ClickBench VS pg_partman

Benchmarks: https://benchmark.clickhouse.com

So definitely compared against PostgreSQL, MariaDB it is significantly faster.

On par with lower-end Snowflake.

TimescaleDB primarily serves operational use cases: Developers building products on top of live data, where you are regularly streaming in fresh data, and you often know what many queries look like a priori, because those are powering your live APIs, dashboards, and product experience.

That's different from a data warehouse or many traditional "OLAP" use cases, where you might dump a big dataset statically, and then people will occasionally do ad-hoc queries against it. This is the big weather dataset file sitting on your desktop that you occasionally query while on holidays.

So it's less about "can you store weather data", but what does that use case look like? How are the queries shaped? Are you saving a single dataset for ad-hoc queries across the entire dataset, or continuously streaming in new data, and aging out or de-prioritizing old data?

In most of the products we serve, customers are often interested in recent data in a very granular format ("shallow and wide"), or longer historical queries along a well defined axis ("deep and narrow").

For example, this is where the benefits of TimescaleDB's segmented columnar compression emerges. It optimizes for those queries which are very common in your application, e.g., an IoT application that groups by or selected by deviceID, crypto/fintech analysis based on the ticker symbol, product analytics based on tenantID, etc.

If you look at Clickbench, what most of the queries say are: Scan ALL the data in your database, and GROUP BY one of the 100 columns in the web analytics logs.

- https://github.com/ClickHouse/ClickBench/blob/main/clickhous...

There are almost no time-predicates in the benchmark that Clickhouse created, but perhaps that is not surprising given it was designed for ad-hoc weblog analytics at Yandex.

So yes, Timescale serves many products today that use weather data, but has made different choices than Clickhouse (or things like DuckDB, pg_analytics, etc) to serve those more operational use cases.

We tested with 43 Clickbench SQL queries. Queries on the Variant columns are about 10% slower than those on pre-defined static columns, and 8 times faster than those on JSON columns. (For I/O reasons, most cold runs on JSONB data failed with OOM.)

I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench

It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.

I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398

Indeed, ClickHouse results were run on an older instance type of the same family and size (c5.4xlarge for ClickHouse and c6a.4xlarge for Timescale), so if anything ClickHouse results are at a slight disadvantage.

This is an open source benchmark - we'd love contributions from Timescale enthusiasts if we missed something: https://github.com/ClickHouse/ClickBench/

Interesting project! Thank you for open sourcing and sharing. Agree that local and embedded analytics are an increasing trend, I see it too.

A couple of questions:

* I’m curious what the difficulties were in the implementation. I suspect it is quite a challenge to implement this support in the current SQLite architecture, and would curious to know which parts were tricky and any design trade-off you were faced with.

* Aside from ease-of-use (install extension, no need for a separate analytical database system), I wonder if there are additional benefits users can anticipate resulting from a single system architecture vs running an embedded OLAP store like DuckDB or clickhouse-local / chdb side-by-side with SQLite? Do you anticipate performance or resource efficiency gains, for instance?

* I am also curious, what the main difficulty with bringing in a separate analytical database is, assuming it natively integrates with SQLite. I may be biased, but I doubt anything can approach the performance of native column-oriented systems, so I'm curious what the tipping point might be for using this extension vs using an embedded OLAP store in practice.

Btw, would love for you or someone in the community to benchmark Stanchion in ClickBench and submit results! (https://github.com/ClickHouse/ClickBench/)

Disclaimer: I work on ClickHouse.

Assuming these are the types of insights you're looking for, you'll probably look for a way to aggregate data points across/within geographies. postgis is an open source extension for postgres that can help you with this, but there's also quite a few python tools that can help you explore the data, such as geopandas, folium, geoplot. Depending on volume, you might want to partition the data for query performance, and there's another extension pg_partman that can help with that. Just noticed some other posts have recommended something similar.

pg_partman is in RDS >12.5 (here). Pg_partman makes managing data retention relatively easy.

Whenever I see these posts from TimescaleDB, I always want to ask them how it compares in performance to alternative extensions that implement the same features, rather than just comparing TimescaleDB to vanilla PostgreSQL.

For example, they mention their automated data retention and how it's achieved with one SQL command, and how DELETEing records is a very costly operation, and how "even if you were using Postgres declarative partitioning you’d still need to automate the process yourself, wasting precious developer time, adding additional requirements, and implementing bespoke code that needs to be supported moving forward".

There's zero mention anywhere of pg_partman, which does all of these things for you equally as simply, and is a fully OSS free alternative [0].

I get that it's a PG extension that competes with their product. I know that TimescaleDB does a few other things that pg_partman does not. But I can't help but find its (seemingly) purposeful omission in these, otherwise very thorough blog posts, misleading.

[0] https://github.com/pgpartman/pg_partman/blob/master/doc/pg_p...

Take a look into this extension which would take care of a good amount of automation for you.

You might want to look into pg_partman which has many useful tools around semi-automatic partitioning. According to their documentation they already have a procedure that will do exactly that: create new partitions based on the rows in the default partition.