# README
bitcask
A high performance Key/Value store written in Go with a predictable read/write performance and high throughput. Uses a Bitcask on-disk layout (LSM+WAL) similar to Riak
For a more feature-complete Redis-compatible server, distributed key/value store have a look at Bitraft which uses this library as its backend. Use Bitcask as a starting point or if you want to embed in your application, use Bitraft if you need a complete server/client solution with high availability with a Redis-compatible API.
Features
- Embedded (
import "git.mills.io/prologic/bitcask") - Builtin CLI (
bitcask) - Builtin Redis-compatible server (
bitcaskd) - Predictable read/write performance
- Low latency
- High throughput (See: Performance )
Is Bitcask right for my project?
NOTE: Please read this carefully to identify whether using Bitcask is suitable for your needs.
bitcask is a great fit for:
-
Storing hundreds of thousands to millions of key/value pairs based on default configuration. With the default configuration (configurable) of 64 bytes per key and 64kB values, 1M keys would consume roughly ~600-700MB of memory ~65-70GB of disk storage. These are all configurable when you create a new database with
bitcask.Open(...)with functional-style options you can pass withWithXXX(). -
As the backing store to a distributed key/value store. See for example the bitraft as an example of this.
-
For high performance, low latency read/write workloads where you cannot fit a typical hash-map into memory, but require the highest level of performance and predicate read latency. Bitcask ensures only 1 read/write IOPS are ever required for reading and writing key/value pairs.
-
As a general purpose embedded key/value store where you would have used BoltDB, LevelDB, BuntDB or similar...
bitcask is not suited for:
- Storing billions of records The reason for this is the key-space is held in memory using a highly performant and memory optimized adaptive radix tree thanks to go-adaptive-radix-tree however this means the more keys you have in your key space, the more memory is consumed. Consider using a disk-backed B-Tree like BoltDB or LevelDB if you intend to store a large quantity of key/value pairs.
Note however that storing large amounts of data in terms of value(s) is totally fine. In other wise thousands to millions of keys with large values will work just fine.
- Write intensive workloads. Due to the Bitcask design
heavy write workloads that lots of key/value pairs will over time cause
problems like "Too many open files" (#193) errors to occur. This can be mitigated by
periodically compacting the data files by issuing a
.Merge()operation however if key/value pairs do not change or are never deleted, as-in only new key/value pairs are ever written this will have no effect. Eventually you will run out of file descriptors!
You should consider your read/write workloads carefully and ensure you set appropriate file descriptor limits with
ulimit -nthat suit your needs.
Development
$ git clone https://git.mills.io/prologic/bitcask.git
$ make
Install
$ go get git.mills.io/prologic/bitcask
Usage (library)
Install the package into your project:
$ go get git.mills.io/prologic/bitcask
package main
import (
"log"
"git.mills.io/prologic/bitcask"
)
func main() {
db, _ := bitcask.Open("/tmp/db")
defer db.Close()
db.Put([]byte("Hello"), []byte("World"))
val, _ := db.Get([]byte("Hello"))
log.Printf(string(val))
}
See the GoDoc for further documentation and other examples.
Usage (tool)
$ bitcask -p /tmp/db set Hello World
$ bitcask -p /tmp/db get Hello
World
Usage (server)
There is also a builtin very simple Redis-compatible server called bitcaskd:
$ ./bitcaskd ./tmp
INFO[0000] starting bitcaskd v0.0.7@146f777 bind=":6379" path=./tmp
Example session:
$ telnet localhost 6379
Trying ::1...
Connected to localhost.
Escape character is '^]'.
SET foo bar
+OK
GET foo
$3
bar
DEL foo
:1
GET foo
$-1
PING
+PONG
QUIT
+OK
Connection closed by foreign host.
