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knizhnik
imcs
C2014–2015
knizhnik / imcs

imcs

In-Memory Columnar Store extension for PostgreSQL

Data & Databases
Explore on GitHubRead Hacker News discussions
2Distinct people
2Mentions
0Explicit recommenders
203GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

In-Memory Columnar Store extension for 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 month2 dated mentions

Observed mentions: 2014-01-03 23:27:35 UTC to 2015-02-24 13:18:45 UTC.

012014-01-01 00:00:00 UTC to 2014-02-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJan 20142014-02-01 00:00:00 UTC to 2014-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-03-01 00:00:00 UTC to 2014-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-04-01 00:00:00 UTC to 2014-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-05-01 00:00:00 UTC to 2014-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-06-01 00:00:00 UTC to 2014-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-07-01 00:00:00 UTC to 2014-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJul 20142014-08-01 00:00:00 UTC to 2014-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-09-01 00:00:00 UTC to 2014-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-10-01 00:00:00 UTC to 2014-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-11-01 00:00:00 UTC to 2014-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-12-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-01-01 00:00:00 UTC to 2015-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-02-01 00:00:00 UTC to 2015-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsFeb 2015

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
Jan 20142014-01-01 00:00:00 UTC2014-02-01 00:00:00 UTC110
Feb 20142014-02-01 00:00:00 UTC2014-03-01 00:00:00 UTC000
Mar 20142014-03-01 00:00:00 UTC2014-04-01 00:00:00 UTC000
Apr 20142014-04-01 00:00:00 UTC2014-05-01 00:00:00 UTC000
May 20142014-05-01 00:00:00 UTC2014-06-01 00:00:00 UTC000
Jun 20142014-06-01 00:00:00 UTC2014-07-01 00:00:00 UTC000
Jul 20142014-07-01 00:00:00 UTC2014-08-01 00:00:00 UTC000
Aug 20142014-08-01 00:00:00 UTC2014-09-01 00:00:00 UTC000
Sep 20142014-09-01 00:00:00 UTC2014-10-01 00:00:00 UTC000
Oct 20142014-10-01 00:00:00 UTC2014-11-01 00:00:00 UTC000
Nov 20142014-11-01 00:00:00 UTC2014-12-01 00:00:00 UTC000
Dec 20142014-12-01 00:00:00 UTC2015-01-01 00:00:00 UTC000
Jan 20152015-01-01 00:00:00 UTC2015-02-01 00:00:00 UTC000
Feb 20152015-02-01 00:00:00 UTC2015-03-01 00:00:00 UTC101

What Hacker News says

2 sources
ddorian43·Feb 24, 2015
From a glance, you have to define the queries before. Are you using anything currently? What is your insert/update/delete rate? If low, maybe look at postgresql in-memory column-store https://github.com/knizhnik/imcs/ Another way may be voltdb. Since it's partitioned+single-thread/partition, and cross-partition queries are slow, you'll probably have to make a query for each partition and group results on the app. Also curious.
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