Reliable and efficient key–value database for Linux?
Question:
I need a fast, reliable and memory-efficient key–value database for Linux. My keys are about 128 bytes, and the maximum value size can be 128K or 256K. The database subsystem shouldn’t use more than about 1 MB of RAM. The total database size is 20G (!), but only a small random fraction of the data is accessed at a time. If necessary, I can move some data blobs out of the database (to regular files), so the size gets down to 2 GB maximum. The database must survive a system crash without any loss in recently unmodified data. I’ll have about 100 times more reads than writes. It is a plus if it can use a block device (without a filesystem) as storage. I don’t need client-server functionality, just a library. I need Python bindings (but I can implement them if they are not available).
Which solutions should I consider, and which one do you recommend?
Candidates I know of which could work:
- Tokyo Cabinet (Python bindings are pytc, see also pytc example code, supports hashes and B+trees, transaction log files and more, the size of the bucket array is fixed at database creation time; the writer must close the file to give others a chance; lots of small writes with reopening the file for each of them are very slow; the Tyrant server can help with the lots of small writes; speed comparison between Tokyo Cabinet, Tokyo Tyrant and Berkeley DB)
- VSDB (safe even on NFS, without locking; what about barriers?; updates are very slow, but not as slow as in cdb; last version in 2003)
- BerkeleyDB (provides crash recovery; provides transactions; the
bsddb Python module provides bindings)
- Samba’s TDB (with transactions and Python bindings, some users experienced corruption, sometimes
mmap()s the whole file, the repack operation sometimes doubles the file size, produces mysterious failures if the database is larger than 2G (even on 64-bit systems), cluster implementation (CTDB) also available; file grows too large after lots of modifications; file becomes too slow after lots of hash contention; no built-in way to rebuild the file; very fast parallel updates by locking individual hash buckets)
- aodbm (append-only so survives a system crash, with Python bindings)
- hamsterdb (with Python bindings)
- C-tree (mature, versatile commercial solution with high performance, has a free edition with reduced functionality)
- the old TDB (from 2001)
- bitcask (log-structured, written in Erlang)
- various other DBM implementations (such as GDBM, NDBM, QDBM,, Perl’s SDBM or Ruby’s; probably they don’t have proper crash recovery)
I won’t use these:
- MemcacheDB (client-server, uses BereleleyDB as a backend)
- cdb (needs to regenerate the whole database upon each write)
- http://www.wildsparx.com/apbcdb/ (ditto)
- Redis (keeps the whole database in memory)
- SQLite (it becomes very slow without periodic vacuuming, see autocompletion in the in the location bar in Firefox 3.0, even though versions 3.1 and later of sqlite allow
auto_vacuuming; beware: small writing transactions can be very slow; beware: if a busy process is doing many transactions, other processes starve, and they can never get the lock)
- MongoDB (too heavy-weight, treats values as objects with internal structure)
- Firebird (SQL-based RDBMS, too heavy-weight)
FYI, a recent article about key–value databases in the Linux magazine.
FYI, an older software list
FYI, a speed comparison of MemcacheDB, Redis and Tokyo Cabinet Tyrant
Related questions on StackOverflow:
Answers:
how about a SQLite?
I’ve used bsddb.hashlib() with Python, it worked pretty good.
how about Python 3.0’s dbm.ndbm ?
I’ve had good luck with the Tokyo Cabinet/pytc solution. It’s very fast (a bit faster than using the shelve module using anydbm in my implementation), both for reading and writing (though I too do far more reading). The problem for me was the spartan documentation on the python bindings, but there’s enough example code around to figure out how to do what you need to do. Additionally, tokyo cabinet is quite easy to install (as are the python bindings), doesn’t require a server (as you mention) and seems to be actively supported (stable but no longer under active development). You can open files in read-only mode, allowing concurrent access, or read/write mode, preventing other processes from accessing the database.
I was looking at various options over the summer, and the advice I got then was this: try out the different options and see what works best for you. It’d be nice if there were simply a “best” option, but everyone is looking for slightly different features and are willing to make different trade-offs. You know best.
(That said, it’d be useful to others if you shared what ended up working the best for you, and why you chose that solution over others!)
In my query for a cross-platform ISAM-style database (similar), I also received suggestions for the embedded version of Firebird and GLib.
cdb can handle any database up to 4 GB, making it too small for the 20GB matter at hand.
Riak runs on Linux, and allows you to dynamically add nodes
LMDB is the most memory-efficient database around
http://symas.com/mdb/inmem/
and also proven to be the most reliable – completely crash-proof.
http://wisdom.cs.wisc.edu/workshops/spring-14/talks/Thanu.pdf
Of the ones you’ve mentioned, Tokyo Cabinet has documented corruption issues
https://www.google.com/search?q=cfengine+tokyo+cabinet+corruption
BerkeleyDB also has well-documented corruption issues, as does Bitcask. (And bitcask is an in-memory-only DB anyway, so useless for your 1MB RAM requirement.)
LMDB is also well-supported in Python, with a couple different bindings available.
https://github.com/dw/py-lmdb/
https://github.com/tspurway/pymdb-lightning
Disclaimer – I am the author of LMDB. But these are documented facts: LMDB is the smallest, most efficient, and most reliable key/value store in the world and nothing else comes anywhere close.
