oracular (1) sqlite-utils.1.gz
NAME
sqlite-utils - sqlite-utils documentation
CLI TOOL AND PYTHON LIBRARY FOR MANIPULATING SQLITE DATABASES
This library and command-line utility helps create SQLite databases from an existing collection of data.
Most of the functionality is available as either a Python API or through the sqlite-utils command-line
tool.
sqlite-utils is not intended to be a full ORM: the focus is utility helpers to make creating the initial
database and populating it with data as productive as possible.
It is designed as a useful complement to Datasette.
Cleaning data with sqlite-utils and Datasette provides a tutorial introduction (and accompanying ten
minute video) about using this tool.
Contents
Installation
sqlite-utils is tested on Linux, macOS and Windows.
Using Homebrew
The sqlite-utils command-line tool can be installed on macOS using Homebrew:
brew install sqlite-utils
If you have it installed and want to upgrade to the most recent release, you can run:
brew upgrade sqlite-utils
Then run sqlite-utils --version to confirm the installed version.
Using pip
The sqlite-utils package on PyPI includes both the sqlite_utils Python library and the sqlite-utils
command-line tool. You can install them using pip like so:
pip install sqlite-utils
Using pipx
pipx is a tool for installing Python command-line applications in their own isolated environments. You
can use pipx to install the sqlite-utils command-line tool like this:
pipx install sqlite-utils
Alternatives to sqlite3
By default, sqlite-utils uses the sqlite3 package bundled with the Python standard library.
Depending on your operating system, this may come with some limitations.
On some platforms the ability to load additional extensions (via conn.load_extension(...) or
--load-extension=/path/to/extension) may be disabled.
You may also see the error sqlite3.OperationalError: table sqlite_master may not be modified when trying
to alter an existing table.
You can work around these limitations by installing either the pysqlite3 package or the sqlean.py
package, both of which provide drop-in replacements for the standard library sqlite3 module but with a
recent version of SQLite and full support for loading extensions.
To install sqlean.py (which has compiled binary wheels available for all major platforms) run the
following:
sqlite-utils install sqlean.py
pysqlite3 and sqlean.py do not provide implementations of the .iterdump() method. To use that method (see
Dumping the database to SQL) or the sqlite-utils dump command you should also install the sqlite-dump
package:
sqlite-utils install sqlite-dump
Setting up shell completion
You can configure shell tab completion for the sqlite-utils command using these commands.
For bash:
eval "$(_SQLITE_UTILS_COMPLETE=bash_source sqlite-utils)"
For zsh:
eval "$(_SQLITE_UTILS_COMPLETE=zsh_source sqlite-utils)"
Add this code to ~/.zshrc or ~/.bashrc to automatically run it when you start a new shell.
See the Click documentation for more details.
sqlite-utils command-line tool
The sqlite-utils command-line tool can be used to manipulate SQLite databases in a number of different
ways.
Once installed the tool should be available as sqlite-utils. It can also be run using python -m
sqlite_utils.
• Running SQL queries
• Returning JSON
• Newline-delimited JSON
• JSON arrays
• Binary data in JSON
• Nested JSON values
• Returning CSV or TSV
• Table-formatted output
• Returning raw data, such as binary content
• Using named parameters
• UPDATE, INSERT and DELETE
• Defining custom SQL functions
• SQLite extensions
• Attaching additional databases
• Querying data directly using an in-memory database
• Running queries directly against CSV or JSON
• Explicitly specifying the format
• Joining in-memory data against existing databases using --attach
• --schema, --analyze, --dump and --save
• Returning all rows in a table
• Listing tables
• Listing views
• Listing indexes
• Listing triggers
• Showing the schema
• Analyzing tables
• Saving the analyzed table details
• Creating an empty database
• Inserting JSON data
• Inserting binary data
• Inserting newline-delimited JSON
• Flattening nested JSON objects
• Inserting CSV or TSV data
• Alternative delimiters and quote characters
• CSV files without a header row
• Inserting unstructured data with --lines and --text
• Applying conversions while inserting data
• --convert with --lines
• --convert with --text
• Insert-replacing data
• Upserting data
• Executing SQL in bulk
• Inserting data from files
• Converting data in columns
• Importing additional modules
• Using the debugger
• Defining a convert() function
• sqlite-utils convert recipes
• Saving the result to a different column
• Converting a column into multiple columns
• Creating tables
• Renaming a table
• Duplicating tables
• Dropping tables
• Transforming tables
• Adding a primary key to a rowid table
• Extracting columns into a separate table
• Creating views
• Dropping views
• Adding columns
• Adding columns automatically on insert/update
• Adding foreign key constraints
• Adding multiple foreign keys at once
• Adding indexes for all foreign keys
• Setting defaults and not null constraints
• Creating indexes
• Configuring full-text search
• Executing searches
• Enabling cached counts
• Optimizing index usage with ANALYZE
• Vacuum
• Optimize
• WAL mode
• Dumping the database to SQL
• Loading SQLite extensions
• SpatiaLite helpers
• Adding spatial indexes
• Installing packages
• Uninstalling packages
• Experimental TUI
Running SQL queries
The sqlite-utils query command lets you run queries directly against a SQLite database file. This is the
default subcommand, so the following two examples work the same way:
sqlite-utils query dogs.db "select * from dogs"
sqlite-utils dogs.db "select * from dogs"
NOTE:
In Python: db.query() CLI reference: sqlite-utils query
Returning JSON
The default format returned for queries is JSON:
sqlite-utils dogs.db "select * from dogs"
[{"id": 1, "age": 4, "name": "Cleo"},
{"id": 2, "age": 2, "name": "Pancakes"}]
Newline-delimited JSON
Use --nl to get back newline-delimited JSON objects:
sqlite-utils dogs.db "select * from dogs" --nl
{"id": 1, "age": 4, "name": "Cleo"}
{"id": 2, "age": 2, "name": "Pancakes"}
JSON arrays
You can use --arrays to request arrays instead of objects:
sqlite-utils dogs.db "select * from dogs" --arrays
[[1, 4, "Cleo"],
[2, 2, "Pancakes"]]
You can also combine --arrays and --nl:
sqlite-utils dogs.db "select * from dogs" --arrays --nl
[1, 4, "Cleo"]
[2, 2, "Pancakes"]
If you want to pretty-print the output further, you can pipe it through python -mjson.tool:
sqlite-utils dogs.db "select * from dogs" | python -mjson.tool
[
{
"id": 1,
"age": 4,
"name": "Cleo"
},
{
"id": 2,
"age": 2,
"name": "Pancakes"
}
]
Binary data in JSON
Binary strings are not valid JSON, so BLOB columns containing binary data will be returned as a JSON
object containing base64 encoded data, that looks like this:
sqlite-utils dogs.db "select name, content from images" | python -mjson.tool
[
{
"name": "transparent.gif",
"content": {
"$base64": true,
"encoded": "R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"
}
}
]
Nested JSON values
If one of your columns contains JSON, by default it will be returned as an escaped string:
sqlite-utils dogs.db "select * from dogs" | python -mjson.tool
[
{
"id": 1,
"name": "Cleo",
"friends": "[{\"name\": \"Pancakes\"}, {\"name\": \"Bailey\"}]"
}
]
You can use the --json-cols option to automatically detect these JSON columns and output them as nested
JSON data:
sqlite-utils dogs.db "select * from dogs" --json-cols | python -mjson.tool
[
{
"id": 1,
"name": "Cleo",
"friends": [
{
"name": "Pancakes"
},
{
"name": "Bailey"
}
]
}
]
Returning CSV or TSV
You can use the --csv option to return results as CSV:
sqlite-utils dogs.db "select * from dogs" --csv
id,age,name
1,4,Cleo
2,2,Pancakes
This will default to including the column names as a header row. To exclude the headers, use
--no-headers:
sqlite-utils dogs.db "select * from dogs" --csv --no-headers
1,4,Cleo
2,2,Pancakes
Use --tsv instead of --csv to get back tab-separated values:
sqlite-utils dogs.db "select * from dogs" --tsv
id age name
1 4 Cleo
2 2 Pancakes
Table-formatted output
You can use the --table option (or -t shortcut) to output query results as a table:
sqlite-utils dogs.db "select * from dogs" --table
id age name
---- ----- --------
1 4 Cleo
2 2 Pancakes
You can use the --fmt option to specify different table formats, for example rst for reStructuredText:
sqlite-utils dogs.db "select * from dogs" --fmt rst
==== ===== ========
id age name
==== ===== ========
1 4 Cleo
2 2 Pancakes
==== ===== ========
Available --fmt options are:
• asciidoc
• double_grid
• double_outline
• fancy_grid
• fancy_outline
• github
• grid
• heavy_grid
• heavy_outline
• html
• jira
• latex
• latex_booktabs
• latex_longtable
• latex_raw
• mediawiki
• mixed_grid
• mixed_outline
• moinmoin
• orgtbl
• outline
• pipe
• plain
• presto
• pretty
• psql
• rounded_grid
• rounded_outline
• rst
• simple
• simple_grid
• simple_outline
• textile
• tsv
• unsafehtml
• youtrack
This list can also be found by running sqlite-utils query --help.
Returning raw data, such as binary content
If your table contains binary data in a BLOB you can use the --raw option to output specific columns
directly to standard out.
For example, to retrieve a binary image from a BLOB column and store it in a file you can use the
following:
sqlite-utils photos.db "select contents from photos where id=1" --raw > myphoto.jpg
To return the first column of each result as raw data, separated by newlines, use --raw-lines:
sqlite-utils photos.db "select caption from photos" --raw-lines > captions.txt
Using named parameters
You can pass named parameters to the query using -p:
sqlite-utils query dogs.db "select :num * :num2" -p num 5 -p num2 6
[{":num * :num2": 30}]
These will be correctly quoted and escaped in the SQL query, providing a safe way to combine other values
with SQL.
UPDATE, INSERT and DELETE
If you execute an UPDATE, INSERT or DELETE query the command will return the number of affected rows:
sqlite-utils dogs.db "update dogs set age = 5 where name = 'Cleo'"
[{"rows_affected": 1}]
Defining custom SQL functions
You can use the --functions option to pass a block of Python code that defines additional functions which
can then be called by your SQL query.
This example defines a function which extracts the domain from a URL:
sqlite-utils query sites.db "select url, domain(url) from urls" --functions '
from urllib.parse import urlparse
def domain(url):
return urlparse(url).netloc
'
Every callable object defined in the block will be registered as a SQL function with the same name, with
the exception of functions with names that begin with an underscore.
SQLite extensions
You can load SQLite extension modules using the --load-extension option, see Loading SQLite extensions.
sqlite-utils dogs.db "select spatialite_version()" --load-extension=spatialite
[{"spatialite_version()": "4.3.0a"}]
Attaching additional databases
SQLite supports cross-database SQL queries, which can join data from tables in more than one database
file.
You can attach one or more additional databases using the --attach option, providing an alias to use for
that database and the path to the SQLite file on disk.
This example attaches the books.db database under the alias books and then runs a query that combines
data from that database with the default dogs.db database:
sqlite-utils dogs.db --attach books books.db \
'select * from sqlite_master union all select * from books.sqlite_master'
NOTE:
In Python: db.attach()
Querying data directly using an in-memory database
The sqlite-utils memory command works similar to sqlite-utils query, but allows you to execute queries
against an in-memory database.
You can also pass this command CSV or JSON files which will be loaded into a temporary in-memory table,
allowing you to execute SQL against that data without a separate step to first convert it to SQLite.
Without any extra arguments, this command executes SQL against the in-memory database directly:
sqlite-utils memory 'select sqlite_version()'
[{"sqlite_version()": "3.35.5"}]
It takes all of the same output formatting options as sqlite-utils query: --csv and --csv and --table and
--nl:
sqlite-utils memory 'select sqlite_version()' --csv
sqlite_version()
3.35.5
sqlite-utils memory 'select sqlite_version()' --fmt grid
+--------------------+
| sqlite_version() |
+====================+
| 3.35.5 |
+--------------------+
Running queries directly against CSV or JSON
If you have data in CSV or JSON format you can load it into an in-memory SQLite database and run queries
against it directly in a single command using sqlite-utils memory like this:
sqlite-utils memory data.csv "select * from data"
You can pass multiple files to the command if you want to run joins between data from different files:
sqlite-utils memory one.csv two.json \
"select * from one join two on one.id = two.other_id"
If your data is JSON it should be the same format supported by the sqlite-utils insert command - so
either a single JSON object (treated as a single row) or a list of JSON objects.
CSV data can be comma- or tab- delimited.
The in-memory tables will be named after the files without their extensions. The tool also sets up
aliases for those tables (using SQL views) as t1, t2 and so on, or you can use the alias t to refer to
the first table:
sqlite-utils memory example.csv "select * from t"
If two files have the same name they will be assigned a numeric suffix:
sqlite-utils memory foo/data.csv bar/data.csv "select * from data_2"
To read from standard input, use either - or stdin as the filename - then use stdin or t or t1 as the
table name:
cat example.csv | sqlite-utils memory - "select * from stdin"
Incoming CSV data will be assumed to use utf-8. If your data uses a different character encoding you can
specify that with --encoding:
cat example.csv | sqlite-utils memory - "select * from stdin" --encoding=latin-1
If you are joining across multiple CSV files they must all use the same encoding.
Column types will be automatically detected in CSV or TSV data, using the same mechanism as
--detect-types described in Inserting CSV or TSV data. You can pass the --no-detect-types option to
disable this automatic type detection and treat all CSV and TSV columns as TEXT.
Explicitly specifying the format
By default, sqlite-utils memory will attempt to detect the incoming data format (JSON, TSV or CSV)
automatically.
You can instead specify an explicit format by adding a :csv, :tsv, :json or :nl (for newline-delimited
JSON) suffix to the filename. For example:
sqlite-utils memory one.dat:csv two.dat:nl \
"select * from one union select * from two"
Here the contents of one.dat will be treated as CSV and the contents of two.dat will be treated as
newline-delimited JSON.
To explicitly specify the format for data piped into the tool on standard input, use stdin:format - for
example:
cat one.dat | sqlite-utils memory stdin:csv "select * from stdin"
Joining in-memory data against existing databases using --attach
The attach option can be used to attach database files to the in-memory connection, enabling joins
between in-memory data loaded from a file and tables in existing SQLite database files. An example:
echo "id\n1\n3\n5" | sqlite-utils memory - --attach trees trees.db \
"select * from trees.trees where rowid in (select id from stdin)"
Here the --attach trees trees.db option makes the trees.db database available with an alias of trees.
select * from trees.trees where ... can then query the trees table in that database.
The CSV data that was piped into the script is available in the stdin table, so ... where rowid in
(select id from stdin) can be used to return rows from the trees table that match IDs that were piped in
as CSV content.
--schema, --analyze, --dump and --save
To see the in-memory database schema that would be used for a file or for multiple files, use --schema:
sqlite-utils memory dogs.csv --schema
CREATE TABLE [dogs] (
[id] INTEGER,
[age] INTEGER,
[name] TEXT
);
CREATE VIEW t1 AS select * from [dogs];
CREATE VIEW t AS select * from [dogs];
You can run the equivalent of the analyze-tables command using --analyze:
sqlite-utils memory dogs.csv --analyze
dogs.id: (1/3)
Total rows: 2
Null rows: 0
Blank rows: 0
Distinct values: 2
dogs.name: (2/3)
Total rows: 2
Null rows: 0
Blank rows: 0
Distinct values: 2
dogs.age: (3/3)
Total rows: 2
Null rows: 0
Blank rows: 0
Distinct values: 2
You can output SQL that will both create the tables and insert the full data used to populate the
in-memory database using --dump:
sqlite-utils memory dogs.csv --dump
BEGIN TRANSACTION;
CREATE TABLE [dogs] (
[id] INTEGER,
[age] INTEGER,
[name] TEXT
);
INSERT INTO "dogs" VALUES('1','4','Cleo');
INSERT INTO "dogs" VALUES('2','2','Pancakes');
CREATE VIEW t1 AS select * from [dogs];
CREATE VIEW t AS select * from [dogs];
COMMIT;
Passing --save other.db will instead use that SQL to populate a new database file:
sqlite-utils memory dogs.csv --save dogs.db
These features are mainly intended as debugging tools - for much more finely grained control over how
data is inserted into a SQLite database file see Inserting JSON data and Inserting CSV or TSV data.
Returning all rows in a table
You can return every row in a specified table using the rows command:
sqlite-utils rows dogs.db dogs
[{"id": 1, "age": 4, "name": "Cleo"},
{"id": 2, "age": 2, "name": "Pancakes"}]
This command accepts the same output options as query - so you can pass --nl, --csv, --tsv, --no-headers,
--table and --fmt.
You can use the -c option to specify a subset of columns to return:
sqlite-utils rows dogs.db dogs -c age -c name
[{"age": 4, "name": "Cleo"},
{"age": 2, "name": "Pancakes"}]
You can filter rows using a where clause with the --where option:
sqlite-utils rows dogs.db dogs -c name --where 'name = "Cleo"'
[{"name": "Cleo"}]
Or pass named parameters using --where in combination with -p:
sqlite-utils rows dogs.db dogs -c name --where 'name = :name' -p name Cleo
[{"name": "Cleo"}]
You can define a sort order using --order column or --order 'column desc'.
Use --limit N to only return the first N rows. Use --offset N to return rows starting from the specified
offset.
NOTE:
In Python: table.rows CLI reference: sqlite-utils rows
Listing tables
You can list the names of tables in a database using the tables command:
sqlite-utils tables mydb.db
[{"table": "dogs"},
{"table": "cats"},
{"table": "chickens"}]
You can output this list in CSV using the --csv or --tsv options:
sqlite-utils tables mydb.db --csv --no-headers
dogs
cats
chickens
If you just want to see the FTS4 tables, you can use --fts4 (or --fts5 for FTS5 tables):
sqlite-utils tables docs.db --fts4
[{"table": "docs_fts"}]
Use --counts to include a count of the number of rows in each table:
sqlite-utils tables mydb.db --counts
[{"table": "dogs", "count": 12},
{"table": "cats", "count": 332},
{"table": "chickens", "count": 9}]
Use --columns to include a list of columns in each table:
sqlite-utils tables dogs.db --counts --columns
[{"table": "Gosh", "count": 0, "columns": ["c1", "c2", "c3"]},
{"table": "Gosh2", "count": 0, "columns": ["c1", "c2", "c3"]},
{"table": "dogs", "count": 2, "columns": ["id", "age", "name"]}]
Use --schema to include the schema of each table:
sqlite-utils tables dogs.db --schema --table
table schema
------- -----------------------------------------------
Gosh CREATE TABLE Gosh (c1 text, c2 text, c3 text)
Gosh2 CREATE TABLE Gosh2 (c1 text, c2 text, c3 text)
dogs CREATE TABLE [dogs] (
[id] INTEGER,
[age] INTEGER,
[name] TEXT)
The --nl, --csv, --tsv, --table and --fmt options are also available.
NOTE:
In Python: db.tables or db.table_names() CLI reference: sqlite-utils tables
Listing views
The views command shows any views defined in the database:
sqlite-utils views sf-trees.db --table --counts --columns --schema
view count columns schema
--------- ------- -------------------- --------------------------------------------------------------
demo_view 189144 ['qSpecies'] CREATE VIEW demo_view AS select qSpecies from Street_Tree_List
hello 1 ['sqlite_version()'] CREATE VIEW hello as select sqlite_version()
It takes the same options as the tables command:
• --columns
• --schema
• --counts
• --nl
• --csv
• --tsv
• --table
NOTE:
In Python: db.views or db.view_names() CLI reference: sqlite-utils views
Listing indexes
The indexes command lists any indexes configured for the database:
sqlite-utils indexes covid.db --table
table index_name seqno cid name desc coll key
-------------------------------- ------------------------------------------------------ ------- ----- ----------------- ------ ------ -----
johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_combined_key 0 12 combined_key 0 BINARY 1
johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_country_or_region 0 1 country_or_region 0 BINARY 1
johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_province_or_state 0 2 province_or_state 0 BINARY 1
johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_day 0 0 day 0 BINARY 1
ny_times_us_counties idx_ny_times_us_counties_date 0 0 date 1 BINARY 1
ny_times_us_counties idx_ny_times_us_counties_fips 0 3 fips 0 BINARY 1
ny_times_us_counties idx_ny_times_us_counties_county 0 1 county 0 BINARY 1
ny_times_us_counties idx_ny_times_us_counties_state 0 2 state 0 BINARY 1
It shows indexes across all tables. To see indexes for specific tables, list those after the database:
sqlite-utils indexes covid.db johns_hopkins_csse_daily_reports --table
The command defaults to only showing the columns that are explicitly part of the index. To also include
auxiliary columns use the --aux option - these columns will be listed with a key of 0.
The command takes the same format options as the tables and views commands.
NOTE:
In Python: table.indexes CLI reference: sqlite-utils indexes
Listing triggers
The triggers command shows any triggers configured for the database:
sqlite-utils triggers global-power-plants.db --table
name table sql
--------------- --------- -----------------------------------------------------------------
plants_insert plants CREATE TRIGGER [plants_insert] AFTER INSERT ON [plants]
BEGIN
INSERT OR REPLACE INTO [_counts]
VALUES (
'plants',
COALESCE(
(SELECT count FROM [_counts] WHERE [table] = 'plants'),
0
) + 1
);
END
It defaults to showing triggers for all tables. To see triggers for one or more specific tables pass
their names as arguments:
sqlite-utils triggers global-power-plants.db plants
The command takes the same format options as the tables and views commands.
NOTE:
In Python: table.triggers or db.triggers CLI reference: sqlite-utils triggers
Showing the schema
The sqlite-utils schema command shows the full SQL schema for the database:
sqlite-utils schema dogs.db
CREATE TABLE "dogs" (
[id] INTEGER PRIMARY KEY,
[name] TEXT
);
This will show the schema for every table and index in the database. To view the schema just for a
specified subset of tables pass those as additional arguments:
sqlite-utils schema dogs.db dogs chickens
NOTE:
In Python: table.schema or db.schema CLI reference: sqlite-utils schema
Analyzing tables
When working with a new database it can be useful to get an idea of the shape of the data. The
sqlite-utils analyze-tables command inspects specified tables (or all tables) and calculates some useful
details about each of the columns in those tables.
To inspect the tags table in the github.db database, run the following:
sqlite-utils analyze-tables github.db tags
tags.repo: (1/3)
Total rows: 261
Null rows: 0
Blank rows: 0
Distinct values: 14
Most common:
88: 107914493
75: 140912432
27: 206156866
Least common:
1: 209590345
2: 206649770
2: 303218369
tags.name: (2/3)
Total rows: 261
Null rows: 0
Blank rows: 0
Distinct values: 175
Most common:
10: 0.2
9: 0.1
7: 0.3
Least common:
1: 0.1.1
1: 0.11.1
1: 0.1a2
tags.sha: (3/3)
Total rows: 261
Null rows: 0
Blank rows: 0
Distinct values: 261
For each column this tool displays the number of null rows, the number of blank rows (rows that contain
an empty string), the number of distinct values and, for columns that are not entirely distinct, the most
common and least common values.
If you do not specify any tables every table in the database will be analyzed:
sqlite-utils analyze-tables github.db
If you wish to analyze one or more specific columns, use the -c option:
sqlite-utils analyze-tables github.db tags -c sha
To show more than 10 common values, use --common-limit 20. To skip the most common or least common value
analysis, use --no-most or --no-least:
sqlite-utils analyze-tables github.db tags --common-limit 20 --no-least
Saving the analyzed table details
analyze-tables can take quite a while to run for large database files. You can save the results of the
analysis to a database table called _analyze_tables_ using the --save option:
sqlite-utils analyze-tables github.db --save
The _analyze_tables_ table has the following schema:
CREATE TABLE [_analyze_tables_] (
[table] TEXT,
[column] TEXT,
[total_rows] INTEGER,
[num_null] INTEGER,
[num_blank] INTEGER,
[num_distinct] INTEGER,
[most_common] TEXT,
[least_common] TEXT,
PRIMARY KEY ([table], [column])
);
The most_common and least_common columns will contain nested JSON arrays of the most common and least
common values that look like this:
[
["Del Libertador, Av", 5068],
["Alberdi Juan Bautista Av.", 4612],
["Directorio Av.", 4552],
["Rivadavia, Av", 4532],
["Yerbal", 4512],
["Cosquín", 4472],
["Estado Plurinacional de Bolivia", 4440],
["Gordillo Timoteo", 4424],
["Montiel", 4360],
["Condarco", 4288]
]
Creating an empty database
You can create a new empty database file using the create-database command:
sqlite-utils create-database empty.db
To enable WAL mode on the newly created database add the --enable-wal option:
sqlite-utils create-database empty.db --enable-wal
To enable SpatiaLite metadata on a newly created database, add the --init-spatialite flag:
sqlite-utils create-database empty.db --init-spatialite
That will look for SpatiaLite in a set of predictable locations. To load it from somewhere else, use the
--load-extension option:
sqlite-utils create-database empty.db --init-spatialite --load-extension /path/to/spatialite.so
Inserting JSON data
If you have data as JSON, you can use sqlite-utils insert tablename to insert it into a database. The
table will be created with the correct (automatically detected) columns if it does not already exist.
You can pass in a single JSON object or a list of JSON objects, either as a filename or piped directly to
standard-in (by using - as the filename).
Here's the simplest possible example:
echo '{"name": "Cleo", "age": 4}' | sqlite-utils insert dogs.db dogs -
To specify a column as the primary key, use --pk=column_name.
To create a compound primary key across more than one column, use --pk multiple times.
If you feed it a JSON list it will insert multiple records. For example, if dogs.json looks like this:
[
{
"id": 1,
"name": "Cleo",
"age": 4
},
{
"id": 2,
"name": "Pancakes",
"age": 2
},
{
"id": 3,
"name": "Toby",
"age": 6
}
]
You can import all three records into an automatically created dogs table and set the id column as the
primary key like so:
sqlite-utils insert dogs.db dogs dogs.json --pk=id
You can skip inserting any records that have a primary key that already exists using --ignore:
sqlite-utils insert dogs.db dogs dogs.json --ignore
You can delete all the existing rows in the table before inserting the new records using --truncate:
sqlite-utils insert dogs.db dogs dogs.json --truncate
You can add the --analyze option to run ANALYZE against the table after the rows have been inserted.
Inserting binary data
You can insert binary data into a BLOB column by first encoding it using base64 and then structuring it
like this:
[
{
"name": "transparent.gif",
"content": {
"$base64": true,
"encoded": "R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"
}
}
]
Inserting newline-delimited JSON
You can also import newline-delimited JSON using the --nl option:
echo '{"id": 1, "name": "Cleo"}
{"id": 2, "name": "Suna"}' | sqlite-utils insert creatures.db creatures - --nl
Newline-delimited JSON consists of full JSON objects separated by newlines.
If you are processing data using jq you can use the jq -c option to output valid newline-delimited JSON.
Since Datasette can export newline-delimited JSON, you can combine the Datasette and sqlite-utils like
so:
curl -L "https://latest.datasette.io/fixtures/facetable.json?_shape=array&_nl=on" \
| sqlite-utils insert nl-demo.db facetable - --pk=id --nl
You can also pipe sqlite-utils together to create a new SQLite database file containing the results of a
SQL query against another database:
sqlite-utils sf-trees.db \
"select TreeID, qAddress, Latitude, Longitude from Street_Tree_List" --nl \
| sqlite-utils insert saved.db trees - --nl
sqlite-utils saved.db "select * from trees limit 5" --csv
TreeID,qAddress,Latitude,Longitude
141565,501X Baker St,37.7759676911831,-122.441396661871
232565,940 Elizabeth St,37.7517102172731,-122.441498017841
119263,495X Lakeshore Dr,,
207368,920 Kirkham St,37.760210314285,-122.47073935813
188702,1501 Evans Ave,37.7422086702947,-122.387293152263
Flattening nested JSON objects
sqlite-utils insert and sqlite-utils memory both expect incoming JSON data to consist of an array of JSON
objects, where the top-level keys of each object will become columns in the created database table.
If your data is nested you can use the --flatten option to create columns that are derived from the
nested data.
