Hyukjin Kwon created SPARK-21693:
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Summary: AppVeyor tests reach the time limit, 1.5 hours, sometimes
in SparkR tests
Key: SPARK-21693
URL: https://issues.apache.org/jira/browse/SPARK-21693
Project: Spark
Issue Type: Test
Components: Build, SparkR
Affects Versions: 2.3.0
Reporter: Hyukjin Kwon
We finally sometimes reach the time limit, 1.5 hours,
https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark/build/1676-master
I requested to increase this from an hour to 1.5 hours before but it looks we
should fix this in AppVeyor. I asked this for my account few times before but
it looks we can't increase this time limit again and again.
I could identify three things that take a quite a bit of times:
1. Disabled cache feature in pull request builder, which ends up downloading
Maven dependencies (15-20ish mins)
https://www.appveyor.com/docs/build-cache/
{code}
Note: Saving cache is disabled in Pull Request builds.
{code}
and also see
http://help.appveyor.com/discussions/problems/4159-cache-doesnt-seem-to-be-working
This seems difficult to fix within Spark.
2. "MLlib classification algorithms" tests (30-35ish mins)
This test below looks taking 30-35ish mins.
{code}
MLlib classification algorithms, except for tree-based algorithms: Spark
package found in SPARK_HOME: C:\projects\spark\bin\..
......................................................................
{code}
As a (I think) last resort, we could make a matrix for this test alone, so that
we run the other tests after a build and then run this test after another
build, for example, I run Scala tests by this workaround -
https://ci.appveyor.com/project/spark-test/spark/build/757-20170716 (a matrix
with 7 build and test each).
3. Disabled {{spark.sparkr.use.daemon}} on Windows due to the limitation of
{{mcfork}}
See [this
codes|https://github.com/apache/spark/blob/478fbc866fbfdb4439788583281863ecea14e8af/core/src/main/scala/org/apache/spark/api/r/RRunner.scala#L362-L392].
We disabled this feature and currently fork processes from Java that is
expensive. I haven't tested this yet but maybe reducing
{{spark.sql.shuffle.partitions}} can be an approach to work around this.
Currently, if I understood correctly, this is 200 by default in R tests, which
ends up with 200 Java processes for every shuffle.
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