[SPARK-16826] java.util.Hashtable limits the throughput of PARSE_URL()

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 2.0.0
• Fix Version/s: 2.1.0
• Component/s: SQL
• Assignee: Sylvain Zimmer
• Reporter: Sylvain Zimmer
• Created: 31/Jul/16 21:31
• Updated: 12/Jan/18 08:50
• Resolved: 05/Aug/16 19:56

Description

Hello!

I'm using c4.8xlarge instances on EC2 with 36 cores and doing lots of parse_url(url, "host") in Spark SQL.

Unfortunately it seems that there is an internal thread-safe cache in there, and the instances end up being 90% idle.

When I view the thread dump for my executors, most of the executor threads are "BLOCKED", in that state:

java.util.Hashtable.get(Hashtable.java:362)
java.net.URL.getURLStreamHandler(URL.java:1135)
java.net.URL.<init>(URL.java:599)
java.net.URL.<init>(URL.java:490)
java.net.URL.<init>(URL.java:439)
org.apache.spark.sql.catalyst.expressions.ParseUrl.getUrl(stringExpressions.scala:731)
org.apache.spark.sql.catalyst.expressions.ParseUrl.parseUrlWithoutKey(stringExpressions.scala:772)
org.apache.spark.sql.catalyst.expressions.ParseUrl.eval(stringExpressions.scala:785)
org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:69)
org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:69)
org.apache.spark.sql.execution.FilterExec$$anonfun$17$$anonfun$apply$2.apply(basicPhysicalOperators.scala:203)
org.apache.spark.sql.execution.FilterExec$$anonfun$17$$anonfun$apply$2.apply(basicPhysicalOperators.scala:202)
scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:147)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
org.apache.spark.scheduler.Task.run(Task.scala:85)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
java.lang.Thread.run(Thread.java:745)

However, when I switch from 1 executor with 36 cores to 9 executors with 4 cores, throughput is almost 10x higher and the CPUs are back at ~100% use.

Thanks!

Issue Links

• incorporates: SPARK-23056 parse_url regression when switched to using java.net.URI instead of java.net.URL
• links to: [Github] Pull Request #14488 (sylvinus)