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cybertec-postgresql
pg_squeeze
C2017–2022
cybertec-postgresql / pg_squeeze

pg_squeeze

A PostgreSQL extension for automatic bloat cleanup

4Distinct people
4Mentions
0Explicit recommenders
705GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A PostgreSQL extension for automatic bloat cleanup

GitHub

Where it is used

No documented product use has been verified yet.

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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 6:01:08 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 year4 dated mentions

Observed mentions: 2017-05-31 22:53:22 UTC to 2022-10-04 15:29:27 UTC.

0122017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022

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
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC220
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC101
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101

What Hacker News says

1 sources
> I am estimating that your database space isn't MySQL, which is just fine of course. You are absolutely right :) My background is strongly on Postgres, you can see from my profile more information if you want to. So yes, I apologize if some of my questions are not applying or become to obvious for cases that are MySQL-based. But for the most part, I believe principles of operation are the same. > [other comments] As mentioned, thank you very much for the detailed information. This completes the picture that I was looking for. I will definitely go in more detail for some of the links provided. This principle of operation is not too different from something I proposed to a Postgres project some time ago (https://github.com/cybertec-postgresql/pg_squeeze/issues/18). This tool indeed is conceptually pretty similar. It's a shame that supporti
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ng schema changes is not part of their focus at this point. It wouldn't do throttling either, but it shouldn't be a difficult feature to add, I guess. For other users here that may be interested in the Postgres world, there are two tools that perform similar operation (creating a shadow table and filling it in the background), but are both focused on rewriting the table to avoid bloat, rather than for doing a schema migration: * pg_repack (https://reorg.github.io/pg_repack/): the most used one, relies on triggers * pg_squeeze: already mentioned, uses logical replication
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