Consider this example document, in a file called log.json:
{
"httpRequest": {
"latency": "0.112114537s",
"requestMethod": "GET",
"requestSize": "534",
"status": 200
},
"insertId": "6111722f000b5b4c4d4071e2",
"labels": {
"service": "datasette-io"
}
}
Inserting this into a table using sqlite-utils insert logs.db logs log.json will create a table with the
following schema:
CREATE TABLE [logs] (
[httpRequest] TEXT,
[insertId] TEXT,
[labels] TEXT
);
With the --flatten option columns will be created using topkey_nextkey column names - so running
sqlite-utils insert logs.db logs log.json --flatten will create the following schema instead:
CREATE TABLE [logs] (
[httpRequest_latency] TEXT,
[httpRequest_requestMethod] TEXT,
[httpRequest_requestSize] TEXT,
[httpRequest_status] INTEGER,
[insertId] TEXT,
[labels_service] TEXT
);
Inserting CSV or TSV data
If your data is in CSV format, you can insert it using the --csv option:
sqlite-utils insert dogs.db dogs dogs.csv --csv
For tab-delimited data, use --tsv:
sqlite-utils insert dogs.db dogs dogs.tsv --tsv
Data is expected to be encoded as Unicode UTF-8. If your data is an another character encoding you can
specify it using the --encoding option:
sqlite-utils insert dogs.db dogs dogs.tsv --tsv --encoding=latin-1
To stop inserting after a specified number of records - useful for getting a faster preview of a large
file - use the --stop-after option:
sqlite-utils insert dogs.db dogs dogs.csv --csv --stop-after=10
A progress bar is displayed when inserting data from a file. You can hide the progress bar using the
--silent option.
By default every column inserted from a CSV or TSV file will be of type TEXT. To automatically detect
column types - resulting in a mix of TEXT, INTEGER and FLOAT columns, use the --detect-types option (or
its shortcut -d).
For example, given a creatures.csv file containing this:
name,age,weight
Cleo,6,45.5
Dori,1,3.5
The following command:
sqlite-utils insert creatures.db creatures creatures.csv --csv --detect-types
Will produce this schema:
sqlite-utils schema creatures.db
CREATE TABLE "creatures" (
[name] TEXT,
[age] INTEGER,
[weight] FLOAT
);
You can set the SQLITE_UTILS_DETECT_TYPES environment variable if you want --detect-types to be the
default behavior:
export SQLITE_UTILS_DETECT_TYPES=1
If a CSV or TSV file includes empty cells, like this one:
name,age,weight
Cleo,6,
Dori,,3.5
They will be imported into SQLite as empty string values, "".
To import them as NULL values instead, use the --empty-null option:
sqlite-utils insert creatures.db creatures creatures.csv --csv --empty-null
Alternative delimiters and quote characters
If your file uses a delimiter other than , or a quote character other than " you can attempt to detect
delimiters or you can specify them explicitly.
The --sniff option can be used to attempt to detect the delimiters:
sqlite-utils insert dogs.db dogs dogs.csv --sniff
Alternatively, you can specify them using the --delimiter and --quotechar options.
Here's a CSV file that uses ; for delimiters and the | symbol for quote characters:
name;description
Cleo;|Very fine; a friendly dog|
Pancakes;A local corgi
You can import that using:
sqlite-utils insert dogs.db dogs dogs.csv --delimiter=";" --quotechar="|"
Passing --delimiter, --quotechar or --sniff implies --csv, so you can omit the --csv option.
CSV files without a header row
The first row of any CSV or TSV file is expected to contain the names of the columns in that file.
If your file does not include this row, you can use the --no-headers option to specify that the tool
should not use that fist row as headers.
If you do this, the table will be created with column names called untitled_1 and untitled_2 and so on.
You can then rename them using the sqlite-utils transform ... --rename command, see Transforming tables.
Inserting unstructured data with --lines and --text
If you have an unstructured file you can insert its contents into a table with a single line column
containing each line from the file using --lines. This can be useful if you intend to further analyze
those lines using SQL string functions or sqlite-utils convert:
sqlite-utils insert logs.db loglines logfile.log --lines
This will produce the following schema:
CREATE TABLE [loglines] (
[line] TEXT
);
You can also insert the entire contents of the file into a single column called text using --text:
sqlite-utils insert content.db content file.txt --text
The schema here will be:
CREATE TABLE [content] (
[text] TEXT
);
Applying conversions while inserting data
The --convert option can be used to apply a Python conversion function to imported data before it is
inserted into the database. It works in a similar way to sqlite-utils convert.
Your Python function will be passed a dictionary called row for each item that is being imported. You can
modify that dictionary and return it - or return a fresh dictionary - to change the data that will be
inserted.
Given a JSON file called dogs.json containing this:
[
{"id": 1, "name": "Cleo"},
{"id": 2, "name": "Pancakes"}
]
The following command will insert that data and add an is_good column set to 1 for each dog:
sqlite-utils insert dogs.db dogs dogs.json --convert 'row["is_good"] = 1'
The --convert option also works with the --csv, --tsv and --nl insert options.
As with sqlite-utils convert you can use --import to import additional Python modules, see Importing
additional modules for details.
You can also pass code that runs some initialization steps and defines a convert(value) function, see
Defining a convert() function.
--convert with --lines
Things work slightly differently when combined with the --lines or --text options.
With --lines, instead of being passed a row dictionary your function will be passed a line string
representing each line of the input. Given a file called access.log containing the following:
INFO: 127.0.0.1:60581 - GET / HTTP/1.1 200 OK
INFO: 127.0.0.1:60581 - GET /foo/-/static/app.css?cead5a HTTP/1.1 200 OK
You could convert it into structured data like so:
sqlite-utils insert logs.db loglines access.log --convert '
type, source, _, verb, path, _, status, _ = line.split()
return {
"type": type,
"source": source,
"verb": verb,
"path": path,
"status": status,
}' --lines
The resulting table would look like this:
┌──────┬─────────────────┬──────┬──────────────────────────────┬────────┐
│type │ source │ verb │ path │ status │
├──────┼─────────────────┼──────┼──────────────────────────────┼────────┤
│INFO: │ 127.0.0.1:60581 │ GET │ / │ 200 │
├──────┼─────────────────┼──────┼──────────────────────────────┼────────┤
│INFO: │ 127.0.0.1:60581 │ GET │ /foo/-/static/app.css?cead5a │ 200 │
└──────┴─────────────────┴──────┴──────────────────────────────┴────────┘
--convert with --text
With --text the entire input to the command will be made available to the function as a variable called
text.
The function can return a single dictionary which will be inserted as a single row, or it can return a
list or iterator of dictionaries, each of which will be inserted.
Here's how to use --convert and --text to insert one record per word in the input:
echo 'A bunch of words' | sqlite-utils insert words.db words - \
--text --convert '({"word": w} for w in text.split())'
The result looks like this:
sqlite-utils dump words.db
BEGIN TRANSACTION;
CREATE TABLE [words] (
[word] TEXT
);
INSERT INTO "words" VALUES('A');
INSERT INTO "words" VALUES('bunch');
INSERT INTO "words" VALUES('of');
INSERT INTO "words" VALUES('words');
COMMIT;
Insert-replacing data
The --replace option to insert causes any existing records with the same primary key to be replaced
entirely by the new records.
To replace a dog with in ID of 2 with a new record, run the following:
echo '{"id": 2, "name": "Pancakes", "age": 3}' | \
sqlite-utils insert dogs.db dogs - --pk=id --replace
Upserting data
Upserting is update-or-insert. If a row exists with the specified primary key the provided columns will
be updated. If no row exists that row will be created.
Unlike insert --replace, an upsert will ignore any column values that exist but are not present in the
upsert document.
For example:
echo '{"id": 2, "age": 4}' | \
sqlite-utils upsert dogs.db dogs - --pk=id
This will update the dog with an ID of 2 to have an age of 4, creating a new record (with a null name) if
one does not exist. If a row DOES exist the name will be left as-is.
The command will fail if you reference columns that do not exist on the table. To automatically create
missing columns, use the --alter option.
NOTE:
upsert in sqlite-utils 1.x worked like insert ... --replace does in 2.x. See issue #66 for details of
this change.
Executing SQL in bulk
If you have a JSON, newline-delimited JSON, CSV or TSV file you can execute a bulk SQL query using each
of the records in that file using the sqlite-utils bulk command.
The command takes the database file, the SQL to be executed and the file containing records to be used
when evaluating the SQL query.
The SQL query should include :named parameters that match the keys in the records.
For example, given a chickens.csv CSV file containing the following:
id,name
1,Blue
2,Snowy
3,Azi
4,Lila
5,Suna
6,Cardi
You could insert those rows into a pre-created chickens table like so:
sqlite-utils bulk chickens.db \
'insert into chickens (id, name) values (:id, :name)' \
chickens.csv --csv
This command takes the same options as the sqlite-utils insert command - so it defaults to expecting JSON
but can accept other formats using --csv or --tsv or --nl or other options described above.
By default all of the SQL queries will be executed in a single transaction. To commit every 20 records,
use --batch-size 20.
Inserting data from files
The insert-files command can be used to insert the content of files, along with their metadata, into a
SQLite table.
Here's an example that inserts all of the GIF files in the current directory into a gifs.db database,
placing the file contents in an images table:
sqlite-utils insert-files gifs.db images *.gif
You can also pass one or more directories, in which case every file in those directories will be added
recursively:
sqlite-utils insert-files gifs.db images path/to/my-gifs
By default this command will create a table with the following schema:
CREATE TABLE [images] (
[path] TEXT PRIMARY KEY,
[content] BLOB,
[size] INTEGER
);
Content will be treated as binary by default and stored in a BLOB column. You can use the --text option
to store that content in a TEXT column instead.
You can customize the schema using one or more -c options. For a table schema that includes just the
path, MD5 hash and last modification time of the file, you would use this:
sqlite-utils insert-files gifs.db images *.gif -c path -c md5 -c mtime --pk=path
This will result in the following schema:
CREATE TABLE [images] (
[path] TEXT PRIMARY KEY,
[md5] TEXT,
[mtime] FLOAT
);
Note that there's no content column here at all - if you specify custom columns using -c you need to
include -c content to create that column.
You can change the name of one of these columns using a -c colname:coldef parameter. To rename the mtime
column to last_modified you would use this:
sqlite-utils insert-files gifs.db images *.gif \
-c path -c md5 -c last_modified:mtime --pk=path
You can pass --replace or --upsert to indicate what should happen if you try to insert a file with an
existing primary key. Pass --alter to cause any missing columns to be added to the table.
The full list of column definitions you can use is as follows:
name The name of the file, e.g. cleo.jpg
path The path to the file relative to the root folder, e.g. pictures/cleo.jpg
fullpath
The fully resolved path to the image, e.g. /home/simonw/pictures/cleo.jpg
sha256 The SHA256 hash of the file contents
md5 The MD5 hash of the file contents
mode The permission bits of the file, as an integer - you may want to convert this to octal
content
The binary file contents, which will be stored as a BLOB
content_text
The text file contents, which will be stored as TEXT
mtime The modification time of the file, as floating point seconds since the Unix epoch
ctime The creation time of the file, as floating point seconds since the Unix epoch
mtime_int
The modification time as an integer rather than a float
ctime_int
The creation time as an integer rather than a float
mtime_iso
The modification time as an ISO timestamp, e.g. 2020-07-27T04:24:06.654246
ctime_iso
The creation time is an ISO timestamp
size The integer size of the file in bytes
stem The filename without the extension - for file.txt.gz this would be file.txt
suffix The file extension - for file.txt.gz this would be .gz
You can insert data piped from standard input like this:
cat dog.jpg | sqlite-utils insert-files dogs.db pics - --name=dog.jpg
The - argument indicates data should be read from standard input. The string passed using the --name
option will be used for the file name and path values.
When inserting data from standard input only the following column definitions are supported: name, path,
content, content_text, sha256, md5 and size.
Converting data in columns
The convert command can be used to transform the data in a specified column - for example to parse a date
string into an ISO timestamp, or to split a string of tags into a JSON array.
The command accepts a database, table, one or more columns and a string of Python code to be executed
against the values from those columns. The following example would replace the values in the headline
column in the articles table with an upper-case version:
sqlite-utils convert content.db articles headline 'value.upper()'
The Python code is passed as a string. Within that Python code the value variable will be the value of
the current column.
The code you provide will be compiled into a function that takes value as a single argument. If you break
your function body into multiple lines the last line should be a return statement:
sqlite-utils convert content.db articles headline '
value = str(value)
return value.upper()'
Your code will be automatically wrapped in a function, but you can also define a function called
convert(value) which will be called, if available:
sqlite-utils convert content.db articles headline '
def convert(value):
return value.upper()'
Use a CODE value of - to read from standard input:
cat mycode.py | sqlite-utils convert content.db articles headline -
Where mycode.py contains a fragment of Python code that looks like this:
def convert(value):
return value.upper()
The conversion will be applied to every row in the specified table. You can limit that to just rows that
match a WHERE clause using --where:
sqlite-utils convert content.db articles headline 'value.upper()' \
--where "headline like '%cat%'"
You can include named parameters in your where clause and populate them using one or more --param
options:
sqlite-utils convert content.db articles headline 'value.upper()' \
--where "headline like :query" \
--param query '%cat%'
The --dry-run option will output a preview of the conversion against the first ten rows, without
modifying the database.
By default any rows with a falsey value for the column - such as 0 or null - will be skipped. Use the
--no-skip-false option to disable this behaviour.
Importing additional modules
You can specify Python modules that should be imported and made available to your code using one or more
--import options. This example uses the textwrap module to wrap the content column at 100 characters:
sqlite-utils convert content.db articles content \
'"\n".join(textwrap.wrap(value, 100))' \
--import=textwrap
This supports nested imports as well, for example to use ElementTree:
sqlite-utils convert content.db articles content \
'xml.etree.ElementTree.fromstring(value).attrib["title"]' \
--import=xml.etree.ElementTree
Using the debugger
If an error occurs while running your conversion operation you may see a message like this:
user-defined function raised exception
Add the --pdb option to catch the error and open the Python debugger at that point. The conversion
operation will exit after you type q in the debugger.
Here's an example debugging session. First, create a articles table with invalid XML in the content
column:
echo '{"content": "This is not XML"}' | sqlite-utils insert content.db articles -
Now run the conversion with the --pdb option:
sqlite-utils convert content.db articles content \
'xml.etree.ElementTree.fromstring(value).attrib["title"]' \
--import=xml.etree.ElementTree \
--pdb
When the error occurs the debugger will open:
Exception raised, dropping into pdb...: syntax error: line 1, column 0
> .../python3.11/xml/etree/ElementTree.py(1338)XML()
-> parser.feed(text)
(Pdb) args
text = 'This is not XML'
parser = <xml.etree.ElementTree.XMLParser object at 0x102c405e0>
(Pdb) q
args here shows the arguments to the current function in the stack. The Python pdb documentation has full
details on the other available commands.
Defining a convert() function
Instead of providing a single line of code to be executed against each value, you can define a function
called convert(value).
This mechanism can be used to execute one-off initialization code that runs once at the start of the
conversion run.
The following example adds a new score column, then updates it to list a random number - after first
seeding the random number generator to ensure that multiple runs produce the same results:
sqlite-utils add-column content.db articles score float --not-null-default 1.0
sqlite-utils convert content.db articles score '
import random
random.seed(10)
def convert(value):
return random.random()
'
sqlite-utils convert recipes
Various built-in recipe functions are available for common operations. These are:
r.jsonsplit(value, delimiter=',', type=<class 'str'>)
Convert a string like a,b,c into a JSON array ["a", "b", "c"]
The delimiter parameter can be used to specify a different delimiter.
The type parameter can be set to float or int to produce a JSON array of different types, for
example if the column's string value was 1.2,3,4.5 the following:
r.jsonsplit(value, type=float)
Would produce an array like this: [1.2, 3.0, 4.5]
r.parsedate(value, dayfirst=False, yearfirst=False, errors=None)
Parse a date and convert it to ISO date format: yyyy-mm-dd
In the case of dates such as 03/04/05 U.S. MM/DD/YY format is assumed - you can use dayfirst=True
or yearfirst=True to change how these ambiguous dates are interpreted.
Use the errors= parameter to specify what should happen if a value cannot be parsed.
By default, if any value cannot be parsed an error will be occurred and all values will be left as
they were.
Set errors=r.IGNORE to ignore any values that cannot be parsed, leaving them unchanged.
Set errors=r.SET_NULL to set any values that cannot be parsed to null.
r.parsedatetime(value, dayfirst=False, yearfirst=False, errors=None)
Parse a datetime and convert it to ISO datetime format: yyyy-mm-ddTHH:MM:SS
These recipes can be used in the code passed to sqlite-utils convert like this:
sqlite-utils convert my.db mytable mycolumn \
'r.jsonsplit(value)'
To use any of the documented parameters, do this:
sqlite-utils convert my.db mytable mycolumn \
'r.jsonsplit(value, delimiter=":")'
Saving the result to a different column
The --output and --output-type options can be used to save the result of the conversion to a separate
column, which will be created if that column does not already exist:
sqlite-utils convert content.db articles headline 'value.upper()' \
--output headline_upper
The type of the created column defaults to text, but a different column type can be specified using
--output-type. This example will create a new floating point column called id_as_a_float with a copy of
each item's ID increased by 0.5:
sqlite-utils convert content.db articles id 'float(value) + 0.5' \
--output id_as_a_float \
--output-type float
You can drop the original column at the end of the operation by adding --drop.
Converting a column into multiple columns
Sometimes you may wish to convert a single column into multiple derived columns. For example, you may
have a location column containing latitude,longitude values which you wish to split out into separate
latitude and longitude columns.
You can achieve this using the --multi option to sqlite-utils convert. This option expects your Python
code to return a Python dictionary: new columns well be created and populated for each of the keys in
that dictionary.
For the latitude,longitude example you would use the following:
sqlite-utils convert demo.db places location \
'bits = value.split(",")
return {
"latitude": float(bits[0]),
"longitude": float(bits[1]),
}' --multi
The type of the returned values will be taken into account when creating the new columns. In this
example, the resulting database schema will look like this:
CREATE TABLE [places] (
[location] TEXT,
[latitude] FLOAT,
[longitude] FLOAT
);
The code function can also return None, in which case its output will be ignored. You can drop the
original column at the end of the operation by adding --drop.
Creating tables
Most of the time creating tables by inserting example data is the quickest approach. If you need to
create an empty table in advance of inserting data you can do so using the create-table command:
sqlite-utils create-table mydb.db mytable id integer name text --pk=id
This will create a table called mytable with two columns - an integer id column and a text name column.
It will set the id column to be the primary key.
You can pass as many column-name column-type pairs as you like. Valid types are integer, text, float and
blob.
You can specify columns that should be NOT NULL using --not-null colname. You can specify default values
for columns using --default colname defaultvalue.
sqlite-utils create-table mydb.db mytable \
id integer \
name text \
age integer \
is_good integer \
--not-null name \
--not-null age \
--default is_good 1 \
--pk=id
sqlite-utils tables mydb.db --schema -t
table schema
------- --------------------------------
mytable CREATE TABLE [mytable] (
[id] INTEGER PRIMARY KEY,
[name] TEXT NOT NULL,
[age] INTEGER NOT NULL,
[is_good] INTEGER DEFAULT '1'
)
You can specify foreign key relationships between the tables you are creating using --fk colname
othertable othercolumn:
sqlite-utils create-table books.db authors \
id integer \
name text \
--pk=id
sqlite-utils create-table books.db books \
id integer \
title text \
author_id integer \
--pk=id \
--fk author_id authors id
sqlite-utils tables books.db --schema -t
table schema
------- -------------------------------------------------
authors CREATE TABLE [authors] (
[id] INTEGER PRIMARY KEY,
[name] TEXT
)
books CREATE TABLE [books] (
[id] INTEGER PRIMARY KEY,
[title] TEXT,
[author_id] INTEGER REFERENCES [authors]([id])
)
You can create a table in SQLite STRICT mode using --strict:
sqlite-utils create-table mydb.db mytable id integer name text --strict
sqlite-utils tables mydb.db --schema -t
table schema
------- ------------------------
mytable CREATE TABLE [mytable] (
[id] INTEGER,
[name] TEXT
) STRICT
If a table with the same name already exists, you will get an error. You can choose to silently ignore
this error with --ignore, or you can replace the existing table with a new, empty table using --replace.
You can also pass --transform to transform the existing table to match the new schema. See Explicitly
creating a table in the Python library documentation for details of how this option works.
Renaming a table
Yo ucan rename a table using the rename-table command:
sqlite-utils rename-table mydb.db oldname newname
Pass --ignore to ignore any errors caused by the table not existing, or the new name already being in
use.
Duplicating tables
The duplicate command duplicates a table - creating a new table with the same schema and a copy of all of
the rows:
sqlite-utils duplicate books.db authors authors_copy
Dropping tables
You can drop a table using the drop-table command:
sqlite-utils drop-table mydb.db mytable
Use --ignore to ignore the error if the table does not exist.
Transforming tables
The transform command allows you to apply complex transformations to a table that cannot be implemented
using a regular SQLite ALTER TABLE command. See Transforming a table for details of how this works. The
transform command preserves a table's STRICT mode.
sqlite-utils transform mydb.db mytable \
--drop column1 \
--rename column2 column_renamed
Every option for this table (with the exception of --pk-none) can be specified multiple times. The
options are as follows:
--type column-name new-type
Change the type of the specified column. Valid types are integer, text, float, blob.
--drop column-name
Drop the specified column.
--rename column-name new-name
Rename this column to a new name.
--column-order column
Use this multiple times to specify a new order for your columns. -o shortcut is also available.
--not-null column-name
Set this column as NOT NULL.
--not-null-false column-name
For a column that is currently set as NOT NULL, remove the NOT NULL.
--pk column-name
Change the primary key column for this table. Pass --pk multiple times if you want to create a
compound primary key.
--pk-none
Remove the primary key from this table, turning it into a rowid table.
--default column-name value
Set the default value of this column.
--default-none column
Remove the default value for this column.
--drop-foreign-key column
Drop the specified foreign key.
--add-foregn-key column other_table other_column
Add a foreign key constraint to column pointing to other_table.other_column.
If you want to see the SQL that will be executed to make the change without actually executing it, add
the --sql flag. For example:
sqlite-utils transform fixtures.db roadside_attractions \
--rename pk id \
--default name Untitled \
--column-order id \
--column-order longitude \
--column-order latitude \
--drop address \
--sql
CREATE TABLE [roadside_attractions_new_4033a60276b9] (
[id] INTEGER PRIMARY KEY,
[longitude] FLOAT,
[latitude] FLOAT,
[name] TEXT DEFAULT 'Untitled'
);
INSERT INTO [roadside_attractions_new_4033a60276b9] ([longitude], [latitude], [id], [name])
SELECT [longitude], [latitude], [pk], [name] FROM [roadside_attractions];
DROP TABLE [roadside_attractions];
ALTER TABLE [roadside_attractions_new_4033a60276b9] RENAME TO [roadside_attractions];
Adding a primary key to a rowid table
SQLite tables that are created without an explicit primary key are created as rowid tables. They still
have a numeric primary key which is available in the rowid column, but that column is not included in the
output of select *. Here's an example:
echo '[{"name": "Azi"}, {"name": "Suna"}]' | \
sqlite-utils insert chickens.db chickens -
sqlite-utils schema chickens.db
CREATE TABLE [chickens] (
[name] TEXT
);
sqlite-utils chickens.db 'select * from chickens'
[{"name": "Azi"},
{"name": "Suna"}]
sqlite-utils chickens.db 'select rowid, * from chickens'
[{"rowid": 1, "name": "Azi"},
{"rowid": 2, "name": "Suna"}]
You can use sqlite-utils transform ... --pk id to add a primary key column called id to the table. The
primary key will be created as an INTEGER PRIMARY KEY and the existing rowid values will be copied across
to it. It will automatically increment as new rows are added to the table:
sqlite-utils transform chickens.db chickens --pk id
sqlite-utils schema chickens.db
CREATE TABLE "chickens" (
[id] INTEGER PRIMARY KEY,
[name] TEXT
);
sqlite-utils chickens.db 'select * from chickens'
[{"id": 1, "name": "Azi"},
{"id": 2, "name": "Suna"}]
echo '{"name": "Cardi"}' | sqlite-utils insert chickens.db chickens -
sqlite-utils chickens.db 'select * from chickens'
[{"id": 1, "name": "Azi"},
{"id": 2, "name": "Suna"},
{"id": 3, "name": "Cardi"}]
Extracting columns into a separate table
The sqlite-utils extract command can be used to extract specified columns into a separate table.
Take a look at the Python API documentation for Extracting columns into a separate table for a detailed
description of how this works, including examples of table schemas before and after running an extraction
operation.
The command takes a database, table and one or more columns that should be extracted. To extract the
species column from the trees table you would run:
sqlite-utils extract my.db trees species
This would produce the following schema:
CREATE TABLE "trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[species_id] INTEGER,
FOREIGN KEY(species_id) REFERENCES species(id)
);
CREATE TABLE [species] (
[id] INTEGER PRIMARY KEY,
[species] TEXT
);
CREATE UNIQUE INDEX [idx_species_species]
ON [species] ([species]);
The command takes the following options:
--table TEXT
The name of the lookup to extract columns to. This defaults to using the name of the columns that
are being extracted.
--fk-column TEXT
The name of the foreign key column to add to the table. Defaults to columnname_id.
--rename <TEXT TEXT>
Use this option to rename the columns created in the new lookup table.
--silent
Don't display the progress bar.
Here's a more complex example that makes use of these options. It converts this CSV file full of global
power plants into SQLite, then extracts the country and country_long columns into a separate countries
table:
wget 'https://github.com/wri/global-power-plant-database/blob/232a6666/output_database/global_power_plant_database.csv?raw=true'
sqlite-utils insert global.db power_plants \
'global_power_plant_database.csv?raw=true' --csv
# Extract those columns:
sqlite-utils extract global.db power_plants country country_long \
--table countries \
--fk-column country_id \
--rename country_long name
After running the above, the command sqlite-utils schema global.db reveals the following schema:
CREATE TABLE [countries] (
[id] INTEGER PRIMARY KEY,
[country] TEXT,
[name] TEXT
);
CREATE TABLE "power_plants" (
[country_id] INTEGER,
[name] TEXT,
[gppd_idnr] TEXT,
[capacity_mw] TEXT,
[latitude] TEXT,
[longitude] TEXT,
[primary_fuel] TEXT,
[other_fuel1] TEXT,
[other_fuel2] TEXT,
[other_fuel3] TEXT,
[commissioning_year] TEXT,
[owner] TEXT,
[source] TEXT,
[url] TEXT,
[geolocation_source] TEXT,
[wepp_id] TEXT,
[year_of_capacity_data] TEXT,
[generation_gwh_2013] TEXT,
[generation_gwh_2014] TEXT,
[generation_gwh_2015] TEXT,
[generation_gwh_2016] TEXT,
[generation_gwh_2017] TEXT,
[generation_data_source] TEXT,
[estimated_generation_gwh] TEXT,
FOREIGN KEY([country_id]) REFERENCES [countries]([id])
);
CREATE UNIQUE INDEX [idx_countries_country_name]
ON [countries] ([country], [name]);
Creating views
You can create a view using the create-view command:
sqlite-utils create-view mydb.db version "select sqlite_version()"
sqlite-utils mydb.db "select * from version"
[{"sqlite_version()": "3.31.1"}]
Use --replace to replace an existing view of the same name, and --ignore to do nothing if a view already
exists.
Dropping views
You can drop a view using the drop-view command:
sqlite-utils drop-view myview
Use --ignore to ignore the error if the view does not exist.
Adding columns
You can add a column using the add-column command:
sqlite-utils add-column mydb.db mytable nameofcolumn text
The last argument here is the type of the column to be created. This can be one of:
• text or str
• integer or int
• float
• blob or bytes
This argument is optional and defaults to text.
You can add a column that is a foreign key reference to another table using the --fk option:
sqlite-utils add-column mydb.db dogs species_id --fk species
This will automatically detect the name of the primary key on the species table and use that (and its
type) for the new column.
