I've setup a 3-node cluster (1-master & 2-workers) of Hadoop with Yarn along with Spark.
My Pyspark scripts need org.elasticsearch.spark in order to write to Elasticsearch. I'm providing this with parameter --packages org.elasticsearch:elasticsearch-spark-30_2.12:8.4.1 while executing my Pyspark script , that is while executing using spark-submit .
Stuck with this error :
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/shaded/javax/ws/rs/core/NoContentException
at org.apache.hadoop.yarn.util.timeline.TimelineUtils.<clinit>(TimelineUtils.java:60)
at org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:200)
at org.apache.hadoop.service.AbstractService.init(AbstractService.java:164)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:191)
at org.apache.spark.deploy.yarn.Client.run(Client.scala:1327)
at org.apache.spark.deploy.yarn.YarnClusterApplication.start(Client.scala:1764)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:958)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1046)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1055)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.shaded.javax.ws.rs.core.NoContentException
at java.net.URLClassLoader.findClass(URLClassLoader.java:387)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
... 13 more
What have I tried :
I have tried to add all the paths listed on this answer - https://stackoverflow.com/a/25393369/6490744 - doesn't work.
I had Hadoop-3.1.1, after checking https://github.com/apache/incubator-kyuubi/issues/2904 (they've mentioned that the issue is resolved in Hadoop 3.3.3) I have upgraded to 3.3.3. But the issue still persists.
I have also tried by manually downloading the jar to my spark/jars directory using wget -U "Any User Agent" https://repo1.maven.org/maven2/org/elasticsearch/elasticsearch-spark-30_2.12/8.4.1/elasticsearch-spark-30_2.12-8.4.1.jar => after downloading, tried to do spark-submit without passing --packages (since I have the jar in path).
All of this has been giving me the same error
After 2 hours of struggle, got the clue from - https://github.com/apache/incubator-kyuubi/issues/2904#issuecomment-1158643036 :
I had yarn.timeline-service.enabled set to true in my /etc/hadoop/yarn-site.xml - updated to false , now the error is gone.
Wonder how to setup the yarn-timeline-server now
Hadoop 2.7.3, Spark 2.2.1 and Hive 2.1.1.
Hive log shows that java.lang.NoClassDefFoundError: org/apache/spark/JavaSparkListener when I try to load data to any of the tables.
Caused by: java.lang.ClassNotFoundException: org.apache.spark.JavaSparkListener
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 19 more
What can be the issue ? These was a spark bug fix for the similar issue which says spark 2.2.0 should have the solution . My question is when I am using spark 2.2.1 then why even I am getting this issue .The requested jar should already be inside the spark core jar in the classpath .
I am trying to stream Twitter data into spark streaming. I have exported additional jars using following command -
spark-shell --jars twitter4j-core-4.0.1.jar,spark-streaming-twitter_2.11-1.6.2.jar
But at time of ssc.start() , getting following error -
scala> ssc.start()
Exception in thread "streaming-start" java.lang.NoClassDefFoundError:
twitter4j/StreamListener
at org.apache.spark.streaming.twitter.TwitterInputDStream.getReceiver(TwitterInputDStream.scala:55)
at
org.apache.spark.streaming.scheduler.ReceiverTracker$$anonfun$8.apply(ReceiverTracker.scala:415)
at
org.apache.spark.streaming.scheduler.ReceiverTracker$$anonfun$8.apply(ReceiverTracker.scala:414)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at
scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at
org.apache.spark.streaming.scheduler.ReceiverTracker.launchReceivers(ReceiverTracker.scala:414)
at
org.apache.spark.streaming.scheduler.ReceiverTracker.start(ReceiverTracker.scala:157)
at
org.apache.spark.streaming.scheduler.JobScheduler.start(JobScheduler.scala:82)
at
org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply$mcV$sp(StreamingContext.scala:610)
at
org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply(StreamingContext.scala:606)
at
org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply(StreamingContext.scala:606)
at
org.apache.spark.util.ThreadUtils$$anon$1.run(ThreadUtils.scala:122)
Caused by: java.lang.ClassNotFoundException: twitter4j.StreamListener
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 16 more
18/03/29 16:20:55 INFO StreamingContext: StreamingContext started
I am code given in following link-
https://acadgild.com/blog/streaming-twitter-data-using-spark/
Thanks..
It seems there is a missing dependency , that you need to add
please try to add this jar as a dependency , it has the class that you need
twitter4j.StreamListener
https://mvnrepository.com/artifact/org.twitter4j/twitter4j-stream/4.0.7
You will need to find the jar version which is build for your spark version and scala/java/python version
I am trying to integrate Spark 2.1 job's metrics to Ganglia.
