I am running spark streaming in yarn cluster mode and i want to capture logs and write it in driver local file for this I have created custom log4j.properties files in which I have mentioned driver's local file path but I can only see drivers logs in this file, Why my executors logs are not captured in this file and how can I capture executor log. I have tried different approaches and my spark-submit command is as follows:-
spark-submit --master yarn --deploy-mode yarn-cluster
--conf "spark.driver.extraJavaOptions=-Dlog4j.configuration=file:/home/log/conf/log4j.properties"
--conf "spark.executor.extraJavaOptions=-Dlog4j.configuration=file:/home/log/conf/log4j.properties" --class com.Word.count.SparkStream /home/project/WordCount/target/Count-0.0.1-SNAPSHOT.jar
you may post your log4j.properties.I assume that you can see executor logs in executor node local dir
Related
I am trying to run the spark-submit command on my Hadoop cluster
Here is a summary of my Hadoop Cluster:
The cluster is built using 5 VirtualBox VM's connected on an internal network
There is 1 namenode and 4 datanodes created.
All the VM's were built from the Bitnami Hadoop Stack VirtualBox image
When I run the following command:
spark-submit --class org.apache.spark.examples.SparkPi $SPARK_HOME/examples/jars/spark-examples_2.12-3.0.3.jar 10
I receive the following error:
java.io.FileNotFoundException: File file:/home/bitnami/sparkStaging/bitnami/.sparkStaging/application_1658417340986_0002/__spark_conf__.zip does not exist
I also get a similar error when trying to create a sparkSession using PySpark:
spark = SparkSession.builder.appName('appName').getOrCreate()
I have tried/verified the following
environment variables: HADOOP_HOME, SPARK_HOME AND HADOOP_CONF_DIR have been set in my .bashrc file
SPARK_DIST_CLASSPATH and HADOOP_CONF_DIR have been defined in spark-env.sh
Added spark.master yarn, spark.yarn.stagingDir file:///home/bitnami/sparkStaging and spark.yarn.jars file:///opt/bitnami/hadoop/spark/jars/ in spark-defaults.conf
I believe spark.yarn.stagingDir needs to be an HDFS path.
More specifically, the "YARN Staging directory" needs to be available on all Spark executors, not just a local file path from where you run spark-submit
The path that isn't found is being reported from the YARN cluster, where /home/bitnami might not exist, or the Unix user running the Spark executor containers does not have access to that path.
Similarly, spark.yarn.jars (or spark.yarn.archive) should be HDFS paths because these will get downloaded, in parallel, across all executors.
Since the spark job is supposed to be submitted to the Hadoop cluster managed by YARN, master and deploy-mode has to be set. From the spark 3.3.0 docs:
# Run on a YARN cluster in cluster deploy mode
export HADOOP_CONF_DIR=XXX
./bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode cluster \
--executor-memory 20G \
--num-executors 50 \
/path/to/examples.jar \
1000
Or programatically:
spark = SparkSession.builder().appName('appName').master("yarn").config("spark.submit.deployMode","cluster").getOrCreate()
I'm using Spark on YARN with
Ambari 2.7.4
HDP Standalone 3.1.4
Spark 2.3.2
Hadoop 3.1.1
Graphite on Docker latest
I was trying to get Spark metrics with Graphite sink following this tutorial.
Advanced spark2-metrics-properties in Ambari are:
driver.sink.graphite.class=org.apache.spark.metrics.sink.GraphiteSink
executor.sink.graphite.class=org.apache.spark.metrics.sink.GraphiteSink
worker.sink.graphite.class=org.apache.spark.metrics.sink.GraphiteSink
master.sink.graphite.class=org.apache.spark.metrics.sink.GraphiteSink
*.sink.graphite.host=ap-test-m.c.gcp-ps.internal
*.sink.graphite.port=2003
*.sink.graphite.protocol=tcp
*.sink.graphite.period=10
*.sink.graphite.unit=seconds
*.sink.graphite.prefix=app-test
*.source.jvm.class=org.apache.spark.metrics.source.JvmSource
Spark submit:
export HADOOP_CONF_DIR=/usr/hdp/3.1.4.0-315/hadoop/conf/; spark-submit --class com.Main --master yarn --deploy-mode client --driver-memory 1g --executor-memory 10g --num-executors 2 --executor-cores 2 spark-app.jar /data
As a result I'm only getting driver metrics.
Also, I was trying to add metrics.properties to spark-submit command together with global spark metrics props, but that didn't help.