I need a fast, reliable and memory-efficient key–value database for Linux. My keys are about 128 bytes, and the maximum value size can be 128K or 256K. The database subsystem shouldn’t use more than about 1 MB of RAM. The total database size is 20G (!), but only a small random fraction of the data is accessed at a time. If necessary, I can move some data blobs out of the database (to regular files), so the size gets down to 2 GB maximum. The database must survive a system crash without any loss in recently unmodified data. I’ll have about 100 times more reads than writes. It is a plus if it can use a block device (without a filesystem) as storage. I don’t need client-server functionality, just a library. I need Python bindings (but I can implement them if they are not available).
Which solutions should I consider, and which one do you recommend?
Candidates I know of which could work:
- Tokyo Cabinet (Python bindings are pytc, see also pytc example code, supports hashes and B+trees, transaction log files and more, the size of the bucket array is fixed at database creation time; the writer must close the file to give others a chance; lots of small writes with reopening the file for each of them are very slow; the Tyrant server can help with the lots of small writes; speed comparison between Tokyo Cabinet, Tokyo Tyrant and Berkeley DB)
- VSDB (safe even on NFS, without locking; what about barriers?; updates are very slow, but not as slow as in cdb; last version in 2003)
- BerkeleyDB (provides crash recovery; provides transactions; the
bsddbPython module provides bindings) - Samba’s TDB (with transactions and Python bindings, some users experienced corruption, sometimes
mmap()s the whole file, therepackoperation sometimes doubles the file size, produces mysterious failures if the database is larger than 2G (even on 64-bit systems), cluster implementation (CTDB) also available; file grows too large after lots of modifications; file becomes too slow after lots of hash contention; no built-in way to rebuild the file; very fast parallel updates by locking individual hash buckets) - aodbm (append-only so survives a system crash, with Python bindings)
- hamsterdb (with Python bindings)
- C-tree (mature, versatile commercial solution with high performance, has a free edition with reduced functionality)
- the old TDB (from 2001)
- bitcask (log-structured, written in Erlang)
- various other DBM implementations (such as GDBM, NDBM, QDBM,, Perl’s SDBM or Ruby’s; probably they don’t have proper crash recovery)
I won’t use these:
- MemcacheDB (client-server, uses BereleleyDB as a backend)
- cdb (needs to regenerate the whole database upon each write)
- http://www.wildsparx.com/apbcdb/ (ditto)
- Redis (keeps the whole database in memory)
- SQLite (it becomes very slow without periodic vacuuming, see autocompletion in the in the location bar in Firefox 3.0, even though versions 3.1 and later of sqlite allow
auto_vacuuming; beware: small writing transactions can be very slow; beware: if a busy process is doing many transactions, other processes starve, and they can never get the lock) - MongoDB (too heavy-weight, treats values as objects with internal structure)
- Firebird (SQL-based RDBMS, too heavy-weight)
FYI, a recent article about key–value databases in the Linux magazine.
FYI, an older software list
FYI, a speed comparison of MemcacheDB, Redis and Tokyo Cabinet Tyrant
Related questions on StackOverflow:
how about a SQLite?
I’ve used bsddb.hashlib() with Python, it worked pretty good.
how about Python 3.0’s dbm.ndbm ?
I’ve had good luck with the Tokyo Cabinet/pytc solution. It’s very fast (a bit faster than using the shelve module using anydbm in my implementation), both for reading and writing (though I too do far more reading). The problem for me was the spartan documentation on the python bindings, but there’s enough example code around to figure out how to do what you need to do. Additionally, tokyo cabinet is quite easy to install (as are the python bindings), doesn’t require a server (as you mention) and seems to be actively supported (stable but no longer under active development). You can open files in read-only mode, allowing concurrent access, or read/write mode, preventing other processes from accessing the database.
I was looking at various options over the summer, and the advice I got then was this: try out the different options and see what works best for you. It’d be nice if there were simply a “best” option, but everyone is looking for slightly different features and are willing to make different trade-offs. You know best.
(That said, it’d be useful to others if you shared what ended up working the best for you, and why you chose that solution over others!)
In my query for a cross-platform ISAM-style database (similar), I also received suggestions for the embedded version of Firebird and GLib.
cdb can handle any database up to 4 GB, making it too small for the 20GB matter at hand.
Riak runs on Linux, and allows you to dynamically add nodes
LMDB is the most memory-efficient database around
http://symas.com/mdb/inmem/
and also proven to be the most reliable – completely crash-proof.
http://wisdom.cs.wisc.edu/workshops/spring-14/talks/Thanu.pdf
Of the ones you’ve mentioned, Tokyo Cabinet has documented corruption issues
https://www.google.com/search?q=cfengine+tokyo+cabinet+corruption
BerkeleyDB also has well-documented corruption issues, as does Bitcask. (And bitcask is an in-memory-only DB anyway, so useless for your 1MB RAM requirement.)
LMDB is also well-supported in Python, with a couple different bindings available.
https://github.com/dw/py-lmdb/
https://github.com/tspurway/pymdb-lightning
Disclaimer – I am the author of LMDB. But these are documented facts: LMDB is the smallest, most efficient, and most reliable key/value store in the world and nothing else comes anywhere close.