Ubuntu Manpage: tokyocabinet - a modern implementation of DBM

Provided by: libtokyocabinet-dev_1.4.48-15.1build1_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'.