150 lines
6.4 KiB
HTML
150 lines
6.4 KiB
HTML
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset='utf-8'>
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<title>RocksDB | A persistent key-value store for fast storage environments</title>
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<link rel='stylesheet' href='static/rocksdb.css' type='text/css'/>
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<link rel='shortcut icon' href='static/favicon.ico'>
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<meta name='viewport' content='width=device-width'>
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<meta property='og:type' content='website'>
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<meta property='og:title' content='RocksDB | A persistent key-value store for fast storage environments'>
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<meta property='og:description' content='A persistent key-value store for fast storage environments'>
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<meta property='og:image' content='static/rocksdb.png'>
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</head>
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<body>
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<header class='topbar'><nav class='width'>
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<a href='index.html'><h1>RocksDB</h1></a>
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<ul>
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<li><a href='overview.html'>Overview</a></li>
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<li><a href='https://github.com/facebook/rocksdb/wiki/_pages'>Wiki</a></li>
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<li><a href='https://github.com/facebook/rocksdb'>GitHub</a>
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</ul>
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</nav></header>
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<header class='hero'><div class='width'>
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<hgroup>
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<h1>
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A persistent key-value store for fast storage environments
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</h1>
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<a class='button' href='overview.html'>Get Started</a>
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</hgroup>
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<aside>
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<img src='static/rocksdbhero.png' />
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</aside>
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</div></header>
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<section class='content'><div class='width'>
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<article>
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<h2>What is RocksDB?</h2>
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<p>
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RocksDB is an embeddable persistent key-value store for fast
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storage. RocksDB can also be the foundation for a client-server
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database but our current focus is on embedded workloads.
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<p>
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<p>
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RocksDB builds on <a
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href='https://code.google.com/p/leveldb/'>LevelDB</a> to be scalable
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to run on servers with many CPU cores, to efficiently use
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fast storage, to support IO-bound, in-memory and write-once
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workloads, and to be flexible to allow for innovation.
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</p>
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<h2>How does performance compare?</h2>
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<p>
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We benchmarked LevelDB and found that it was unsuitable for our
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server workloads. The <a
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href='http://leveldb.googlecode.com/svn/trunk/doc/benchmark.html'>
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benchmark results</a> look awesome at first sight, but we quickly
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realized that those results were for a database whose size was
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smaller than the size of RAM on the test machine - where the entire
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database could fit in the OS page cache. When we performed the same
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benchmarks on a database that was at least 5 times larger than main
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memory, the performance results were dismal.
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</p>
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<p>
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By contrast, we've published the <a
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href='https://github.com/facebook/rocksdb/wiki/Performance-Benchmarks'>
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RocksDB benchmark results</a> for server side workloads on Flash. We
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also measured the performance of LevelDB on these server-workload
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benchmarks and found that RocksDB solidly outperforms LevelDB for
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these IO bound workloads. We found that LevelDB's single-threaded
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compaction process was insufficient to drive server workloads. We
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saw frequent write-stalls with LevelDB that caused 99-percentile
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latency to be tremendously large. We found that mmap-ing a file into
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the OS cache introduced performance bottlenecks for reads. We could
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not make LevelDB consume all the IOs offered by the underlying Flash
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storage.
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</p>
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<h2>What is RocksDB suitable for?</h2>
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<p>
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RocksDB can be used by applications that need low latency database
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accesses. A user-facing application that stores the viewing history
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and state of users of a website can potentially store this content
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on RocksDB. A spam detection application that needs fast access to
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big data sets can use RocksDB. A graph-search query that needs to
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scan a data set in realtime can use RocksDB. RocksDB can be used to
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cache data from Hadoop, thereby allowing applications to query
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Hadoop data in realtime. A message-queue that supports a high number
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of inserts and deletes can use RocksDB.
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</p>
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<h2>Why is RocksDB open source?</h2>
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<p>
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We are open sourcing this project <a
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href='http://github.com/facebook/rocksdb'>on GitHub</a> because we
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think it will be useful beyond Facebook. We are hoping that software
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programmers and database developers will use, enhance, and customize
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RocksDB for their use-cases. We would also like to engage with the
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academic community on topics related to efficiency for modern
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database algorithms.
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</p>
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</article>
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<aside>
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<h3>Quick links</h3>
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<ul>
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<li><a href='overview.html'>Overview</a></li>
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<li><a href='https://github.com/facebook/rocksdb/wiki/_pages'>Wiki</a></li>
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<li><a href='https://github.com/facebook/rocksdb'>GitHub</a>
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</ul>
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<h3>Community</h3>
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<ul>
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<li><a href='https://github.com/facebook/rocksdb/issues'>Report an issue</a></li>
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<li><a href='https://www.facebook.com/rocksdb'>Facebook Page</a></li>
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<li>
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<div class='fb-like'
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data-href='https://www.facebook.com/rocksdb'
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data-layout='button_count' data-action='like'
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data-show-faces='false' data-share='true'></div>
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</li>
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</ul>
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<h3>Open source</h3>
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<ul>
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<li><a href='https://github.com/facebook/rocksdb/blob/master/LICENSE'>BSD license</a></li>
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<li><a href='https://github.com/facebook/rocksdb/blob/master/CONTRIBUTING.md'>Contributing</a></li>
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<li><a href='https://github.com/facebook/rocksdb/blob/master/PATENTS'>Patent grant</a></li>
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</ul>
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</aside>
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</div></section>
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<footer><div class='width'>
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© Copyright 2013, Facebook
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</div></footer>
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}(document, 'script', 'facebook-jssdk'));</script>
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</body>
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</html>
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