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citusdata
postgres_vectorization_test
C2014–2023
citusdata / postgres_vectorization_test

postgres_vectorization_test

Vectorized executor to speed up PostgreSQL

Data & DatabasesTesting & Quality
Explore on GitHubRead Hacker News discussions
6Distinct people
7Mentions
0Explicit recommenders
334GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Vectorized executor to speed up PostgreSQL

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, 5:04:57 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 year7 dated mentions

Observed mentions: 2014-10-07 20:30:09 UTC to 2023-11-18 17:30:26 UTC.

01232014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments20142015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-01-01 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): 1 mentions; 0 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2023

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
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC312
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 UTC000
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
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 UTC101
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 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC110

What Hacker News says

1 sources
mslot·Sep 15, 2015
For basic reads and writes the FDW APIs are quite suitable and they work well for cstore_fdw. However, to get the most out of columnar storage you also need vectorized execution that can calculate aggregates on blocks of values. The FDW APIs are not well-suited for this purpose, since they pull one row of data at a time. We've done some experiments in which we used the executor hooks to implement a vectorized executor for cstore_fdw [1] and it can give dramatic performance gains. However, the SQL support of this solution is limited as the APIs force you to build a new executor from scratch. With sharding, the distributed executor can work on the SQL level and translate one SQL query to many SQL queries. In that case your distributed executor can focus on issues such as partitioning and merging data, while the low-level work is still being
Read the rest
done by the regular PostgreSQL executor, so you don't have to implement it from scratch. [1] https://github.com/citusdata/postgres_vectorization_test
In PostgreSQL, pg_shard, and what we learned from our failures

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