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tsbs
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timescale / tsbs

tsbs

Time Series Benchmark Suite, a tool for comparing and evaluating databases for time series data

Data & DatabasesTesting & Quality
Explore on GitHubRead Hacker News discussions
21Distinct people
32Mentions
0Explicit recommenders
1.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Time Series Benchmark Suite, a tool for comparing and evaluating databases for time series data

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 4:00:59 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year32 dated mentions

Observed mentions: 2018-08-15 19:18:39 UTC to 2024-05-24 07:59:27 UTC.

0510152018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 7 mentions; 0 submissions, 7 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 13 mentions; 0 submissions, 13 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC303
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC707
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC13013
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC303
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC303
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101

What Hacker News says

13 sources
akulkarni·Nov 3, 2021
(Timescale co-founder) I'll answer this here with a similar response that I gave Pradeep (the author) via Twitter. I think ClickHouse is a great technology. It totally beats TimescaleDB for OLAP queries. I'll be the first to admit that. What our (100+ hour, 3 month analysis) benchmark showed is that for _time-series workloads_, TimescaleDB fared better. [0] Pradeep's analysis - while earnest - is essentially comparing OLAP style queries using a dataset that is not very representative of time-series workloads. Which is why the time-series benchmark suite (TSBS) [1] exists (which we did not create, although we now maintain it). I've asked Pradeep to compare using the TSBS - and he said he'd look into it. [2] As a developer, I'm very wary of technologies that claim to be better at everything - especially those who hide their weaknesses. We d
Read the rest
on't do that at TimescaleDB. For those who read our benchmark closely, we clearly show where ClickHouse beats TimescaleDB, and where TimescaleDB does better. And - despite what many commenters on here may want you to think - we heap loads of praise on ClickHouse. As a reader of HackerNews, I'm also tired of all the negativity that's developing on this site. People who bully. People who default to accusing others of dishonesty instead of trying to have a meaningful dialogue and reach mutual understanding. People who enter debates wanting to be right, versus wanting to identify the right answer. Disappointingly, this includes some visible influencers whom I personally know. We should all strive to do better, to assume positive intent, and have productive dialogues. (This is why one of our values at TimescaleDB is "Assume Positive Intent." [3] I think Hacker News - and the world in general - would be a much better, happier, healthier place if we all just did that.) [0] https://blog.timescale.com/blog/what-is-clickhouse-how-does-... [1] https://github.com/timescale/tsbs [2] https://twitter.com/p_chhetri/status/1455216425807745025 [3] https://www.timescale.com/careers
In TimescaleDB vs ClickHouse
clickok·Nov 3, 2021
I see a lot of really divergent results with these time series database benchmarking posts. Timescale's open source benchmark suite[0] is a great contribution towards making different software comparable, but it seems like the tasks/metrics heavily favor TimescaleDB. This article has Clickhouse more-or-less spanking TimescaleDB, but the blog post it references[1] is basically the reverse. Are the use cases just that different? ----- 0. https://github.com/timescale/tsbs 1. https://blog.timescale.com/blog/what-is-clickhouse-how-does-...
In TimescaleDB vs ClickHouse
ryanbooz·Oct 21, 2021
Two quick responses: - The code that TSBS uses was contributed by Altinity[1]. If there is a better setup, please feel free to submit a PR. As stated elsewhere, we did have a former CH engineer review and even updated ClickHouse to the newest version __yesterday__ based on his suggestion to ensure we had the best numbers. (and some queries did improve after upgrading, which are the numbers we presented) - It seems like you read the article (great job - it was long!!), so I'm sure you understand that we were trying to answer performance and feature questions at a deeper level than almost any benchmark we've seen to date. Many just show a few graphs and walk away. We fully acknowledged that smaller batches are not recommended by CH, but something many (normally OLTP) users would probably have. It matters and nobody (that we know of) has sho
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wn those numbers before. And in our test, larger batch sizes do work well, but not to some great magnitude in this one server setup. Did 10k or 20k rows maybe go a little faster for CH? Sometimes yes, sometimes negligible. The illustration was that we literally spent months and hundreds of benchmark cycles trying to understand the nuances. I think we're pretty clear in the post that CH is a great database for the intended cases, but it has shortcomings just like TimescaleDB does and we tried to faithfully explore each side. [1]: https://github.com/timescale/tsbs/pull/26
In Comparing ClickHouse to PostgreSQL and TimescaleDB for time-series data
ryanbooz·Oct 21, 2021
