Ubuntu Manpage: tokyocabinet - a modern implementation of DBM

Provided by: libtokyocabinet-dev_1.4.48-11_amd64

NAME

       tokyocabinet - a modern implementation of DBM

INTRODUCTION

Tokyo Cabinet is a library of routines for managing a database. The database is a simple
data file containing records, each is a pair of a key and a value. Every key and value is
serial bytes with variable length. Both binary data and character string can be used as a
key and a value. There is neither concept of data tables nor data types. Records are
organized in hash table, B+ tree, or fixed-length array.

As for database of hash table, each key must be unique within a database, so it is
impossible to store two or more records with a key overlaps. The following access methods
are provided to the database: storing a record with a key and a value, deleting a record
by a key, retrieving a record by a key. Moreover, traversal access to every key are
provided, although the order is arbitrary. These access methods are similar to ones of
DBM (or its followers: NDBM and GDBM) library defined in the UNIX standard. Tokyo Cabinet
is an alternative for DBM because of its higher performance.

As for database of B+ tree, records whose keys are duplicated can be stored. Access
methods of storing, deleting, and retrieving are provided as with the database of hash
table. Records are stored in order by a comparison function assigned by a user. It is
possible to access each record with the cursor in ascending or descending order.
According to this mechanism, forward matching search for strings and range search for
integers are realized.

As for database of fixed-length array, records are stored with unique natural numbers. It
is impossible to store two or more records with a key overlaps. Moreover, the length of
each record is limited by the specified length. Provided operations are the same as ones
of hash database.

Table database is also provided as a variant of hash database. Each record is identified
by the primary key and has a set of named columns. Although there is no concept of data
schema, it is possible to search for records with complex conditions efficiently by using
indices of arbitrary columns.

Tokyo Cabinet is written in the C language, and provided as API of C, Perl, Ruby, Java,
and Lua. Tokyo Cabinet is available on platforms which have API conforming to C99 and
POSIX. Tokyo Cabinet is a free software licensed under the GNU Lesser General Public
License.

THE DINOSAUR WING OF THE DBM FORKS

Tokyo Cabinet is developed as the successor of GDBM and QDBM on the following purposes.
They are achieved and Tokyo Cabinet replaces conventional DBM products.

improves space efficiency : smaller size of database file.
improves time efficiency : faster processing speed.
improves parallelism : higher performance in multi-thread environment.
improves usability : simplified API.
improves robustness : database file is not corrupted even under catastrophic
situation.
supports 64-bit architecture : enormous memory space and database file are
available.

As with QDBM, the following three restrictions of traditional DBM: a process can handle
only one database, the size of a key and a value is bounded, a database file is sparse,
are cleared. Moreover, the following three restrictions of QDBM: the size of a database
file is limited to 2GB, environments with different byte orders can not share a database
file, only one thread can search a database at the same time, are cleared.

Tokyo Cabinet runs very fast. For example, elapsed time to store 1 million records is 0.7
seconds for hash database, and 1.6 seconds for B+ tree database. Moreover, the size of
database of Tokyo Cabinet is very small. For example, overhead for a record is 16 bytes
for hash database, and 5 bytes for B+ tree database. Furthermore, scalability of Tokyo
Cabinet is great. The database size can be up to 8EB (9.22e18 bytes).

EFFECTIVE IMPLEMENTATION OF HASH DATABASE

Tokyo Cabinet uses hash algorithm to retrieve records. If a bucket array has sufficient
number of elements, the time complexity of retrieval is "O(1)". That is, time required
for retrieving a record is constant, regardless of the scale of a database. It is also
the same about storing and deleting. Collision of hash values is managed by separate
chaining. Data structure of the chains is binary search tree. Even if a bucket array has
unusually scarce elements, the time complexity of retrieval is "O(log n)".

Tokyo Cabinet attains improvement in retrieval by loading RAM with the whole of a bucket
array. If a bucket array is on RAM, it is possible to access a region of a target record
by about one path of file operations. A bucket array saved in a file is not read into RAM
with the `read' call but directly mapped to RAM with the `mmap' call. Therefore,
preparation time on connecting to a database is very short, and two or more processes can
share the same memory map.

If the number of elements of a bucket array is about half of records stored within a
database, although it depends on characteristic of the input, the probability of collision
of hash values is about 56.7% (36.8% if the same, 21.3% if twice, 11.5% if four times,
6.0% if eight times). In such case, it is possible to retrieve a record by two or less
paths of file operations. If it is made into a performance index, in order to handle a
database containing one million of records, a bucket array with half a million of elements
is needed. The size of each element is 4 bytes. That is, if 2M bytes of RAM is
available, a database containing one million records can be handled.

