You can stop your streaming context in cluster mode by running the following command without needing to sending a SIGTERM. This will stop the streaming context without you needing to explicitly stop it using a thread hook. $SPARK_HOME_DIR/bin/spark-submit –master $MASTER_REST_URL –kill $DRIVER_ID -$MASTER_REST_URL is the rest url of the spark driver, ie something like spark://localhost:6066 … Read more
FROM THE QUESTION ASKER: Added: To interfere the deserialization part directly without modifying your own classes use the following spring-spark project by parapluplu. This projects autowires your bean when it gets deserialized by spring. EDIT: In order to use Spark, you need the following setup (also seen in this repository): Spring Boot + Spark: . … Read more
You are branching the query plan: from the same ds1 you are trying to: ds1.collect.foreach(…) ds1.writeStream.format(…){…} But you are only calling .start() on the second branch, leaving the other dangling without a termination, which in turn throws the exception you are getting back. The solution is to start both branches and await termination. val ds1 … Read more
spark.yarn.executor.memoryOverhead Is just the max value .The goal is to calculate OVERHEAD as a percentage of real executor memory, as used by RDDs and DataFrames –executor-memory/spark.executor.memory controls the executor heap size, but JVMs can also use some memory off heap, for example for interned Strings and direct byte buffers. The value of the spark.yarn.executor.memoryOverhead property … Read more
groupBy after orderBy doesn’t maintain order, as others have pointed out. What you want to do is use a Window function, partitioned on id and ordered by hours. You can collect_list over this and then take the max (largest) of the resulting lists since they go cumulatively (i.e. the first hour will only have itself … Read more
Reason is association with yarn cluster may be lost due to the Java 8 excessive memory allocation issue: https://issues.apache.org/jira/browse/YARN-4714 You can force YARN to ignore this by setting up the following properties in yarn-site.xml <property> <name>yarn.nodemanager.pmem-check-enabled</name> <value>false</value> </property> <property> <name>yarn.nodemanager.vmem-check-enabled</name> <value>false</value> </property> Thanks to simplejack, Reference from Spark Pi Example in Cluster mode with Yarn: … Read more
Extending the answer By @Rohan Aletty. Here is a sample code of a BroadcastWrapper that refresh broadcast variable based on some ttl public class BroadcastWrapper { private Broadcast<ReferenceData> broadcastVar; private Date lastUpdatedAt = Calendar.getInstance().getTime(); private static BroadcastWrapper obj = new BroadcastWrapper(); private BroadcastWrapper(){} public static BroadcastWrapper getInstance() { return obj; } public JavaSparkContext getSparkContext(SparkContext sc) … Read more
The problem is that you are mixing Scala 2.11 and 2.10 artifacts. You have: scalaVersion := “2.11.8” And then: libraryDependencies += “org.apache.spark” % “spark-streaming_2.10” % “1.4.1” Where the 2.10 artifact is being required. You are also mixing Spark versions instead of using a consistent version: // spark 1.6.1 libraryDependencies += “org.apache.spark” %% “spark-core” % “1.6.1” … Read more
The general idea is that PySpark creates as many java processes than there are executors, and then ships data to each process. If there are too few processes, a memory bottleneck happens on the java heap space. In your case, the specific error is that the RDD that you created with sc.parallelize([…]) did not specify … Read more
just do the following: df1.unpersist() df2.unpersist() Spark automatically monitors cache usage on each node and drops out old data partitions in a least-recently-used (LRU) fashion. If you would like to manually remove an RDD instead of waiting for it to fall out of the cache, use the RDD.unpersist() method.