Docker
You can also use the Bitcask Docker Image:
$ docker pull prologic/bitcask
$ docker run -d -p 6379:6379 prologic/bitcask
Performance
Benchmarks run on a 11" MacBook with a 1.4Ghz Intel Core i7:
$ make bench
...
goos: darwin
goarch: amd64
pkg: git.mills.io/prologic/bitcask
BenchmarkGet/128B-4 316515 3263 ns/op 39.22 MB/s 160 B/op 1 allocs/op
BenchmarkGet/256B-4 382551 3204 ns/op 79.90 MB/s 288 B/op 1 allocs/op
BenchmarkGet/512B-4 357216 3835 ns/op 133.51 MB/s 576 B/op 1 allocs/op
BenchmarkGet/1K-4 274958 4429 ns/op 231.20 MB/s 1152 B/op 1 allocs/op
BenchmarkGet/2K-4 227764 5013 ns/op 408.55 MB/s 2304 B/op 1 allocs/op
BenchmarkGet/4K-4 187557 5534 ns/op 740.15 MB/s 4864 B/op 1 allocs/op
BenchmarkGet/8K-4 153546 7652 ns/op 1070.56 MB/s 9472 B/op 1 allocs/op
BenchmarkGet/16K-4 115549 10272 ns/op 1594.95 MB/s 18432 B/op 1 allocs/op
BenchmarkGet/32K-4 69592 16405 ns/op 1997.39 MB/s 40960 B/op 1 allocs/op
BenchmarkPut/128BNoSync-4 123519 11094 ns/op 11.54 MB/s 49 B/op 2 allocs/op
BenchmarkPut/256BNoSync-4 84662 13398 ns/op 19.11 MB/s 50 B/op 2 allocs/op
BenchmarkPut/1KNoSync-4 46345 24855 ns/op 41.20 MB/s 58 B/op 2 allocs/op
BenchmarkPut/2KNoSync-4 28820 43817 ns/op 46.74 MB/s 68 B/op 2 allocs/op
BenchmarkPut/4KNoSync-4 13976 90059 ns/op 45.48 MB/s 89 B/op 2 allocs/op
BenchmarkPut/8KNoSync-4 7852 155101 ns/op 52.82 MB/s 130 B/op 2 allocs/op
BenchmarkPut/16KNoSync-4 4848 238113 ns/op 68.81 MB/s 226 B/op 2 allocs/op
BenchmarkPut/32KNoSync-4 2564 391483 ns/op 83.70 MB/s 377 B/op 3 allocs/op
BenchmarkPut/128BSync-4 260 4611273 ns/op 0.03 MB/s 48 B/op 2 allocs/op
BenchmarkPut/256BSync-4 265 4665506 ns/op 0.05 MB/s 48 B/op 2 allocs/op
BenchmarkPut/1KSync-4 256 4757334 ns/op 0.22 MB/s 48 B/op 2 allocs/op
BenchmarkPut/2KSync-4 255 4996788 ns/op 0.41 MB/s 92 B/op 2 allocs/op
BenchmarkPut/4KSync-4 222 5136481 ns/op 0.80 MB/s 98 B/op 2 allocs/op
BenchmarkPut/8KSync-4 223 5530824 ns/op 1.48 MB/s 99 B/op 2 allocs/op
BenchmarkPut/16KSync-4 213 5717880 ns/op 2.87 MB/s 202 B/op 2 allocs/op
BenchmarkPut/32KSync-4 211 5835948 ns/op 5.61 MB/s 355 B/op 3 allocs/op
BenchmarkScan-4 568696 2036 ns/op 392 B/op 33 allocs/op
PASS
For 128B values:
- ~300,000 reads/sec
- ~90,000 writes/sec
- ~490,000 scans/sec
The full benchmark above shows linear performance as you increase key/value sizes.
Support
Support the ongoing development of Bitcask!
Sponsor
- Become a Sponsor
Contributors
Thank you to all those that have contributed to this project, battle-tested it, used it in their own projects or products, fixed bugs, improved performance and even fix tiny typos in documentation! Thank you and keep contributing!
You can find an AUTHORS file where we keep a list of contributors to the project. If you contribute a PR please consider adding your name there.
Related Projects
- bitraft -- A Distributed Key/Value store (using Raft) with a Redis compatible protocol.
- bitcaskfs -- A FUSE file system for mounting a Bitcask database.
- bitcask-bench -- A benchmarking tool comparing Bitcask and several other Go key/value libraries.
License
bitcask is licensed under the term of the MIT License