You can explicitly specify the column you wish to reference using --fk-col:
sqlite-utils add-column mydb.db dogs species_id --fk species --fk-col ref
You can set a NOT NULL DEFAULT 'x' constraint on the new column using --not-null-default:
sqlite-utils add-column mydb.db dogs friends_count integer --not-null-default 0
Adding columns automatically on insert/update
You can use the --alter option to automatically add new columns if the data you are inserting or
upserting is of a different shape:
sqlite-utils insert dogs.db dogs new-dogs.json --pk=id --alter
Adding foreign key constraints
The add-foreign-key command can be used to add new foreign key references to an existing table -
something which SQLite's ALTER TABLE command does not support.
To add a foreign key constraint pointing the books.author_id column to authors.id in another table, do
this:
sqlite-utils add-foreign-key books.db books author_id authors id
If you omit the other table and other column references sqlite-utils will attempt to guess them - so the
above example could instead look like this:
sqlite-utils add-foreign-key books.db books author_id
Add --ignore to ignore an existing foreign key (as opposed to returning an error):
sqlite-utils add-foreign-key books.db books author_id --ignore
See Adding foreign key constraints in the Python API documentation for further details, including how the
automatic table guessing mechanism works.
Adding multiple foreign keys at once
Adding a foreign key requires a VACUUM. On large databases this can be an expensive operation, so if you
are adding multiple foreign keys you can combine them into one operation (and hence one VACUUM) using
add-foreign-keys:
sqlite-utils add-foreign-keys books.db \
books author_id authors id \
authors country_id countries id
When you are using this command each foreign key needs to be defined in full, as four arguments - the
table, column, other table and other column.
Adding indexes for all foreign keys
If you want to ensure that every foreign key column in your database has a corresponding index, you can
do so like this:
sqlite-utils index-foreign-keys books.db
Setting defaults and not null constraints
You can use the --not-null and --default options (to both insert and upsert) to specify columns that
should be NOT NULL or to set database defaults for one or more specific columns:
sqlite-utils insert dogs.db dogs_with_scores dogs-with-scores.json \
--not-null=age \
--not-null=name \
--default age 2 \
--default score 5
Creating indexes
You can add an index to an existing table using the create-index command:
sqlite-utils create-index mydb.db mytable col1 [col2...]
This can be used to create indexes against a single column or multiple columns.
The name of the index will be automatically derived from the table and columns. To specify a different
name, use --name=name_of_index.
Use the --unique option to create a unique index.
Use --if-not-exists to avoid attempting to create the index if one with that name already exists.
To add an index on a column in descending order, prefix the column with a hyphen. Since this can be
confused for a command-line option you need to construct that like this:
sqlite-utils create-index mydb.db mytable -- col1 -col2 col3
This will create an index on that table on (col1, col2 desc, col3).
If your column names are already prefixed with a hyphen you'll need to manually execute a CREATE INDEX
SQL statement to add indexes to them rather than using this tool.
Add the --analyze option to run ANALYZE against the index after it has been created.
Configuring full-text search
You can enable SQLite full-text search on a table and a set of columns like this:
sqlite-utils enable-fts mydb.db documents title summary
This will use SQLite's FTS5 module by default. Use --fts4 if you want to use FTS4:
sqlite-utils enable-fts mydb.db documents title summary --fts4
The enable-fts command will populate the new index with all existing documents. If you later add more
documents you will need to use populate-fts to cause them to be indexed as well:
sqlite-utils populate-fts mydb.db documents title summary
A better solution here is to use database triggers. You can set up database triggers to automatically
update the full-text index using the --create-triggers option when you first run enable-fts:
sqlite-utils enable-fts mydb.db documents title summary --create-triggers
To set a custom FTS tokenizer, e.g. to enable Porter stemming, use --tokenize=:
sqlite-utils populate-fts mydb.db documents title summary --tokenize=porter
To remove the FTS tables and triggers you created, use disable-fts:
sqlite-utils disable-fts mydb.db documents
To rebuild one or more FTS tables (see Rebuilding a full-text search table), use rebuild-fts:
sqlite-utils rebuild-fts mydb.db documents
You can rebuild every FTS table by running rebuild-fts without passing any table names:
sqlite-utils rebuild-fts mydb.db
Executing searches
Once you have configured full-text search for a table, you can search it using sqlite-utils search:
sqlite-utils search mydb.db documents searchterm
This command accepts the same output options as sqlite-utils query: --table, --csv, --tsv, --nl etc.
By default it shows the most relevant matches first. You can specify a different sort order using the -o
option, which can take a column or a column followed by desc:
# Sort by rowid
sqlite-utils search mydb.db documents searchterm -o rowid
# Sort by created in descending order
sqlite-utils search mydb.db documents searchterm -o 'created desc'
SQLite advanced search syntax is enabled by default. To run a search with automatic quoting applied to
the terms to avoid them being potentially interpreted as advanced search syntax use the --quote option.
You can specify a subset of columns to be returned using the -c option one or more times:
sqlite-utils search mydb.db documents searchterm -c title -c created
By default all search results will be returned. You can use --limit 20 to return just the first 20
results.
Use the --sql option to output the SQL that would be executed, rather than running the query:
sqlite-utils search mydb.db documents searchterm --sql
with original as (
select
rowid,
*
from [documents]
)
select
[original].*
from
[original]
join [documents_fts] on [original].rowid = [documents_fts].rowid
where
[documents_fts] match :query
order by
[documents_fts].rank
Enabling cached counts
select count(*) queries can take a long time against large tables. sqlite-utils can speed these up by
adding triggers to maintain a _counts table, see Cached table counts using triggers for details.
The sqlite-utils enable-counts command can be used to configure these triggers, either for every table in
the database or for specific tables.
# Configure triggers for every table in the database
sqlite-utils enable-counts mydb.db
# Configure triggers just for specific tables
sqlite-utils enable-counts mydb.db table1 table2
If the _counts table ever becomes out-of-sync with the actual table counts you can repair it using the
reset-counts command:
sqlite-utils reset-counts mydb.db
Optimizing index usage with ANALYZE
The SQLite ANALYZE command builds a table of statistics which the query planner can use to make better
decisions about which indexes to use for a given query.
You should run ANALYZE if your database is large and you do not think your indexes are being efficiently
used.
To run ANALYZE against every index in a database, use this:
sqlite-utils analyze mydb.db
You can run it against specific tables, or against specific named indexes, by passing them as optional
arguments:
sqlite-utils analyze mydb.db mytable idx_mytable_name
You can also run ANALYZE as part of another command using the --analyze option. This is supported by the
create-index, insert and upsert commands.
Vacuum
You can run VACUUM to optimize your database like so:
sqlite-utils vacuum mydb.db
Optimize
The optimize command can dramatically reduce the size of your database if you are using SQLite full-text
search. It runs OPTIMIZE against all of your FTS4 and FTS5 tables, then runs VACUUM.
If you just want to run OPTIMIZE without the VACUUM, use the --no-vacuum flag.
# Optimize all FTS tables and then VACUUM
sqlite-utils optimize mydb.db
# Optimize but skip the VACUUM
sqlite-utils optimize --no-vacuum mydb.db
To optimize specific tables rather than every FTS table, pass those tables as extra arguments:
sqlite-utils optimize mydb.db table_1 table_2
WAL mode
You can enable Write-Ahead Logging for a database file using the enable-wal command:
sqlite-utils enable-wal mydb.db
You can disable WAL mode using disable-wal:
sqlite-utils disable-wal mydb.db
Both of these commands accept one or more database files as arguments.
Dumping the database to SQL
The dump command outputs a SQL dump of the schema and full contents of the specified database file:
sqlite-utils dump mydb.db
BEGIN TRANSACTION;
CREATE TABLE ...
...
COMMIT;
Loading SQLite extensions
Many of these commands have the ability to load additional SQLite extensions using the
--load-extension=/path/to/extension option - use --help to check for support, e.g. sqlite-utils rows
--help.
This option can be applied multiple times to load multiple extensions.
Since SpatiaLite is commonly used with SQLite, the value spatialite is special: it will search for
SpatiaLite in the most common installation locations, saving you from needing to remember exactly where
that module is located:
sqlite-utils memory "select spatialite_version()" --load-extension=spatialite
[{"spatialite_version()": "4.3.0a"}]
SpatiaLite helpers
SpatiaLite adds geographic capability to SQLite (similar to how PostGIS builds on PostgreSQL). The
SpatiaLite cookbook is a good resource for learning what's possible with it.
You can convert an existing table to a geographic table by adding a geometry column, use the sqlite-utils
add-geometry-column command:
sqlite-utils add-geometry-column spatial.db locations geometry --type POLYGON --srid 4326
The table (locations in the example above) must already exist before adding a geometry column. Use
sqlite-utils create-table first, then add-geometry-column.
Use the --type option to specify a geometry type. By default, add-geometry-column uses a generic
GEOMETRY, which will work with any type, though it may not be supported by some desktop GIS applications.
Eight (case-insensitive) types are allowed:
• POINT
• LINESTRING
• POLYGON
• MULTIPOINT
• MULTILINESTRING
• MULTIPOLYGON
• GEOMETRYCOLLECTION
• GEOMETRY
Adding spatial indexes
Once you have a geometry column, you can speed up bounding box queries by adding a spatial index:
sqlite-utils create-spatial-index spatial.db locations geometry
See this SpatiaLite Cookbook recipe for examples of how to use a spatial index.
Installing packages
The convert command and the insert -\-convert and query -\-functions options can be provided with a
Python script that imports additional modules from the sqlite-utils environment.
You can install packages from PyPI directly into the correct environment using sqlite-utils install
<package>. This is a wrapper around pip install.
sqlite-utils install beautifulsoup4
Use -U to upgrade an existing package.
Uninstalling packages
You can uninstall packages that were installed using sqlite-utils install with sqlite-utils uninstall
<package>:
sqlite-utils uninstall beautifulsoup4
Use -y to skip the request for confirmation.
Experimental TUI
A TUI is a "text user interface" (or "terminal user interface") - a keyboard and mouse driven graphical
interface running in your terminal.
sqlite-utils has experimental support for a TUI for building command-line invocations, built on top of
the Trogon TUI library.
To enable this feature you will need to install the trogon dependency. You can do that like so:
sqite-utils install trogon
Once installed, running the sqlite-utils tui command will launch the TUI interface:
sqlite-utils tui
You can then construct a command by selecting options from the menus, and execute it using Ctrl+R.
[image: A TUI interface for sqlite-utils - the left column shows a list of commands, while the right
panel has a form for constructing arguments to the add-column command.] [image]
sqlite_utils Python library
• Getting started
• Connecting to or creating a database
• Attaching additional databases
• Tracing queries
• Executing queries
• db.query(sql, params)
• db.execute(sql, params)
• Passing parameters
• Accessing tables
• Listing tables
• Listing views
• Listing rows
• Counting rows
• Listing rows with their primary keys
• Retrieving a specific record
• Showing the schema
• Creating tables
• Custom column order and column types
• Explicitly creating a table
• Compound primary keys
• Specifying foreign keys
• Table configuration options
• Setting defaults and not null constraints
• Renaming a table
• Duplicating tables
• Bulk inserts
• Insert-replacing data
• Updating a specific record
• Deleting a specific record
• Deleting multiple records
• Upserting data
• Converting data in columns
• Working with lookup tables
• Creating lookup tables explicitly
• Populating lookup tables automatically during insert/upsert
• Working with many-to-many relationships
• Using m2m and lookup tables together
• Analyzing a column
• Adding columns
• Adding columns automatically on insert/update
• Adding foreign key constraints
• Adding multiple foreign key constraints at once
• Adding indexes for all foreign keys
• Dropping a table or view
• Transforming a table
• Altering column types
• Renaming columns
• Dropping columns
• Changing primary keys
• Changing not null status
• Altering column defaults
• Changing column order
• Adding foreign key constraints
• Replacing foreign key constraints
• Dropping foreign key constraints
• Custom transformations with .transform_sql()
• Extracting columns into a separate table
• Setting an ID based on the hash of the row contents
• Creating views
• Storing JSON
• Converting column values using SQL functions
• Checking the SQLite version
• Dumping the database to SQL
• Introspecting tables and views
• .exists()
• .count
• .columns
• .columns_dict
• .default_values
• .pks
• .use_rowid
• .foreign_keys
• .schema
• .strict
• .indexes
• .xindexes
• .triggers
• .triggers_dict
• .detect_fts()
• .virtual_table_using
• .has_counts_triggers
• db.supports_strict
• Full-text search
• Enabling full-text search for a table
• Quoting characters for use in search
• Searching with table.search()
• Building SQL queries with table.search_sql()
• Rebuilding a full-text search table
• Optimizing a full-text search table
• Cached table counts using triggers
• Creating indexes
• Optimizing index usage with ANALYZE
• Vacuum
• WAL mode
• Suggesting column types
• Registering custom SQL functions
• Quoting strings for use in SQL
• Reading rows from a file
• Setting the maximum CSV field size limit
• Detecting column types using TypeTracker
• SpatiaLite helpers
• Initialize SpatiaLite
• Finding SpatiaLite
• Adding geometry columns
• Creating a spatial index
Getting started
Here's how to create a new SQLite database file containing a new chickens table, populated with four
records:
from sqlite_utils import Database
db = Database("chickens.db")
db["chickens"].insert_all([{
"name": "Azi",
"color": "blue",
}, {
"name": "Lila",
"color": "blue",
}, {
"name": "Suna",
"color": "gold",
}, {
"name": "Cardi",
"color": "black",
}])
You can loop through those rows like this:
for row in db["chickens"].rows:
print(row)
Which outputs the following:
{'name': 'Azi', 'color': 'blue'}
{'name': 'Lila', 'color': 'blue'}
{'name': 'Suna', 'color': 'gold'}
{'name': 'Cardi', 'color': 'black'}
To run a SQL query, use db.query():
for row in db.query("""
select color, count(*)
from chickens group by color
order by count(*) desc
"""):
print(row)
Which outputs:
{'color': 'blue', 'count(*)': 2}
{'color': 'gold', 'count(*)': 1}
{'color': 'black', 'count(*)': 1}
Connecting to or creating a database
Database objects are constructed by passing in either a path to a file on disk or an existing SQLite3
database connection:
from sqlite_utils import Database
db = Database("my_database.db")
This will create my_database.db if it does not already exist.
If you want to recreate a database from scratch (first removing the existing file from disk if it already
exists) you can use the recreate=True argument:
db = Database("my_database.db", recreate=True)
Instead of a file path you can pass in an existing SQLite connection:
import sqlite3
db = Database(sqlite3.connect("my_database.db"))
If you want to create an in-memory database, you can do so like this:
db = Database(memory=True)
You can also create a named in-memory database. Unlike regular memory databases these can be accessed by
multiple threads, provided at least one reference to the database still exists. del db will clear the
database from memory.
db = Database(memory_name="my_shared_database")
Connections use PRAGMA recursive_triggers=on by default. If you don't want to use recursive triggers you
can turn them off using:
db = Database(memory=True, recursive_triggers=False)
By default, any sqlite-utils plugins that implement the prepare_connection(conn) hook will be executed
against the connection when you create the Database object. You can opt out of executing plugins using
execute_plugins=False like this:
db = Database(memory=True, execute_plugins=False)
You can pass strict=True to enable SQLite STRICT mode for all tables created using this database object:
db = Database("my_database.db", strict=True)
Attaching additional databases
SQLite supports cross-database SQL queries, which can join data from tables in more than one database
file.
You can attach an additional database using the .attach() method, providing an alias to use for that
database and the path to the SQLite file on disk.
db = Database("first.db")
db.attach("second", "second.db")
# Now you can run queries like this one:
print(db.query("""
select * from table_in_first
union all
select * from second.table_in_second
"""))
You can reference tables in the attached database using the alias value you passed to db.attach(alias,
filepath) as a prefix, for example the second.table_in_second reference in the SQL query above.
Tracing queries
You can use the tracer mechanism to see SQL queries that are being executed by SQLite. A tracer is a
function that you provide which will be called with sql and params arguments every time SQL is executed,
for example:
def tracer(sql, params):
print("SQL: {} - params: {}".format(sql, params))
You can pass this function to the Database() constructor like so:
db = Database(memory=True, tracer=tracer)
You can also turn on a tracer function temporarily for a block of code using the with db.tracer(...)
context manager:
db = Database(memory=True)
# ... later
with db.tracer(print):
db["dogs"].insert({"name": "Cleo"})
This example will print queries only for the duration of the with block.
Executing queries
The Database class offers several methods for directly executing SQL queries.
db.query(sql, params)
The db.query(sql) function executes a SQL query and returns an iterator over Python dictionaries
representing the resulting rows:
db = Database(memory=True)
db["dogs"].insert_all([{"name": "Cleo"}, {"name": "Pancakes"}])
for row in db.query("select * from dogs"):
print(row)
# Outputs:
# {'name': 'Cleo'}
# {'name': 'Pancakes'}
db.execute(sql, params)
The db.execute() and db.executescript() methods provide wrappers around .execute() and .executescript()
on the underlying SQLite connection. These wrappers log to the tracer function if one has been
registered.
db.execute(sql) returns a sqlite3.Cursor that was used to execute the SQL.
db = Database(memory=True)
db["dogs"].insert({"name": "Cleo"})
cursor = db.execute("update dogs set name = 'Cleopaws'")
print(cursor.rowcount)
# Outputs the number of rows affected by the update
# In this case 2
Other cursor methods such as .fetchone() and .fetchall() are also available, see the standard library
documentation.
Passing parameters
Both db.query() and db.execute() accept an optional second argument for parameters to be passed to the
SQL query.
This can take the form of either a tuple/list or a dictionary, depending on the type of parameters used
in the query. Values passed in this way will be correctly quoted and escaped, helping avoid SQL injection
vulnerabilities.
? parameters in the SQL query can be filled in using a list:
db.execute("update dogs set name = ?", ["Cleopaws"])
# This will rename ALL dogs to be called "Cleopaws"
Named parameters using :name can be filled using a dictionary:
dog = next(db.query(
"select rowid, name from dogs where name = :name",
{"name": "Cleopaws"}
))
# dog is now {'rowid': 1, 'name': 'Cleopaws'}
In this example next() is used to retrieve the first result in the iterator returned by the db.query()
method.
Accessing tables
Tables are accessed using the indexing operator, like so:
table = db["my_table"]
If the table does not yet exist, it will be created the first time you attempt to insert or upsert data
into it.
You can also access tables using the .table() method like so:
table = db.table("my_table")
Using this factory function allows you to set Table configuration options.
Listing tables
You can list the names of tables in a database using the .table_names() method:
>>> db.table_names()
['dogs']
To see just the FTS4 tables, use .table_names(fts4=True). For FTS5, use .table_names(fts5=True).
You can also iterate through the table objects themselves using the .tables property:
>>> db.tables
[<Table dogs>]
Listing views
.view_names() shows you a list of views in the database:
>>> db.view_names()
['good_dogs']
You can iterate through view objects using the .views property:
>>> db.views
[<View good_dogs>]
View objects are similar to Table objects, except that any attempts to insert or update data will throw
an error. The full list of methods and properties available on a view object is as follows:
• columns
• columns_dict
• count
• schema
• rows
• rows_where(where, where_args, order_by, select)
• drop()
Listing rows
To iterate through dictionaries for each of the rows in a table, use .rows:
>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows:
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
{'id': 2, 'age': 2, 'name': 'Pancakes'}
You can filter rows by a WHERE clause using .rows_where(where, where_args):
>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows_where("age > ?", [3]):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
The first argument is a fragment of SQL. The second, optional argument is values to be passed to that
fragment - you can use ? placeholders and pass an array, or you can use :named parameters and pass a
dictionary, like this:
>>> for row in db["dogs"].rows_where("age > :age", {"age": 3}):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
To return custom columns (instead of the default that uses select *) pass select="column1, column2":
>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows_where(select='name, age'):
... print(row)
{'name': 'Cleo', 'age': 4}
To specify an order, use the order_by= argument:
>>> for row in db["dogs"].rows_where("age > 1", order_by="age"):
... print(row)
{'id': 2, 'age': 2, 'name': 'Pancakes'}
{'id': 1, 'age': 4, 'name': 'Cleo'}
You can use order_by="age desc" for descending order.
You can order all records in the table by excluding the where argument:
>>> for row in db["dogs"].rows_where(order_by="age desc"):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
{'id': 2, 'age': 2, 'name': 'Pancakes'}
This method also accepts offset= and limit= arguments, for specifying an OFFSET and a LIMIT for the SQL
query:
>>> for row in db["dogs"].rows_where(order_by="age desc", limit=1):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
Counting rows
To count the number of rows that would be returned by a where filter, use .count_where(where,
where_args):
>>> db["dogs"].count_where("age > ?", [1])
2
Listing rows with their primary keys
Sometimes it can be useful to retrieve the primary key along with each row, in order to pass that key (or
primary key tuple) to the .get() or .update() methods.
The .pks_and_rows_where() method takes the same signature as .rows_where() (with the exception of the
select= parameter) but returns a generator that yields pairs of (primary key, row dictionary).
The primary key value will usually be a single value but can also be a tuple if the table has a compound
primary key.
If the table is a rowid table (with no explicit primary key column) then that ID will be returned.
>>> db = sqlite_utils.Database(memory=True)
>>> db["dogs"].insert({"name": "Cleo"})
>>> for pk, row in db["dogs"].pks_and_rows_where():
... print(pk, row)
1 {'rowid': 1, 'name': 'Cleo'}
>>> db["dogs_with_pk"].insert({"id": 5, "name": "Cleo"}, pk="id")
>>> for pk, row in db["dogs_with_pk"].pks_and_rows_where():
... print(pk, row)
5 {'id': 5, 'name': 'Cleo'}
>>> db["dogs_with_compound_pk"].insert(
... {"species": "dog", "id": 3, "name": "Cleo"},
... pk=("species", "id")
... )
>>> for pk, row in db["dogs_with_compound_pk"].pks_and_rows_where():
... print(pk, row)
('dog', 3) {'species': 'dog', 'id': 3, 'name': 'Cleo'}
Retrieving a specific record
You can retrieve a record by its primary key using table.get():
>>> db = sqlite_utils.Database("dogs.db")
>>> print(db["dogs"].get(1))
{'id': 1, 'age': 4, 'name': 'Cleo'}
If the table has a compound primary key you can pass in the primary key values as a tuple:
>>> db["compound_dogs"].get(("mixed", 3))
If the record does not exist a NotFoundError will be raised:
from sqlite_utils.db import NotFoundError
try:
row = db["dogs"].get(5)
except NotFoundError:
print("Dog not found")
Showing the schema
The db.schema property returns the full SQL schema for the database as a string:
>>> db = sqlite_utils.Database("dogs.db")
>>> print(db.schema)
CREATE TABLE "dogs" (
[id] INTEGER PRIMARY KEY,
[name] TEXT
);
Creating tables
The easiest way to create a new table is to insert a record into it:
from sqlite_utils import Database
import sqlite3
db = Database("dogs.db")
dogs = db["dogs"]
dogs.insert({
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
})
This will automatically create a new table called "dogs" with the following schema:
CREATE TABLE dogs (
name TEXT,
twitter TEXT,
age INTEGER,
is_good_dog INTEGER
)
You can also specify a primary key by passing the pk= parameter to the .insert() call. This will only be
obeyed if the record being inserted causes the table to be created:
dogs.insert({
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, pk="id")
After inserting a row like this, the dogs.last_rowid property will return the SQLite rowid assigned to
the most recently inserted record.
The dogs.last_pk property will return the last inserted primary key value, if you specified one. This can
be very useful when writing code that creates foreign keys or many-to-many relationships.
Custom column order and column types
The order of the columns in the table will be derived from the order of the keys in the dictionary,
provided you are using Python 3.6 or later.
If you want to explicitly set the order of the columns you can do so using the column_order= parameter:
db["dogs"].insert({
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, pk="id", column_order=("id", "twitter", "name"))
You don't need to pass all of the columns to the column_order parameter. If you only pass a subset of the
columns the remaining columns will be ordered based on the key order of the dictionary.
Column types are detected based on the example data provided. Sometimes you may find you need to
over-ride these detected types - to create an integer column for data that was provided as a string for
example, or to ensure that a table where the first example was None is created as an INTEGER rather than
a TEXT column. You can do this using the columns= parameter:
db["dogs"].insert({
"id": 1,
"name": "Cleo",
"age": "5",
}, pk="id", columns={"age": int, "weight": float})
This will create a table with the following schema:
CREATE TABLE [dogs] (
[id] INTEGER PRIMARY KEY,
[name] TEXT,
[age] INTEGER,
[weight] FLOAT
)
Explicitly creating a table
You can directly create a new table without inserting any data into it using the .create() method:
db["cats"].create({
"id": int,
"name": str,
"weight": float,
}, pk="id")
The first argument here is a dictionary specifying the columns you would like to create. Each column is
paired with a Python type indicating the type of column. See Adding columns for full details on how these
types work.
This method takes optional arguments pk=, column_order=, foreign_keys=, not_null=set() and
defaults=dict() - explained below.
A sqlite_utils.utils.sqlite3.OperationalError will be raised if a table of that name already exists.
You can pass ignore=True to ignore that error. You can also use if_not_exists=True to use the SQL CREATE
TABLE IF NOT EXISTS pattern to achieve the same effect:
db["cats"].create({
"id": int,
"name": str,
}, pk="id", if_not_exists=True)
To drop and replace any existing table of that name, pass replace=True. This is a dangerous operation
that will result in loss of existing data in the table.
You can also pass transform=True to have any existing tables transformed to match your new table
specification. This is a dangerous operation as it will drop columns that are no longer listed in your
call to .create(), so be careful when running this.
db["cats"].create({
"id": int,
"name": str,
"weight": float,
}, pk="id", transform=True)
The transform=True option will update the table schema if any of the following have changed:
• The specified columns or their types
• The specified primary key
• The order of the columns, defined using column_order=
• The not_null= or defaults= arguments
Changes to foreign_keys= are not currently detected and applied by transform=True.
You can pass strict=True to create a table in STRICT mode:
db["cats"].create({
"id": int,
"name": str,
}, strict=True)
Compound primary keys
If you want to create a table with a compound primary key that spans multiple columns, you can do so by
passing a tuple of column names to any of the methods that accept a pk= parameter. For example:
db["cats"].create({
"id": int,
"breed": str,
"name": str,
"weight": float,
}, pk=("breed", "id"))
This also works for the .insert(), .insert_all(), .upsert() and .upsert_all() methods.
Specifying foreign keys
Any operation that can create a table (.create(), .insert(), .insert_all(), .upsert() and .upsert_all())
accepts an optional foreign_keys= argument which can be used to set up foreign key constraints for the
table that is being created.
If you are using your database with Datasette, Datasette will detect these constraints and use them to
generate hyperlinks to associated records.
The foreign_keys argument takes a list that indicates which foreign keys should be created. The list can
take several forms. The simplest is a list of columns:
foreign_keys=["author_id"]
The library will guess which tables you wish to reference based on the column names using the rules
described in Adding foreign key constraints.
You can also be more explicit, by passing in a list of tuples:
foreign_keys=[
("author_id", "authors", "id")
]
This means that the author_id column should be a foreign key that references the id column in the authors
table.
You can leave off the third item in the tuple to have the referenced column automatically set to the
primary key of that table. A full example:
db["authors"].insert_all([
{"id": 1, "name": "Sally"},
{"id": 2, "name": "Asheesh"}
], pk="id")
db["books"].insert_all([
{"title": "Hedgehogs of the world", "author_id": 1},
{"title": "How to train your wolf", "author_id": 2},
], foreign_keys=[
("author_id", "authors")
])
Table configuration options
The .insert(), .upsert(), .insert_all() and .upsert_all() methods each take a number of keyword
arguments, some of which influence what happens should they cause a table to be created and some of which
affect the behavior of those methods.
You can set default values for these methods by accessing the table through the db.table(...) method
(instead of using db["table_name"]), like so:
table = db.table(
"authors",
pk="id",
not_null={"name", "score"},
column_order=("id", "name", "score", "url")
)
# Now you can call .insert() like so:
table.insert({"id": 1, "name": "Tracy", "score": 5})
The configuration options that can be specified in this way are pk, foreign_keys, column_order, not_null,
defaults, batch_size, hash_id, hash_id_columns, alter, ignore, replace, extracts, conversions, columns,
strict. These are all documented below.