Hello @PeterZaitsev! Actually Altinity is the one that contributed the bits to TSBS for benchmarking ClickHouse[1], so we are using the work that they contributed (and anyone is welcome to make a PR for updates or changes). We also had a former ClickHouse engineer look at the setup to verify it matched best practices with how CH is currently designed, given the TSBS dataset. As for the optimizations in the article you pointed to from 2019 (specifically how to query "last point" data more efficiently in ClickHouse), it uses a different table type (AggregatedMergeTree) and a materialized view to get better query response times for this query type. We (or someone in the community) could certainly add that optimization to the benchmark, but it wouldn't be using raw data - which we didn't think was appropriate for the benchmark analysis. But i
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f one wanted to use that optimization, then one should also use Continuous Aggregates for TimescaleDB - ie for an apples to apples comparison - which I think would also lead to similar results to what we show today. It's actually something we've talked about adding to TSBS for TimescaleDB (as an option to turn on/off) and maybe other DBs could do the same. [1]: https://github.com/timescale/tsbs/pull/26
In Comparing ClickHouse to PostgreSQL and TimescaleDB for time-series data
akulkarni·Oct 21, 2021
If you are referring to this post: https://altinity.com/blog/clickhouse-for-time-series That post was written in November 2018 - 3 years ago - when TimescaleDB was barely 1.0. A lot has changed since then: 1. TimescaleDB launched native columnar compression in 2019, which completely changed its story around storage footprint and query performance [0] 2. TimescaleDB has gotten much better 3. PostgreSQL has also gotten better (which in turn makes TimescaleDB better) In fact, IIRC Altinity used and contributed ClickHouse to the TSBS [1], which is also what this newer benchmark uses as well (Disclaimer: TimescaleDB co-founder) [0] https://blog.timescale.com/blog/building-columnar-compressio... [1] https://github.com/timescale/tsbs
In Comparing ClickHouse to PostgreSQL and TimescaleDB for time-series data
ryanbooz·Sep 20, 2021
[Timescale DevRel here] @zX41ZdbW@ - Thanks for pointing out the various benchmarks that have been run by other companies between Clickhouse and TimescaleDB using TSBS[1]. As we mentioned, we'll dig deeper into a similar benchmark with much more detail than any of those examples in an upcoming blog post. One notable omission on all of the benchmarks that we've seen is that none of them enable TimescaleDB compression (which also transforms row-oriented data into a columnar-type format). In our detailed benchmarking, queries on compressed columnar data in Timescale outperformed Clickhouse in most queries, particularly as cardinality increases, often by 5x or more. And with compression of 90% or more, storage is often comparable. (Again, blog post coming soon - we are just making sure our results are accurate before rushing to publish.) The
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beauty of TimescaleDB columnar compression model is that it allows the user to decide when their workload can benefit from deep/narrow queries of data that doesn't change often (although it can still be modified just like regular row data), verses shallow/wide queries for things like inserting data and near-time queries. It's a hybrid model that provides a lot of flexibility for users AND significantly improves the performance of historical queries. So yes, we do agree that columnar storage is a huge performance win for many types of queries. And of course, with TimescaleDB, one also gets all of the benefits of PostgreSQL and its vibrant ecosystem. Can't wait to share the details in the coming weeks! [1]: https://github.com/timescale/tsbs
In ClickHouse, Inc.
bluestreak·May 11, 2021
We launched QuestDB last summer [1, 2]. Our storage model is vector-based and append-only. This meant that all incoming data had to arrive in the correct time order. This worked well for some use cases but we increasingly saw real-world cases where data doesn't always land at the database in chronological order. We saw plenty of developers and users come and go specifically because of this technical limitation. So it became a priority to deal with out-of-order data. The big decision was which direction to take to tackle the problem. LSM trees seemed an obvious choice, but we chose an alternative route so we wouldn't lose the performance we spent years building. Our latest release supports out-of-order ingestion by re-ordering data on the fly. That's what this article is about. Also, we had many people asking about the differences between
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QuestDB and other open-source databases and why users should consider giving it a try instead of other systems. When we launched on HN, readers showed a lot of interest in side-by-side comparisons to other databases on the market. One suggestion [3] that we thought would be great to try out was to benchmark ingestion and query speeds using the Time Series Benchmark Suite (TSBS) [4] developed by TimescaleDB. We're super excited to share the results in the article. [1] https://news.ycombinator.com/item?id=23975807 [2] https://news.ycombinator.com/item?id=23616878 [3] https://news.ycombinator.com/item?id=23977183 [4] https://github.com/timescale/tsbs
In Building a new vector based storage model
mfreed·May 5, 2021
Hi @spmurrayzzz thanks for the feedback. (Timescale person) Always strive to do the best and fairest benchmarks we can, and for that reason, all our benchmarks are fully open-source for both repeatability and improvements/contributions: https://github.com/timescale/tsbs/blob/master/docs/mongo.md We also really did spend a lot of time investigating approaches with MongoDB, so you'll see our benchmarks actually evaluate two _different_ ways to use time-series data with MongoDB (culled & optimized from suggestions in MongoDB forums). But always welcome to feedback: https://blog.timescale.com/blog/how-to-store-time-series-dat... Thanks!
In TimescaleDB raises $40M

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