Traditional DBM provides two modes of the storing operations: "insert" and "replace". In
the case a key overlaps an existing record, the insert mode keeps the existing value,
while the replace mode transposes it to the specified value. In addition to the two
modes, Tokyo Cabinet provides "concatenate" mode. In the mode, the specified value is
concatenated at the end of the existing value and stored. This feature is useful when
adding an element to a value as an array.

Generally speaking, while succession of updating, fragmentation of available regions
occurs, and the size of a database grows rapidly. Tokyo Cabinet deal with this problem by
coalescence of dispensable regions and reuse of them. When overwriting a record with a
value whose size is greater than the existing one, it is necessary to remove the region to
another position of the file. Because the time complexity of the operation depends on the
size of the region of a record, extending values successively is inefficient. However,
Tokyo Cabinet deal with this problem by alignment. If increment can be put in padding, it
is not necessary to remove the region.

The "free block pool" to reuse dispensable regions efficiently is also implemented. It
keeps a list of dispensable regions and reuse the "best fit" region, that is the smallest
region in the list, when a new block is requested. Because fragmentation is inevitable
even then, two kinds of optimization (defragmentation) mechanisms are implemented. The
first is called static optimization which deploys all records into another file and then
writes them back to the original file at once. The second is called dynamic optimization
which gathers up dispensable regions by replacing the locations of records and dispensable
regions gradually.

USEFUL IMPLEMENTATION OF B+ TREE DATABASE

Although B+ tree database is slower than hash database, it features ordering access to
each record. The order can be assigned by users. Records of B+ tree are sorted and
arranged in logical pages. Sparse index organized in B tree that is multiway balanced
tree are maintained for each page. Thus, the time complexity of retrieval and so on is
"O(log n)". Cursor is provided to access each record in order. The cursor can jump to a
position specified by a key and can step forward or backward from the current position.
Because each page is arranged as double linked list, the time complexity of stepping
cursor is "O(1)".

B+ tree database is implemented, based on above hash database. Because each page of B+
tree is stored as each record of hash database, B+ tree database inherits efficiency of
storage management of hash database. Because the header of each record is smaller and
alignment of each page is adjusted according to the page size, in most cases, the size of
database file is cut by half compared to one of hash database. Although operation of many
pages are required to update B+ tree, QDBM expedites the process by caching pages and
reducing file operations. In most cases, because whole of the sparse index is cached on
memory, it is possible to retrieve a record by one or less path of file operations.

Each pages of B+ tree can be stored with compressed. Two compression method; Deflate of
ZLIB and Block Sorting of BZIP2, are supported. Because each record in a page has similar
patterns, high efficiency of compression is expected due to the Lempel-Ziv or the BWT
algorithms. In case handling text data, the size of a database is reduced to about 25%.
If the scale of a database is large and disk I/O is the bottleneck, featuring compression
makes the processing speed improved to a large extent.

NAIVE IMPLEMENTATION OF FIXED-LENGTH DATABASE

       Fixed-length  database  has restrictions that each key should be a natural number and that
       the length of each value is limited.  However, time efficiency and  space  efficiency  are
       higher than the other data structures as long as the use case is within the restriction.

       Because  the  whole  region  of  the  database  is mapped on memory by the `mmap' call and
       referred as a multidimensional array, the overhead related to the file I/O  is  minimized.
       Due  to  this simple structure, fixed-length database works faster than hash database, and
       its concurrency in multi-thread environment is prominent.

       The size of the database is proportional to the range of keys and the limit size  of  each
       value.   That is, the smaller the range of keys is or the smaller the length of each value
       is, the higher the space efficiency is.  For example, if the maximum key  is  1000000  and
       the  limit  size  of the value is 100 bytes, the size of the database will be about 100MB.
       Because regions around referred records are only loaded on the RAM, you can  increase  the
       size of the database to the size of the virtual memory.

FLEXIBLE IMPLEMENTATION OF TABLE DATABASE

Table database does not express simple key/value structure but expresses a structure like
a table of relational database. Each record is identified by the primary key and has a
set of multiple columns named with arbitrary strings. For example, a stuff in your
company can be expressed by a record identified by the primary key of the employee ID
number and structured by columns of his name, division, salary, and so on. Unlike
relational database, table database does not need to define any data schema and can
contain records of various structures different from each other.

Table database supports query functions with not only the primary key but also with
conditions about arbitrary columns. Each column condition is composed of the name of a
column and a condition expression. Operators of full matching, forward matching, regular
expression matching, and so on are provided for the string type. Operators of full
matching, range matching and so on are provided for the number type. Operators for tag
search and full-text search are also provided. A query can contain multiple conditions
for logical intersection. Search by multiple queries for logical union is also available.
The order of the result set can be specified as the ascending or descending order of
strings or numbers.

