Initial volume tests have been [based on standard basho_bench eleveldb test](../test/volume/single_node/examples) to run multiple stores in parallel on the same node and and subjecting them to concurrent pressure.
This showed a [relative positive performance for leveled](VOLUME_PRERIAK.md) for both population and load. This also showed that although the LevelEd throughput was relatively stable it was still subject to fluctuations related to CPU constraints. Prior to moving on to full Riak testing, a number of changes where then made to LevelEd to reduce the CPU load in particular during merge events.
The aim was to try and make the burden of running LevelEd easier on disk I/O, whilst accepting that although CPU load may be higher, it should be constant and allow for predictable and less volatile performance.
Testing in a Riak cluster, has been based on like-for-like comparisons between leveldb and leveled - except that leveled was run within a Riak [modified](FUTURE.md) to use HEAD not GET requests when HEAD requests are sufficient.
The initial testing was based on simple gets and updates, with 5 gets for every update.
The first thing to note about the test is the impact of the pareto distribution and the start from an empty store, on what is actually being tested. At the start of the test there is a 0% chance of a GET request actually finding an object. Normally, it will be 3 hours into the test before a GET request will have a 50% chance of finding an object.
Both leveled and leveldb are optimised for finding non-presence through the use of bloom filters, so the comparison is not unduly influenced by this. However, the workload at the end of the test is both more realistic (in that objects are found), and harder if the previous throughput had been greater. So it is better to focus on the results at the tail of the tests, and bear in mind that workload is harder at the tail if overall throughput had been greater in the test.
Leveled like bitcask will defer compaction work until a designated compaction window, and these tests were run outside of that compaction window. So although the throughput of leveldb is lower, it has no deferred work at the end of the test. Future tetsing work is scheduled to examine leveled throughput during a compaction window.
- When unconstrained by disk I/O limits, leveldb can achieve a greater throughput rate than leveled.
- During these tests leveldb is frequently constrained by disk I/O limits, and the frequency with which it is constrained increases the longer the test is run for.
- leveled is almost always constrained by CPU, or by the limits imposed by response latency and the number of concurrent workers.
- Write amplification is the primary delta in disk contention between leveldb and leveled - as leveldb is amplifying the writing of values not just keys it is creating a significantly larger 'background noise' of disk activity, and that noise is sufficiently variable that it invokes response time volatility even when r and w values are less than n.
- leveled has substantially lower tail latency, especially on PUTs.
- leveled throughput would be increased by adding concurrent workers, and increasing the available CPU
- leveldb throughput would be increased by having improved disk i/o.