My spark-default.conf looks like
*.sink.ganglia.class org.apache.spark.metrics.sink.GangliaSink
*.sink.ganglia.name Name
*.sink.ganglia.host $MASTERIP
*.sink.ganglia.port $PORT
*.sink.ganglia.mode unicast
*.sink.ganglia.period 10
*.sink.ganglia.unit seconds
When i submit my job i can see the warn
Warning: Ignoring non-spark config property: *.sink.ganglia.host=host
Warning: Ignoring non-spark config property: *.sink.ganglia.name=Name
Warning: Ignoring non-spark config property: *.sink.ganglia.mode=unicast
Warning: Ignoring non-spark config property: *.sink.ganglia.class=org.apache.spark.metrics.sink.GangliaSink
Warning: Ignoring non-spark config property: *.sink.ganglia.period=10
Warning: Ignoring non-spark config property: *.sink.ganglia.port=8649
Warning: Ignoring non-spark config property: *.sink.ganglia.unit=seconds
My environment details are
Hadoop : Amazon 2.7.3 - emr-5.7.0
Spark : Spark 2.1.1,
Ganglia: 3.7.2
If you have any inputs or any other alternative of Ganglia please reply.
according to the spark docs
The metrics system is configured via a configuration file that Spark expects to be present at $SPARK_HOME/conf/metrics.properties. A custom file location can be specified via the spark.metrics.conf configuration property.
so instead of having these confs in spark-default.conf, move them to $SPARK_HOME/conf/metrics.properties
For EMR specifically, you'll need to put these settings in /etc/spark/conf/metrics.properties on the master node.
Spark on EMR does include the Ganglia library:
$ ls -l /usr/lib/spark/external/lib/spark-ganglia-lgpl_*
-rw-r--r-- 1 root root 28376 Mar 22 00:43 /usr/lib/spark/external/lib/spark-ganglia-lgpl_2.11-2.3.0.jar
In addition, your example is missing the equals sign (=) between the config names and values - unsure if that's an issue. Below is an example config that worked successfully for me.
*.sink.ganglia.class=org.apache.spark.metrics.sink.GangliaSink
*.sink.ganglia.name=AMZN-EMR
*.sink.ganglia.host=$MASTERIP
*.sink.ganglia.port=8649
*.sink.ganglia.mode=unicast
*.sink.ganglia.period=10
*.sink.ganglia.unit=seconds
From this page:
https://spark.apache.org/docs/latest/monitoring.html
Spark also supports a Ganglia sink which is not included in the default build due to licensing restrictions:
GangliaSink: Sends metrics to a Ganglia node or multicast group.
**To install the GangliaSink you’ll need to perform a custom build of Spark**. Note that by embedding this library you will include LGPL-licensed code in your Spark package. For sbt users, set the SPARK_GANGLIA_LGPL environment variable before building. For Maven users, enable the -Pspark-ganglia-lgpl profile. In addition to modifying the cluster’s Spark build user
I don't know if anyone still needs this. But you have to make the full Ganglia configurations:
# Ganglia conf
*.sink.ganglia.class=org.apache.spark.metrics.sink.GangliaSink
*.sink.ganglia.name=AMZN-EMR
*.sink.ganglia.host=$MASTERIP
*.sink.ganglia.port=8649
*.sink.ganglia.mode=unicast
*.sink.ganglia.period=10
*.sink.ganglia.unit=seconds
# Enable JvmSource for instance master, worker, driver and executor
master.source.jvm.class=org.apache.spark.metrics.source.JvmSource
worker.source.jvm.class=org.apache.spark.metrics.source.JvmSource
driver.source.jvm.class=org.apache.spark.metrics.source.JvmSource
executor.source.jvm.class=org.apache.spark.metrics.source.JvmSource
Even with the full configuration, I'm running into this issue from AWS EMR 5.33.0
21/05/26 14:18:20 ERROR org.apache.spark.metrics.MetricsSystem: Source class org.apache.spark.metrics.source.JvmSource cannot be instantiated
java.lang.ClassNotFoundException: org.apache.spark.metrics.source.JvmSource
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:239)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSources$1.apply(MetricsSystem.scala:184)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSources$1.apply(MetricsSystem.scala:181)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:130)
at org.apache.spark.metrics.MetricsSystem.registerSources(MetricsSystem.scala:181)
at org.apache.spark.metrics.MetricsSystem.start(MetricsSystem.scala:102)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:528)
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
21/05/26 14:18:20 ERROR org.apache.spark.metrics.MetricsSystem: Sink class org.apache.spark.metrics.sink.GangliaSink cannot be instantiated
21/05/26 14:18:20 ERROR org.apache.spark.SparkContext: Error initializing SparkContext.
java.lang.ClassNotFoundException: org.apache.spark.metrics.sink.GangliaSink
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:239)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSinks$1.apply(MetricsSystem.scala:200)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSinks$1.apply(MetricsSystem.scala:196)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:130)
at org.apache.spark.metrics.MetricsSystem.registerSinks(MetricsSystem.scala:196)
at org.apache.spark.metrics.MetricsSystem.start(MetricsSystem.scala:104)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:528)
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
It's weird because AWS EMR should provide this dependency (org.apache.spark:spark-core_2.11:2.4.7) and I hope that the Spark distribution with AWS EMR is compiled with the Ganglia option. Forcing this jar on --packages or --jars spark options doesn't help either.
