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Compression reduces the size of +data on disk by compressing the SSTable in user-configurable compression <code class="docutils literal"><span class="pre">chunk_length_in_kb</span></code>. Because Cassandra +SSTables are immutable, the CPU cost of compressing is only necessary when the SSTable is written - subsequent updates +to data will land in different SSTables, so Cassandra will not need to decompress, overwrite, and recompress data when +UPDATE commands are issued. On reads, Cassandra will locate the relevant compressed chunks on disk, decompress the full +chunk, and then proceed with the remainder of the read path (merging data from disks and memtables, read repair, and so +on).</p> +<div class="section" id="configuring-compression"> +<h2>Configuring Compression<a class="headerlink" href="#configuring-compression" title="Permalink to this headline">¶</a></h2> +<p>Compression is configured on a per-table basis as an optional argument to <code class="docutils literal"><span class="pre">CREATE</span> <span class="pre">TABLE</span></code> or <code class="docutils literal"><span class="pre">ALTER</span> <span class="pre">TABLE</span></code>. By +default, three options are relevant:</p> +<ul class="simple"> +<li><code class="docutils literal"><span class="pre">class</span></code> specifies the compression class - Cassandra provides three classes (<code class="docutils literal"><span class="pre">LZ4Compressor</span></code>, +<code class="docutils literal"><span class="pre">SnappyCompressor</span></code>, and <code class="docutils literal"><span class="pre">DeflateCompressor</span></code> ). The default is <code class="docutils literal"><span class="pre">SnappyCompressor</span></code>.</li> +<li><code class="docutils literal"><span class="pre">chunk_length_in_kb</span></code> specifies the number of kilobytes of data per compression chunk. The default is 64KB.</li> +<li><code class="docutils literal"><span class="pre">crc_check_chance</span></code> determines how likely Cassandra is to verify the checksum on each compression chunk during +reads. The default is 1.0.</li> +</ul> +<p>Users can set compression using the following syntax:</p> +<div class="highlight-none"><div class="highlight"><pre><span></span>CREATE TABLE keyspace.table (id int PRIMARY KEY) WITH compression = {'class': 'LZ4Compressor'}; +</pre></div> +</div> +<p>Or</p> +<div class="highlight-none"><div class="highlight"><pre><span></span>ALTER TABLE keyspace.table WITH compression = {'class': 'SnappyCompressor', 'chunk_length_in_kb': 128, 'crc_check_chance': 0.5}; +</pre></div> +</div> +<p>Once enabled, compression can be disabled with <code class="docutils literal"><span class="pre">ALTER</span> <span class="pre">TABLE</span></code> setting <code class="docutils literal"><span class="pre">enabled</span></code> to <code class="docutils literal"><span class="pre">false</span></code>:</p> +<div class="highlight-none"><div class="highlight"><pre><span></span>ALTER TABLE keyspace.table WITH compression = {'enabled':'false'}; +</pre></div> +</div> +<p>Operators should be aware, however, that changing compression is not immediate. The data is compressed when the SSTable +is written, and as SSTables are immutable, the compression will not be modified until the table is compacted. Upon +issuing a change to the compression options via <code class="docutils literal"><span class="pre">ALTER</span> <span class="pre">TABLE</span></code>, the existing SSTables will not be modified until they +are compacted - if an operator needs compression changes to take effect immediately, the operator can trigger an SSTable +rewrite using <code class="docutils literal"><span class="pre">nodetool</span> <span class="pre">scrub</span></code> or <code class="docutils literal"><span class="pre">nodetool</span> <span class="pre">upgradesstables</span> <span class="pre">-a</span></code>, both of which will rebuild the SSTables on disk, +re-compressing the data in the process.</p> +</div> +<div class="section" id="benefits-and-uses"> +<h2>Benefits and Uses<a class="headerlink" href="#benefits-and-uses" title="Permalink to this headline">¶</a></h2> +<p>Compression’s primary benefit is that it reduces the amount of data written to disk. Not only does the reduced size save +in storage requirements, it often increases read and write throughput, as the CPU overhead of compressing data is faster +than the time it would take to read or write the larger volume of uncompressed data from disk.</p> +<p>Compression is most useful in tables comprised of many rows, where the rows are similar in nature. Tables containing +similar text columns (such as repeated JSON blobs) often compress very well.