DEDeathArrow·Jun 4, 2022
This might interest someone: "Vectorized Operations extension for PostgreSQL"
https://github.com/postgrespro/vops
In Vectorized and performance-portable Quicksortpostgrespro
vops
C2020–2022
postgrespro / vops
vops
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2Distinct people
2Mentions
0Explicit recommenders
164GitHub stars
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 11:56:56 AM UTC.
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Mentions over time
HN mentions by month2 dated mentions
Observed mentions: 2020-06-10 11:01:09 UTC to 2022-06-04 19:57:36 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jun 2020 | 2020-06-01 00:00:00 UTC | 2020-07-01 00:00:00 UTC | 1 | 0 | 1 |
| Jul 2020 | 2020-07-01 00:00:00 UTC | 2020-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2020 | 2020-08-01 00:00:00 UTC | 2020-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2020 | 2020-09-01 00:00:00 UTC | 2020-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2020 | 2020-10-01 00:00:00 UTC | 2020-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2020 | 2020-11-01 00:00:00 UTC | 2020-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2020 | 2020-12-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2021 | 2021-01-01 00:00:00 UTC | 2021-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2021 | 2021-02-01 00:00:00 UTC | 2021-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2021 | 2021-03-01 00:00:00 UTC | 2021-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2021 | 2021-04-01 00:00:00 UTC | 2021-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2021 | 2021-05-01 00:00:00 UTC | 2021-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2021 | 2021-06-01 00:00:00 UTC | 2021-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2021 | 2021-07-01 00:00:00 UTC | 2021-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2021 | 2021-08-01 00:00:00 UTC | 2021-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2021 | 2021-09-01 00:00:00 UTC | 2021-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2021 | 2021-10-01 00:00:00 UTC | 2021-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2021 | 2021-11-01 00:00:00 UTC | 2021-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2021 | 2021-12-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2022 | 2022-01-01 00:00:00 UTC | 2022-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2022 | 2022-02-01 00:00:00 UTC | 2022-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2022 | 2022-03-01 00:00:00 UTC | 2022-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2022 | 2022-04-01 00:00:00 UTC | 2022-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2022 | 2022-05-01 00:00:00 UTC | 2022-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2022 | 2022-06-01 00:00:00 UTC | 2022-07-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
2 sourcesI am also using TimescaleDB and face similar issues around the OLAP workloads. I tested Clickstream and QuestDB, both of which claim superior OLAP performance, however the ingest rates and cardinality of my unstructured data did not perform on par with TimescaleDB in my limited tests. Lacking time, instead I implemented a number of strategies within Postgres - trading space for speed with additional computed index columns and partitioning around these, applying table layering at various time resolutions, and using Real-time Aggregates as introduced in TimescaleDB 1.7. The Real-time Aggregates make a significant improvement to performance on large datasets for very little effort and appear to be a feature well-suited to solving your problem. This has alleviated performance enough to keep me looking within the Postgres ecosystem. For no
Read the rest
w the performance is within target budgets again. Going forward, time permitting, I’ll be looking at additional Postgres plugins which target OLAP performance using combinations of columnar storage and SIMD processing: Swarm64, VOPs (https://github.com/postgrespro/vops), pg_strom