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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

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

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:04:58 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

2 sources
(TimescaleDB co-founder) TimescaleDB is more performant that you may think. We've benchmarked this extensively: eg outperforming vs InfluxDB [1] [2], vs Cassandra [3], vs Mongo [4]. We've also open-sourced the benchmarking suite so others can run these themselves and verify our results. [5] We also beat MemSQL regularly for enterprise engagements (unfortunately can't share those results publicly). I think the scalability of ClickHouse is quite compelling, and if you need more than 1-2M inserts a second and 100TBs of storage, then that would be one reason where I'd recommend another database over our own. But horizontal scalability is something we have been working on for nearly a year, so we expect this to be a less of an issue in the near future (will have more to share later this month). You are correct however that TimescaleDB requires
Read the rest
more storage than some of these other options. If storage is the most important criteria for you (ie more important than usability or performance), then again I would recommend you to one of the other databases that are more optimized for compression. However, you can get 6-8x compression by running TimescaleDB on ZFS today, and we are also currently working on additional techniques for achieving higher compression rates. [1] https://blog.timescale.com/timescaledb-vs-influxdb-for-time-... [2] https://blog.timescale.com/what-is-high-cardinality-how-do-t... [3] https://blog.timescale.com/time-series-data-cassandra-vs-tim... [4] https://blog.timescale.com/how-to-store-time-series-data-mon... [5] https://github.com/timescale/tsbs
In Zabbix, Time Series Data and TimescaleDB

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