Setting defaults and not null constraints
Each of the methods that can cause a table to be created take optional arguments not_null=set() and
defaults=dict(). The methods that take these optional arguments are:
• db.create_table(...)
• table.create(...)
• table.insert(...)
• table.insert_all(...)
• table.upsert(...)
• table.upsert_all(...)
You can use not_null= to pass a set of column names that should have a NOT NULL constraint set on them
when they are created.
You can use defaults= to pass a dictionary mapping columns to the default value that should be specified
in the CREATE TABLE statement.
Here's an example that uses these features:
db["authors"].insert_all(
[{"id": 1, "name": "Sally", "score": 2}],
pk="id",
not_null={"name", "score"},
defaults={"score": 1},
)
db["authors"].insert({"name": "Dharma"})
list(db["authors"].rows)
# Outputs:
# [{'id': 1, 'name': 'Sally', 'score': 2},
# {'id': 3, 'name': 'Dharma', 'score': 1}]
print(db["authors"].schema)
# Outputs:
# CREATE TABLE [authors] (
# [id] INTEGER PRIMARY KEY,
# [name] TEXT NOT NULL,
# [score] INTEGER NOT NULL DEFAULT 1
# )
Renaming a table
The db.rename_table(old_name, new_name) method can be used to rename a table:
db.rename_table("my_table", "new_name_for_my_table")
This executes the following SQL:
ALTER TABLE [my_table] RENAME TO [new_name_for_my_table]
Duplicating tables
The table.duplicate() method creates a copy of the table, copying both the table schema and all of the
rows in that table:
db["authors"].duplicate("authors_copy")
The new authors_copy table will now contain a duplicate copy of the data from authors.
This method raises sqlite_utils.db.NoTable if the table does not exist.
Bulk inserts
If you have more than one record to insert, the insert_all() method is a much more efficient way of
inserting them. Just like insert() it will automatically detect the columns that should be created, but
it will inspect the first batch of 100 items to help decide what those column types should be.
Use it like this:
db["dogs"].insert_all([{
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, {
"id": 2,
"name": "Marnie",
"twitter": "MarnieTheDog",
"age": 16,
"is_good_dog": True,
}], pk="id", column_order=("id", "twitter", "name"))
The column types used in the CREATE TABLE statement are automatically derived from the types of data in
that first batch of rows. Any additional columns in subsequent batches will cause a
sqlite3.OperationalError exception to be raised unless the alter=True argument is supplied, in which case
the new columns will be created.
The function can accept an iterator or generator of rows and will commit them according to the batch
size. The default batch size is 100, but you can specify a different size using the batch_size parameter:
db["big_table"].insert_all(({
"id": 1,
"name": "Name {}".format(i),
} for i in range(10000)), batch_size=1000)
You can skip inserting any records that have a primary key that already exists using ignore=True. This
works with both .insert({...}, ignore=True) and .insert_all([...], ignore=True).
You can delete all the existing rows in the table before inserting the new records using truncate=True.
This is useful if you want to replace the data in the table.
Pass analyze=True to run ANALYZE against the table after inserting the new records.
Insert-replacing data
If you try to insert data using a primary key that already exists, the .insert() or .insert_all() method
will raise a sqlite3.IntegrityError exception.
This example that catches that exception:
from sqlite_utils.utils import sqlite3
try:
db["dogs"].insert({"id": 1, "name": "Cleo"}, pk="id")
except sqlite3.IntegrityError:
print("Record already exists with that primary key")
Importing from sqlite_utils.utils.sqlite3 ensures your code continues to work even if you are using the
pysqlite3 library instead of the Python standard library sqlite3 module.
Use the ignore=True parameter to ignore this error:
# This fails silently if a record with id=1 already exists
db["dogs"].insert({"id": 1, "name": "Cleo"}, pk="id", ignore=True)
To replace any existing records that have a matching primary key, use the replace=True parameter to
.insert() or .insert_all():
db["dogs"].insert_all([{
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, {
"id": 2,
"name": "Marnie",
"twitter": "MarnieTheDog",
"age": 16,
"is_good_dog": True,
}], pk="id", replace=True)
NOTE:
Prior to sqlite-utils 2.0 the .upsert() and .upsert_all() methods worked the same way as
.insert(replace=True) does today. See Upserting data for the new behaviour of those methods introduced
in 2.0.
Updating a specific record
You can update a record by its primary key using table.update():
>>> db = sqlite_utils.Database("dogs.db")
>>> print(db["dogs"].get(1))
{'id': 1, 'age': 4, 'name': 'Cleo'}
>>> db["dogs"].update(1, {"age": 5})
>>> print(db["dogs"].get(1))
{'id': 1, 'age': 5, 'name': 'Cleo'}
The first argument to update() is the primary key. This can be a single value, or a tuple if that table
has a compound primary key:
>>> db["compound_dogs"].update((5, 3), {"name": "Updated"})
The second argument is a dictionary of columns that should be updated, along with their new values.
You can cause any missing columns to be added automatically using alter=True:
>>> db["dogs"].update(1, {"breed": "Mutt"}, alter=True)
Deleting a specific record
You can delete a record using table.delete():
>>> db = sqlite_utils.Database("dogs.db")
>>> db["dogs"].delete(1)
The delete() method takes the primary key of the record. This can be a tuple of values if the row has a
compound primary key:
>>> db["compound_dogs"].delete((5, 3))
Deleting multiple records
You can delete all records in a table that match a specific WHERE statement using table.delete_where():
>>> db = sqlite_utils.Database("dogs.db")
>>> # Delete every dog with age less than 3
>>> db["dogs"].delete_where("age < ?", [3])
Calling table.delete_where() with no other arguments will delete every row in the table.
Pass analyze=True to run ANALYZE against the table after deleting the rows.
Upserting data
Upserting allows you to insert records if they do not exist and update them if they DO exist, based on
matching against their primary key.
For example, given the dogs database you could upsert the record for Cleo like so:
db["dogs"].upsert({
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 4,
"is_good_dog": True,
}, pk="id", column_order=("id", "twitter", "name"))
If a record exists with id=1, it will be updated to match those fields. If it does not exist it will be
created.
Any existing columns that are not referenced in the dictionary passed to .upsert() will be unchanged. If
you want to replace a record entirely, use .insert(doc, replace=True) instead.
Note that the pk and column_order parameters here are optional if you are certain that the table has
already been created. You should pass them if the table may not exist at the time the first upsert is
performed.
An upsert_all() method is also available, which behaves like insert_all() but performs upserts instead.
NOTE:
.upsert() and .upsert_all() in sqlite-utils 1.x worked like .insert(..., replace=True) and
.insert_all(..., replace=True) do in 2.x. See issue #66 for details of this change.
Converting data in columns
The table.convert(...) method can be used to apply a conversion function to the values in a column,
either to update that column or to populate new columns. It is the Python library equivalent of the
sqlite-utils convert command.
This feature works by registering a custom SQLite function that applies a Python transformation, then
running a SQL query equivalent to UPDATE table SET column = convert_value(column);
To transform a specific column to uppercase, you would use the following:
db["dogs"].convert("name", lambda value: value.upper())
You can pass a list of columns, in which case the transformation will be applied to each one:
db["dogs"].convert(["name", "twitter"], lambda value: value.upper())
To save the output to of the transformation to a different column, use the output= parameter:
db["dogs"].convert("name", lambda value: value.upper(), output="name_upper")
This will add the new column, if it does not already exist. You can pass output_type=int or some other
type to control the type of the new column - otherwise it will default to text.
If you want to drop the original column after saving the results in a separate output column, pass
drop=True.
By default any rows with a falsey value for the column - such as 0 or None - will be skipped. Pass
skip_false=False to disable this behaviour.
You can create multiple new columns from a single input column by passing multi=True and a conversion
function that returns a Python dictionary. This example creates new upper and lower columns populated
from the single title column:
table.convert(
"title", lambda v: {"upper": v.upper(), "lower": v.lower()}, multi=True
)
The .convert() method accepts optional where= and where_args= parameters which can be used to apply the
conversion to a subset of rows specified by a where clause. Here's how to apply the conversion only to
rows with an id that is higher than 20:
table.convert("title", lambda v: v.upper(), where="id > :id", where_args={"id": 20})
These behave the same as the corresponding parameters to the .rows_where() method, so you can use ?
placeholders and a list of values instead of :named placeholders with a dictionary.
Working with lookup tables
A useful pattern when populating large tables in to break common values out into lookup tables. Consider
a table of Trees, where each tree has a species. Ideally these species would be split out into a separate
Species table, with each one assigned an integer primary key that can be referenced from the Trees table
species_id column.
Creating lookup tables explicitly
Calling db["Species"].lookup({"name": "Palm"}) creates a table called Species (if one does not already
exist) with two columns: id and name. It sets up a unique constraint on the name column to guarantee it
will not contain duplicate rows. It then inserts a new row with the name set to Palm and returns the new
integer primary key value.
If the Species table already exists, it will insert the new row and return the primary key. If a row with
that name already exists, it will return the corresponding primary key value directly.
If you call .lookup() against an existing table without the unique constraint it will attempt to add the
constraint, raising an IntegrityError if the constraint cannot be created.
If you pass in a dictionary with multiple values, both values will be used to insert or retrieve the
corresponding ID and any unique constraint that is created will cover all of those columns, for example:
db["Trees"].insert({
"latitude": 49.1265976,
"longitude": 2.5496218,
"species": db["Species"].lookup({
"common_name": "Common Juniper",
"latin_name": "Juniperus communis"
})
})
The .lookup() method has an optional second argument which can be used to populate other columns in the
table but only if the row does not exist yet. These columns will not be included in the unique index.
To create a species record with a note on when it was first seen, you can use this:
db["Species"].lookup({"name": "Palm"}, {"first_seen": "2021-03-04"})
The first time this is called the record will be created for name="Palm". Any subsequent calls with that
name will ignore the second argument, even if it includes different values.
.lookup() also accepts keyword arguments, which are passed through to the insert() method and can be used
to influence the shape of the created table. Supported parameters are:
• pk - which defaults to id
• foreign_keys
• column_order
• not_null
• defaults
• extracts
• conversions
• columns
• strict
Populating lookup tables automatically during insert/upsert
A more efficient way to work with lookup tables is to define them using the extracts= parameter, which is
accepted by .insert(), .upsert(), .insert_all(), .upsert_all() and by the .table(...) factory function.
extracts= specifies columns which should be "extracted" out into a separate lookup table during the data
insertion.
It can be either a list of column names, in which case the extracted table names will match the column
names exactly, or it can be a dictionary mapping column names to the desired name of the extracted table.
To extract the species column out to a separate Species table, you can do this:
# Using the table factory
trees = db.table("Trees", extracts={"species": "Species"})
trees.insert({
"latitude": 49.1265976,
"longitude": 2.5496218,
"species": "Common Juniper"
})
# If you want the table to be called 'species', you can do this:
trees = db.table("Trees", extracts=["species"])
# Using .insert() directly
db["Trees"].insert({
"latitude": 49.1265976,
"longitude": 2.5496218,
"species": "Common Juniper"
}, extracts={"species": "Species"})
Working with many-to-many relationships
sqlite-utils includes a shortcut for creating records using many-to-many relationships in the form of the
table.m2m(...) method.
Here's how to create two new records and connect them via a many-to-many table in a single line of code:
db["dogs"].insert({"id": 1, "name": "Cleo"}, pk="id").m2m(
"humans", {"id": 1, "name": "Natalie"}, pk="id"
)
Running this example actually creates three tables: dogs, humans and a many-to-many dogs_humans table. It
will insert a record into each of those tables.
The .m2m() method executes against the last record that was affected by .insert() or .update() - the
record identified by the table.last_pk property. To execute .m2m() against a specific record you can
first select it by passing its primary key to .update():
db["dogs"].update(1).m2m(
"humans", {"id": 2, "name": "Simon"}, pk="id"
)
The first argument to .m2m() can be either the name of a table as a string or it can be the table object
itself.
The second argument can be a single dictionary record or a list of dictionaries. These dictionaries will
be passed to .upsert() against the specified table.
Here's alternative code that creates the dog record and adds two people to it:
db = Database(memory=True)
dogs = db.table("dogs", pk="id")
humans = db.table("humans", pk="id")
dogs.insert({"id": 1, "name": "Cleo"}).m2m(
humans, [
{"id": 1, "name": "Natalie"},
{"id": 2, "name": "Simon"}
]
)
The method will attempt to find an existing many-to-many table by looking for a table that has foreign
key relationships against both of the tables in the relationship.
If it cannot find such a table, it will create a new one using the names of the two tables - dogs_humans
in this example. You can customize the name of this table using the m2m_table= argument to .m2m().
It it finds multiple candidate tables with foreign keys to both of the specified tables it will raise a
sqlite_utils.db.NoObviousTable exception. You can avoid this error by specifying the correct table using
m2m_table=.
The .m2m() method also takes an optional pk= argument to specify the primary key that should be used if
the table is created, and an optional alter=True argument to specify that any missing columns of an
existing table should be added if they are needed.
Using m2m and lookup tables together
You can work with (or create) lookup tables as part of a call to .m2m() using the lookup= parameter. This
accepts the same argument as table.lookup() does - a dictionary of values that should be used to lookup
or create a row in the lookup table.
This example creates a dogs table, populates it, creates a characteristics table, populates that and sets
up a many-to-many relationship between the two. It chains .m2m() twice to create two associated
characteristics:
db = Database(memory=True)
dogs = db.table("dogs", pk="id")
dogs.insert({"id": 1, "name": "Cleo"}).m2m(
"characteristics", lookup={
"name": "Playful"
}
).m2m(
"characteristics", lookup={
"name": "Opinionated"
}
)
You can inspect the database to see the results like this:
>>> db.table_names()
['dogs', 'characteristics', 'characteristics_dogs']
>>> list(db["dogs"].rows)
[{'id': 1, 'name': 'Cleo'}]
>>> list(db["characteristics"].rows)
[{'id': 1, 'name': 'Playful'}, {'id': 2, 'name': 'Opinionated'}]
>>> list(db["characteristics_dogs"].rows)
[{'characteristics_id': 1, 'dogs_id': 1}, {'characteristics_id': 2, 'dogs_id': 1}]
>>> print(db["characteristics_dogs"].schema)
CREATE TABLE [characteristics_dogs] (
[characteristics_id] INTEGER REFERENCES [characteristics]([id]),
[dogs_id] INTEGER REFERENCES [dogs]([id]),
PRIMARY KEY ([characteristics_id], [dogs_id])
)
Analyzing a column
The table.analyze_column(column) method is used by the analyze-tables CLI command.
It takes the following arguments and options:
column - required
The name of the column to analyze
common_limit
The number of most common values to return. Defaults to 10.
value_truncate
If set to an integer, values longer than this will be truncated to this length. Defaults to None.
most_common
If set to False, the most_common field of the returned ColumnDetails will be set to None. Defaults
to True.
least_common
If set to False, the least_common field of the returned ColumnDetails will be set to None.
Defaults to True.
And returns a ColumnDetails named tuple with the following fields:
table The name of the table
column The name of the column
total_rows
The total number of rows in the table
num_null
The number of rows for which this column is null
num_blank
The number of rows for which this column is blank (the empty string)
num_distinct
The number of distinct values in this column
most_common
The N most common values as a list of (value, count) tuples`, or None if the table consists
entirely of distinct values
least_common
The N least common values as a list of (value, count) tuples`, or None if the table is entirely
distinct or if the number of distinct values is less than N (since they will already have been
returned in most_common)
Adding columns
You can add a new column to a table using the .add_column(col_name, col_type) method:
db["dogs"].add_column("instagram", str)
db["dogs"].add_column("weight", float)
db["dogs"].add_column("dob", datetime.date)
db["dogs"].add_column("image", "BLOB")
db["dogs"].add_column("website") # str by default
You can specify the col_type argument either using a SQLite type as a string, or by directly passing a
Python type e.g. str or float.
The col_type is optional - if you omit it the type of TEXT will be used.
SQLite types you can specify are "TEXT", "INTEGER", "FLOAT" or "BLOB".
If you pass a Python type, it will be mapped to SQLite types as shown here:
float: "FLOAT"
int: "INTEGER"
bool: "INTEGER"
str: "TEXT"
bytes: "BLOB"
datetime.datetime: "TEXT"
datetime.date: "TEXT"
datetime.time: "TEXT"
datetime.timedelta: "TEXT"
# If numpy is installed
np.int8: "INTEGER"
np.int16: "INTEGER"
np.int32: "INTEGER"
np.int64: "INTEGER"
np.uint8: "INTEGER"
np.uint16: "INTEGER"
np.uint32: "INTEGER"
np.uint64: "INTEGER"
np.float16: "FLOAT"
np.float32: "FLOAT"
np.float64: "FLOAT"
You can also add a column that is a foreign key reference to another table using the fk parameter:
db["dogs"].add_column("species_id", fk="species")
This will automatically detect the name of the primary key on the species table and use that (and its
type) for the new column.
You can explicitly specify the column you wish to reference using fk_col:
db["dogs"].add_column("species_id", fk="species", fk_col="ref")
You can set a NOT NULL DEFAULT 'x' constraint on the new column using not_null_default:
db["dogs"].add_column("friends_count", int, not_null_default=0)
Adding columns automatically on insert/update
You can insert or update data that includes new columns and have the table automatically altered to fit
the new schema using the alter=True argument. This can be passed to all four of .insert(), .upsert(),
.insert_all() and .upsert_all(), or it can be passed to db.table(table_name, alter=True) to enable it by
default for all method calls against that table instance.
db["new_table"].insert({"name": "Gareth"})
# This will throw an exception:
db["new_table"].insert({"name": "Gareth", "age": 32})
# This will succeed and add a new "age" integer column:
db["new_table"].insert({"name": "Gareth", "age": 32}, alter=True)
# You can see confirm the new column like so:
print(db["new_table"].columns_dict)
# Outputs this:
# {'name': <class 'str'>, 'age': <class 'int'>}
# This works too:
new_table = db.table("new_table", alter=True)
new_table.insert({"name": "Gareth", "age": 32, "shoe_size": 11})
Adding foreign key constraints
The SQLite ALTER TABLE statement doesn't have the ability to add foreign key references to an existing
column.
The add_foreign_key() method here is a convenient wrapper around table.transform().
It's also possible to add foreign keys by directly updating the sqlite_master table. The
sqlite-utils-fast-fks plugin implements this pattern, using code that was included with sqlite-utils
prior to version 3.35.
Here's an example of this mechanism in action:
db["authors"].insert_all([
{"id": 1, "name": "Sally"},
{"id": 2, "name": "Asheesh"}
], pk="id")
db["books"].insert_all([
{"title": "Hedgehogs of the world", "author_id": 1},
{"title": "How to train your wolf", "author_id": 2},
])
db["books"].add_foreign_key("author_id", "authors", "id")
The table.add_foreign_key(column, other_table, other_column) method takes the name of the column, the
table that is being referenced and the key column within that other table. If you omit the other_column
argument the primary key from that table will be used automatically. If you omit the other_table argument
the table will be guessed based on some simple rules:
• If the column is of format author_id, look for tables called author or authors
• If the column does not end in _id, try looking for a table with the exact name of the column or that
name with an added s
This method first checks that the specified foreign key references tables and columns that exist and does
not clash with an existing foreign key. It will raise a sqlite_utils.db.AlterError exception if these
checks fail.
To ignore the case where the key already exists, use ignore=True:
db["books"].add_foreign_key("author_id", "authors", "id", ignore=True)
Adding multiple foreign key constraints at once
You can use db.add_foreign_keys(...) to add multiple foreign keys in one go. This method takes a list of
four-tuples, each one specifying a table, column, other_table and other_column.
Here's an example adding two foreign keys at once:
db.add_foreign_keys([
("dogs", "breed_id", "breeds", "id"),
("dogs", "home_town_id", "towns", "id")
])
This method runs the same checks as .add_foreign_keys() and will raise sqlite_utils.db.AlterError if
those checks fail.
Adding indexes for all foreign keys
If you want to ensure that every foreign key column in your database has a corresponding index, you can
do so like this:
db.index_foreign_keys()
Dropping a table or view
You can drop a table or view using the .drop() method:
db["my_table"].drop()
Pass ignore=True if you want to ignore the error caused by the table or view not existing.
db["my_table"].drop(ignore=True)
Transforming a table
The SQLite ALTER TABLE statement is limited. It can add and drop columns and rename tables, but it cannot
change column types, change NOT NULL status or change the primary key for a table.
The table.transform() method can do all of these things, by implementing a multi-step pattern described
in the SQLite documentation:
1. Start a transaction
2. CREATE TABLE tablename_new_x123 with the required changes
3. Copy the old data into the new table using INSERT INTO tablename_new_x123 SELECT * FROM tablename;
4. DROP TABLE tablename;
5. ALTER TABLE tablename_new_x123 RENAME TO tablename;
6. Commit the transaction
The .transform() method takes a number of parameters, all of which are optional.
As a bonus, calling .transform() will reformat the schema for the table that is stored in SQLite to make
it more readable. This works even if you call it without any arguments.
To keep the original table around instead of dropping it, pass the keep_table= option and specify the
name of the table you would like it to be renamed to:
table.transform(types={"age": int}, keep_table="original_table")
Altering column types
To alter the type of a column, use the types= argument:
# Convert the 'age' column to an integer, and 'weight' to a float
table.transform(types={"age": int, "weight": float})
See Adding columns for a list of available types.
Renaming columns
The rename= parameter can rename columns:
# Rename 'age' to 'initial_age':
table.transform(rename={"age": "initial_age"})
Dropping columns
To drop columns, pass them in the drop= set:
# Drop the 'age' column:
table.transform(drop={"age"})
Changing primary keys
To change the primary key for a table, use pk=. This can be passed a single column for a regular primary
key, or a tuple of columns to create a compound primary key. Passing pk=None will remove the primary key
and convert the table into a rowid table.
# Make `user_id` the new primary key
table.transform(pk="user_id")
Changing not null status
You can change the NOT NULL status of columns by using not_null=. You can pass this a set of columns to
make those columns NOT NULL:
# Make the 'age' and 'weight' columns NOT NULL
table.transform(not_null={"age", "weight"})
If you want to take existing NOT NULL columns and change them to allow null values, you can do so by
passing a dictionary of true/false values instead:
# 'age' is NOT NULL but we want to allow NULL:
table.transform(not_null={"age": False})
# Make age allow NULL and switch weight to being NOT NULL:
table.transform(not_null={"age": False, "weight": True})
Altering column defaults
The defaults= parameter can be used to set or change the defaults for different columns:
# Set default age to 1:
table.transform(defaults={"age": 1})
# Now remove the default from that column:
table.transform(defaults={"age": None})
Changing column order
The column_order= parameter can be used to change the order of the columns. If you pass the names of a
subset of the columns those will go first and columns you omitted will appear in their existing order
after them.
# Change column order
table.transform(column_order=("name", "age", "id")
Adding foreign key constraints
You can add one or more foreign key constraints to a table using the add_foreign_keys= parameter:
db["places"].transform(
add_foreign_keys=(
("country", "country", "id"),
("continent", "continent", "id")
)
)
This accepts the same arguments described in specifying foreign keys - so you can specify them as a full
tuple of (column, other_table, other_column), or you can take a shortcut and pass just the name of the
column, provided the table can be automatically derived from the column name:
db["places"].transform(
add_foreign_keys=(("country", "continent"))
)
Replacing foreign key constraints
The foreign_keys= parameter is similar to to add_foreign_keys= but can be be used to replace all foreign
key constraints on a table, dropping any that are not explicitly mentioned:
db["places"].transform(
foreign_keys=(
("continent", "continent", "id"),
)
)
Dropping foreign key constraints
You can use .transform() to remove foreign key constraints from a table.
This example drops two foreign keys - the one from places.country to country.id and the one from
places.continent to continent.id:
db["places"].transform(
drop_foreign_keys=("country", "continent")
)
Custom transformations with .transform_sql()
The .transform() method can handle most cases, but it does not automatically upgrade indexes, views or
triggers associated with the table that is being transformed.
If you want to do something more advanced, you can call the table.transform_sql(...) method with the same
arguments that you would have passed to table.transform(...).
This method will return a list of SQL statements that should be executed to implement the change. You can
then make modifications to that SQL - or add additional SQL statements - before executing it yourself.
Extracting columns into a separate table
The table.extract() method can be used to extract specified columns into a separate table.
Imagine a Trees table that looks like this:
┌───┬──────────────┬─────────┐
│id │ TreeAddress │ Species │
├───┼──────────────┼─────────┤
│1 │ 52 Vine St │ Palm │
├───┼──────────────┼─────────┤
│2 │ 12 Draft St │ Oak │
├───┼──────────────┼─────────┤
│3 │ 51 Dark Ave │ Palm │
├───┼──────────────┼─────────┤
│4 │ 1252 Left St │ Palm │
└───┴──────────────┴─────────┘
The Species column contains duplicate values. This database could be improved by extracting that column
out into a separate Species table and pointing to it using a foreign key column.
The schema of the above table is:
CREATE TABLE [Trees] (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[Species] TEXT
)
Here's how to extract the Species column using .extract():
db["Trees"].extract("Species")
After running this code the table schema now looks like this:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[Species_id] INTEGER,
FOREIGN KEY(Species_id) REFERENCES Species(id)
)
A new Species table will have been created with the following schema:
CREATE TABLE [Species] (
[id] INTEGER PRIMARY KEY,
[Species] TEXT
)
The .extract() method defaults to creating a table with the same name as the column that was extracted,
and adding a foreign key column called tablename_id.
You can specify a custom table name using table=, and a custom foreign key name using fk_column=. This
example creates a table called tree_species and a foreign key column called tree_species_id:
db["Trees"].extract("Species", table="tree_species", fk_column="tree_species_id")
The resulting schema looks like this:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[tree_species_id] INTEGER,
FOREIGN KEY(tree_species_id) REFERENCES tree_species(id)
)
CREATE TABLE [tree_species] (
[id] INTEGER PRIMARY KEY,
[Species] TEXT
)
You can also extract multiple columns into the same external table. Say for example you have a table like
this:
┌───┬──────────────┬────────────┬───────────┐
│id │ TreeAddress │ CommonName │ LatinName │
├───┼──────────────┼────────────┼───────────┤
│1 │ 52 Vine St │ Palm │ Arecaceae │
├───┼──────────────┼────────────┼───────────┤
│2 │ 12 Draft St │ Oak │ Quercus │
├───┼──────────────┼────────────┼───────────┤
│3 │ 51 Dark Ave │ Palm │ Arecaceae │
├───┼──────────────┼────────────┼───────────┤
│4 │ 1252 Left St │ Palm │ Arecaceae │
└───┴──────────────┴────────────┴───────────┘
You can pass ["CommonName", "LatinName"] to .extract() to extract both of those columns:
db["Trees"].extract(["CommonName", "LatinName"])
This produces the following schema:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[CommonName_LatinName_id] INTEGER,
FOREIGN KEY(CommonName_LatinName_id) REFERENCES CommonName_LatinName(id)
)
CREATE TABLE [CommonName_LatinName] (
[id] INTEGER PRIMARY KEY,
[CommonName] TEXT,
[LatinName] TEXT
)
The table name CommonName_LatinName is derived from the extract columns. You can use table= and
fk_column= to specify custom names like this:
db["Trees"].extract(["CommonName", "LatinName"], table="Species", fk_column="species_id")
This produces the following schema:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[species_id] INTEGER,
FOREIGN KEY(species_id) REFERENCES Species(id)
)
CREATE TABLE [Species] (
[id] INTEGER PRIMARY KEY,
[CommonName] TEXT,
[LatinName] TEXT
)
You can use the rename= argument to rename columns in the lookup table. To create a Species table with
columns called name and latin you can do this:
db["Trees"].extract(
["CommonName", "LatinName"],
table="Species",
fk_column="species_id",
rename={"CommonName": "name", "LatinName": "latin"}
)
This produces a lookup table like so:
CREATE TABLE [Species] (
[id] INTEGER PRIMARY KEY,
[name] TEXT,
[latin] TEXT
)
Setting an ID based on the hash of the row contents
Sometimes you will find yourself working with a dataset that includes rows that do not have a provided
obvious ID, but where you would like to assign one so that you can later upsert into that table without
creating duplicate records.
