Hyukjin Kwon created SPARK-21693: ------------------------------------ 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org