The TimescaleDB benchmark code is a fork of code I wrote, as an independent consultant, for InfluxData in 2016 and 2017. The purpose of my project was to rigorously compare InfluxDB and InfluxDB Enterprise to Cassandra, Elasticsearch, MongoDB, and OpenTSDB. It's called influxdb-comparisons and is an actively-maintained project on Github at [0]. I am no longer affiliated with InfluxData, and these are my own opinions.
I designed and built the influxdb-comparisons benchmark suite to be easy to understand for customers. From a technical perspective, it is simulation-based, verifiable, fast, fair, and extensible. In particular, I created the "use-case approach" so that, no matter how technical our benchmark reports got, customers could say to themselves: "I understand this!". For example, in the devops use-case, we generate data and queries from a realistic simulation of telemetry collected from a server fleet. Doing it this way creates benchmarking stories that appeal to a wide variety of both technical and nontechnical customers.
This user-first design of a benchmarking suite was a novel innovation, and was a large factor in the success of the project.
Another aspect of the project is that we tried to do right by the competition. That means that we spoke with experts (sometimes, the creators of the databases themselves) on how to best achieve our goals. In particular, I worked hard to make the Cassandra, Elasticsearch MongoDB, and OpenTSDB benchmarks show their respective databases in the best light possible. Concretely, each database was configured in a way that is 1) featureful, like InfluxDB, 2) fast at writes, 3) fast at reads, and 4) efficient with disk space.
As an example of my diligence in implementing this benchmark suite for InfluxData, I included a mechanism by which the benchmark query results can be verified for correctness across competing databases, to within floating point tolerances. This is important because, when building adapters for drastically different databases, it is easy to introduce bugs that could give a false advantage to one side or the other (e.g. by accidentally throwing data away, or by executing queries that don't range over the whole dataset).
I don't see that TimescaleDB is using the verification functionality I created. I encourage TimescaleDB to run query verification, and write up their benchmarking methods in detail, like I did here: [1].
I think it's great that TimescaleDB is taking these ideas and extending them. At InfluxData, we made the code open-source so that others could build and learn from our work. In that tradition, I hope that the ongoing discussion about how to do excellent benchmarking of time-series databases keeps evolving.
[0] https://github.com/influxdata/influxdb-comparisons (Note that others maintain this project now.)
[1] https://rwinslow.com/rwinslow-benchmark-tech-paper-influxdb-...
Verification of the correctness of the query results is obviously something we take very seriously, otherwise running these benchmarks would be pretty pointless. We carefully verified the correctness of all of the query benchmarks we published. However, it’s a process we haven’t fully automated yet. From what we can tell, the same is true of influxdb-comparisons — the validation pretty prints full responses but each database has a significantly different format, so one needs to manually parse the results or set up a separate tool to do so. We have our own methods for doing that internally — once we get the process more standardized and automated we will definitely be adding it to TSBS. We encourage anyone with ideas around that (or anything else) to take a look at the open source TSBS code and consider contributing [2].
[1] https://blog.timescale.com/time-series-database-benchmarks-t...