In these cases, a useful technique is to create an ID that is derived from the sha1 hash of the row
contents.
sqlite-utils can do this for you using the hash_id= option. For example:
db = sqlite_utils.Database("dogs.db")
db["dogs"].upsert({"name": "Cleo", "twitter": "cleopaws"}, hash_id="id")
print(list(db["dogs]))
Outputs:
[{'id': 'f501265970505d9825d8d9f590bfab3519fb20b1', 'name': 'Cleo', 'twitter': 'cleopaws'}]
If you are going to use that ID straight away, you can access it using last_pk:
dog_id = db["dogs"].upsert({
"name": "Cleo",
"twitter": "cleopaws"
}, hash_id="id").last_pk
# dog_id is now "f501265970505d9825d8d9f590bfab3519fb20b1"
The hash will be created using all of the column values. To create a hash using a subset of the columns,
pass the hash_id_columns= parameter:
db["dogs"].upsert(
{"name": "Cleo", "twitter": "cleopaws", "age": 7},
hash_id_columns=("name", "twitter")
)
The hash_id= parameter is optional if you specify hash_id_columns= - it will default to putting the hash
in a column called id.
You can manually calculate these hashes using the hash_record(record, keys=...) utility function.
Creating views
The .create_view() method on the database class can be used to create a view:
db.create_view("good_dogs", """
select * from dogs where is_good_dog = 1
""")
This will raise a sqlite_utils.utils.OperationalError if a view with that name already exists.
You can pass ignore=True to silently ignore an existing view and do nothing, or replace=True to replace
an existing view with a new definition if your select statement differs from the current view:
db.create_view("good_dogs", """
select * from dogs where is_good_dog = 1
""", replace=True)
Storing JSON
SQLite has excellent JSON support, and sqlite-utils can help you take advantage of this: if you attempt
to insert a value that can be represented as a JSON list or dictionary, sqlite-utils will create TEXT
column and store your data as serialized JSON. This means you can quickly store even complex data
structures in SQLite and query them using JSON features.
For example:
db["niche_museums"].insert({
"name": "The Bigfoot Discovery Museum",
"url": "http://bigfootdiscoveryproject.com/"
"hours": {
"Monday": [11, 18],
"Wednesday": [11, 18],
"Thursday": [11, 18],
"Friday": [11, 18],
"Saturday": [11, 18],
"Sunday": [11, 18]
},
"address": {
"streetAddress": "5497 Highway 9",
"addressLocality": "Felton, CA",
"postalCode": "95018"
}
})
db.execute("""
select json_extract(address, '$.addressLocality')
from niche_museums
""").fetchall()
# Returns [('Felton, CA',)]
Converting column values using SQL functions
Sometimes it can be useful to run values through a SQL function prior to inserting them. A simple example
might be converting a value to upper case while it is being inserted.
The conversions={...} parameter can be used to specify custom SQL to be used as part of a INSERT or
UPDATE SQL statement.
You can specify an upper case conversion for a specific column like so:
db["example"].insert({
"name": "The Bigfoot Discovery Museum"
}, conversions={"name": "upper(?)"})
# list(db["example"].rows) now returns:
# [{'name': 'THE BIGFOOT DISCOVERY MUSEUM'}]
The dictionary key is the column name to be converted. The value is the SQL fragment to use, with a ?
placeholder for the original value.
A more useful example: if you are working with SpatiaLite you may find yourself wanting to create
geometry values from a WKT value. Code to do that could look like this:
import sqlite3
import sqlite_utils
from shapely.geometry import shape
import httpx
db = sqlite_utils.Database("places.db")
# Initialize SpatiaLite
db.init_spatialite()
# Use sqlite-utils to create a places table
places = db["places"].create({"id": int, "name": str})
# Add a SpatiaLite 'geometry' column
places.add_geometry_column("geometry", "MULTIPOLYGON")
# Fetch some GeoJSON from Who's On First:
geojson = httpx.get(
"https://raw.githubusercontent.com/whosonfirst-data/"
"whosonfirst-data-admin-gb/master/data/404/227/475/404227475.geojson"
).json()
# Convert to "Well Known Text" format using shapely
wkt = shape(geojson["geometry"]).wkt
# Insert the record, converting the WKT to a SpatiaLite geometry:
db["places"].insert(
{"name": "Wales", "geometry": wkt},
conversions={"geometry": "GeomFromText(?, 4326)"},
)
This example uses gographical data from Who's On First and depends on the Shapely and HTTPX Python
libraries.
Checking the SQLite version
The db.sqlite_version property returns a tuple of integers representing the version of SQLite used for
that database object:
>>> db.sqlite_version
(3, 36, 0)
Dumping the database to SQL
The db.iterdump() method returns a sequence of SQL strings representing a complete dump of the database.
Use it like this:
full_sql = "".join(db.iterdump())
This uses the sqlite3.Connection.iterdump() method.
If you are using pysqlite3 or sqlean.py the underlying method may be missing. If you install the
sqlite-dump package then the db.iterdump() method will use that implementation instead:
pip install sqlite-dump
Introspecting tables and views
If you have loaded an existing table or view, you can use introspection to find out more about it:
>>> db["PlantType"]
<Table PlantType (id, value)>
.exists()
The .exists() method can be used to find out if a table exists or not:
>>> db["PlantType"].exists()
True
>>> db["PlantType2"].exists()
False
.count
The .count property shows the current number of rows (select count(*) from table):
>>> db["PlantType"].count
3
>>> db["Street_Tree_List"].count
189144
This property will take advantage of Cached table counts using triggers if the use_counts_table property
is set on the database. You can avoid that optimization entirely by calling table.count_where() instead
of accessing the property.
.columns
The .columns property shows the columns in the table or view. It returns a list of Column(cid, name,
type, notnull, default_value, is_pk) named tuples.
>>> db["PlantType"].columns
[Column(cid=0, name='id', type='INTEGER', notnull=0, default_value=None, is_pk=1),
Column(cid=1, name='value', type='TEXT', notnull=0, default_value=None, is_pk=0)]
.columns_dict
The .columns_dict property returns a dictionary version of the columns with just the names and Python
types:
>>> db["PlantType"].columns_dict
{'id': <class 'int'>, 'value': <class 'str'>}
.default_values
The .default_values property returns a dictionary of default values for each column that has a default:
>>> db["table_with_defaults"].default_values
{'score': 5}
.pks
The .pks property returns a list of strings naming the primary key columns for the table:
>>> db["PlantType"].pks
['id']
If a table has no primary keys but is a rowid table, this property will return ['rowid'].
.use_rowid
Almost all SQLite tables have a rowid column, but a table with no explicitly defined primary keys must
use that rowid as the primary key for identifying individual rows. The .use_rowid property checks to see
if a table needs to use the rowid in this way - it returns True if the table has no explicitly defined
primary keys and False otherwise.
>>> db["PlantType"].use_rowid
False
.foreign_keys
The .foreign_keys property returns any foreign key relationships for the table, as a list of
ForeignKey(table, column, other_table, other_column) named tuples. It is not available on views.
>>> db["Street_Tree_List"].foreign_keys
[ForeignKey(table='Street_Tree_List', column='qLegalStatus', other_table='qLegalStatus', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qCareAssistant', other_table='qCareAssistant', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qSiteInfo', other_table='qSiteInfo', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qSpecies', other_table='qSpecies', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qCaretaker', other_table='qCaretaker', other_column='id'),
ForeignKey(table='Street_Tree_List', column='PlantType', other_table='PlantType', other_column='id')]
.schema
The .schema property outputs the table's schema as a SQL string:
>>> print(db["Street_Tree_List"].schema)
CREATE TABLE "Street_Tree_List" (
"TreeID" INTEGER,
"qLegalStatus" INTEGER,
"qSpecies" INTEGER,
"qAddress" TEXT,
"SiteOrder" INTEGER,
"qSiteInfo" INTEGER,
"PlantType" INTEGER,
"qCaretaker" INTEGER,
"qCareAssistant" INTEGER,
"PlantDate" TEXT,
"DBH" INTEGER,
"PlotSize" TEXT,
"PermitNotes" TEXT,
"XCoord" REAL,
"YCoord" REAL,
"Latitude" REAL,
"Longitude" REAL,
"Location" TEXT
,
FOREIGN KEY ("PlantType") REFERENCES [PlantType](id),
FOREIGN KEY ("qCaretaker") REFERENCES [qCaretaker](id),
FOREIGN KEY ("qSpecies") REFERENCES [qSpecies](id),
FOREIGN KEY ("qSiteInfo") REFERENCES [qSiteInfo](id),
FOREIGN KEY ("qCareAssistant") REFERENCES [qCareAssistant](id),
FOREIGN KEY ("qLegalStatus") REFERENCES [qLegalStatus](id))
.strict
The .strict property identifies if the table is a SQLite STRICT table.
>>> db["ny_times_us_counties"].strict
False
.indexes
The .indexes property returns all indexes created for a table, as a list of Index(seq, name, unique,
origin, partial, columns) named tuples. It is not available on views.
>>> db["Street_Tree_List"].indexes
[Index(seq=0, name='"Street_Tree_List_qLegalStatus"', unique=0, origin='c', partial=0, columns=['qLegalStatus']),
Index(seq=1, name='"Street_Tree_List_qCareAssistant"', unique=0, origin='c', partial=0, columns=['qCareAssistant']),
Index(seq=2, name='"Street_Tree_List_qSiteInfo"', unique=0, origin='c', partial=0, columns=['qSiteInfo']),
Index(seq=3, name='"Street_Tree_List_qSpecies"', unique=0, origin='c', partial=0, columns=['qSpecies']),
Index(seq=4, name='"Street_Tree_List_qCaretaker"', unique=0, origin='c', partial=0, columns=['qCaretaker']),
Index(seq=5, name='"Street_Tree_List_PlantType"', unique=0, origin='c', partial=0, columns=['PlantType'])]
.xindexes
The .xindexes property returns more detailed information about the indexes on the table, using the SQLite
PRAGMA index_xinfo() mechanism. It returns a list of XIndex(name, columns) named tuples, where columns is
a list of XIndexColumn(seqno, cid, name, desc, coll, key) named tuples.
>>> db["ny_times_us_counties"].xindexes
[
XIndex(
name='idx_ny_times_us_counties_date',
columns=[
XIndexColumn(seqno=0, cid=0, name='date', desc=1, coll='BINARY', key=1),
XIndexColumn(seqno=1, cid=-1, name=None, desc=0, coll='BINARY', key=0)
]
),
XIndex(
name='idx_ny_times_us_counties_fips',
columns=[
XIndexColumn(seqno=0, cid=3, name='fips', desc=0, coll='BINARY', key=1),
XIndexColumn(seqno=1, cid=-1, name=None, desc=0, coll='BINARY', key=0)
]
)
]
.triggers
The .triggers property lists database triggers. It can be used on both database and table objects. It
returns a list of Trigger(name, table, sql) named tuples.
>>> db["authors"].triggers
[Trigger(name='authors_ai', table='authors', sql='CREATE TRIGGER [authors_ai] AFTER INSERT...'),
Trigger(name='authors_ad', table='authors', sql="CREATE TRIGGER [authors_ad] AFTER DELETE..."),
Trigger(name='authors_au', table='authors', sql="CREATE TRIGGER [authors_au] AFTER UPDATE")]
>>> db.triggers
... similar output to db["authors"].triggers
.triggers_dict
The .triggers_dict property returns the triggers for that table as a dictionary mapping their names to
their SQL definitions.
>>> db["authors"].triggers_dict
{'authors_ai': 'CREATE TRIGGER [authors_ai] AFTER INSERT...',
'authors_ad': 'CREATE TRIGGER [authors_ad] AFTER DELETE...',
'authors_au': 'CREATE TRIGGER [authors_au] AFTER UPDATE'}
The same property exists on the database, and will return all triggers across all tables:
>>> db.triggers_dict
{'authors_ai': 'CREATE TRIGGER [authors_ai] AFTER INSERT...',
'authors_ad': 'CREATE TRIGGER [authors_ad] AFTER DELETE...',
'authors_au': 'CREATE TRIGGER [authors_au] AFTER UPDATE'}
.detect_fts()
The detect_fts() method returns the associated SQLite FTS table name, if one exists for this table. If
the table has not been configured for full-text search it returns None.
>>> db["authors"].detect_fts()
"authors_fts"
.virtual_table_using
The .virtual_table_using property reveals if a table is a virtual table. It returns None for regular
tables and the upper case version of the type of virtual table otherwise. For example:
>>> db["authors"].enable_fts(["name"])
>>> db["authors_fts"].virtual_table_using
"FTS5"
.has_counts_triggers
The .has_counts_triggers property shows if a table has been configured with triggers for updating a
_counts table, as described in Cached table counts using triggers.
>>> db["authors"].has_counts_triggers
False
>>> db["authors"].enable_counts()
>>> db["authors"].has_counts_triggers
True
db.supports_strict
This property on the database object returns True if the available SQLite version supports STRICT mode,
which was added in SQLite 3.37.0 (on 2021-11-27).
>>> db.supports_strict
True
Full-text search
SQLite includes bundled extensions that implement powerful full-text search.
Enabling full-text search for a table
You can enable full-text search on a table using .enable_fts(columns):
db["dogs"].enable_fts(["name", "twitter"])
You can then run searches using the .search() method:
rows = list(db["dogs"].search("cleo"))
This method returns a generator that can be looped over to get dictionaries for each row, similar to
Listing rows.
If you insert additional records into the table you will need to refresh the search index using
populate_fts():
db["dogs"].insert({
"id": 2,
"name": "Marnie",
"twitter": "MarnieTheDog",
"age": 16,
"is_good_dog": True,
}, pk="id")
db["dogs"].populate_fts(["name", "twitter"])
A better solution is to use database triggers. You can set up database triggers to automatically update
the full-text index using create_triggers=True:
db["dogs"].enable_fts(["name", "twitter"], create_triggers=True)
.enable_fts() defaults to using FTS5. If you wish to use FTS4 instead, use the following:
db["dogs"].enable_fts(["name", "twitter"], fts_version="FTS4")
You can customize the tokenizer configured for the table using the tokenize= parameter. For example, to
enable Porter stemming, where English words like "running" will match stemmed alternatives such as "run",
use tokenize="porter":
db["articles"].enable_fts(["headline", "body"], tokenize="porter")
The SQLite documentation has more on FTS5 tokenizers and FTS4 tokenizers. porter is a valid option for
both.
If you attempt to configure a FTS table where one already exists, a sqlite3.OperationalError exception
will be raised.
You can replace the existing table with a new configuration using replace=True:
db["articles"].enable_fts(["headline"], tokenize="porter", replace=True)
This will have no effect if the FTS table already exists, otherwise it will drop and recreate the table
with the new settings. This takes into consideration the columns, the tokenizer, the FTS version used and
whether or not the table has triggers.
To remove the FTS tables and triggers you created, use the disable_fts() table method:
db["dogs"].disable_fts()
Quoting characters for use in search
SQLite supports advanced search query syntax. In some situations you may wish to disable this, since
characters such as . may have special meaning that causes errors when searching for strings provided by
your users.
The db.quote_fts(query) method returns the query with SQLite full-text search quoting applied such that
the query should be safe to use in a search:
db.quote_fts("Search term.")
# Returns: '"Search" "term."'
Searching with table.search()
The table.search(q) method returns a generator over Python dictionaries representing rows that match the
search phrase q, ordered by relevance with the most relevant results first.
for article in db["articles"].search("jquery"):
print(article)
The .search() method also accepts the following optional parameters:
order_by string
The column to sort by. Defaults to relevance score. Can optionally include a desc, e.g. rowid
desc.
columns array of strings
Columns to return. Defaults to all columns.
limit integer
Number of results to return. Defaults to all results.
offset integer
Offset to use along side the limit parameter.
where string
Extra SQL fragment for the WHERE clause
where_args dictionary
Arguments to use for :param placeholders in the extra WHERE clause
quote bool
Apply FTS quoting rules to the search query, disabling advanced query syntax in a way that avoids
surprising errors.
To return just the title and published columns for three matches for "dog" where the id is greater than
10 ordered by published with the most recent first, use the following:
for article in db["articles"].search(
"dog",
order_by="published desc",
limit=3,
where="id > :min_id",
where_args={"min_id": 10},
columns=["title", "published"]
):
print(article)
Building SQL queries with table.search_sql()
You can generate the SQL query that would be used for a search using the table.search_sql() method. It
takes the same arguments as table.search(), with the exception of the search query and the where_args
parameter, since those should be provided when the returned SQL is executed.
print(db["articles"].search_sql(columns=["title", "author"]))
Outputs:
with original as (
select
rowid,
[title],
[author]
from [articles]
)
select
[original].[title],
[original].[author]
from
[original]
join [articles_fts] on [original].rowid = [articles_fts].rowid
where
[articles_fts] match :query
order by
[articles_fts].rank
This method detects if a SQLite table uses FTS4 or FTS5, and outputs the correct SQL for ordering by
relevance depending on the search type.
The FTS4 output looks something like this:
with original as (
select
rowid,
[title],
[author]
from [articles]
)
select
[original].[title],
[original].[author]
from
[original]
join [articles_fts] on [original].rowid = [articles_fts].rowid
where
[articles_fts] match :query
order by
rank_bm25(matchinfo([articles_fts], 'pcnalx'))
This uses the rank_bm25() custom SQL function from sqlite-fts4. You can register that custom function
against a Database connection using this method:
db.register_fts4_bm25()
Rebuilding a full-text search table
You can rebuild a table using the table.rebuild_fts() method. This is useful for if the table
configuration changes or the indexed data has become corrupted in some way.
db["dogs"].rebuild_fts()
This method can be called on a table that has been configured for full-text search - dogs in this
instance - or directly on a _fts table:
db["dogs_fts"].rebuild_fts()
This runs the following SQL:
INSERT INTO dogs_fts (dogs_fts) VALUES ("rebuild");
Optimizing a full-text search table
Once you have populated a FTS table you can optimize it to dramatically reduce its size like so:
db["dogs"].optimize()
This runs the following SQL:
INSERT INTO dogs_fts (dogs_fts) VALUES ("optimize");
Cached table counts using triggers
The select count(*) query in SQLite requires a full scan of the primary key index, and can take an
increasingly long time as the table grows larger.
The table.enable_counts() method can be used to configure triggers to continuously update a record in a
_counts table. This value can then be used to quickly retrieve the count of rows in the associated table.
db["dogs"].enable_counts()
This will create the _counts table if it does not already exist, with the following schema:
CREATE TABLE [_counts] (
[table] TEXT PRIMARY KEY,
[count] INTEGER DEFAULT 0
)
You can enable cached counts for every table in a database (except for virtual tables and the _counts
table itself) using the database enable_counts() method:
db.enable_counts()
Once enabled, table counts will be stored in the _counts table. The count records will be automatically
kept up-to-date by the triggers when rows are added or deleted to the table.
To access these counts you can query the _counts table directly or you can use the db.cached_counts()
method. This method returns a dictionary mapping tables to their counts:
>>> db.cached_counts()
{'global-power-plants': 33643,
'global-power-plants_fts_data': 136,
'global-power-plants_fts_idx': 199,
'global-power-plants_fts_docsize': 33643,
'global-power-plants_fts_config': 1}
You can pass a list of table names to this method to retrieve just those counts:
>>> db.cached_counts(["global-power-plants"])
{'global-power-plants': 33643}
The table.count property executes a select count(*) query by default, unless the db.use_counts_table
property is set to True.
You can set use_counts_table to True when you instantiate the database object:
db = Database("global-power-plants.db", use_counts_table=True)
If the property is True any calls to the table.count property will first attempt to find the cached count
in the _counts table, and fall back on a count(*) query if the value is not available or the table is
missing.
Calling the .enable_counts() method on a database or table object will set use_counts_table to True for
the lifetime of that database object.
If the _counts table ever becomes out-of-sync with the actual table counts you can repair it using the
.reset_counts() method:
db.reset_counts()
Creating indexes
You can create an index on a table using the .create_index(columns) method. The method takes a list of
columns:
db["dogs"].create_index(["is_good_dog"])
By default the index will be named idx_{table-name}_{columns}. If you pass find_unique_name=True and the
automatically derived name already exists, an available name will be found by incrementing a suffix
number, for example idx_items_title_2.
You can customize the name of the created index by passing the index_name parameter:
db["dogs"].create_index(
["is_good_dog", "age"],
index_name="good_dogs_by_age"
)
To create an index in descending order for a column, wrap the column name in db.DescIndex() like this:
from sqlite_utils.db import DescIndex
db["dogs"].create_index(
["is_good_dog", DescIndex("age")],
index_name="good_dogs_by_age"
)
You can create a unique index by passing unique=True:
db["dogs"].create_index(["name"], unique=True)
Use if_not_exists=True to do nothing if an index with that name already exists.
Pass analyze=True to run ANALYZE against the new index after creating it.
Optimizing index usage with ANALYZE
The SQLite ANALYZE command builds a table of statistics which the query planner can use to make better
decisions about which indexes to use for a given query.
You should run ANALYZE if your database is large and you do not think your indexes are being efficiently
used.
To run ANALYZE against every index in a database, use this:
db.analyze()
To run it just against a specific named index, pass the name of the index to that method:
db.analyze("idx_countries_country_name")
To run against all indexes attached to a specific table, you can either pass the table name to
db.analyze(...) or you can call the method directly on the table, like this:
db["dogs"].analyze()
Vacuum
You can optimize your database by running VACUUM against it like so:
Database("my_database.db").vacuum()
WAL mode
You can enable Write-Ahead Logging for a database with .enable_wal():
Database("my_database.db").enable_wal()
You can disable WAL mode using .disable_wal():
Database("my_database.db").disable_wal()
You can check the current journal mode for a database using the journal_mode property:
journal_mode = Database("my_database.db").journal_mode
This will usually be wal or delete (meaning WAL is disabled), but can have other values - see the PRAGMA
journal_mode documentation.
Suggesting column types
When you create a new table for a list of inserted or upserted Python dictionaries, those methods detect
the correct types for the database columns based on the data you pass in.
In some situations you may need to intervene in this process, to customize the columns that are being
created in some way - see Explicitly creating a table.
That table .create() method takes a dictionary mapping column names to the Python type they should store:
db["cats"].create({
"id": int,
"name": str,
"weight": float,
})
You can use the suggest_column_types() helper function to derive a dictionary of column names and types
from a list of records, suitable to be passed to table.create().
For example:
from sqlite_utils import Database, suggest_column_types
cats = [{
"id": 1,
"name": "Snowflake"
}, {
"id": 2,
"name": "Crabtree",
"age": 4
}]
types = suggest_column_types(cats)
# types now looks like this:
# {"id": <class 'int'>,
# "name": <class 'str'>,
# "age": <class 'int'>}
# Manually add an extra field:
types["thumbnail"] = bytes
# types now looks like this:
# {"id": <class 'int'>,
# "name": <class 'str'>,
# "age": <class 'int'>,
# "thumbnail": <class 'bytes'>}
# Create the table
db = Database("cats.db")
db["cats"].create(types, pk="id")
# Insert the records
db["cats"].insert_all(cats)
# list(db["cats"].rows) now returns:
# [{"id": 1, "name": "Snowflake", "age": None, "thumbnail": None}
# {"id": 2, "name": "Crabtree", "age": 4, "thumbnail": None}]
# The table schema looks like this:
# print(db["cats"].schema)
# CREATE TABLE [cats] (
# [id] INTEGER PRIMARY KEY,
# [name] TEXT,
# [age] INTEGER,
# [thumbnail] BLOB
# )
Registering custom SQL functions
SQLite supports registering custom SQL functions written in Python. The db.register_function() method
lets you register these functions, and keeps track of functions that have already been registered.
If you use it as a method it will automatically detect the name and number of arguments needed by the
function:
from sqlite_utils import Database
db = Database(memory=True)
def reverse_string(s):
return "".join(reversed(list(s)))
db.register_function(reverse_string)
print(db.execute('select reverse_string("hello")').fetchone()[0])
# This prints "olleh"
You can also use the method as a function decorator like so:
@db.register_function
def reverse_string(s):
return "".join(reversed(list(s)))
print(db.execute('select reverse_string("hello")').fetchone()[0])
By default, the name of the Python function will be used as the name of the SQL function. You can
customize this with the name= keyword argument:
@db.register_function(name="rev")
def reverse_string(s):
return "".join(reversed(list(s)))
print(db.execute('select rev("hello")').fetchone()[0])
Python 3.8 added the ability to register deterministic SQLite functions, allowing you to indicate that a
function will return the exact same result for any given inputs and hence allowing SQLite to apply some
performance optimizations. You can mark a function as deterministic using deterministic=True, like this:
@db.register_function(deterministic=True)
def reverse_string(s):
return "".join(reversed(list(s)))
If you run this on a version of Python prior to 3.8 your code will still work, but the deterministic=True
parameter will be ignored.
By default registering a function with the same name and number of arguments will have no effect - the
Database instance keeps track of functions that have already been registered and skips registering them
if @db.register_function is called a second time.
If you want to deliberately replace the registered function with a new implementation, use the
replace=True argument:
@db.register_function(deterministic=True, replace=True)
def reverse_string(s):
return s[::-1]
Exceptions that occur inside a user-defined function default to returning the following error:
Unexpected error: user-defined function raised exception
You can cause sqlite3 to return more useful errors, including the traceback from the custom function, by
executing the following before your custom functions are executed:
from sqlite_utils.utils import sqlite3
sqlite3.enable_callback_tracebacks(True)
Quoting strings for use in SQL
In almost all cases you should pass values to your SQL queries using the optional parameters argument to
db.query(), as described in Passing parameters.
If that option isn't relevant to your use-case you can to quote a string for use with SQLite using the
db.quote() method, like so:
>>> db = Database(memory=True)
>>> db.quote("hello")
"'hello'"
>>> db.quote("hello'this'has'quotes")
"'hello''this''has''quotes'"
Reading rows from a file
The sqlite_utils.utils.rows_from_file() helper function can read rows (a sequence of dictionaries) from
CSV, TSV, JSON or newline-delimited JSON files.
Setting the maximum CSV field size limit
Sometimes when working with CSV files that include extremely long fields you may see an error that looks
like this:
_csv.Error: field larger than field limit (131072)
The Python standard library csv module enforces a field size limit. You can increase that limit using the
csv.field_size_limit(new_limit) method (documented here) but if you don't want to pick a new level you
may instead want to increase it to the maximum possible.
The maximum possible value for this is not documented, and varies between systems.
Calling sqlite_utils.utils.maximize_csv_field_size_limit() will set the value to the highest possible for
the current system:
from sqlite_utils.utils import maximize_csv_field_size_limit
maximize_csv_field_size_limit()
If you need to reset to the original value after calling this function you can do so like this:
from sqlite_utils.utils import ORIGINAL_CSV_FIELD_SIZE_LIMIT
import csv
csv.field_size_limit(ORIGINAL_CSV_FIELD_SIZE_LIMIT)
Detecting column types using TypeTracker
Sometimes you may find yourself working with data that lacks type information - data from a CSV file for
example.
The TypeTracker class can be used to try to automatically identify the most likely types for data that is
initially represented as strings.
Consider this example:
import csv, io
csv_file = io.StringIO("id,name\n1,Cleo\n2,Cardi")
rows = list(csv.DictReader(csv_file))
# rows is now this:
# [{'id': '1', 'name': 'Cleo'}, {'id': '2', 'name': 'Cardi'}]
If we insert this data directly into a table we will get a schema that is entirely TEXT columns:
from sqlite_utils import Database
db = Database(memory=True)
db["creatures"].insert_all(rows)
print(db.schema)
# Outputs:
# CREATE TABLE [creatures] (
# [id] TEXT,
# [name] TEXT
# );
We can detect the best column types using a TypeTracker instance:
from sqlite_utils.utils import TypeTracker
tracker = TypeTracker()
db["creatures2"].insert_all(tracker.wrap(rows))
print(tracker.types)
# Outputs {'id': 'integer', 'name': 'text'}
We can then apply those types to our new table using the table.transform() method:
db["creatures2"].transform(types=tracker.types)
print(db["creatures2"].schema)
# Outputs:
# CREATE TABLE [creatures2] (
# [id] INTEGER,
# [name] TEXT
# );
SpatiaLite helpers
SpatiaLite is a geographic extension to SQLite (similar to PostgreSQL + PostGIS). Using requires finding,
loading and initializing the extension, adding geometry columns to existing tables and optionally
creating spatial indexes. The utilities here help streamline that setup.