If someone manages to get Ganglia working with Spark on AWS EMR with driver/executors jvm monitoring. Please do tell me how.
I am new to Spark and Cassandra. On trying to submit a spark job, I am getting an error while connecting to Cassandra.
Details:
Versions:
Spark : 1.3.1 (build for hadoop 2.6 or later : spark-1.3.1-bin-hadoop2.6)
Cassandra : 2.0
Spark-Cassandra-Connector: 1.3.0-M1
scala : 2.10.5
Spark and Cassandra is on a virtual cluster
Cluster details:
Spark Master : 192.168.101.13
Spark Slaves : 192.168.101.11 and 192.168.101.12
Cassandra Nodes: 192.168.101.11 (seed node) and 192.168.101.12
I am trying to submit a job through my client machine (laptop) - 172.16.0.6.
After googling for this error, I have made sure that I can ping all the machines on the cluster from the client machine : spark master/slaves and cassandra nodes and also disabled the firewall on all machines. But I am
still struggling with this error.
Cassandra.yaml
listen_address: 192.168.101.11 (192.168.101.12 on other cassandra node)
start_native_transport: true
native_transport_port: 9042
start_rpc: true
rpc_address: 192.168.101.11 (192.168.101.12 on other cassandra node)
rpc_port: 9160
I am trying to run a minimal sample job
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import com.datastax.spark.connector._
val rdd = sc.cassandraTable("test", "words")
rdd.toArray.foreach(println)
To submit the job, I use spark-shell (:paste the code in spark shell):
spark-shell --jars "/home/ameya/.m2/repository/com/datastax/spark/spark-cassandra-connector_2.10/1.3.0-M1/spark-cassandra-connector_2.10-1.3.0-M1.jar","/home/ameya/.m2/repository/com/datastax/cassandra/cassandra-driver-core/2.1.5/cassandra-driver-core-2.1.5.jar","/home/ameya/.m2/repository/com/google/collections/google-collections/1.0/google-collections-1.0.jar","/home/ameya/.m2/repository/io/netty/netty/3.8.0.Final/netty-3.8.0.Final.jar","/home/ameya/.m2/repository/com/google/guava/guava/14.0.1/guava-14.0.1.jar","/home/ameya/.m2/repository/io/dropwizard/metrics/metrics-core/3.1.0/metrics-core-3.1.0.jar","/home/ameya/.m2/repository/org/slf4j/slf4j-api/1.7.10/slf4j-api-1.7.10.jar","/home/ameya/.m2/repository/com/google/collections/google-collections/1.0/google-collections-1.0.jar","/home/ameya/.m2/repository/io/netty/netty/3.8.0.Final/netty-3.8.0.Final.jar","/home/ameya/.m2/repository/com/google/guava/guava/14.0.1/guava-14.0.1.jar","/home/ameya/.m2/repository/org/apache/cassandra/cassandra-clientutil/2.1.5/cassandra-clientutil-2.1.5.jar","/home/ameya/.m2/repository/joda-time/joda-time/2.3/joda-time-2.3.jar","/home/ameya/.m2/repository/org/apache/cassandra/cassandra-thrift/2.1.3/cassandra-thrift-2.1.3.jar","/home/ameya/.m2/repository/org/joda/joda-convert/1.2/joda-convert-1.2.jar","/home/ameya/.m2/repository/org/apache/thrift/libthrift/0.9.2/libthrift-0.9.2.jar","/home/ameya/.m2/repository/org/apache/thrift/libthrift/0.9.2/libthrift-0.9.2.jar" --master spark://192.168.101.13:7077 --conf spark.cassandra.connection.host=192.168.101.11 --conf spark.cassandra.auth.username=cassandra --conf spark.cassandra.auth.password=cassandra
The error I am getting:
warning: there were 1 deprecation warning(s); re-run with -deprecation for details
**java.io.IOException: Failed to open native connection to Cassandra at {192.168.101.11}:9042**
at com.datastax.spark.connector.cql.CassandraConnector$.com$datastax$spark$connector$cql$CassandraConnector$$createSession(CassandraConnector.scala:181)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$2.apply(CassandraConnector.scala:167)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$2.apply(CassandraConnector.scala:167)
at com.datastax.spark.connector.cql.RefCountedCache.createNewValueAndKeys(RefCountedCache.scala:31)
at com.datastax.spark.connector.cql.RefCountedCache.acquire(RefCountedCache.scala:56)
at com.datastax.spark.connector.cql.CassandraConnector.openSession(CassandraConnector.scala:76)
at com.datastax.spark.connector.cql.CassandraConnector.withSessionDo(CassandraConnector.scala:104)
at com.datastax.spark.connector.cql.CassandraConnector.withClusterDo(CassandraConnector.scala:115)
at com.datastax.spark.connector.cql.Schema$.fromCassandra(Schema.scala:243)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.tableDef(CassandraTableRowReaderProvider.scala:49)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef$lzycompute(CassandraTableScanRDD.scala:59)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef(CassandraTableScanRDD.scala:59)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.verify(CassandraTableRowReaderProvider.scala:148)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.verify(CassandraTableScanRDD.scala:59)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.getPartitions(CassandraTableScanRDD.scala:118)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1512)
