tsbs VS TimescaleDB

1. correctness: from small units tests to relatively complex integrations tests. they typically populate a test database and query it via various interfaces, such as REST or the Postgres protocol. we use Azure Pipelines to execute them - testing in MacoOS, Linux (both Intel and ARM) and Windows.

2. performance: we tend to use the TSBS project for most of our performance testing and profiling. fun fact: we actually had to patch it as the vanilla TSBS was a bottleneck in some tests. Sadly, the PR with the improvements is still not merged: https://github.com/timescale/tsbs/pull/186

As usual, we use the industry standard Time Series Benchmark Suite (TSBS) as the benchmark tool. Unfortunately, TSBS upstream does not support MongoDB time series collections.

yes correct - although Clickhouse is more of an OLAP database. Timescale is built on top of Postgres, while QuestDB is built from scratch with Postgres wire compatibility. You can run benchmarks on https://github.com/timescale/tsbs

According their benchmark it is really fast.

We used the nodeIngestBench for all the benchmarking. It is a multi-process Node.js script that runs high-performance ingest benchmarks on CrateDB. It uses a data model that was adapted from Timescale’s Time Series Benchmark Suite (TSBS). One thing that we want to make clear is that nodeIngestBench is a write benchmark. The data structure that it creates is unsuitable for any performance-indicative reading tests because of its high cardinality (due to random data) and no partitioning.

In order to make the benchmark easily reproducible, we're going to use TSBS benchmark utilities to generate the data. We'll be using so-called IoT use case:

Also, some open-source vendors collaboratively maintain benchmarking suites such as Time Series Benchmark Suite to help choose the best tools for particular workloads.

Last year we released QuestDB 6.0 and achieved an ingestion rate of 1.4 million rows per second (per server). We compared those results to popular open source databases [1] and explained how we dealt with out of order ingestion under the hood while keeping the underlying storage model read-friendly. Since then, we focused our efforts on making queries faster, in particular filter queries with WHERE clauses. To do so, we once again decided to make things from scratch and built a JIT (Just-in-Time) compiler for SQL filters, with tons of low-level optimisations such as SIMD. We then parallelized the query execution to improve the execution time even further. In this blog post, we first look at some benchmarks against Clickhouse and TimescaleDB, before digging deeper in how this all works within QuestDB's storage model. Once again, we use the Time Series Benchmark Suite (TSBS) [2], developed by TimescaleDB,: it is an open source and reproducible benchmark.

We'd love to get your feedback!

[1]: https://news.ycombinator.com/item?id=27411307

[2] https://github.com/timescale/tsbs

This table schema: https://github.com/timescale/tsbs/blob/bcc00137d72d889e6059e...

...seems like a quite odd way to store time-series in ClickHouse. If I understood that code correctly (and I am really not sure), they partition their data by some tag value (the first one in a list?) instead of time, which is what timescaledb afaik partitions by. Of course that query filtering by timerange is going to be slower than usual. Whether that makes sense depends on your usecase.

Don't forget PostgreSQL extensions. For something like a chat log, TimescaleDB (https://www.timescale.com/) can be surprisingly efficient. It will handle partitioning for you, with additional features like data reordering, compression, and retention policies.

TimescaleDB{:target="_blank"}: an extension of PostgreSQL that adds time-series capabilities to the relational database model. It provides scalability and performance optimizations for handling large volumes of time-stamped data while maintaining the flexibility of a relational database.

What about getting in touch with commercial organisations that have products/services based on PostgreSQL? For example Timescale, EDB, and Citus Data, or really any hosting provider that offers a managed PostgreSQL service.

Friends don't let their friends choose Mysql :)

A super long time ago (decades) when I was using Oracle regularly I had to make a decision on which way to go. Although Mysql then had the mindshare I thought that Postgres was more similar to Oracle, more standards compliant, and more of a real enterprise type of DB. The rumor was also that Postgres was heavier than MySQL. Too many horror stories of lost data (MyIsam), bad transactions (MyIsam lacks transaction integrity), and the number of Mysql gotchas being a really long list influenced me.

In time I actually found out that I had underestimated one of the most important attributes of Postgres that was a huge strength over Mysql: the power of community. Because Postgres has a really superb community that can be found on Libera Chat and elsewhere, and they are very willing to help out, I think Postgres has a huge advantage over Mysql. RhodiumToad [Andrew Gierth] https://github.com/RhodiumToad & davidfetter [David Fetter] https://www.linkedin.com/in/davidfetter are incredibly helpful folks.

I don't know that Postgres' licensing made a huge difference or not but my perception is that there are a ton of 3rd party products based on Postgres but customized to specific DB needs because of the more liberalness of the PG license which is MIT/BSD derived https://www.postgresql.org/about/licence/

Some of the PG based 3rd party DBs:

Enterprise DB https://www.enterprisedb.com/ - general purpose PG with some variants

Greenplum https://greenplum.org/ - Data warehousing

Crunchydata https://www.crunchydata.com/products/hardened-postgres - high security Postgres for regulated environments

Citus https://www.citusdata.com - Distributed DB & Columnar

Timescale https://www.timescale.com/

Why Choose PG today?

If you want better ACID: Postgres

If you want more compliant SQL: Postgres

If you want more customizability to a variety of use-cases: Postgres using a variant

If you want the flexibility of using NOSQL at times: Postgres

If you want more product knowledge reusability for other backend products: Postgres

TimescaleDB is Postgres with extensions to automatically partition tables for fast processing of time series data.

Have you heard of timescale? https://www.timescale.com/ Seems similar to ocient but specifically for time series data.

Slowly I understood that instead of a vanilla PostgreSQL database I need to use to use Timescale which is based on PostgreSQL. I am sure others would have come to this conclusion much faster than I did.

Good question... haha I haven't really considered it. I'm no too versed in this domain and so the whole project will be a learning experience. One of the things is that it will include time-series harvest data. I was searching around for ways to implement this and found solutions like TimescaleDB and InfluxDB. Seems like also there are just some plugins that can sit on top of PostgreSQL.