Initialize SpatiaLite
Finding SpatiaLite
Adding geometry columns
Creating a spatial index
Plugins
sqlite-utils supports plugins, which can be used to add extra features to the software.
Plugins can add new commands, for example sqlite-utils some-command ...
Plugins can be installed using the sqlite-utils install command:
sqlite-utils install sqlite-utils-name-of-plugin
You can see a JSON list of plugins that have been installed by running this:
sqlite-utils plugins
Plugin hooks such as prepare_connection(conn) affect each instance of the Database class. You can opt-out
of these plugins by creating that class instance like so:
db = Database(memory=True, execute_plugins=False)
Building a plugin
Plugins are created in a directory named after the plugin. To create a "hello world" plugin, first create
a hello-world directory:
mkdir hello-world
cd hello-world
In that folder create two files. The first is a pyproject.toml file describing the plugin:
[project]
name = "sqlite-utils-hello-world"
version = "0.1"
[project.entry-points.sqlite_utils]
hello_world = "sqlite_utils_hello_world"
The [project.entry-points.sqlite_utils] section tells sqlite-utils which module to load when executing
the plugin.
Then create sqlite_utils_hello_world.py with the following content:
import click
import sqlite_utils
@sqlite_utils.hookimpl
def register_commands(cli):
@cli.command()
def hello_world():
"Say hello world"
click.echo("Hello world!")
Install the plugin in "editable" mode - so you can make changes to the code and have them picked up
instantly by sqlite-utils - like this:
sqlite-utils install -e .
Or pass the path to your plugin directory:
sqlite-utils install -e /dev/sqlite-utils-hello-world
Now, running this should execute your new command:
sqlite-utils hello-world
Your command will also be listed in the output of sqlite-utils --help.
See the LLM plugin documentation for tips on distributing your plugin.
Plugin hooks
Plugin hooks allow sqlite-utils to be customized.
register_commands(cli)
This hook can be used to register additional commands with the sqlite-utils CLI. It is called with the
cli object, which is a click.Group instance.
Example implementation:
import click
import sqlite_utils
@sqlite_utils.hookimpl
def register_commands(cli):
@cli.command()
def hello_world():
"Say hello world"
click.echo("Hello world!")
prepare_connection(conn)
This hook is called when a new SQLite database connection is created. You can use it to register custom
SQL functions, aggregates and collations. For example:
import sqlite_utils
@sqlite_utils.hookimpl
def prepare_connection(conn):
conn.create_function(
"hello", 1, lambda name: f"Hello, {name}!"
)
This registers a SQL function called hello which takes a single argument and can be called like this:
select hello("world"); -- "Hello, world!"
API reference
• sqlite_utils.db.Database
• sqlite_utils.db.Queryable
• sqlite_utils.db.Table
• sqlite_utils.db.View
• Other
• sqlite_utils.db.Column
• sqlite_utils.db.ColumnDetails
• sqlite_utils.utils
• sqlite_utils.utils.hash_record
• sqlite_utils.utils.rows_from_file
• sqlite_utils.utils.TypeTracker
• sqlite_utils.utils.chunks
• sqlite_utils.utils.flatten
sqlite_utils.db.Database
sqlite_utils.db.Queryable
Table and View are both subclasses of Queryable, providing access to the following methods:
sqlite_utils.db.Table
sqlite_utils.db.View
Other
sqlite_utils.db.Column
sqlite_utils.db.ColumnDetails
sqlite_utils.utils
sqlite_utils.utils.hash_record
sqlite_utils.utils.rows_from_file
sqlite_utils.utils.TypeTracker
sqlite_utils.utils.chunks
sqlite_utils.utils.flatten
CLI reference
This page lists the --help for every sqlite-utils CLI sub-command.
• query
• memory
• insert
• upsert
• bulk
• search
• transform
• extract
• schema
• insert-files
• analyze-tables
• convert
• tables
• views
• rows
• triggers
• indexes
• create-database
• create-table
• create-index
• enable-fts
• populate-fts
• rebuild-fts
• disable-fts
• tui
• optimize
• analyze
• vacuum
• dump
• add-column
• add-foreign-key
• add-foreign-keys
• index-foreign-keys
• enable-wal
• disable-wal
• enable-counts
• reset-counts
• duplicate
• rename-table
• drop-table
• create-view
• drop-view
• install
• uninstall
• add-geometry-column
• create-spatial-index
• plugins
query
See Running SQL queries.
Usage: sqlite-utils query [OPTIONS] PATH SQL
Execute SQL query and return the results as JSON
Example:
sqlite-utils data.db \
"select * from chickens where age > :age" \
-p age 1
Options:
--attach <TEXT FILE>... Additional databases to attach - specify alias and
filepath
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira,
latex, latex_booktabs, latex_longtable, latex_raw,
mediawiki, mixed_grid, mixed_outline, moinmoin,
orgtbl, outline, pipe, plain, presto, pretty,
psql, rounded_grid, rounded_outline, rst, simple,
simple_grid, simple_outline, textile, tsv,
unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not
escaped strings
-r, --raw Raw output, first column of first row
--raw-lines Raw output, first column of each row
-p, --param <TEXT TEXT>... Named :parameters for SQL query
--functions TEXT Python code defining one or more custom SQL
functions
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
memory
See Querying data directly using an in-memory database.
Usage: sqlite-utils memory [OPTIONS] [PATHS]... SQL
Execute SQL query against an in-memory database, optionally populated by
imported data
To import data from CSV, TSV or JSON files pass them on the command-line:
sqlite-utils memory one.csv two.json \
"select * from one join two on one.two_id = two.id"
For data piped into the tool from standard input, use "-" or "stdin":
cat animals.csv | sqlite-utils memory - \
"select * from stdin where species = 'dog'"
The format of the data will be automatically detected. You can specify the
format explicitly using :json, :csv, :tsv or :nl (for newline-delimited JSON)
- for example:
cat animals.csv | sqlite-utils memory stdin:csv places.dat:nl \
"select * from stdin where place_id in (select id from places)"
Use --schema to view the SQL schema of any imported files:
sqlite-utils memory animals.csv --schema
Options:
--functions TEXT Python code defining one or more custom SQL
functions
--attach <TEXT FILE>... Additional databases to attach - specify alias and
filepath
--flatten Flatten nested JSON objects, so {"foo": {"bar":
1}} becomes {"foo_bar": 1}
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira,
latex, latex_booktabs, latex_longtable, latex_raw,
mediawiki, mixed_grid, mixed_outline, moinmoin,
orgtbl, outline, pipe, plain, presto, pretty,
psql, rounded_grid, rounded_outline, rst, simple,
simple_grid, simple_outline, textile, tsv,
unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not
escaped strings
-r, --raw Raw output, first column of first row
--raw-lines Raw output, first column of each row
-p, --param <TEXT TEXT>... Named :parameters for SQL query
--encoding TEXT Character encoding for CSV input, defaults to
utf-8
-n, --no-detect-types Treat all CSV/TSV columns as TEXT
--schema Show SQL schema for in-memory database
--dump Dump SQL for in-memory database
--save FILE Save in-memory database to this file
--analyze Analyze resulting tables and output results
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
insert
See Inserting JSON data, Inserting CSV or TSV data, Inserting unstructured data with --lines and --text,
Applying conversions while inserting data.
Usage: sqlite-utils insert [OPTIONS] PATH TABLE FILE
Insert records from FILE into a table, creating the table if it does not
already exist.
Example:
echo '{"name": "Lila"}' | sqlite-utils insert data.db chickens -
By default the input is expected to be a JSON object or array of objects.
- Use --nl for newline-delimited JSON objects
- Use --csv or --tsv for comma-separated or tab-separated input
- Use --lines to write each incoming line to a column called "line"
- Use --text to write the entire input to a column called "text"
You can also use --convert to pass a fragment of Python code that will be used
to convert each input.
Your Python code will be passed a "row" variable representing the imported
row, and can return a modified row.
This example uses just the name, latitude and longitude columns from a CSV
file, converting name to upper case and latitude and longitude to floating
point numbers:
sqlite-utils insert plants.db plants plants.csv --csv --convert '
return {
"name": row["name"].upper(),
"latitude": float(row["latitude"]),
"longitude": float(row["longitude"]),
}'
If you are using --lines your code will be passed a "line" variable, and for
--text a "text" variable.
When using --text your function can return an iterator of rows to insert. This
example inserts one record per word in the input:
echo 'A bunch of words' | sqlite-utils insert words.db words - \
--text --convert '({"word": w} for w in text.split())'
Options:
--pk TEXT Columns to use as the primary key, e.g. id
--flatten Flatten nested JSON objects, so {"a": {"b": 1}}
becomes {"a_b": 1}
--nl Expect newline-delimited JSON
-c, --csv Expect CSV input
--tsv Expect TSV input
--empty-null Treat empty strings as NULL
--lines Treat each line as a single value called 'line'
--text Treat input as a single value called 'text'
--convert TEXT Python code to convert each item
--import TEXT Python modules to import
--delimiter TEXT Delimiter to use for CSV files
--quotechar TEXT Quote character to use for CSV/TSV
--sniff Detect delimiter and quote character
--no-headers CSV file has no header row
--encoding TEXT Character encoding for input, defaults to utf-8
--batch-size INTEGER Commit every X records
--stop-after INTEGER Stop after X records
--alter Alter existing table to add any missing columns
--not-null TEXT Columns that should be created as NOT NULL
--default <TEXT TEXT>... Default value that should be set for a column
-d, --detect-types Detect types for columns in CSV/TSV data
--analyze Run ANALYZE at the end of this operation
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
--silent Do not show progress bar
--strict Apply STRICT mode to created table
--ignore Ignore records if pk already exists
--replace Replace records if pk already exists
--truncate Truncate table before inserting records, if table
already exists
-h, --help Show this message and exit.
upsert
See Upserting data.
Usage: sqlite-utils upsert [OPTIONS] PATH TABLE FILE
Upsert records based on their primary key. Works like 'insert' but if an
incoming record has a primary key that matches an existing record the existing
record will be updated.
Example:
echo '[
{"id": 1, "name": "Lila"},
{"id": 2, "name": "Suna"}
]' | sqlite-utils upsert data.db chickens - --pk id
Options:
--pk TEXT Columns to use as the primary key, e.g. id
[required]
--flatten Flatten nested JSON objects, so {"a": {"b": 1}}
becomes {"a_b": 1}
--nl Expect newline-delimited JSON
-c, --csv Expect CSV input
--tsv Expect TSV input
--empty-null Treat empty strings as NULL
--lines Treat each line as a single value called 'line'
--text Treat input as a single value called 'text'
--convert TEXT Python code to convert each item
--import TEXT Python modules to import
--delimiter TEXT Delimiter to use for CSV files
--quotechar TEXT Quote character to use for CSV/TSV
--sniff Detect delimiter and quote character
--no-headers CSV file has no header row
--encoding TEXT Character encoding for input, defaults to utf-8
--batch-size INTEGER Commit every X records
--stop-after INTEGER Stop after X records
--alter Alter existing table to add any missing columns
--not-null TEXT Columns that should be created as NOT NULL
--default <TEXT TEXT>... Default value that should be set for a column
-d, --detect-types Detect types for columns in CSV/TSV data
--analyze Run ANALYZE at the end of this operation
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
--silent Do not show progress bar
--strict Apply STRICT mode to created table
-h, --help Show this message and exit.
bulk
See Executing SQL in bulk.
Usage: sqlite-utils bulk [OPTIONS] PATH SQL FILE
Execute parameterized SQL against the provided list of documents.
Example:
echo '[
{"id": 1, "name": "Lila2"},
{"id": 2, "name": "Suna2"}
]' | sqlite-utils bulk data.db '
update chickens set name = :name where id = :id
' -
Options:
--batch-size INTEGER Commit every X records
--functions TEXT Python code defining one or more custom SQL functions
--flatten Flatten nested JSON objects, so {"a": {"b": 1}} becomes
{"a_b": 1}
--nl Expect newline-delimited JSON
-c, --csv Expect CSV input
--tsv Expect TSV input
--empty-null Treat empty strings as NULL
--lines Treat each line as a single value called 'line'
--text Treat input as a single value called 'text'
--convert TEXT Python code to convert each item
--import TEXT Python modules to import
--delimiter TEXT Delimiter to use for CSV files
--quotechar TEXT Quote character to use for CSV/TSV
--sniff Detect delimiter and quote character
--no-headers CSV file has no header row
--encoding TEXT Character encoding for input, defaults to utf-8
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
search
See Executing searches.
Usage: sqlite-utils search [OPTIONS] PATH DBTABLE Q
Execute a full-text search against this table
Example:
sqlite-utils search data.db chickens lila
Options:
-o, --order TEXT Order by ('column' or 'column desc')
-c, --column TEXT Columns to return
--limit INTEGER Number of rows to return - defaults to everything
--sql Show SQL query that would be run
--quote Apply FTS quoting rules to search term
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
transform
See Transforming tables.
Usage: sqlite-utils transform [OPTIONS] PATH TABLE
Transform a table beyond the capabilities of ALTER TABLE
Example:
sqlite-utils transform mydb.db mytable \
--drop column1 \
--rename column2 column_renamed
Options:
--type <TEXT CHOICE>... Change column type to INTEGER, TEXT, FLOAT or
BLOB
--drop TEXT Drop this column
--rename <TEXT TEXT>... Rename this column to X
-o, --column-order TEXT Reorder columns
--not-null TEXT Set this column to NOT NULL
--not-null-false TEXT Remove NOT NULL from this column
--pk TEXT Make this column the primary key
--pk-none Remove primary key (convert to rowid table)
--default <TEXT TEXT>... Set default value for this column
--default-none TEXT Remove default from this column
--add-foreign-key <TEXT TEXT TEXT>...
Add a foreign key constraint from a column to
another table with another column
--drop-foreign-key TEXT Drop foreign key constraint for this column
--sql Output SQL without executing it
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
extract
See Extracting columns into a separate table.
Usage: sqlite-utils extract [OPTIONS] PATH TABLE COLUMNS...
Extract one or more columns into a separate table
Example:
sqlite-utils extract trees.db Street_Trees species
Options:
--table TEXT Name of the other table to extract columns to
--fk-column TEXT Name of the foreign key column to add to the table
--rename <TEXT TEXT>... Rename this column in extracted table
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
schema
See Showing the schema.
Usage: sqlite-utils schema [OPTIONS] PATH [TABLES]...
Show full schema for this database or for specified tables
Example:
sqlite-utils schema trees.db
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
insert-files
See Inserting data from files.
Usage: sqlite-utils insert-files [OPTIONS] PATH TABLE FILE_OR_DIR...
Insert one or more files using BLOB columns in the specified table
Example:
sqlite-utils insert-files pics.db images *.gif \
-c name:name \
-c content:content \
-c content_hash:sha256 \
-c created:ctime_iso \
-c modified:mtime_iso \
-c size:size \
--pk name
Options:
-c, --column TEXT Column definitions for the table
--pk TEXT Column to use as primary key
--alter Alter table to add missing columns
--replace Replace files with matching primary key
--upsert Upsert files with matching primary key
--name TEXT File name to use
--text Store file content as TEXT, not BLOB
--encoding TEXT Character encoding for input, defaults to utf-8
-s, --silent Don't show a progress bar
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
analyze-tables
See Analyzing tables.
Usage: sqlite-utils analyze-tables [OPTIONS] PATH [TABLES]...
Analyze the columns in one or more tables
Example:
sqlite-utils analyze-tables data.db trees
Options:
-c, --column TEXT Specific columns to analyze
--save Save results to _analyze_tables table
--common-limit INTEGER How many common values
--no-most Skip most common values
--no-least Skip least common values
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
convert
See Converting data in columns.
Usage: sqlite-utils convert [OPTIONS] DB_PATH TABLE COLUMNS... CODE
Convert columns using Python code you supply. For example:
sqlite-utils convert my.db mytable mycolumn \
'"\n".join(textwrap.wrap(value, 10))' \
--import=textwrap
"value" is a variable with the column value to be converted.
Use "-" for CODE to read Python code from standard input.
The following common operations are available as recipe functions:
r.jsonsplit(value, delimiter=',', type=<class 'str'>)
Convert a string like a,b,c into a JSON array ["a", "b", "c"]
r.parsedate(value, dayfirst=False, yearfirst=False, errors=None)
Parse a date and convert it to ISO date format: yyyy-mm-dd
- dayfirst=True: treat xx as the day in xx/yy/zz
- yearfirst=True: treat xx as the year in xx/yy/zz
- errors=r.IGNORE to ignore values that cannot be parsed
- errors=r.SET_NULL to set values that cannot be parsed to null
r.parsedatetime(value, dayfirst=False, yearfirst=False, errors=None)
Parse a datetime and convert it to ISO datetime format: yyyy-mm-ddTHH:MM:SS
- dayfirst=True: treat xx as the day in xx/yy/zz
- yearfirst=True: treat xx as the year in xx/yy/zz
- errors=r.IGNORE to ignore values that cannot be parsed
- errors=r.SET_NULL to set values that cannot be parsed to null
You can use these recipes like so:
sqlite-utils convert my.db mytable mycolumn \
'r.jsonsplit(value, delimiter=":")'
Options:
--import TEXT Python modules to import
--dry-run Show results of running this against first 10
rows
--multi Populate columns for keys in returned
dictionary
--where TEXT Optional where clause
-p, --param <TEXT TEXT>... Named :parameters for where clause
--output TEXT Optional separate column to populate with the
output
--output-type [integer|float|blob|text]
Column type to use for the output column
--drop Drop original column afterwards
--no-skip-false Don't skip falsey values
-s, --silent Don't show a progress bar
--pdb Open pdb debugger on first error
-h, --help Show this message and exit.
tables
See Listing tables.
Usage: sqlite-utils tables [OPTIONS] PATH
List the tables in the database
Example:
sqlite-utils tables trees.db
Options:
--fts4 Just show FTS4 enabled tables
--fts5 Just show FTS5 enabled tables
--counts Include row counts per table
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--columns Include list of columns for each table
--schema Include schema for each table
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
views
See Listing views.
Usage: sqlite-utils views [OPTIONS] PATH
List the views in the database
Example:
sqlite-utils views trees.db
Options:
--counts Include row counts per view
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--columns Include list of columns for each view
--schema Include schema for each view
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
rows
See Returning all rows in a table.
Usage: sqlite-utils rows [OPTIONS] PATH DBTABLE
Output all rows in the specified table
Example:
sqlite-utils rows trees.db Trees
Options:
-c, --column TEXT Columns to return
--where TEXT Optional where clause
-o, --order TEXT Order by ('column' or 'column desc')
-p, --param <TEXT TEXT>... Named :parameters for where clause
--limit INTEGER Number of rows to return - defaults to everything
--offset INTEGER SQL offset to use
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira,
latex, latex_booktabs, latex_longtable, latex_raw,
mediawiki, mixed_grid, mixed_outline, moinmoin,
orgtbl, outline, pipe, plain, presto, pretty,
psql, rounded_grid, rounded_outline, rst, simple,
simple_grid, simple_outline, textile, tsv,
unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not
escaped strings
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
triggers
See Listing triggers.
Usage: sqlite-utils triggers [OPTIONS] PATH [TABLES]...
Show triggers configured in this database
Example:
sqlite-utils triggers trees.db
Options:
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
indexes
See Listing indexes.
Usage: sqlite-utils indexes [OPTIONS] PATH [TABLES]...
Show indexes for the whole database or specific tables
Example:
sqlite-utils indexes trees.db Trees
Options:
--aux Include auxiliary columns
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
create-database
See Creating an empty database.
Usage: sqlite-utils create-database [OPTIONS] PATH
Create a new empty database file
Example:
sqlite-utils create-database trees.db
Options:
--enable-wal Enable WAL mode on the created database
--init-spatialite Enable SpatiaLite on the created database
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
create-table
See Creating tables.
Usage: sqlite-utils create-table [OPTIONS] PATH TABLE COLUMNS...
Add a table with the specified columns. Columns should be specified using
name, type pairs, for example:
sqlite-utils create-table my.db people \
id integer \
name text \
height float \
photo blob --pk id
Valid column types are text, integer, float and blob.
Options:
--pk TEXT Column to use as primary key
--not-null TEXT Columns that should be created as NOT NULL
--default <TEXT TEXT>... Default value that should be set for a column
--fk <TEXT TEXT TEXT>... Column, other table, other column to set as a
foreign key
--ignore If table already exists, do nothing
--replace If table already exists, replace it
--transform If table already exists, try to transform the schema
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
--strict Apply STRICT mode to created table
-h, --help Show this message and exit.
create-index
See Creating indexes.
Usage: sqlite-utils create-index [OPTIONS] PATH TABLE COLUMN...
Add an index to the specified table for the specified columns
Example:
sqlite-utils create-index chickens.db chickens name
To create an index in descending order:
sqlite-utils create-index chickens.db chickens -- -name
Options:
--name TEXT Explicit name for the new index
--unique Make this a unique index
--if-not-exists, --ignore Ignore if index already exists
--analyze Run ANALYZE after creating the index
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
enable-fts
See Configuring full-text search.
Usage: sqlite-utils enable-fts [OPTIONS] PATH TABLE COLUMN...
Enable full-text search for specific table and columns"
Example:
sqlite-utils enable-fts chickens.db chickens name
Options:
--fts4 Use FTS4
--fts5 Use FTS5
--tokenize TEXT Tokenizer to use, e.g. porter
--create-triggers Create triggers to update the FTS tables when the
parent table changes.
--replace Replace existing FTS configuration if it exists
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
populate-fts
Usage: sqlite-utils populate-fts [OPTIONS] PATH TABLE COLUMN...
Re-populate full-text search for specific table and columns
Example:
sqlite-utils populate-fts chickens.db chickens name
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
rebuild-fts
Usage: sqlite-utils rebuild-fts [OPTIONS] PATH [TABLES]...
Rebuild all or specific full-text search tables
Example:
sqlite-utils rebuild-fts chickens.db chickens
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
disable-fts
Usage: sqlite-utils disable-fts [OPTIONS] PATH TABLE
Disable full-text search for specific table
Example:
sqlite-utils disable-fts chickens.db chickens
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
tui
See Experimental TUI.
Usage: sqlite-utils tui [OPTIONS]
Open Textual TUI.
Options:
-h, --help Show this message and exit.
optimize
See Optimize.
Usage: sqlite-utils optimize [OPTIONS] PATH [TABLES]...
Optimize all full-text search tables and then run VACUUM - should shrink the
database file
Example:
sqlite-utils optimize chickens.db
Options:
--no-vacuum Don't run VACUUM
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
analyze
See Optimizing index usage with ANALYZE.
Usage: sqlite-utils analyze [OPTIONS] PATH [NAMES]...
Run ANALYZE against the whole database, or against specific named indexes and
tables
Example:
sqlite-utils analyze chickens.db
Options:
-h, --help Show this message and exit.
vacuum
See Vacuum.
Usage: sqlite-utils vacuum [OPTIONS] PATH
Run VACUUM against the database
Example:
sqlite-utils vacuum chickens.db
Options:
-h, --help Show this message and exit.
dump
See Dumping the database to SQL.
Usage: sqlite-utils dump [OPTIONS] PATH
Output a SQL dump of the schema and full contents of the database
Example:
sqlite-utils dump chickens.db
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
add-column
See Adding columns.
Usage: sqlite-utils add-column [OPTIONS] PATH TABLE COL_NAME
[[integer|int|float|text|str|blob|bytes]]
Add a column to the specified table
Example:
sqlite-utils add-column chickens.db chickens weight float
Options:
--fk TEXT Table to reference as a foreign key
--fk-col TEXT Referenced column on that foreign key table - if
omitted will automatically use the primary key
--not-null-default TEXT Add NOT NULL DEFAULT 'TEXT' constraint
--ignore If column already exists, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
add-foreign-key
See Adding foreign key constraints.
Usage: sqlite-utils add-foreign-key [OPTIONS] PATH TABLE COLUMN [OTHER_TABLE]
[OTHER_COLUMN]
Add a new foreign key constraint to an existing table
Example:
sqlite-utils add-foreign-key my.db books author_id authors id
Options:
--ignore If foreign key already exists, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
add-foreign-keys
See Adding multiple foreign keys at once.
Usage: sqlite-utils add-foreign-keys [OPTIONS] PATH [FOREIGN_KEY]...
Add multiple new foreign key constraints to a database
Example:
sqlite-utils add-foreign-keys my.db \
books author_id authors id \
authors country_id countries id
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
index-foreign-keys
See Adding indexes for all foreign keys.
Usage: sqlite-utils index-foreign-keys [OPTIONS] PATH
Ensure every foreign key column has an index on it
Example:
sqlite-utils index-foreign-keys chickens.db
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
enable-wal
See WAL mode.
Usage: sqlite-utils enable-wal [OPTIONS] PATH...
Enable WAL for database files
Example:
sqlite-utils enable-wal chickens.db
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
disable-wal
Usage: sqlite-utils disable-wal [OPTIONS] PATH...
Disable WAL for database files
Example:
sqlite-utils disable-wal chickens.db
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
enable-counts
See Enabling cached counts.
Usage: sqlite-utils enable-counts [OPTIONS] PATH [TABLES]...
Configure triggers to update a _counts table with row counts
Example:
sqlite-utils enable-counts chickens.db
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
reset-counts
Usage: sqlite-utils reset-counts [OPTIONS] PATH
Reset calculated counts in the _counts table
Example:
sqlite-utils reset-counts chickens.db
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
duplicate
See Duplicating tables.
Usage: sqlite-utils duplicate [OPTIONS] PATH TABLE NEW_TABLE
Create a duplicate of this table, copying across the schema and all row data.
Options:
--ignore If table does not exist, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
rename-table
See Renaming a table.
Usage: sqlite-utils rename-table [OPTIONS] PATH TABLE NEW_NAME
Rename this table.
Options:
--ignore If table does not exist, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
drop-table
See Dropping tables.
Usage: sqlite-utils drop-table [OPTIONS] PATH TABLE
Drop the specified table
Example:
sqlite-utils drop-table chickens.db chickens
Options:
--ignore If table does not exist, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
create-view
See Creating views.
Usage: sqlite-utils create-view [OPTIONS] PATH VIEW SELECT
Create a view for the provided SELECT query
Example:
sqlite-utils create-view chickens.db heavy_chickens \
'select * from chickens where weight > 3'
Options:
--ignore If view already exists, do nothing
--replace If view already exists, replace it
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
drop-view
See Dropping views.
Usage: sqlite-utils drop-view [OPTIONS] PATH VIEW
Drop the specified view
Example:
sqlite-utils drop-view chickens.db heavy_chickens
Options:
--ignore If view does not exist, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
install
See Installing packages.
Usage: sqlite-utils install [OPTIONS] [PACKAGES]...
Install packages from PyPI into the same environment as sqlite-utils
Options:
-U, --upgrade Upgrade packages to latest version
-e, --editable TEXT Install a project in editable mode from this path
-h, --help Show this message and exit.
uninstall
See Uninstalling packages.
Usage: sqlite-utils uninstall [OPTIONS] PACKAGES...
Uninstall Python packages from the sqlite-utils environment
Options:
-y, --yes Don't ask for confirmation
-h, --help Show this message and exit.
add-geometry-column
See SpatiaLite helpers.
Usage: sqlite-utils add-geometry-column [OPTIONS] DB_PATH TABLE COLUMN_NAME
Add a SpatiaLite geometry column to an existing table. Requires SpatiaLite
extension.
By default, this command will try to load the SpatiaLite extension from usual
paths. To load it from a specific path, use --load-extension.