at org.apache.spark.rdd.RDD.collect(RDD.scala:813)
at org.apache.spark.rdd.RDD.toArray(RDD.scala:833)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:38)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:40)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:42)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:44)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:46)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:48)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:50)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:52)
at $iwC$$iwC$$iwC.<init>(<console>:54)
at $iwC$$iwC.<init>(<console>:56)
at $iwC.<init>(<console>:58)
at <init>(<console>:60)
at .<init>(<console>:64)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:856)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:901)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:813)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:656)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:664)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:669)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:996)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:944)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1058)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
**Caused by: com.datastax.driver.core.exceptions.NoHostAvailableException: All host(s) tried for query failed (tried: /192.168.101.11:9042 (com.datastax.driver.core.TransportException: [/192.168.101.11:9042] Connection has been closed))**
at com.datastax.driver.core.ControlConnection.reconnectInternal(ControlConnection.java:223)
at com.datastax.driver.core.ControlConnection.connect(ControlConnection.java:78)
at com.datastax.driver.core.Cluster$Manager.init(Cluster.java:1236)
at com.datastax.driver.core.Cluster.getMetadata(Cluster.java:333)
at com.datastax.spark.connector.cql.CassandraConnector$.com$datastax$spark$connector$cql$CassandraConnector$$createSession(CassandraConnector.scala:174)
... 71 more
Can anyone point out what am I doing wrong here ?
you did not specified spark.cassandra.connection.host by default spark assume that cassandra host is same as spark master node.
var sc:SparkContext=_
val conf = new SparkConf().setAppName("Cassandra Demo").setMaster(master)
.set("spark.cassandra.connection.host", "192.168.101.11")
c=new SparkContext(conf)
val rdd = sc.cassandraTable("test", "words")
rdd.toArray.foreach(println)
it should work if you have properly set seed nodein cassandra.yaml
I struggled with this issue overnight, and finally got a combination that works. I am writing it down for those who may run into similar issue.
First of all, this is a version issue cassandra-driver-core's dependency. But to track down the exact combination that works takes me quite a bit time.
Secondly, this is the combination that works for me.
Spark 1.6.2 with Hadoop 2.6, cassandra 2.1.5 (Ubuntu 14.04, Java 1.8),
In built.sbt (sbt assembly, scalaVersion := "2.10.5"), use
"com.datastax.spark" %% "spark-cassandra-connector" % "1.4.0",
"com.datastax.cassandra" % "cassandra-driver-core" % "2.1.5"
Thirdly, let me clarify my frustrations. With spark-cassandra-connector 1.5.0, I can run the assembly with spark-submit with --master "local[2]" on the same machine with remote cassandra connection without any problem. Any combination of connector 1.5.0, 1.6.0 with Cassandra 2.0, 2.1, 2.2, 3,4 works well. But if I try to submit the job to a cluster from the same machine (NodeManager) with --master yarn --deploy-mode cluster, then I will always run into the problem: Failed to open native connection to Cassandra at {192.168.122.12}:9042
What is going on here? Any from DataStarX can take a look at this issue? I can only guess it has something to do with "cqlversion", which should match the version of Cassandra cluster.
Anybody know a better solution? [cassandra], [apache-spark]
It's worked finally :
steps :
set listen_address to private IP of EC2 instance.
do not set any broadcast_address
set rpc_address to 0.0.0.0
set broadcast_rpc_address to public ip of EC2 instance.
The issue resolved. It was due to some mess up with the dependencies. I built a jar with dependencies and passed it to spark-submit, instead of specifying dependent jars separately.
This is an issue with version of the cassandra-driver-core jar's dependency.
The provided cassandra's version is 2.0
The provided cassandra-driver-core jar's version is 2.1.5
The jar should be the same as the version of the cassandra running.
In this case, the included jar file should be cassandra-driver-core-2.0.0.jar