</p> +</div> +<div class="section" id="operational-impact"> +<h2>Operational Impact<a class="headerlink" href="#operational-impact" title="Permalink to this headline">¶</a></h2> +<ul class="simple"> +<li>Compression metadata is stored off-heap and scales with data on disk. This often requires 1-3GB of off-heap RAM per +terabyte of data on disk, though the exact usage varies with <code class="docutils literal"><span class="pre">chunk_length_in_kb</span></code> and compression ratios.</li> +<li>Streaming operations involve compressing and decompressing data on compressed tables - in some code paths (such as +non-vnode bootstrap), the CPU overhead of compression can be a limiting factor.</li> +<li>The compression path checksums data to ensure correctness - while the traditional Cassandra read path does not have a +way to ensure correctness of data on disk, compressed tables allow the user to set <code class="docutils literal"><span class="pre">crc_check_chance</span></code> (a float from +0.0 to 1.0) to allow Cassandra to probabilistically validate chunks on read to verify bits on disk are not corrupt.</li> +</ul> +</div> +<div class="section" id="advanced-use"> +<h2>Advanced Use<a class="headerlink" href="#advanced-use" title="Permalink to this headline">¶</a></h2> +<p>Advanced users can provide their own compression class by implementing the interface at +<code class="docutils literal"><span class="pre">org.apache.cassandra.io.compress.ICompressor</span></code>.</p> +</div> +</div> + + + + + <div class="doc-prev-next-links" role="navigation" aria-label="footer navigation"> + + <a href="cdc.html" class="btn btn-default pull-right " role="button" title="Change Data Capture" accesskey="n">Next <span class="glyphicon glyphicon-circle-arrow-right" aria-hidden="true"></span></a> + + + <a href="bloom_filters.html" class="btn btn-default" role="button" title="Bloom Filters" accesskey="p"><span class="glyphicon glyphicon-circle-arrow-left" aria-hidden="true"></span> Previous</a> + + </div> + + </div> + </div> + </div> + <div class="col-md-2"> + </div> + </div> +</div> + + <footer> + <div class="container"> + <div class="col-md-4 social-blk"> + <span class="social"> + <a href="https://twitter.com/cassandra"; + class="twitter-follow-button" + data-show-count="false" data-size="large">Follow @cassandra</a> + <script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script> + <a href="https://twitter.com/intent/tweet?button_hashtag=cassandra"; + class="twitter-hashtag-button" + data-size="large" + data-related="ApacheCassandra">Tweet #cassandra</a> + <script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script> + </span> + </div> + + <div class="col-md-8 trademark"> + <p>© 2016 <a href="http://apache.org";>The Apache Software Foundation</a>. + Apache, the Apache feather logo, and Apache Cassandra are trademarks of The Apache Software Foundation. + <p> + </div> + </div><!-- /.container --> +</footer> + +<!-- Javascript. 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While Cassandra can +be made to run on small servers for testing or development environments (including Raspberry Pis), a minimal production +server requires at least 2 cores, and at least 8GB of RAM. Typical production servers have 8 or more cores and at least +32GB of RAM.</p> +<div class="section" id="cpu"> +<h2>CPU<a class="headerlink" href="#cpu" title="Permalink to this headline">¶</a></h2> +<p>Cassandra is highly concurrent, handling many simultaneous requests (both read and write) using multiple threads running +on as many CPU cores as possible. The Cassandra write path tends to be heavily optimized (writing to the commitlog and +then inserting the data into the memtable), so writes, in particular, tend to be CPU bound. Consequently, adding +additional CPU cores often increases throughput of both reads and writes.</p> +</div> +<div class="section" id="memory"> +<h2>Memory<a class="headerlink" href="#memory" title="Permalink to this headline">¶</a></h2> +<p>Cassandra runs within a Java VM, which will pre-allocate a fixed size heap (java’s Xmx system parameter). In addition to +the heap, Cassandra will use significant amounts of RAM offheap for compression metadata, bloom filters, row, key, and +counter caches, and an in process page cache. Finally, Cassandra will take advantage of the operating system’s page +cache, storing recently accessed portions files in RAM for rapid re-use.</p> +<p>For optimal performance, operators should benchmark and tune their clusters based on their individual workload. However, +basic guidelines suggest:</p> +<ul class="simple"> +<li>ECC RAM should always be used, as Cassandra has few internal safeguards to protect against bit level corruption</li> +<li>The Cassandra heap should be no less than 2GB, and no more than 50% of your system RAM</li> +<li>Heaps smaller than 12GB should consider ParNew/ConcurrentMarkSweep garbage collection</li> +<li>Heaps larger than 12GB should consider G1GC</li> +</ul> +</div> +<div class="section" id="disks"> +<h2>Disks<a class="headerlink" href="#disks" title="Permalink to this headline">¶</a></h2> +<p>Cassandra persists data to disk for two very different purposes. The first is to the commitlog when a new write is made +so that it can be replayed after a crash or system shutdown. The second is to the data directory when thresholds are +exceeded and memtables are flushed to disk as SSTables.