<p>In order to test LevelDB's performance, we benchmark it against other well-established database implementations. We compare LevelDB (revision 39) against <ahref="http://www.sqlite.org/">SQLite3</a> (version 3.7.6.3) and <ahref="http://fallabs.com/kyotocabinet/spex.html">Kyoto Cabinet's</a> (version 1.2.67) TreeDB (a B+Tree based key-value store). We would like to acknowledge Scott Hess and Mikio Hirabayashi for their suggestions and contributions to the SQLite3 and Kyoto Cabinet benchmarks, respectively.</p>
<p>Benchmarks were all performed on a six-core Intel(R) Xeon(R) CPU X5650 @ 2.67GHz, with 12288 KB of total L3 cache and 12 GB of DDR3 RAM at 1333 MHz. (Note that LevelDB uses at most two CPUs since the benchmarks are single threaded: one to run the benchmark, and one for background compactions.) We ran the benchmarks on two machines (with identical processors), one with an Ext3 file system and one with an Ext4 file system. The machine with the Ext3 file system has a SATA Hitachi HDS721050CLA362 hard drive. The machine with the Ext4 file system has a SATA Samsung HD502HJ hard drive. Both hard drives spin at 7200 RPM. The numbers reported below are the median of three measurements.</p>
<h4>Benchmark Source Code</h4>
<p>We wrote benchmark tools for SQLite and Kyoto TreeDB based on LevelDB's <spanclass="code">db_bench</span>. The code for each of the benchmarks resides here:</p>
<li>LevelDB: LevelDB was compiled with the <ahref="http://code.google.com/p/google-perftools">tcmalloc</a> library and the <ahref="http://code.google.com/p/snappy/">Snappy</a> compression library. Assertions were disabled.</li>
<li>TreeDB: TreeDB was compiled using the <ahref="http://www.oberhumer.com/opensource/lzo/">LZO</a> compression library. Furthermore, we enabled the TSMALL and TLINEAR options when opening the database in order to reduce the footprint of each record.</li>
<li>SQLite: We tuned SQLite's performance, by setting its locking mode to exclusive. We left SQLite's <ahref="http://www.sqlite.org/draft/wal.html">write-ahead logging</a> disabled since that is the default configuration. (Enabling write-ahead-logging improves SQLite's write performance by roughly 30%, but the character of the comparisons below does not change significantly.)</li>
</ul>
<h2>1. Baseline Performance</h2>
<p>This section gives the baseline performance of a all of the
databases. Following sections show how performance changes as various
parameters are varied. For the baseline:</p>
<ul>
<li> Each database is allowed 4 MB of cache memory.</li>
<li> Databases are opened in <em>asynchronous</em> write mode.
(LevelDB's sync option, TreeDB's OAUTOSYNC option, and
SQLite3's synchronous options are all turned off). I.e.,
every write is pushed to the operating system, but the
benchmark does not wait for the write to reach the disk.</li>
<li> Keys are 16 bytes each.</li>
<li> Value are 100 bytes each (with enough redundancy so that
a simple compressor shrinks them to 50% of their original
size).</li>
<li> Sequential reads/writes traverse the key space in increasing order.</li>
<li> Random reads/writes traverse the key space in random order.</li>
<p>LevelDB outperforms both SQLite3 and TreeDB in sequential and random write operations and sequential read operations. Kyoto Cabinet has the fastest random read operations.</p>
<h2>2. Write Performance under Different Configurations</h2>
<h3>A. Large Values </h3>
<p>For this benchmark, we start with an empty database, and write 100,000 byte values (~50% compressible). To keep the benchmark running time reasonable, we stop after writing 1000 values.</p>
<p>LevelDB doesn't perform as well with large values of 100,000 bytes each. This is because LevelDB writes keys and values at least twice: first time to the transaction log, and second time (during a compaction) to a sorted file.
With larger values, LevelDB's per-operation efficiency is swamped by the
cost of extra copies of large values.</p>
<h3>B. Batch Writes</h3>
<p>A batch write is a set of writes that are applied atomically to the underlying database. A single batch of N writes may be significantly faster than N individual writes. The following benchmark writes one thousand batches where each batch contains one thousand 100-byte values. TreeDB does not support batch writes and is omitted from this benchmark.</p>
<p>LevelDB's write performance is better with compression than without
since compression decreases the amount of data that has to be written
to disk. Therefore LevelDB users can leave compression enabled in
most scenarios without having worry about a tradeoff between space
usage and performance. TreeDB's performance on the other hand is
better without compression than with compression. Presumably this is
because TreeDB's compression library (LZO) is more expensive than
LevelDB's compression library (Snappy).<p>
<h3>E. Using more memory</h3>
<p>We increased the overall cache size for each database to 128 MB. For LevelDB, we partitioned 128 MB into a 120 MB write buffer and 8 MB of cache (up from 2 MB of write buffer and 2 MB of cache). For SQLite3, we kept the page size at 1024 bytes, but increased the number of pages to 131,072 (up from 4096). For TreeDB, we also kept the page size at 1024 bytes, but increased the cache size to 128 MB (up from 4 MB).</p>
<p>As expected, the read performance of all of the databases increases
when the caches are enlarged. In particular, TreeDB seems to make
very effective use of a cache that is large enough to hold the entire
database.</p>
<h3>B. No compression reads </h3>
<p>For this benchmark, we populated a database with 1 million entries consisting of 16 byte keys and 100 byte values. We compiled LevelDB and Kyoto Cabinet without compression support, so results that are read out from the database are already uncompressed. We've listed the SQLite3 baseline read performance as a point of comparison.</p>
<p>Performance of both LevelDB and TreeDB improves a small amount when
compression is disabled. Note however that under different workloads,
performance may very well be better with compression if it allows more
of the working set to fit in memory.</p>
<h2>Note about Ext4 Filesystems</h2>
<p>The preceding numbers are for an ext3 file system. Synchronous writes are much slower under <ahref="http://en.wikipedia.org/wiki/Ext4">ext4</a> (LevelDB drops to ~34 writes / second, TreeDB drops to ~5 writes / second; SQLite3 drops to ~24 writes / second) due to ext4's different handling of <spanclass="code">fsync</span> / <spanclass="code">msync</span> calls. Even LevelDB's asynchronous write performance drops somewhat since it spreads its storage across multiple files and issues <spanclass="code">fsync</span> calls when switching to a new file.</p>
<h2>Acknowledgements</h2>
<p>Jeff Dean and Sanjay Ghemawat wrote LevelDB. Kevin Tseng wrote and compiled these benchmarks. Mikio Hirabayashi, Scott Hess, and Gabor Cselle provided help and advice.</p>
