Debugging & Tuning in Spark

Debugging & Tuning in Spark
Shiao-An Yuan
@sayuan
2016-08-11

Spark Overview
● Cluster Manager (aka Master)
● Worker (aka Slave)
● Driver
● Executor
http://spark.apache.org/docs/latest/cluster-overview.html

RDD (Resilient Distributed Dataset)
A fault-tolerant collection of elements that can be operated
on in parallel

Word Count
val sc: SparkContext = ...
val result = sc.textFile(file) // RDD[String]
.flatMap(_.split(" ")) // RDD[String]
.map(_ -> 1) // RDD[(String, Int)]
.groupByKey() // RDD[(String, Iterable[Int])]
.map(x => (x._1, x._2.sum)) // RDD[(String, Int)]
.collect() // Array[(String, Int])

Lazy, Transformation, Action, Job
groupByKey mapmapflatMap collect

Partition, Shuffle

Stage, Task

DAG (Directed Acyclic Graph)
● RDD operations
○ Transformation
○ Action
● Lazy
● Job
● Shuffle
● Stage
● Partition
● Task

Objective
1. A correct and parallelizable algorithm
2. Parallelism
3. Reduce the overhead from parallelization

Correctness and Parallelizable
● Use small input
● Run locally
○ --master local
○ --master local[4]
○ --master local[*]

Non-RDD Operations
● Avoid long blocking on driver

Data Skew
● repartition() come to rescue?
● Hotspots
○ Choose another partitioned key
○ Filter unreasonable data
● Trace to it’s source

https://databricks.gitbooks.io/databricks-spark-knowledge-base/content/best_practices/prefer_reducebykey_over_groupbykey.html

Prefer reduceByKey() over groupByKey()
● reduceByKey() combines output
before shuffling the data
● Also consider aggregateByKey()
● Use groupByKey() if you really
know what you are doing

Shuffle Spill
● Increase partition count
● spark.shuffle.spill=false (default since Spark 1.6)
● spark.shuffle.memoryFraction
● spark.executor.memory

http://www.slideshare.net/databricks/new-developments-in-spark

Join
● partitionBy()
● repartitionAndSortWithinPartitions()
● spark.sql.autoBroadcastJoinThreshold (default 10 MB)
● Join it manually by mapPartitions()
○ Broadcast small RDD
■ http://stackoverflow.com/a/17690254/406803
○ Query data from database
■ https://groups.google.com/a/lists.datastax.com/d/topic/spark-connector-user/63ILfPqPRYI/discussion

Broadcast Small RDD
val smallRdd = ...
val largeRdd = ...
val smallBroadcast = sc.broadcast(smallRdd.collectAsMap())
val joined = largeRdd.mapPartitions(iter => {
val m = smallBroadcast.value
for {
(k, v) <- iter
if m.contains(k)
} yield (k, (v, m.get(k).get))
}, preservesPartitioning = true)

Query Data from Cassandra
val conf = new SparkConf()
.set("spark.cassandra.connection.host", "127.0.0.1")
val connector = CassandraConnector(conf)
val joined = rdd.mapPartitions(iter => {
connector.withSessionDo(session => {
val stmt = session.prepare("SELECT value FROM table WHERE key=?")
iter.map {
case (k, v) => (k, (v, session.execute(stmt.bind(k)).one()))
}
})
})

Persist
● Storage level
○ MEMORY_ONLY
○ MEMORY_AND_DISK
○ MEMORY_ONLY_SER
○ MEMORY_AND_DISK_SER
○ DISK_ONLY
○ …
● Kryo serialization
○ Much faster
○ Registration needed
http://spark.apache.org/docs/latest/programming-guide.html#which-storage-level-to-choose

Common Failures
● Large shuffle blocks
○ java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
■ Increase partition count
○ MetadataFetchFailedException, FetchFailedException
■ Increase partition count
■ Increase `spark.executor.memory`
■ …
○ java.lang.OutOfMemoryError: GC overhead over limit exceeded
■ May caused by shuffle spill

java.lang.OutOfMemoryError: Java heap space
● Driver
○ Increase `spark.driver.memory`
○ collect()
■ take()
■ saveAsTextFile()
● Executor
○ Increase `spark.executor.memory`
○ More nodes

java.io.IOException: No space left on device
● SPARK_WORKER_DIR
● SPARK_LOCAL_DIRS, spark.local.dir
● Shuffle files
○ Only delete after the RDD object has been GC

Other Tips
● Event logs
○ spark.eventLog.enabled=true
○ ${SPARK_HOME}/sbin/start-history-server.sh

Partitions
● Rule of thumb: ~128 MB per partition
● If #partitions <= 2000, but close, bump to just > 2000
● Increase #partitions by repartition()
● Decrease #partitions by coalesce()
● spark.sql.shuffle.partitions (default 200)
http://www.slideshare.net/cloudera/top-5-mistakes-to-avoid-when-writing-apache-spark-applications

Executors, Cores, Memory!?
● 32 nodes
● 16 cores each
● 64 GB of RAM each
● If you have an application need 32 cores, what is the
correct setting?
http://www.slideshare.net/cloudera/top-5-mistakes-to-avoid-when-writing-apache-spark-applications

Why Spark Debugging / Tuning is Hard?
● Distributed
● Lazy
● Hard to do benchmark
● Spark is sensitive

Conclusion
● When in doubt, repartition!
● Avoid shuffle if you can
● Choose a reasonable partition count
● Premature optimization is the root of all evil -- Donald Knuth

Reference
● Tuning and Debugging in Apache Spark
● Top 5 Mistakes to Avoid When Writing Apache Spark
Applications
● How-to: Tune Your Apache Spark Jobs (Part 1)
● How-to: Tune Your Apache Spark Jobs (Part 2)

Debugging & Tuning in Spark

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