And finally, I tried conf in spark-submit and in java SparkConf:
--conf "spark.metrics.conf.driver.sink.graphite.class"="org.apache.spark.metrics.sink.GraphiteSink"
--conf "spark.metrics.conf.executor.sink.graphite.class"="org.apache.spark.metrics.sink.GraphiteSink"
--conf "worker.sink.graphite.class"="org.apache.spark.metrics.sink.GraphiteSink"
--conf "master.sink.graphite.class"="org.apache.spark.metrics.sink.GraphiteSink"
--conf "spark.metrics.conf.*.sink.graphite.host"="host"
--conf "spark.metrics.conf.*.sink.graphite.port"=2003
--conf "spark.metrics.conf.*.sink.graphite.period"=10
--conf "spark.metrics.conf.*.sink.graphite.unit"=seconds
--conf "spark.metrics.conf.*.sink.graphite.prefix"="app-test"
--conf "spark.metrics.conf.*.source.jvm.class"="org.apache.spark.metrics.source.JvmSource"
But that didn't help either.
CSVSink also gives only driver metrics.
UPD
When I submit job in cluster mode - I'm getting the same metrics as in Spark History Server. But the jvm metrics are still absent.
Posting to a dated question, but maybe it will help.
Seems like executors do not have metrics.properties file on their filesystems.
One way to confirm this would be to look at the executor logs:
2020-01-16 10:00:10 ERROR MetricsConfig:91 - Error loading configuration file metrics.properties
java.io.FileNotFoundException: metrics.properties (No such file or directory)
at org.apache.spark.metrics.MetricsConfig.loadPropertiesFromFile(MetricsConfig.scala:132)
at org.apache.spark.metrics.MetricsConfig.initialize(MetricsConfig.scala:55)
at org.apache.spark.metrics.MetricsSystem.<init>(MetricsSystem.scala:95)
at org.apache.spark.metrics.MetricsSystem$.createMetricsSystem(MetricsSystem.scala:233)
To fix this on yarn pass two parameters to spark-submit:
$ spark-submit \
--files metrics.properties \
--conf spark.metrics.conf=metrics.properties
The --files option ensures that files specified in the option will be shared to executors.
The spark.metrics.conf option specifies a custom file location for the metrics.
Another way to fix the issue would be to place the metrics.properties file into $SPARK_HOME/conf/metrics.properties on both the driver and executor before starting the job.
More on metrics here: https://spark.apache.org/docs/latest/monitoring.html
I launched dispatcher as follows and the launch was successful as seen from the logs
./sbin/start-mesos-dispatcher.sh --master mesos://10.0.0.6:5050
Rest server was activated on port 7078
I submitted the job to the dispatcher as follows
./bin/spark-submit \
--class com.ibm.cds.spark.samples.HelloSpark \
--master mesos://10.0.0.6:7078 \
--deploy-mode cluster \
--verbose \
https://github.com/../helloSpark.jar
On the spark slave, I get the following error in mesos agent sandbox - stderr.
17/11/22 09:22:06 INFO RestSubmissionClient: Submitting a request to launch an application in mesos://10.0.0.6:5050.
Exception in thread "main" org.apache.spark.deploy.rest.SubmitRestProtocolException: Malformed response received from server
at org.apache.spark.deploy.rest.RestSubmissionClient.readResponse(RestSubmissionClient.scala:268)
at org.apache.spark.deploy.rest.RestSubmissionClient.org$apache$spark$deploy$rest$RestSubmissionClient$$postJson(RestSubmissionClient.s
Question:
why is the executor submitting the launch of application to mesos-master? In spark-submit (above), I clearly give spark master address (at port 7078). why is this not taken?
How can I avoid this error?
using mesos version 1.4.1
removed all entries in spark-defaults.conf except the below.
spark.eventLog.enabled true
It works fine now, meaning, I dont get this error.
looks like having a spark.master called out in spark-defaults.conf was causing this issue.
I have Java Spark job that works on manually deployed Spark 1.6.0 in standalone mode on an EC2.
I am spark-submitting this job to a EMR 5.3.0 cluster on the master using YARN but it fails.
Spark-submit line is,
spark-submit --class <startclass> --master yarn --queue default --deploy-mode cluster --conf spark.eventLog.enabled=true --conf spark.eventLog.dir=hdfs://`hostname -f`:8020/tmp/ourSparkLogs --driver-memory 4G --executor-memory 4G --executor-cores 2 hdfs://`hostname -f`:8020/data/x.jar yarn-client
The "yarn-client" is the first argument to the x.jar application and is fed to the SparkContext as setMaster,
conf.setMaster(args[0]);
When I submit it, it starts out running fine, until I initialize the JavaSparkContext from a SparkConf,
JavaSparkContext sc = new JavaSparkContext(conf);
... and then Spark crashes.