You can create indices for arbitrary columns to improve performance of search and sorting.
Although columns do not have data types, indices have types for strings or numbers.
Inverted indices for space separated tokens and character N-gram tokens are also
supported. The query optimizer uses indices in suitable way according to each query.
Indices are implemented as different files of B+ tree database.

PRACTICAL FUNCTIONALITY

Databases on the filesystem feature transaction mechanisms. It is possible to commit a
series of operations between the beginning and the end of the transaction in a lump, or to
abort the transaction and perform rollback to the state before the transaction. Two
isolation levels are supported; serializable and read uncommitted. Durability is secured
by write ahead logging and shadow paging.

Tokyo Cabinet provides two modes to connect to a database: "reader" and "writer". A
reader can perform retrieving but neither storing nor deleting. A writer can perform all
access methods. Exclusion control between processes is performed when connecting to a
database by file locking. While a writer is connected to a database, neither readers nor
writers can be connected. While a reader is connected to a database, other readers can be
connect, but writers can not. According to this mechanism, data consistency is guaranteed
with simultaneous connections in multitasking environment.

Functions of API of Tokyo cabinet are reentrant and available in multi-thread environment.
Discrete database object can be operated in parallel entirely. For simultaneous
operations of the same database object, read-write lock is used for exclusion control.
That is, while a writing thread is operating the database, other reading threads and
writing threads are blocked. However, while a reading thread is operating the database,
reading threads are not blocked. The locking granularity of hash database and
fixed-length database is per record, and that of the other databases is per file.

SIMPLE BUT VARIOUS INTERFACES

Tokyo Cabinet provides simple API based on the object oriented design. Every operation
for database is encapsulated and published as lucid methods as `open' (connect), `close'
(disconnect), `put' (insert), `out' (remove), `get' (retrieve), and so on. Because the
three of hash, B+ tree, and fixed-length array database APIs are very similar with each
other, porting an application from one to the other is easy. Moreover, the abstract API
is provided to handle these databases with the same interface. Applications of the
abstract API can determine the type of the database in runtime.

The utility API is also provided. Such fundamental data structure as list and map are
included. And, some useful features; memory pool, string processing, encoding, are also
included.

Six kinds of API; the utility API, the hash database API, the B+ tree database API, the
fixed-length database API, the table database API, and the abstract database API, are
provided for the C language. Command line interfaces are also provided corresponding to
each API. They are useful for prototyping, test, and debugging. Except for C, Tokyo
Cabinet provides APIs for Perl, Ruby, Java, and Lua. APIs for other languages will
hopefully be provided by third party.

In cases that multiple processes access a database at the same time or some processes
access a database on a remote host, the remote service is useful. The remote service is
composed of a database server and its access library. Applications can access the
database server by using the remote database API. The server implements HTTP and the
memcached protocol partly so that client programs on almost all platforms can access the
server easily.

HOW TO USE THE LIBRARY

       Tokyo Cabinet provides API of the C language and it is available by programs conforming to
       the C89 (ANSI C) standard or the C99 standard.  As the header files of Tokyo  Cabinet  are
       provided  as `tcutil.h', `tchdb.h', and `tcbdb.h', applications should include one or more
       of them accordingly to use the API.  As the library is provided as `libtokyocabinet.a' and
       `libtokyocabinet.so'  and  they depends `libz.so', `librt.so', `libpthread.so', `libm.so',
       and `libc.so', linker  options  `-ltokyocabinet',  `-lz',  `-lbz2',  `-lrt',  `-lpthread',
       `-lm',  and  `-lc'  are  required  for  build  command.   A  typical  build command is the
       following.

              gcc -I/usr/local/include tc_example.c -o tc_example \
                -L/usr/local/lib -ltokyocabinet -lz -lbz2 -lrt -lpthread -lm -lc

       You can also use Tokyo Cabinet in programs written in C++.  Because each header is wrapped
       in C linkage (`extern "C"' block), you can simply include them into your C++ programs.

LICENSE

       Tokyo  Cabinet  is free software; you can redistribute it and/or modify it under the terms
       of the GNU Lesser General Public License as published by  the  Free  Software  Foundation;
       either version 2.1 of the License or any later version.

       Tokyo Cabinet is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
       without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR  PURPOSE.
       See the GNU Lesser General Public License for more details.

       You  should have received a copy of the GNU Lesser General Public License along with Tokyo
       Cabinet (See the file `COPYING'); if not, write to the Free Software Foundation, Inc.,  59
       Temple Place, Suite 330, Boston, MA 02111-1307 USA.

       Tokyo  Cabinet  was  written  by  FAL  Labs.   You  can  contact  the  author by e-mail to
       `info@fallabs.com'.