Options:
-t, --type [POINT|LINESTRING|POLYGON|MULTIPOINT|MULTILINESTRING|MULTIPOLYGON|GEOMETRYCOLLECTION|GEOMETRY]
Specify a geometry type for this column.
[default: GEOMETRY]
--srid INTEGER Spatial Reference ID. See
https://spatialreference.org for details on
specific projections. [default: 4326]
--dimensions TEXT Coordinate dimensions. Use XYZ for three-
dimensional geometries.
--not-null Add a NOT NULL constraint.
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
create-spatial-index
See Adding spatial indexes.
Usage: sqlite-utils create-spatial-index [OPTIONS] DB_PATH TABLE COLUMN_NAME
Create a spatial index on a SpatiaLite geometry column. The table and geometry
column must already exist before trying to add a spatial index.
By default, this command will try to load the SpatiaLite extension from usual
paths. To load it from a specific path, use --load-extension.
Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
plugins
Usage: sqlite-utils plugins [OPTIONS]
List installed plugins
Options:
-h, --help Show this message and exit.
Contributing
Development of sqlite-utils takes place in the sqlite-utils GitHub repository.
All improvements to the software should start with an issue. Read How I build a feature for a detailed
description of the recommended process for building bug fixes or enhancements.
Obtaining the code
To work on this library locally, first checkout the code. Then create a new virtual environment:
git clone git@github.com:simonw/sqlite-utils
cd sqlite-utils
python3 -mvenv venv
source venv/bin/activate
Or if you are using pipenv:
pipenv shell
Within the virtual environment running sqlite-utils should run your locally editable version of the tool.
You can use which sqlite-utils to confirm that you are running the version that lives in your virtual
environment.
Running the tests
To install the dependencies and test dependencies:
pip install -e '.[test]'
To run the tests:
pytest
Building the documentation
To build the documentation, first install the documentation dependencies:
pip install -e '.[docs]'
Then run make livehtml from the docs/ directory to start a server on port 8000 that will serve the
documentation and live-reload any time you make an edit to a .rst file:
cd docs
make livehtml
The cog tool is used to maintain portions of the documentation. You can run it like so:
cog -r docs/*.rst
Linting and formatting
sqlite-utils uses Black for code formatting, and flake8 and mypy for linting and type checking.
Black is installed as part of pip install -e '.[test]' - you can then format your code by running it in
the root of the project:
black .
To install mypy and flake8 run the following:
pip install -e '.[flake8,mypy]'
Both commands can then be run in the root of the project like this:
flake8
mypy sqlite_utils
All three of these tools are run by our CI mechanism against every commit and pull request.
Using Just and pipenv
If you install Just and pipenv you can use them to manage your local development environment.
To create a virtual environment and install all development dependencies, run:
cd sqlite-utils
just init
To run all of the tests and linters:
just
To run tests, or run a specific test module or test by name:
just test # All tests
just test tests/test_cli_memory.py # Just this module
just test -k test_memory_no_detect_types # Just this test
To run just the linters:
just lint
To apply Black to your code:
just black
To update documentation using Cog:
just cog
To run the live documentation server (this will run Cog first):
just docs
And to list all available commands:
just -l
Release process
Releases are performed using tags. When a new release is published on GitHub, a GitHub Actions workflow
will perform the following:
• Run the unit tests against all supported Python versions. If the tests pass...
• Build a wheel bundle of the underlying Python source code
• Push that new wheel up to PyPI: https://pypi.org/project/sqlite-utils/
To deploy new releases you will need to have push access to the GitHub repository.
sqlite-utils follows Semantic Versioning:
major.minor.patch
We increment major for backwards-incompatible releases.
We increment minor for new features.
We increment patch for bugfix releass.
To release a new version, first create a commit that updates the version number in setup.py and the the
changelog with highlights of the new version. An example commit can be seen here:
# Update changelog
git commit -m " Release 3.29
Refs #423, #458, #467, #469, #470, #471, #472, #475" -a
git push
Referencing the issues that are part of the release in the commit message ensures the name of the release
shows up on those issue pages, e.g. here.
You can generate the list of issue references for a specific release by copying and pasting text from the
release notes or GitHub changes-since-last-release view into this Extract issue numbers from pasted text
tool.
To create the tag for the release, create a new release on GitHub matching the new version number. You
can convert the release notes to Markdown by copying and pasting the rendered HTML into this Paste to
Markdown tool.
Changelog
3.36 (2023-12-07)
•
Support for creating tables in SQLite STRICT mode. Thanks, Taj Khattra. (#344)
• CLI commands create-table, insert and upsert all now accept a --strict option.
• Python methods that can create a table - table.create() and
insert/upsert/insert_all/upsert_all all now accept an optional strict=True parameter.
• The transform command and table.transform() method preserve strict mode when transforming a
table.
• The sqlite-utils create-table command now accepts str, int and bytes as aliases for text, integer and
blob respectively. (#606)
3.35.2 (2023-11-03)
• The --load-extension=spatialite option and find_spatialite() utility function now both work correctly
on arm64 Linux. Thanks, Mike Coats. (#599)
• Fix for bug where sqlite-utils insert could cause your terminal cursor to disappear. Thanks, Luke
Plant. (#433)
• datetime.timedelta values are now stored as TEXT columns. Thanks, Harald Nezbeda. (#522)
• Test suite is now also run against Python 3.12.
3.35.1 (2023-09-08)
• Fixed a bug where table.transform() would sometimes re-assign the rowid values for a table rather than
keeping them consistent across the operation. (#592)
3.35 (2023-08-17)
Adding foreign keys to a table no longer uses PRAGMA writable_schema = 1 to directly manipulate the
sqlite_master table. This was resulting in errors in some Python installations where the SQLite library
was compiled in a way that prevented this from working, in particular on macOS. Foreign keys are now
added using the table transformation mechanism instead. (#577)
This new mechanism creates a full copy of the table, so it is likely to be significantly slower for large
tables, but will no longer trigger table sqlite_master may not be modified errors on platforms that do
not support PRAGMA writable_schema = 1.
A new plugin, sqlite-utils-fast-fks, is now available for developers who still want to use that faster
but riskier implementation.
Other changes:
• The table.transform() method has two new parameters: foreign_keys= allows you to replace the foreign
key constraints defined on a table, and add_foreign_keys= lets you specify new foreign keys to add.
These complement the existing drop_foreign_keys= parameter. (#577)
• The sqlite-utils transform command has a new --add-foreign-key option which can be called multiple
times to add foreign keys to a table that is being transformed. (#585)
• sqlite-utils convert now has a --pdb option for opening a debugger on the first encountered error in
your conversion script. (#581)
• Fixed a bug where sqlite-utils install -e '.[test]' option did not work correctly.
3.34 (2023-07-22)
This release introduces a new plugin system. Read more about this in sqlite-utils now supports plugins.
(#567)
• Documentation describing how to build a plugin.
• Plugin hook: register_commands(cli), for plugins to add extra commands to sqlite-utils. (#569)
• Plugin hook: prepare_connection(conn). Plugins can use this to help prepare the SQLite connection to do
things like registering custom SQL functions. Thanks, Alex Garcia. (#574)
• sqlite_utils.Database(..., execute_plugins=False) option for disabling plugin execution. (#575)
• sqlite-utils install -e path-to-directory option for installing editable code. This option is useful
during the development of a plugin. (#570)
• table.create(...) method now accepts replace=True to drop and replace an existing table with the same
name, or ignore=True to silently do nothing if a table already exists with the same name. (#568)
• sqlite-utils insert ... --stop-after 10 option for stopping the insert after a specified number of
records. Works for the upsert command as well. (#561)
• The --csv and --tsv modes for insert now accept a --empty-null option, which causes empty strings in
the CSV file to be stored as null in the database. (#563)
• New db.rename_table(table_name, new_name) method for renaming tables. (#565)
• sqlite-utils rename-table my.db table_name new_name command for renaming tables. (#565)
• The table.transform(...) method now takes an optional keep_table=new_table_name parameter, which will
cause the original table to be renamed to new_table_name rather than being dropped at the end of the
transformation. (#571)
• Documentation now notes that calling table.transform() without any arguments will reformat the SQL
schema stored by SQLite to be more aesthetically pleasing. (#564)
3.33 (2023-06-25)
• sqlite-utils will now use sqlean.py in place of sqlite3 if it is installed in the same virtual
environment. This is useful for Python environments with either an outdated version of SQLite or with
restrictions on SQLite such as disabled extension loading or restrictions resulting in the
sqlite3.OperationalError: table sqlite_master may not be modified error. (#559)
• New with db.ensure_autocommit_off() context manager, which ensures that the database is in autocommit
mode for the duration of a block of code. This is used by db.enable_wal() and db.disable_wal() to
ensure they work correctly with pysqlite3 and sqlean.py.
• New db.iterdump() method, providing an iterator over SQL strings representing a dump of the database.
This uses sqlite-dump if it is available, otherwise falling back on the conn.iterdump() method from
sqlite3. Both pysqlite3 and sqlean.py omit support for iterdump() - this method helps paper over that
difference.
3.32.1 (2023-05-21)
• Examples in the CLI documentation can now all be copied and pasted without needing to remove a leading
$. (#551)
• Documentation now covers Setting up shell completion for bash and zsh. (#552)
3.32 (2023-05-21)
• New experimental sqlite-utils tui interface for interactively building command-line invocations,
powered by Trogon. This requires an optional dependency, installed using sqlite-utils install trogon.
There is a screenshot in the documentation. (#545)
• sqlite-utils analyze-tables command (documentation) now has a --common-limit 20 option for changing the
number of common/least-common values shown for each column. (#544)
• sqlite-utils analyze-tables --no-most and --no-least options for disabling calculation of most-common
and least-common values.
• If a column contains only null values, analyze-tables will no longer attempt to calculate the most
common and least common values for that column. (#547)
• Calling sqlite-utils analyze-tables with non-existent columns in the -c/--column option now results in
an error message. (#548)
• The table.analyze_column() method (documented here) now accepts most_common=False and
least_common=False options for disabling calculation of those values.
3.31 (2023-05-08)
• Dropped support for Python 3.6. Tests now ensure compatibility with Python 3.11. (#517)
• Automatically locates the SpatiaLite extension on Apple Silicon. Thanks, Chris Amico. (#536)
• New --raw-lines option for the sqlite-utils query and sqlite-utils memory commands, which outputs just
the raw value of the first column of every row. (#539)
• Fixed a bug where table.upsert_all() failed if the not_null= option was passed. (#538)
• Fixed a ResourceWarning when using sqlite-utils insert. (#534)
• Now shows a more detailed error message when sqlite-utils insert is called with invalid JSON. (#532)
• table.convert(..., skip_false=False) and sqlite-utils convert --no-skip-false options, for avoiding a
misfeature where the convert() mechanism skips rows in the database with a falsey value for the
specified column. Fixing this by default would be a backwards-incompatible change and is under
consideration for a 4.0 release in the future. (#527)
• Tables can now be created with self-referential foreign keys. Thanks, Scott Perry. (#537)
• sqlite-utils transform no longer breaks if a table defines default values for columns. Thanks, Kenny
Song. (#509)
• Fixed a bug where repeated calls to table.transform() did not work correctly. Thanks, Martin Carpenter.
(#525)
• Improved error message if rows_from_file() is passed a non-binary-mode file-like object. (#520)
3.30 (2022-10-25)
• Now tested against Python 3.11. (#502)
• New table.search_sql(include_rank=True) option, which adds a rank column to the generated SQL. Thanks,
Jacob Chapman. (#480)
• Progress bars now display for newline-delimited JSON files using the --nl option. Thanks, Mischa
Untaga. (#485)
• New db.close() method. (#504)
• Conversion functions passed to table.convert(...) can now return lists or dictionaries, which will be
inserted into the database as JSON strings. (#495)
• sqlite-utils install and sqlite-utils uninstall commands for installing packages into the same virtual
environment as sqlite-utils, described here. (#483)
• New sqlite_utils.utils.flatten() utility function. (#500)
• Documentation on using Just to run tests, linters and build documentation.
• Documentation now covers the Release process for this package.
3.29 (2022-08-27)
• The sqlite-utils query, memory and bulk commands now all accept a new --functions option. This can be
passed a string of Python code, and any callable objects defined in that code will be made available to
SQL queries as custom SQL functions. See Defining custom SQL functions for details. (#471)
• db[table].create(...) method now accepts a new transform=True parameter. If the table already exists it
will be transformed to match the schema configuration options passed to the function. This may result
in columns being added or dropped, column types being changed, column order being updated or not null
and default values for columns being set. (#467)
• Related to the above, the sqlite-utils create-table command now accepts a --transform option.
• New introspection property: table.default_values returns a dictionary mapping each column name with a
default value to the configured default value. (#475)
• The --load-extension option can now be provided a path to a compiled SQLite extension module
accompanied by the name of an entrypoint, separated by a colon - for example --load-extension
./lines0:sqlite3_lines0_noread_init. This feature is modelled on code first contributed to Datasette by
Alex Garcia. (#470)
• Functions registered using the db.register_function() method can now have a custom name specified using
the new db.register_function(fn, name=...) parameter. (#458)
• sqlite-utils rows has a new --order option for specifying the sort order for the returned rows. (#469)
• All of the CLI options that accept Python code blocks can now all be used to define functions that can
access modules imported in that same block of code without needing to use the global keyword. (#472)
• Fixed bug where table.extract() would not behave correctly for columns containing null values. Thanks,
Forest Gregg. (#423)
• New tutorial: Cleaning data with sqlite-utils and Datasette shows how to use sqlite-utils to import and
clean an example CSV file.
• Datasette and sqlite-utils now have a Discord community. Join the Discord here.
3.28 (2022-07-15)
• New table.duplicate(new_name) method for creating a copy of a table with a matching schema and row
contents. Thanks, David. (#449)
• New sqlite-utils duplicate data.db table_name new_name CLI command for Duplicating tables. (#454)
• sqlite_utils.utils.rows_from_file() is now a documented API. It can be used to read a sequence of
dictionaries from a file-like object containing CSV, TSV, JSON or newline-delimited JSON. It can be
passed an explicit format or can attempt to detect the format automatically. (#443)
• sqlite_utils.utils.TypeTracker is now a documented API for detecting the likely column types for a
sequence of string rows, see Detecting column types using TypeTracker. (#445)
• sqlite_utils.utils.chunks() is now a documented API for splitting an iterator into chunks. (#451)
• sqlite-utils enable-fts now has a --replace option for replacing the existing FTS configuration for a
table. (#450)
• The create-index, add-column and duplicate commands all now take a --ignore option for ignoring errors
should the database not be in the right state for them to operate. (#450)
3.27 (2022-06-14)
See also the annotated release notes for this release.
• Documentation now uses the Furo Sphinx theme. (#435)
• Code examples in documentation now have a "copy to clipboard" button. (#436)
• sqlite_utils.utils.utils.rows_from_file() is now a documented API, see Reading rows from a file. (#443)
• rows_from_file() has two new parameters to help handle CSV files with rows that contain more values
than are listed in that CSV file's headings: ignore_extras=True and extras_key="name-of-key". (#440)
• sqlite_utils.utils.maximize_csv_field_size_limit() helper function for increasing the field size limit
for reading CSV files to its maximum, see Setting the maximum CSV field size limit. (#442)
• table.search(where=, where_args=) parameters for adding additional WHERE clauses to a search query. The
where= parameter is available on table.search_sql(...) as well. See Searching with table.search(). (‐
#441)
• Fixed bug where table.detect_fts() and other search-related functions could fail if two FTS-enabled
tables had names that were prefixes of each other. (#434)
3.26.1 (2022-05-02)
• Now depends on click-default-group-wheel, a pure Python wheel package. This means you can install and
use this package with Pyodide, which can run Python entirely in your browser using WebAssembly. (#429)
Try that out using the Pyodide REPL:
>>> import micropip
>>> await micropip.install("sqlite-utils")
>>> import sqlite_utils
>>> db = sqlite_utils.Database(memory=True)
>>> list(db.query("select 3 * 5"))
[{'3 * 5': 15}]
3.26 (2022-04-13)
• New errors=r.IGNORE/r.SET_NULL parameter for the r.parsedatetime() and r.parsedate() convert recipes.
(#416)
• Fixed a bug where --multi could not be used in combination with --dry-run for the convert command. (‐
#415)
• New documentation: Defining a convert() function. (#420)
• More robust detection for whether or not deterministic=True is supported. (#425)
3.25.1 (2022-03-11)
• Improved display of type information and parameters in the API reference documentation. (#413)
3.25 (2022-03-01)
• New hash_id_columns= parameter for creating a primary key that's a hash of the content of specific
columns - see Setting an ID based on the hash of the row contents for details. (#343)
• New db.sqlite_version property, returning a tuple of integers representing the version of SQLite, for
example (3, 38, 0).
• Fixed a bug where register_function(deterministic=True) caused errors on versions of SQLite prior to
3.8.3. (#408)
• New documented hash_record(record, keys=...) function.
3.24 (2022-02-15)
• SpatiaLite helpers for the sqlite-utils command-line tool - thanks, Chris Amico. (#398)
• sqlite-utils create-database --init-spatialite option for initializing SpatiaLite on a newly created
database.
• sqlite-utils add-geometry-column command for adding geometry columns.
• sqlite-utils create-spatial-index command for adding spatial indexes.
• db[table].create(..., if_not_exists=True) option for creating a table only if it does not already
exist. (#397)
• Database(memory_name="my_shared_database") parameter for creating a named in-memory database that can
be shared between multiple connections. (#405)
• Documentation now describes how to add a primary key to a rowid table using sqlite-utils transform. (‐
#403)
3.23 (2022-02-03)
This release introduces four new utility methods for working with SpatiaLite. Thanks, Chris Amico. (#385)
• sqlite_utils.utils.find_spatialite() finds the location of the SpatiaLite module on disk.
• db.init_spatialite() initializes SpatiaLite for the given database.
• table.add_geometry_column(...) adds a geometry column to an existing table.
• table.create_spatial_index(...) creates a spatial index for a column.
• sqlite-utils batch now accepts a --batch-size option. (#392)
3.22.1 (2022-01-25)
• All commands now include example usage in their --help - see CLI reference. (#384)
• Python library documentation has a new Getting started section. (#387)
• Documentation now uses Plausible analytics. (#389)
3.22 (2022-01-11)
• New CLI reference documentation page, listing the output of --help for every one of the CLI commands.
(#383)
• sqlite-utils rows now has --limit and --offset options for paginating through data. (#381)
• sqlite-utils rows now has --where and -p options for filtering the table using a WHERE query, see
Returning all rows in a table. (#382)
3.21 (2022-01-10)
CLI and Python library improvements to help run ANALYZE after creating indexes or inserting rows, to gain
better performance from the SQLite query planner when it runs against indexes.
Three new CLI commands: create-database, analyze and bulk.
More details and examples can be found in the annotated release notes.
• New sqlite-utils create-database command for creating new empty database files. (#348)
• New Python methods for running ANALYZE against a database, table or index: db.analyze() and
table.analyze(), see Optimizing index usage with ANALYZE. (#366)
• New sqlite-utils analyze command for running ANALYZE using the CLI. (#379)
• The create-index, insert and upsert commands now have a new --analyze option for running ANALYZE after
the command has completed. (#379)
• New sqlite-utils bulk command which can import records in the same way as sqlite-utils insert (from
JSON, CSV or TSV) and use them to bulk execute a parametrized SQL query. (#375)
• The CLI tool can now also be run using python -m sqlite_utils. (#368)
• Using --fmt now implies --table, so you don't need to pass both options. (#374)
• The --convert function applied to rows can now modify the row in place. (#371)
• The insert-files command supports two new columns: stem and suffix. (#372)
• The --nl import option now ignores blank lines in the input. (#376)
• Fixed bug where streaming input to the insert command with --batch-size 1 would appear to only commit
after several rows had been ingested, due to unnecessary input buffering. (#364)
3.20 (2022-01-05)
• sqlite-utils insert ... --lines to insert the lines from a file into a table with a single line column,
see Inserting unstructured data with --lines and --text.
• sqlite-utils insert ... --text to insert the contents of the file into a table with a single text
column and a single row.
• sqlite-utils insert ... --convert allows a Python function to be provided that will be used to convert
each row that is being inserted into the database. See Applying conversions while inserting data,
including details on special behavior when combined with --lines and --text. (#356)
• sqlite-utils convert now accepts a code value of - to read code from standard input. (#353)
• sqlite-utils convert also now accepts code that defines a named convert(value) function, see Converting
data in columns.
• db.supports_strict property showing if the database connection supports SQLite strict tables.
• table.strict property (see .strict) indicating if the table uses strict mode. (#344)
• Fixed bug where sqlite-utils upsert ... --detect-types ignored the --detect-types option. (#362)
3.19 (2021-11-20)
• The table.lookup() method now accepts keyword arguments that match those on the underlying
table.insert() method: foreign_keys=, column_order=, not_null=, defaults=, extracts=, conversions= and
columns=. You can also now pass pk= to specify a different column name to use for the primary key. (‐
#342)
3.18 (2021-11-14)
• The table.lookup() method now has an optional second argument which can be used to populate columns
only the first time the record is created, see Working with lookup tables. (#339)
• sqlite-utils memory now has a --flatten option for flattening nested JSON objects into separate
columns, consistent with sqlite-utils insert. (#332)
• table.create_index(..., find_unique_name=True) parameter, which finds an available name for the created
index even if the default name has already been taken. This means that index-foreign-keys will work
even if one of the indexes it tries to create clashes with an existing index name. (#335)
• Added py.typed to the module, so mypy should now correctly pick up the type annotations. Thanks,
Andreas Longo. (#331)
• Now depends on python-dateutil instead of depending on dateutils. Thanks, Denys Pavlov. (#324)
• table.create() (see Explicitly creating a table) now handles dict, list and tuple types, mapping them
to TEXT columns in SQLite so that they can be stored encoded as JSON. (#338)
• Inserted data with square braces in the column names (for example a CSV file containing a item[price])
column now have the braces converted to underscores: item_price_. Previously such columns would be
rejected with an error. (#329)
• Now also tested against Python 3.10. (#330)
3.17.1 (2021-09-22)
• sqlite-utils memory now works if files passed to it share the same file name. (#325)
• sqlite-utils query now returns [] in JSON mode if no rows are returned. (#328)
3.17 (2021-08-24)
• The sqlite-utils memory command has a new --analyze option, which runs the equivalent of the
analyze-tables command directly against the in-memory database created from the incoming CSV or JSON
data. (#320)
• sqlite-utils insert-files now has the ability to insert file contents in to TEXT columns in addition to
the default BLOB. Pass the --text option or use content_text as a column specifier. (#319)
3.16 (2021-08-18)
• Type signatures added to more methods, including table.resolve_foreign_keys(), db.create_table_sql(),
db.create_table() and table.create(). (#314)
• New db.quote_fts(value) method, see Quoting characters for use in search - thanks, Mark Neumann. (#246)
• table.search() now accepts an optional quote=True parameter. (#296)
• CLI command sqlite-utils search now accepts a --quote option. (#296)
• Fixed bug where --no-headers and --tsv options to sqlite-utils insert could not be used together. (‐
#295)
• Various small improvements to API reference documentation.
3.15.1 (2021-08-10)
• Python library now includes type annotations on almost all of the methods, plus detailed docstrings
describing each one. (#311)
• New API reference documentation page, powered by those docstrings.
• Fixed bug where .add_foreign_keys() failed to raise an error if called against a View. (#313)
• Fixed bug where .delete_where() returned a [] instead of returning self if called against a
non-existent table. (#315)
3.15 (2021-08-09)
• sqlite-utils insert --flatten option for flattening nested JSON objects to create tables with column
names like topkey_nestedkey. (#310)
• Fixed several spelling mistakes in the documentation, spotted using codespell.
• Errors that occur while using the sqlite-utils CLI tool now show the responsible SQL and query
parameters, if possible. (#309)
3.14 (2021-08-02)
This release introduces the new sqlite-utils convert command (#251) and corresponding table.convert(...)
Python method (#302). These tools can be used to apply a Python conversion function to one or more
columns of a table, either updating the column in place or using transformed data from that column to
populate one or more other columns.
This command-line example uses the Python standard library textwrap module to wrap the content of the
content column in the articles table to 100 characters:
$ sqlite-utils convert content.db articles content \
'"\n".join(textwrap.wrap(value, 100))' \
--import=textwrap
The same operation in Python code looks like this:
import sqlite_utils, textwrap
db = sqlite_utils.Database("content.db")
db["articles"].convert("content", lambda v: "\n".join(textwrap.wrap(v, 100)))
See the full documentation for the sqlite-utils convert command and the table.convert(...) Python method
for more details.
Also in this release:
• The new table.count_where(...) method, for counting rows in a table that match a specific SQL WHERE
clause. (#305)
• New --silent option for the sqlite-utils insert-files command to hide the terminal progress bar,
consistent with the --silent option for sqlite-utils convert. (#301)
3.13 (2021-07-24)
• sqlite-utils schema my.db table1 table2 command now accepts optional table names. (#299)
• sqlite-utils memory --help now describes the --schema option.
3.12 (2021-06-25)
• New db.query(sql, params) method, which executes a SQL query and returns the results as an iterator
over Python dictionaries. (#290)
• This project now uses flake8 and has started to use mypy. (#291)
• New documentation on contributing to this project. (#292)
3.11 (2021-06-20)
• New sqlite-utils memory data.csv --schema option, for outputting the schema of the in-memory database
generated from one or more files. See --schema, --analyze, --dump and --save. (#288)
• Added installation instructions. (#286)
3.10 (2021-06-19)
This release introduces the sqlite-utils memory command, which can be used to load CSV or JSON data into
a temporary in-memory database and run SQL queries (including joins across multiple files) directly
against that data.
Also new: sqlite-utils insert --detect-types, sqlite-utils dump, table.use_rowid plus some smaller fixes.
sqlite-utils memory
This example of sqlite-utils memory retrieves information about the all of the repositories in the
Dogsheep organization on GitHub using this JSON API, sorts them by their number of stars and outputs a
table of the top five (using -t):
$ curl -s 'https://api.github.com/users/dogsheep/repos' \
| sqlite-utils memory - '
select full_name, forks_count, stargazers_count
from stdin order by stargazers_count desc limit 5
' -t
full_name forks_count stargazers_count
--------------------------------- ------------- ------------------
dogsheep/twitter-to-sqlite 12 225
dogsheep/github-to-sqlite 14 139
dogsheep/dogsheep-photos 5 116
dogsheep/dogsheep.github.io 7 90
dogsheep/healthkit-to-sqlite 4 85
The tool works against files on disk as well. This example joins data from two CSV files:
$ cat creatures.csv
species_id,name
1,Cleo
2,Bants
2,Dori
2,Azi
$ cat species.csv
id,species_name
1,Dog
2,Chicken
$ sqlite-utils memory species.csv creatures.csv '
select * from creatures join species on creatures.species_id = species.id
'
[{"species_id": 1, "name": "Cleo", "id": 1, "species_name": "Dog"},
{"species_id": 2, "name": "Bants", "id": 2, "species_name": "Chicken"},
{"species_id": 2, "name": "Dori", "id": 2, "species_name": "Chicken"},
{"species_id": 2, "name": "Azi", "id": 2, "species_name": "Chicken"}]
Here the species.csv file becomes the species table, the creatures.csv file becomes the creatures table
and the output is JSON, the default output format.
You can also use the --attach option to attach existing SQLite database files to the in-memory database,
in order to join data from CSV or JSON directly against your existing tables.
Full documentation of this new feature is available in Querying data directly using an in-memory
database. (#272)
sqlite-utils insert --detect-types
The sqlite-utils insert command can be used to insert data from JSON, CSV or TSV files into a SQLite
database file. The new --detect-types option (shortcut -d), when used in conjunction with a CSV or TSV
import, will automatically detect if columns in the file are integers or floating point numbers as
opposed to treating everything as a text column and create the new table with the corresponding schema.
