Compressor Microbenchmark: HyperLevelDB

Symas Corp., February 2015

Test Results

Synchronous Random Write

The synchronous tests only use 1000 records so there's not much to see here. 1000 records with 100 byte values and 16 byte keys should only occupy 110KB but the size is consistently over 140KB here, showing a fixed amount of incompressible overhead in the DB engine. Surprisingly, several of the compressors turn in faster throughput than the uncompressed case.

Random Write

The asynchronous tests use 1000000 records and show snappy, lz4, and lzo to be the fastest compressors here, while zlib gets the most compression.

Random Batched Write

While the throughput matches the results of the non-batched case, with batching lzma gets the best compression.

Synchronous Sequential Write

The synchronous tests only use 1000 records so there's not much to see here. Still, at this small test size, it's surprising to see lz4 and zlib getting significantly better throughput than the uncompressed case.

Sequential Write

Sequential Batched Write

Random Overwrite

Read-Only Throughput

There's a pretty wide range of variation in the throughput here but the overall rankings of each compressor don't change.


These charts show the final stats at the end of the run, after all compactions completed. The RSS shows the maximum size of the process for a given run. The times are the total User and System CPU time, and the total Wall Clock time to run all of the test operations for a given compressor.

The huge amount of system CPU time in the lzma run indicates a lot of malloc overhead in that library. The difference is small but overall it appears that lzo performs best for both compression and speed.


The files used to perform these tests are all available for download.

The command script: Raw output: out.hyper.txt. OpenOffice spreadsheet Hyper.ods.