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apache
spark
Scala2014–2026
apache / spark

spark

Apache Spark - A unified analytics engine for large-scale data processing

Data & Databases
Explore on GitHubRead Hacker News discussions
28Distinct people
31Mentions
0Explicit recommenders
44.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Apache Spark - A unified analytics engine for large-scale data processing

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, 7:02:50 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 year31 dated mentions

Observed mentions: 2014-09-12 21:21:57 UTC to 2026-01-28 20:16:34 UTC.

024682014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20142015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 8 mentions; 0 submissions, 8 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 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): 2 mentions; 0 submissions, 2 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 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
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC101
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC808
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC606
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC303
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 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC404
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC303
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC211
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC211

What Hacker News says

3 sources
rdubz·Apr 6, 2017
There may be some subjectivity around what constitutes added/removed complexity. The ability to factor out repeated logic and configuration in SBT, by dint of using Scala instead of XML, removes a lot of complexity. Of course, arbitrary Scala can get more complex than arbitrary XML. As some points of reference, not saying these clearly argue for one side or the other: Here is the Maven POM for ADAM's core module: https://github.com/bigdatagenomics/adam/blob/adam-parent_2.1... and its parent POM: https://github.com/bigdatagenomics/adam/blob/adam-parent_2.1... For comparison, build.sbt from my fork of ADAM: https://github.com/hammerlab/adam/blob/e4bee3227b5979e65a73d... And a plugin that it inherits that factors out a lot of functionality I reuse across projects: https://github.com/hammerlab/sbt-parent/blob/1.7.5/src/main/... Spark's POMs (
Read the rest
e.g. https://github.com/apache/spark/blob/v2.1.0/pom.xml) vs. SBT project folder (https://github.com/apache/spark/tree/v2.1.0/project) are another point of comparison. It's not hard for me to imagine how someone used to staring at one or the other of these kinds of configs, maybe for years, would feel that it made more sense than the other, but having spent some significant time with both, I think the points about having the option to express more sophisticated logic – that SBT provides – are pretty important, and the lessons extrapolate-able beyond building Scala projects :)
In Building Scala Projects: Maven vs. SBT

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