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142 lines
4.4 KiB
Markdown
142 lines
4.4 KiB
Markdown
# What is diskv?
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Diskv (disk-vee) is a simple, persistent key-value store written in the Go
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language. It starts with an incredibly simple API for storing arbitrary data on
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a filesystem by key, and builds several layers of performance-enhancing
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abstraction on top. The end result is a conceptually simple, but highly
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performant, disk-backed storage system.
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[![Build Status][1]][2]
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[1]: https://drone.io/github.com/peterbourgon/diskv/status.png
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[2]: https://drone.io/github.com/peterbourgon/diskv/latest
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# Installing
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Install [Go 1][3], either [from source][4] or [with a prepackaged binary][5].
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Then,
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```bash
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$ go get github.com/peterbourgon/diskv
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```
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[3]: http://golang.org
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[4]: http://golang.org/doc/install/source
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[5]: http://golang.org/doc/install
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# Usage
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```go
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package main
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import (
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"fmt"
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"github.com/peterbourgon/diskv"
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)
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func main() {
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// Simplest transform function: put all the data files into the base dir.
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flatTransform := func(s string) []string { return []string{} }
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// Initialize a new diskv store, rooted at "my-data-dir", with a 1MB cache.
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d := diskv.New(diskv.Options{
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BasePath: "my-data-dir",
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Transform: flatTransform,
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CacheSizeMax: 1024 * 1024,
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})
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// Write three bytes to the key "alpha".
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key := "alpha"
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d.Write(key, []byte{'1', '2', '3'})
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// Read the value back out of the store.
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value, _ := d.Read(key)
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fmt.Printf("%v\n", value)
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// Erase the key+value from the store (and the disk).
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d.Erase(key)
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}
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```
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More complex examples can be found in the "examples" subdirectory.
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# Theory
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## Basic idea
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At its core, diskv is a map of a key (`string`) to arbitrary data (`[]byte`).
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The data is written to a single file on disk, with the same name as the key.
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The key determines where that file will be stored, via a user-provided
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`TransformFunc`, which takes a key and returns a slice (`[]string`)
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corresponding to a path list where the key file will be stored. The simplest
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TransformFunc,
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```go
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func SimpleTransform (key string) []string {
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return []string{}
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}
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```
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will place all keys in the same, base directory. The design is inspired by
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[Redis diskstore][6]; a TransformFunc which emulates the default diskstore
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behavior is available in the content-addressable-storage example.
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[6]: http://groups.google.com/group/redis-db/browse_thread/thread/d444bc786689bde9?pli=1
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**Note** that your TransformFunc should ensure that one valid key doesn't
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transform to a subset of another valid key. That is, it shouldn't be possible
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to construct valid keys that resolve to directory names. As a concrete example,
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if your TransformFunc splits on every 3 characters, then
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```go
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d.Write("abcabc", val) // OK: written to <base>/abc/abc/abcabc
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d.Write("abc", val) // Error: attempted write to <base>/abc/abc, but it's a directory
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```
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This will be addressed in an upcoming version of diskv.
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Probably the most important design principle behind diskv is that your data is
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always flatly available on the disk. diskv will never do anything that would
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prevent you from accessing, copying, backing up, or otherwise interacting with
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your data via common UNIX commandline tools.
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## Adding a cache
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An in-memory caching layer is provided by combining the BasicStore
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functionality with a simple map structure, and keeping it up-to-date as
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appropriate. Since the map structure in Go is not threadsafe, it's combined
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with a RWMutex to provide safe concurrent access.
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## Adding order
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diskv is a key-value store and therefore inherently unordered. An ordering
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system can be injected into the store by passing something which satisfies the
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diskv.Index interface. (A default implementation, using Google's
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[btree][7] package, is provided.) Basically, diskv keeps an ordered (by a
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user-provided Less function) index of the keys, which can be queried.
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[7]: https://github.com/google/btree
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## Adding compression
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Something which implements the diskv.Compression interface may be passed
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during store creation, so that all Writes and Reads are filtered through
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a compression/decompression pipeline. Several default implementations,
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using stdlib compression algorithms, are provided. Note that data is cached
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compressed; the cost of decompression is borne with each Read.
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## Streaming
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diskv also now provides ReadStream and WriteStream methods, to allow very large
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data to be handled efficiently.
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# Future plans
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* Needs plenty of robust testing: huge datasets, etc...
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* More thorough benchmarking
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* Your suggestions for use-cases I haven't thought of
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