See Inserting CSV or TSV data for details. (#282)
Other changes
• Bug fix: table.transform(), when run against a table without explicit primary keys, would incorrectly
create a new version of the table with an explicit primary key column called rowid. (#284)
• New table.use_rowid introspection property, see .use_rowid. (#285)
• The new sqlite-utils dump file.db command outputs a SQL dump that can be used to recreate a database.
(#274)
• -h now works as a shortcut for --help, thanks Loren McIntyre. (#276)
• Now using pytest-cov and Codecov to track test coverage - currently at 96%. (#275)
• SQL errors that occur when using sqlite-utils query are now displayed as CLI errors.
3.9.1 (2021-06-12)
• Fixed bug when using table.upsert_all() to create a table with only a single column that is treated as
the primary key. (#271)
3.9 (2021-06-11)
• New sqlite-utils schema command showing the full SQL schema for a database, see Showing the schema
(CLI). (#268)
• db.schema introspection property exposing the same feature to the Python library, see Showing the
schema (Python library).
3.8 (2021-06-02)
• New sqlite-utils indexes command to list indexes in a database, see Listing indexes. (#263)
• table.xindexes introspection property returning more details about that table's indexes, see .xindexes.
(#261)
3.7 (2021-05-28)
• New table.pks_and_rows_where() method returning (primary_key, row_dictionary) tuples - see Listing rows
with their primary keys. (#240)
• Fixed bug with table.add_foreign_key() against columns containing spaces. (#238)
• table_or_view.drop(ignore=True) option for avoiding errors if the table or view does not exist. (#237)
• sqlite-utils drop-view --ignore and sqlite-utils drop-table --ignore options. (#237)
• Fixed a bug with inserts of nested JSON containing non-ascii strings - thanks, Dylan Wu. (#257)
• Suggest --alter if an error occurs caused by a missing column. (#259)
• Support creating indexes with columns in descending order, see API documentation and CLI documentation.
(#260)
• Correctly handle CSV files that start with a UTF-8 BOM. (#250)
3.6 (2021-02-18)
This release adds the ability to execute queries joining data from more than one database file - similar
to the cross database querying feature introduced in Datasette 0.55.
• The db.attach(alias, filepath) Python method can be used to attach extra databases to the same
connection, see db.attach() in the Python API documentation. (#113)
• The --attach option attaches extra aliased databases to run SQL queries against directly on the
command-line, see attaching additional databases in the CLI documentation. (#236)
3.5 (2021-02-14)
• sqlite-utils insert --sniff option for detecting the delimiter and quote character used by a CSV file,
see Alternative delimiters and quote characters. (#230)
• The table.rows_where(), table.search() and table.search_sql() methods all now take optional offset= and
limit= arguments. (#231)
• New --no-headers option for sqlite-utils insert --csv to handle CSV files that are missing the header
row, see CSV files without a header row. (#228)
• Fixed bug where inserting data with extra columns in subsequent chunks would throw an error. Thanks
@nieuwenhoven for the fix. (#234)
• Fixed bug importing CSV files with columns containing more than 128KB of data. (#229)
• Test suite now runs in CI against Ubuntu, macOS and Windows. Thanks @nieuwenhoven for the Windows test
fixes. (#232)
3.4.1 (2021-02-05)
• Fixed a code import bug that slipped in to 3.4. (#226)
3.4 (2021-02-05)
• sqlite-utils insert --csv now accepts optional --delimiter and --quotechar options. See Alternative
delimiters and quote characters. (#223)
3.3 (2021-01-17)
• The table.m2m() method now accepts an optional alter=True argument to specify that any missing columns
should be added to the referenced table. See Working with many-to-many relationships. (#222)
3.2.1 (2021-01-12)
• Fixed a bug where .add_missing_columns() failed to take case insensitive column names into account. (‐
#221)
3.2 (2021-01-03)
This release introduces a new mechanism for speeding up count(*) queries using cached table counts,
stored in a _counts table and updated by triggers. This mechanism is described in Cached table counts
using triggers, and can be enabled using Python API methods or the new enable-counts CLI command. (#212)
• table.enable_counts() method for enabling these triggers on a specific table.
• db.enable_counts() method for enabling triggers on every table in the database. (#213)
• New sqlite-utils enable-counts my.db command for enabling counts on all or specific tables, see
Enabling cached counts. (#214)
• New sqlite-utils triggers command for listing the triggers defined for a database or specific tables,
see Listing triggers. (#218)
• New db.use_counts_table property which, if True, causes table.count to read from the _counts table. (‐
#215)
• table.has_counts_triggers property revealing if a table has been configured with the new _counts
database triggers.
• db.reset_counts() method and sqlite-utils reset-counts command for resetting the values in the _counts
table. (#219)
• The previously undocumented db.escape() method has been renamed to db.quote() and is now covered by the
documentation: Quoting strings for use in SQL. (#217)
• New table.triggers_dict and db.triggers_dict introspection properties. (#211, #216)
• sqlite-utils insert now shows a more useful error message for invalid JSON. (#206)
3.1.1 (2021-01-01)
• Fixed failing test caused by optimize sometimes creating larger database files. (#209)
• Documentation now lives on https://sqlite-utils.datasette.io/
• README now includes brew install sqlite-utils installation method.
3.1 (2020-12-12)
• New command: sqlite-utils analyze-tables my.db outputs useful information about the table columns in
the database, such as the number of distinct values and how many rows are null. See Analyzing tables
for documentation. (#207)
• New table.analyze_column(column) Python method used by the analyze-tables command - see Analyzing a
column.
• The table.update() method now correctly handles values that should be stored as JSON. Thanks, Andreas
Madsack. (#204)
3.0 (2020-11-08)
This release introduces a new sqlite-utils search command for searching tables, see Executing searches.
(#192)
The table.search() method has been redesigned, see Searching with table.search(). (#197)
The release includes minor backwards-incompatible changes, hence the version bump to 3.0. Those changes,
which should not affect most users, are:
• The -c shortcut option for outputting CSV is no longer available. The full --csv option is required
instead.
• The -f shortcut for --fmt has also been removed - use --fmt.
• The table.search() method now defaults to sorting by relevance, not sorting by rowid. (#198)
• The table.search() method now returns a generator over a list of Python dictionaries. It previously
returned a list of tuples.
Also in this release:
• The query, tables, rows and search CLI commands now accept a new --tsv option which outputs the results
in TSV. (#193)
• A new table.virtual_table_using property reveals if a table is a virtual table, and returns the upper
case type of virtual table (e.g. FTS4 or FTS5) if it is. It returns None if the table is not a virtual
table. (#196)
• The new table.search_sql() method returns the SQL for searching a table, see Building SQL queries with
table.search_sql().
• sqlite-utils rows now accepts multiple optional -c parameters specifying the columns to return. (#200)
Changes since the 3.0a0 alpha release:
• The sqlite-utils search command now defaults to returning every result, unless you add a --limit 20
option.
• The sqlite-utils search -c and table.search(columns=[]) options are now fully respected. (#201)
2.23 (2020-10-28)
• table.m2m(other_table, records) method now takes any iterable, not just a list or tuple. Thanks, Adam
Wolf. (#189)
• sqlite-utils insert now displays a progress bar for CSV or TSV imports. (#173)
• New @db.register_function(deterministic=True) option for registering deterministic SQLite functions in
Python 3.8 or higher. (#191)
2.22 (2020-10-16)
• New --encoding option for processing CSV and TSV files that use a non-utf-8 encoding, for both the
insert and update commands. (#182)
• The --load-extension option is now available to many more commands. (#137)
• --load-extension=spatialite can be used to load SpatiaLite from common installation locations, if it is
available. (#136)
• Tests now also run against Python 3.9. (#184)
• Passing pk=["id"] now has the same effect as passing pk="id". (#181)
2.21 (2020-09-24)
• table.extract() and sqlite-utils extract now apply much, much faster - one example operation reduced
from twelve minutes to just four seconds! (#172)
• sqlite-utils extract no longer shows a progress bar, because it's fast enough not to need one.
• New column_order= option for table.transform() which can be used to alter the order of columns in a
table. (#175)
• sqlite-utils transform --column-order= option (with a -o shortcut) for changing column order. (#176)
• The table.transform(drop_foreign_keys=) parameter and the sqlite-utils transform --drop-foreign-key
option have changed. They now accept just the name of the column rather than requiring all three of the
column, other table and other column. This is technically a backwards-incompatible change but I chose
not to bump the major version number because the transform feature is so new. (#177)
• The table .disable_fts(), .rebuild_fts(), .delete(), .delete_where() and .add_missing_columns() methods
all now return self, which means they can be chained together with other table operations.
2.20 (2020-09-22)
This release introduces two key new capabilities: transform (#114) and extract (#42).
Transform
SQLite's ALTER TABLE has several documented limitations. The table.transform() Python method and
sqlite-utils transform CLI command work around these limitations using a pattern where a new table with
the desired structure is created, data is copied over to it and the old table is then dropped and
replaced by the new one.
You can use these tools to change column types, rename columns, drop columns, add and remove NOT NULL and
defaults, remove foreign key constraints and more. See the transforming tables (CLI) and transforming
tables (Python library) documentation for full details of how to use them.
Extract
Sometimes a database table - especially one imported from a CSV file - will contain duplicate data. A
Trees table may include a Species column with only a few dozen unique values, when the table itself
contains thousands of rows.
The table.extract() method and sqlite-utils extract commands can extract a column - or multiple columns -
out into a separate lookup table, and set up a foreign key relationship from the original table.
The Python library extract() documentation describes how extraction works in detail, and Extracting
columns into a separate table in the CLI documentation includes a detailed example.
Other changes
• The @db.register_function decorator can be used to quickly register Python functions as custom SQL
functions, see Registering custom SQL functions. (#162)
• The table.rows_where() method now accepts an optional select= argument for specifying which columns
should be selected, see Listing rows.
2.19 (2020-09-20)
• New sqlite-utils add-foreign-keys command for Adding multiple foreign keys at once. (#157)
• New table.enable_fts(..., replace=True) argument for replacing an existing FTS table with a new
configuration. (#160)
• New table.add_foreign_key(..., ignore=True) argument for ignoring a foreign key if it already exists.
(#112)
2.18 (2020-09-08)
• table.rebuild_fts() method for rebuilding a FTS index, see Rebuilding a full-text search table. (#155)
• sqlite-utils rebuild-fts data.db command for rebuilding FTS indexes across all tables, or just specific
tables. (#155)
• table.optimize() method no longer deletes junk rows from the *_fts_docsize table. This was added in
2.17 but it turns out running table.rebuild_fts() is a better solution to this problem.
• Fixed a bug where rows with additional columns that are inserted after the first batch of records could
cause an error due to breaking SQLite's maximum number of parameters. Thanks, Simon Wiles. (#145)
2.17 (2020-09-07)
This release handles a bug where replacing rows in FTS tables could result in growing numbers of
unnecessary rows in the associated *_fts_docsize table. (#149)
• PRAGMA recursive_triggers=on by default for all connections. You can turn it off with
Database(recursive_triggers=False). (#152)
• table.optimize() method now deletes unnecessary rows from the *_fts_docsize table. (#153)
• New tracer method for tracking underlying SQL queries, see Tracing queries. (#150)
• Neater indentation for schema SQL. (#148)
• Documentation for sqlite_utils.AlterError exception thrown by in add_foreign_keys().
2.16.1 (2020-08-28)
• insert_all(..., alter=True) now works for columns introduced after the first 100 records. Thanks, Simon
Wiles! (#139)
• Continuous Integration is now powered by GitHub Actions. (#143)
2.16 (2020-08-21)
• --load-extension option for sqlite-utils query for loading SQLite extensions. (#134)
• New sqlite_utils.utils.find_spatialite() function for finding SpatiaLite in common locations. (#135)
2.15.1 (2020-08-12)
• Now available as a sdist package on PyPI in addition to a wheel. (#133)
2.15 (2020-08-10)
• New db.enable_wal() and db.disable_wal() methods for enabling and disabling Write-Ahead Logging for a
database file - see WAL mode in the Python API documentation.
• Also sqlite-utils enable-wal file.db and sqlite-utils disable-wal file.db commands for doing the same
thing on the command-line, see WAL mode (CLI). (#132)
2.14.1 (2020-08-05)
• Documentation improvements.
2.14 (2020-08-01)
• The insert-files command can now read from standard input: cat dog.jpg | sqlite-utils insert-files
dogs.db pics - --name=dog.jpg. (#127)
• You can now specify a full-text search tokenizer using the new tokenize= parameter to enable_fts().
This means you can enable Porter stemming on a table by running db["articles"].enable_fts(["headline",
"body"], tokenize="porter"). (#130)
• You can also set a custom tokenizer using the sqlite-utils enable-fts CLI command, via the new
--tokenize option.
2.13 (2020-07-29)
• memoryview and uuid.UUID objects are now supported. memoryview objects will be stored using BLOB and
uuid.UUID objects will be stored using TEXT. (#128)
2.12 (2020-07-27)
The theme of this release is better tools for working with binary data. The new insert-files command can
be used to insert binary files directly into a database table, and other commands have been improved with
better support for BLOB columns.
• sqlite-utils insert-files my.db gifs *.gif can now insert the contents of files into a specified table.
The columns in the table can be customized to include different pieces of metadata derived from the
files. See Inserting data from files. (#122)
• --raw option to sqlite-utils query - for outputting just a single raw column value - see Returning raw
data, such as binary content. (#123)
• JSON output now encodes BLOB values as special base64 objects - see Returning JSON. (#125)
• The same format of JSON base64 objects can now be used to insert binary data - see Inserting JSON data.
(#126)
• The sqlite-utils query command can now accept named parameters, e.g. sqlite-utils :memory: "select :num
* :num2" -p num 5 -p num2 6 - see Returning JSON. (#124)
2.11 (2020-07-08)
• New --truncate option to sqlite-utils insert, and truncate=True argument to .insert_all(). Thanks,
Thomas Sibley. (#118)
• The sqlite-utils query command now runs updates in a transaction. Thanks, Thomas Sibley. (#120)
2.10.1 (2020-06-23)
• Added documentation for the table.pks introspection property. (#116)
2.10 (2020-06-12)
• The sqlite-utils command now supports UPDATE/INSERT/DELETE in addition to SELECT. (#115)
2.9.1 (2020-05-11)
• Added custom project links to the PyPI listing.
2.9 (2020-05-10)
• New sqlite-utils drop-table command, see Dropping tables. (#111)
• New sqlite-utils drop-view command, see Dropping views.
• Python decimal.Decimal objects are now stored as FLOAT. (#110)
2.8 (2020-05-03)
• New sqlite-utils create-table command, see Creating tables. (#27)
• New sqlite-utils create-view command, see Creating views. (#107)
2.7.2 (2020-05-02)
• db.create_view(...) now has additional parameters ignore=True or replace=True, see Creating views. (‐
#106)
2.7.1 (2020-05-01)
• New sqlite-utils views my.db command for listing views in a database, see Listing views. (#105)
• sqlite-utils tables (and views) has a new --schema option which outputs the table/view schema, see
Listing tables. (#104)
• Nested structures containing invalid JSON values (e.g. Python bytestrings) are now serialized using
repr() instead of throwing an error. (#102)
2.7 (2020-04-17)
• New columns= argument for the .insert(), .insert_all(), .upsert() and .upsert_all() methods, for
over-riding the auto-detected types for columns and specifying additional columns that should be added
when the table is created. See Custom column order and column types. (#100)
2.6 (2020-04-15)
• New table.rows_where(..., order_by="age desc") argument, see Listing rows. (#76)
2.5 (2020-04-12)
• Panda's Timestamp is now stored as a SQLite TEXT column. Thanks, b0b5h4rp13! (#96)
• table.last_pk is now only available for inserts or upserts of a single record. (#98)
• New Database(filepath, recreate=True) parameter for deleting and recreating the database. (#97)
2.4.4 (2020-03-23)
• Fixed bug where columns with only null values were not correctly created. (#95)
2.4.3 (2020-03-23)
• Column type suggestion code is no longer confused by null values. (#94)
2.4.2 (2020-03-14)
• table.column_dicts now works with all column types - previously it would throw errors on types other
than TEXT, BLOB, INTEGER or FLOAT. (#92)
• Documentation for NotFoundError thrown by table.get(pk) - see Retrieving a specific record.
2.4.1 (2020-03-01)
• table.enable_fts() now works with columns that contain spaces. (#90)
2.4 (2020-02-26)
• table.disable_fts() can now be used to remove FTS tables and triggers that were created using
table.enable_fts(...). (#88)
• The sqlite-utils disable-fts command can be used to remove FTS tables and triggers from the
command-line. (#88)
• Trying to create table columns with square braces ([ or ]) in the name now raises an error. (#86)
• Subclasses of dict, list and tuple are now detected as needing a JSON column. (#87)
2.3.1 (2020-02-10)
table.create_index() now works for columns that contain spaces. (#85)
2.3 (2020-02-08)
table.exists() is now a method, not a property. This was not a documented part of the API before so I'm
considering this a non-breaking change. (#83)
2.2.1 (2020-02-06)
Fixed a bug where .upsert(..., hash_id="pk") threw an error (#84).
2.2 (2020-02-01)
New feature: sqlite_utils.suggest_column_types([records]) returns the suggested column types for a list
of records. See Suggesting column types. (#81).
This replaces the undocumented table.detect_column_types() method.
2.1 (2020-01-30)
New feature: conversions={...} can be passed to the .insert() family of functions to specify SQL
conversions that should be applied to values that are being inserted or updated. See Converting column
values using SQL functions . (#77).
2.0.1 (2020-01-05)
The .upsert() and .upsert_all() methods now raise a sqlite_utils.db.PrimaryKeyRequired exception if you
call them without specifying the primary key column using pk= (#73).
2.0 (2019-12-29)
This release changes the behaviour of upsert. It's a breaking change, hence 2.0.
The upsert command-line utility and the .upsert() and .upsert_all() Python API methods have had their
behaviour altered. They used to completely replace the affected records: now, they update the specified
values on existing records but leave other columns unaffected.
See Upserting data using the Python API and Upserting data using the CLI for full details.
If you want the old behaviour - where records were completely replaced - you can use $ sqlite-utils
insert ... --replace on the command-line and .insert(..., replace=True) and .insert_all(...,
replace=True) in the Python API. See Insert-replacing data using the Python API and Insert-replacing data
using the CLI for more.
For full background on this change, see issue #66.
1.12.1 (2019-11-06)
• Fixed error thrown when .insert_all() and .upsert_all() were called with empty lists (#52)
1.12 (2019-11-04)
Python library utilities for deleting records (#62)
• db["tablename"].delete(4) to delete by primary key, see Deleting a specific record
• db["tablename"].delete_where("id > ?", [3]) to delete by a where clause, see Deleting multiple records
1.11 (2019-09-02)
Option to create triggers to automatically keep FTS tables up-to-date with newly inserted, updated and
deleted records. Thanks, Amjith Ramanujam! (#57)
• sqlite-utils enable-fts ... --create-triggers - see Configuring full-text search using the CLI
• db["tablename"].enable_fts(..., create_triggers=True) - see Configuring full-text search using the
Python library
• Support for introspecting triggers for a database or table - see Introspecting tables and views (#59)
1.10 (2019-08-23)
Ability to introspect and run queries against views (#54)
• db.view_names() method and and db.views property
• Separate View and Table classes, both subclassing new Queryable class
• view.drop() method
See Listing views.
1.9 (2019-08-04)
• table.m2m(...) method for creating many-to-many relationships: Working with many-to-many relationships
(#23)
1.8 (2019-07-28)
• table.update(pk, values) method: Updating a specific record (#35)
1.7.1 (2019-07-28)
• Fixed bug where inserting records with 11 columns in a batch of 100 triggered a "too many SQL
variables" error (#50)
• Documentation and tests for table.drop() method: Dropping a table or view
1.7 (2019-07-24)
Support for lookup tables.
• New table.lookup({...}) utility method for building and querying lookup tables - see Working with
lookup tables (#44)
• New extracts= table configuration option, see Populating lookup tables automatically during
insert/upsert (#46)
• Use pysqlite3 if it is available, otherwise use sqlite3 from the standard library
• Table options can now be passed to the new db.table(name, **options) factory function in addition to
being passed to insert_all(records, **options) and friends - see Table configuration options
• In-memory databases can now be created using db = Database(memory=True)
1.6 (2019-07-18)
• sqlite-utils insert can now accept TSV data via the new --tsv option (#41)
1.5 (2019-07-14)
• Support for compound primary keys (#36)
• Configure these using the CLI tool by passing --pk multiple times
• In Python, pass a tuple of columns to the pk=(..., ...) argument: Compound primary keys
• New table.get() method for retrieving a record by its primary key: Retrieving a specific record (#39)
1.4.1 (2019-07-14)
• Assorted minor documentation fixes: changes since 1.4
1.4 (2019-06-30)
• Added sqlite-utils index-foreign-keys command (docs) and db.index_foreign_keys() method (docs) (#33)
1.3 (2019-06-28)
• New mechanism for adding multiple foreign key constraints at once: db.add_foreign_keys() documentation
(#31)
1.2.2 (2019-06-25)
• Fixed bug where datetime.time was not being handled correctly
1.2.1 (2019-06-20)
• Check the column exists before attempting to add a foreign key (#29)
1.2 (2019-06-12)
• Improved foreign key definitions: you no longer need to specify the column, other_table AND
other_column to define a foreign key - if you omit the other_table or other_column the script will
attempt to guess the correct values by introspecting the database. See Adding foreign key constraints
for details. (#25)
• Ability to set NOT NULL constraints and DEFAULT values when creating tables (#24). Documentation:
Setting defaults and not null constraints (Python API), Setting defaults and not null constraints (CLI)
• Support for not_null_default=X / --not-null-default for setting a NOT NULL DEFAULT 'x' when adding a
new column. Documentation: Adding columns (Python API), Adding columns (CLI)
1.1 (2019-05-28)
• Support for ignore=True / --ignore for ignoring inserted records if the primary key already exists (‐
#21) - documentation: Inserting data (Python API), Inserting data (CLI)
• Ability to add a column that is a foreign key reference using fk=... / --fk (#16) - documentation:
Adding columns (Python API), Adding columns (CLI)
1.0.1 (2019-05-27)
• sqlite-utils rows data.db table --json-cols - fixed bug where --json-cols was not obeyed
1.0 (2019-05-24)
•
Option to automatically add new columns if you attempt to insert or upsert data with extra fields:
sqlite-utils insert ... --alter - see Adding columns automatically with the sqlite-utils CLI
db["tablename"].insert(record, alter=True) - see Adding columns automatically using the Python
API
• New --json-cols option for outputting nested JSON, see Nested JSON values
0.14 (2019-02-24)
• Ability to create unique indexes: db["mytable"].create_index(["name"], unique=True)
• db["mytable"].create_index(["name"], if_not_exists=True)
• $ sqlite-utils create-index mydb.db mytable col1 [col2...], see Creating indexes
• table.add_column(name, type) method, see Adding columns
• $ sqlite-utils add-column mydb.db mytable nameofcolumn, see Adding columns (CLI)
• db["books"].add_foreign_key("author_id", "authors", "id"), see Adding foreign key constraints
• $ sqlite-utils add-foreign-key books.db books author_id authors id, see Adding foreign key constraints
(CLI)
• Improved (but backwards-incompatible) foreign_keys= argument to various methods, see Specifying foreign
keys
0.13 (2019-02-23)
• New --table and --fmt options can be used to output query results in a variety of visual table formats,
see Table-formatted output
• New hash_id= argument can now be used for Setting an ID based on the hash of the row contents
• Can now derive correct column types for numpy int, uint and float values
• table.last_id has been renamed to table.last_rowid
• table.last_pk now contains the last inserted primary key, if pk= was specified
• Prettier indentation in the CREATE TABLE generated schemas
0.12 (2019-02-22)
• Added db[table].rows iterator - see Listing rows
• Replaced sqlite-utils json and sqlite-utils csv with a new default subcommand called sqlite-utils query
which defaults to JSON and takes formatting options --nl, --csv and --no-headers - see Returning JSON
and Returning CSV or TSV
• New sqlite-utils rows data.db name-of-table command, see Returning all rows in a table
• sqlite-utils table command now takes options --counts and --columns plus the standard output format
options, see Listing tables
0.11 (2019-02-07)
New commands for enabling FTS against a table and columns:
sqlite-utils enable-fts db.db mytable col1 col2
See Configuring full-text search.
0.10 (2019-02-06)
Handle datetime.date and datetime.time values.
New option for efficiently inserting rows from a CSV:
sqlite-utils insert db.db foo - --csv
0.9 (2019-01-27)
Improved support for newline-delimited JSON.
sqlite-utils insert has two new command-line options:
• --nl means "expect newline-delimited JSON". This is an extremely efficient way of loading in large
amounts of data, especially if you pipe it into standard input.
• --batch-size=1000 lets you increase the batch size (default is 100). A commit will be issued every X
records. This also control how many initial records are considered when detecting the desired SQL table
schema for the data.
In the Python API, the table.insert_all(...) method can now accept a generator as well as a list of
objects. This will be efficiently used to populate the table no matter how many records are produced by
the generator.
The Database() constructor can now accept a pathlib.Path object in addition to a string or an existing
SQLite connection object.
0.8 (2019-01-25)
Two new commands: sqlite-utils csv and sqlite-utils json
These commands execute a SQL query and return the results as CSV or JSON. See Returning CSV or TSV and
Returning JSON for more details.
$ sqlite-utils json --help
Usage: sqlite-utils json [OPTIONS] PATH SQL
Execute SQL query and return the results as JSON
Options:
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--help Show this message and exit.
$ sqlite-utils csv --help
Usage: sqlite-utils csv [OPTIONS] PATH SQL
Execute SQL query and return the results as CSV
Options:
--no-headers Exclude headers from CSV output
--help Show this message and exit.
0.7 (2019-01-24)
This release implements the sqlite-utils command-line tool with a number of useful subcommands.
• sqlite-utils tables demo.db lists the tables in the database
• sqlite-utils tables demo.db --fts4 shows just the FTS4 tables
• sqlite-utils tables demo.db --fts5 shows just the FTS5 tables
• sqlite-utils vacuum demo.db runs VACUUM against the database
• sqlite-utils optimize demo.db runs OPTIMIZE against all FTS tables, then VACUUM
• sqlite-utils optimize demo.db --no-vacuum runs OPTIMIZE but skips VACUUM
The two most useful subcommands are upsert and insert, which allow you to ingest JSON files with one or
more records in them, creating the corresponding table with the correct columns if it does not already
exist. See Inserting JSON data for more details.
• sqlite-utils insert demo.db dogs dogs.json --pk=id inserts new records from dogs.json into the dogs
table
• sqlite-utils upsert demo.db dogs dogs.json --pk=id upserts records, replacing any records with
duplicate primary keys
One backwards incompatible change: the db["table"].table_names property is now a method:
• db["table"].table_names() returns a list of table names
• db["table"].table_names(fts4=True) returns a list of just the FTS4 tables
• db["table"].table_names(fts5=True) returns a list of just the FTS5 tables
A few other changes:
• Plenty of updated documentation, including full coverage of the new command-line tool
• Allow column names to be reserved words (use correct SQL escaping)
• Added automatic column support for bytes and datetime.datetime
0.6 (2018-08-12)
• .enable_fts() now takes optional argument fts_version, defaults to FTS5. Use FTS4 if the version of
SQLite bundled with your Python does not support FTS5
• New optional column_order= argument to .insert() and friends for providing a partial or full desired
order of the columns when a database table is created
• New documentation for .insert_all() and .upsert() and .upsert_all()
0.5 (2018-08-05)
• db.tables and db.table_names introspection properties
• db.indexes property for introspecting indexes
• table.create_index(columns, index_name) method
• db.create_view(name, sql) method
• Table methods can now be chained, plus added table.last_id for accessing the last inserted row ID
0.4 (2018-07-31)
• enable_fts(), populate_fts() and search() table methods
0.3.1 (2018-07-31)
• Documented related projects
• Added badges to the documentation
0.3 (2018-07-31)
• New Table class representing a table in the SQLite database
0.2 (2018-07-28)
• Initial release to PyPI
AUTHOR
Simon Willison
COPYRIGHT
2018-2024, Simon Willison
Mar 07, 2024 SQLITE-UTILS(1)