In the YARN log, I can see the following,
yarn logs -applicationId application_1487325147456_0051
...
17/02/17 16:27:13 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
17/02/17 16:27:13 INFO Client: Deleted staging directory hdfs://ip-172-31-8-237.eu-west-1.compute.internal:8020/user/ec2-user/.sparkStaging/application_1487325147456_0052
17/02/17 16:27:13 ERROR SparkContext: Error initializing SparkContext.
java.lang.IllegalStateException: Library directory '/mnt/yarn/usercache/ec2-user/appcache/application_1487325147456_0051/container_1487325147456_0051_01_000001/assembly/target/scala-2.11/jars' does not exist; make sure Spark is built.
...
Noting the WARN of spark.yarn.jars flag missing, I found a spark yarn JAR file in
/usr/lib/spark/jars/
... and uploaded it to HDFS per Cloudera's guide on how to run YARN applications on Spark and tried to add that conf, so this became my spark-submit line,
spark-submit --class <startclass> --master yarn --queue default --deploy-mode cluster --conf spark.eventLog.enabled=true --conf spark.eventLog.dir=hdfs://`hostname -f`:8020/tmp/ourSparkLogs --conf spark.yarn.jars=hdfs://`hostname -f`:8020/sparkyarnlibs/spark-yarn_2.11-2.1.0.jar --driver-memory 4G --executor-memory 4G --executor-cores 2 hdfs://`hostname -f`:8020/data/x.jar yarn-client
But that did not work and gave this:
Could not find or load main class org.apache.spark.deploy.yarn.ApplicationMaster
I am really puzzled as to what that Library error is caused by and how to proceed onwards from here.
You have specified "--deploy-mode cluster" and yet are calling conf.setMaster("yarn-client") from the code. Using a master URL of "yarn-client" means "use YARN as the master, and use client mode (not cluster mode)", so I wouldn't be surprised if this is somehow confusing Spark because on one hand you're telling it to use cluster mode and on the other you're telling it to use client mode.
By the way, using a master URL like "yarn-client" or "yarn-cluster" is actually deprecated because the "-client" or "-cluster" part is not really part of the Master but rather is the deploy mode. That is, "--master yarn-client" is really more of a shortcut/alias for "--master yarn --deploy-mode client", and similarly "--master yarn-cluster" just means "--master yarn --deploy-mode cluster".
My recommendation would be to not call conf.setMaster() from your code, since the master is already set to "yarn" automatically in /etc/spark/conf/spark-defaults.conf. For this reason, you also don't need to pass "--master yarn" to spark-submit.
Lastly, it sounds like you need to decide whether you really want to use client deploy mode or cluster deploy mode. With client deploy mode, the driver runs on the master instance, and with cluster deploy mode, the driver runs in a YARN container on one of the core/task instances. See https://spark.apache.org/docs/latest/running-on-yarn.html for more information.
If you want to use client deploy mode, you don't need to pass anything extra because it's already the default. If you want to use cluster deploy mode, pass "--deploy-mode cluster".
I'm trying to submit a Spark app from local machine Terminal to my Cluster. I'm using --master yarn-cluster. I need to run the driver program on my Cluster too, not on the machine I do submit the application i.e my local machine
I'm using
bin/spark-submit
--class com.my.application.XApp
--master yarn-cluster --executor-memory 100m
--num-executors 50 hdfs://name.node.server:8020/user/root/x-service-1.0.0-201512141101-assembly.jar
1000
and getting error
Diagnostics: java.io.FileNotFoundException: File
file:/Users/nish1013/Dev/spark-1.4.1-bin-hadoop2.6/lib/spark-assembly-1.4.1-hadoop2.6.0.jar
does not exist
I can see in my service list ,
YARN + MapReduce2 2.7.1.2.3 Apache Hadoop NextGen MapReduce (YARN)
Spark 1.4.1.2.3 Apache Spark is a fast and general engine for
large-scale data processing.
already installed.
My spark-env.sh in local machine
export HADOOP_CONF_DIR=/Users/nish1013/Dev/hadoop-2.7.1/etc/hadoop
Has anyone encountered similar before ?
I think the right command to call is like following:
bin/spark-submit
--class com.my.application.XApp
--master yarn-cluster --executor-memory 100m
--num-executors 50 --conf spark.yarn.jars=hdfs://name.node.server:8020/user/root/x-service-1.0.0-201512141101-assembly.jar
1000
or you can add
spark.yarn.jars hdfs://name.node.server:8020/user/root/x-service-1.0.0-201512141101-assembly.jar
in your spark.default.conf file