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pgpartman
pg_partman
PLpgSQL2015–2026
pgpartman / pg_partman

pg_partman

Partition management extension for PostgreSQL

Data & Databases
Explore on GitHubRead Hacker News discussions
12Distinct people
14Mentions
0Explicit recommenders
2.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Partition management extension for PostgreSQL

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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 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 year14 dated mentions

Observed mentions: 2015-07-23 18:18:42 UTC to 2026-08-08 06:30:50 UTC.

01232015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-01-01 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): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

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
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC101
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC303
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
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 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC202
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC110
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC110
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC202

What Hacker News says

14 sources
hasyimibhar·Aug 8, 2026
I'm guessing it's something like MongoDB TTL index[0]. It's useful for huge append-only tables where you want a row to be automatically removed after a period of time. The postgres approach is usually to partition your table by the event/insertion timestamp, and use something like pg_partman[1] to automatically drop entire partitions over time. [0] https://www.mongodb.com/docs/manual/core/index-ttl/ [1] https://github.com/pgpartman/pg_partman
In Making Postgres 300x faster for analytics: batching, operator fusion, and SIMD
boomskats·Sep 22, 2022
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 kno
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w 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...
In TimescaleDB 2.7 vs. PostgreSQL 14
gshulegaard·Jul 13, 2020
This is a sort of broad question, but if I were to try and answer I would say that if there is anything you want to be handled by the database you would gravitate towards an extension. PostGIS is a great example since one of the most well known it additions it makes is a datatype, but that's not the only thing it adds. A datatype on it's own wouldn't be that useful without Spatial Indexing (https://postgis.net/workshops/postgis-intro/indexing.html) or spatial utilities to make spatial joins more expressive (https://postgis.net/workshops/postgis-intro/joins_exercises....). Rich data types aren't the only candidates for extensions, automated partition management (https://github.com/pgpartman/pg_partman), data sharding (https://www.citusdata.com/), or even database cron scheduling (https://github.com/citusdata/pg_cron) are also good example
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s of things that are well suited to be extensions. To be honest, I have a hard time imagining alternative paths where some of this functionality _isn't_ an extension. You might imagine an external daemon for things like pg_cron and pg_partman, but if PostGIS wasn't an extension you would probably be using a different database/tool (or a fork) if you had geospatial requirements. It's worth noting that Citus was a fork of PostgreSQL before it was refactored to be an extension. I know this isn't a direct answer to your question, but hopefully you find it somewhat useful. Edit: I totally forgot to add 1 more thing about extensions. When functionality is packaged as an extension there is a much higher likelihood you can mix and match them. For example, I currently run a PostgreSQL cluster using Citus, with pg_partman for automatic time-based partitioning, pg_cron regularly scheduling partition creation, and PostGIS for geospatial datatypes. You could extrapolate the various ways you might use this kind of setup...one that jumps to my mind is a scalable GPS history that could be queried by time period as well as by geographic region.
In PGX: Write Postgres extensions in Rust instead of C
gshulegaard·Jul 11, 2020
I am sure MySQL is a great fit for your use case. For others reading your comment though, I did want to list some things I have used with Postgres that relate to connection pooling and data partitioning: * PGBouncer for connection pooling/sharing. * Postgres Table Inheritance for table partitioning (https://www.postgresql.org/docs/12/ddl-inherit.html) * PGPartman for automating the creation of partitions (https://github.com/pgpartman/pg_partman) * Citus for low-barrier data sharding (it's a Postgres Extension like PostGIS) (https://www.citusdata.com/)
In Making Netflix's Data Infrastructure Cost-Effective
boomskats·May 7, 2019
Every time TimescaleDB is brought up, I feel the need to point people to their shadily worded proprietary licence[0], and pg_partman[1]. Do the same benchmarks against a pg_partman managed partitioned db and you'll get the exact same performance. We do, at least - 150k or so metrics per second, 10 columns per metric. Not trying to crap on the TimescaleDB guys, I've found a lot of their writeups extremely useful and can totally see how their commercially supported product fits. However, I like to see pg_partman at least mentioned somewhere in the article/comments. It's awesome and does the same job. [0]https://github.com/timescale/timescaledb/blob/master/LICENSE [1]https://github.com/pgpartman/pg_partman
In Zabbix, Time Series Data and TimescaleDB
michaelbryzek·Sep 21, 2016
Great to see more options for partition management in postgresql. We took a similar approach using a ruby library to manage partitions at gilt groupe starting many years ago (2009?). It worked quite well and is still in use today. Since then have also used the referenced https://github.com/keithf4/pg_partman on RDS - and it works great! Very happy to have all the partition management in the core database vs. in other languages, and many of the features Keith provides in pg_partman just work. He's done a great job with the project and would encourage folks to take a serious look at his work. If you are interested in details of how we applied pg_partment to RDS w/out requiring extensions, take a look at: https://github.com/flowcommerce/lib-postgresql/blob/master/R... We created a simple process to load the scripts directly into the database
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similar to any one of our other migrations. We then create higher level wrappers for our teams to use to apply consistent use.
In Pgslice: Postgres partitioning as easy as pie
Kassandry·Jul 23, 2015
From their article: "Additionally we had been running with far too much delay in vacuuming which meant lower load on the system, but more idle time in maintenance." So, vacuum wasn't given the resources to keep up with their load, and it's not clear if they were supplementing with manual vacuums during quiet times. Nor was it clear when they started reacting, as PostgreSQL (also outlined in their documentation link), will start squawking about wraparound well before it shuts down, or the monitoring they had in place for whether or not autovacuum/vacuum was keeping up with the workload, or the number of locks their application was taking (locks can block autovacuum). Adjusting the autovacuum settings by table will give you finer control over the postgresql.conf parameters to better match the workload for specific tables, as well. Partition
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ing would have also helped make the actively written part of the table smaller, and older data could be vacuumed with VACUUM FREEZE, or deleted later. There are extensions to help make that easier. https://github.com/keithf4/pg_partman Josh Berkus of PgExperts has some great advice here: https://dba.stackexchange.com/questions/21068/aggressive-aut... So does Jim Nasby, here: http://bluetreble.com/2014/10/postgres-mvcc-and-a-look-at-va... Regarding monitoring, check_postgres.pl would give them an idea that their vacuuming settings needed adjustment sooner, or their application locking needed to be adjusted to not block autovacuum. https://bucardo.org/check_postgres/check_postgres.pl.html#tx... https://bucardo.org/check_postgres/check_postgres.pl.html#la... https://bucardo.org/check_postgres/check_postgres.pl.html#lo... It's easy to mention things after the fact, though, and it's good they got things up and running again for their customers.
In Transaction ID Wraparound in Postgres

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