spark-druid-olap

Sparkline BI Accelerator provides fast ad-hoc query capability over Logical Cubes. This has been folded into our SNAP Platform(http://bit.ly/2oBJSpP) an Integrated BI platform on Apache Spark.

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2Distinct people

2Mentions

0Explicit recommenders

281GitHub stars

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What to know before exploring

What it is

Sparkline BI Accelerator provides fast ad-hoc query capability over Logical Cubes. This has been folded into our SNAP Platform(http://bit.ly/2oBJSpP) an Integrated BI platform on Apache Spark.

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Where it is used

No documented product use has been verified yet.

What people are saying

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Also in these discussions

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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, 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 3-hour interval2 dated mentions

Observed mentions: 2016-03-31 23:55:20 UTC to 2016-04-02 17:39:42 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

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
Mar 31, 21:00	2016-03-31 21:00:00 UTC	2016-04-01 00:00:00 UTC	1	1
Apr 1, 00:00	2016-04-01 00:00:00 UTC	2016-04-01 03:00:00 UTC	0	0
Apr 1, 03:00	2016-04-01 03:00:00 UTC	2016-04-01 06:00:00 UTC	0	0
Apr 1, 06:00	2016-04-01 06:00:00 UTC	2016-04-01 09:00:00 UTC	0	0
Apr 1, 09:00	2016-04-01 09:00:00 UTC	2016-04-01 12:00:00 UTC	0	0
Apr 1, 12:00	2016-04-01 12:00:00 UTC	2016-04-01 15:00:00 UTC	0	0
Apr 1, 15:00	2016-04-01 15:00:00 UTC	2016-04-01 18:00:00 UTC	0	0
Apr 1, 18:00	2016-04-01 18:00:00 UTC	2016-04-01 21:00:00 UTC	0	0
Apr 1, 21:00	2016-04-01 21:00:00 UTC	2016-04-02 00:00:00 UTC	0	0
Apr 2, 00:00	2016-04-02 00:00:00 UTC	2016-04-02 03:00:00 UTC	0	0
Apr 2, 03:00	2016-04-02 03:00:00 UTC	2016-04-02 06:00:00 UTC	0	0
Apr 2, 06:00	2016-04-02 06:00:00 UTC	2016-04-02 09:00:00 UTC	0	0
Apr 2, 09:00	2016-04-02 09:00:00 UTC	2016-04-02 12:00:00 UTC	0	0
Apr 2, 12:00	2016-04-02 12:00:00 UTC	2016-04-02 15:00:00 UTC	0	0
Apr 2, 15:00	2016-04-02 15:00:00 UTC	2016-04-02 18:00:00 UTC	1	1

What Hacker News says

2 sources

hbutani·Apr 2, 2016

I don't know the specific state of the DataSources you mention, but yes just providing a DataSource only is not enough; you can only push Filters and Projections to the underlying engine. You need to develop Query Rewrite Rules to rewrite Joins, Group Bys, Having, Limit etc to the underlying engine. For example our Rewrite engine for Druid is here: https://github.com/SparklineData/spark-druid-olap/blob/maste... Can you be specific about ANSI SQL compliance requirements. Spark SQL is closing the gap on Hive SQL; both have decent support for analytical queries: Cubes/Rollups/Windowing etc. The only major gap between Spark and Hive SQL I know off is SubQuery predicates(exists/not exists).

In Druid: fast column-oriented distributed data store

fangjin·Mar 31, 2016

Druid has a SparkSQL connector: https://github.com/SparklineData/spark-druid-olap

In Druid: fast column-oriented distributed data store