</p> +<p>Commitlogs receive every write made to a Cassandra node and have the potential to block client operations, but they are +only ever read on node start-up. SSTable (data file) writes on the other hand occur asynchronously, but are read to +satisfy client look-ups. SSTables are also periodically merged and rewritten in a process called compaction. The data +held in the commitlog directory is data that has not been permanently saved to the SSTable data directories - it will be +periodically purged once it is flushed to the SSTable data files.</p> +<p>Cassandra performs very well on both spinning hard drives and solid state disks. In both cases, Cassandra’s sorted +immutable SSTables allow for linear reads, few seeks, and few overwrites, maximizing throughput for HDDs and lifespan of +SSDs by avoiding write amplification. However, when using spinning disks, it’s important that the commitlog +(<code class="docutils literal"><span class="pre">commitlog_directory</span></code>) be on one physical disk (not simply a partition, but a physical disk), and the data files +(<code class="docutils literal"><span class="pre">data_file_directories</span></code>) be set to a separate physical disk. By separating the commitlog from the data directory, +writes can benefit from sequential appends to the commitlog without having to seek around the platter as reads request +data from various SSTables on disk.</p> +<p>In most cases, Cassandra is designed to provide redundancy via multiple independent, inexpensive servers. For this +reason, using NFS or a SAN for data directories is an antipattern and should typically be avoided. Similarly, servers +with multiple disks are often better served by using RAID0 or JBOD than RAID1 or RAID5 - replication provided by +Cassandra obsoletes the need for replication at the disk layer, so it’s typically recommended that operators take +advantage of the additional throughput of RAID0 rather than protecting against failures with RAID1 or RAID5.</p> +</div> +<div class="section" id="common-cloud-choices"> +<h2>Common Cloud Choices<a class="headerlink" href="#common-cloud-choices" title="Permalink to this headline">¶</a></h2> +<p>Many large users of Cassandra run in various clouds, including AWS, Azure, and GCE - Cassandra will happily run in any +of these environments. Users should choose similar hardware to what would be needed in physical space. In EC2, popular +options include:</p> +<ul class="simple"> +<li>m1.xlarge instances, which provide 1.6TB of local ephemeral spinning storage and sufficient RAM to run moderate +workloads</li> +<li>i2 instances, which provide both a high RAM:CPU ratio and local ephemeral SSDs</li> +<li>m4.2xlarge / c4.4xlarge instances, which provide modern CPUs, enhanced networking and work well with EBS GP2 (SSD) +storage</li> +</ul> +<p>Generally, disk and network performance increases with instance size and generation, so newer generations of instances +and larger instance types within each family often perform better than their smaller or older alternatives.</p> +</div> +</div> + + + + + <div class="doc-prev-next-links" role="navigation" aria-label="footer navigation"> + + <a href="../tools/index.html" class="btn btn-default pull-right " role="button" title="Cassandra Tools" accesskey="n">Next <span class="glyphicon glyphicon-circle-arrow-right" aria-hidden="true"></span></a> + + + <a href="security.html" class="btn btn-default" role="button" title="Security" accesskey="p"><span class="glyphicon glyphicon-circle-arrow-left" aria-hidden="true"></span> Previous</a> + + </div> + + </div> + </div> + </div> + <div class="col-md-2"> + </div> + </div> +</div> + + <footer> + <div class="container"> + <div class="col-md-4 social-blk"> + <span class="social"> + <a href="https://twitter.com/cassandra"; + class="twitter-follow-button" + data-show-count="false" data-size="large">Follow @cassandra</a> + <script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script> + <a href="https://twitter.com/intent/tweet?button_hashtag=cassandra"; + class="twitter-hashtag-button" + data-size="large" + data-related="ApacheCassandra">Tweet #cassandra</a> + <script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script> + </span> + </div> + + <div class="col-md-8 trademark"> + <p>© 2016 <a href="http://apache.org";>The Apache Software Foundation</a>. + Apache, the Apache feather logo, and Apache Cassandra are trademarks of The Apache Software Foundation. + <p> + </div> + </div><!-- /.container --> +</footer> + +<!-- Javascript. 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