My spark program on EMR is constantly getting this error:
Caused by: javax.net.ssl.SSLPeerUnverifiedException: peer not authenticated
at sun.security.ssl.SSLSessionImpl.getPeerCertificates(SSLSessionImpl.java:421)
at org.apache.http.conn.ssl.AbstractVerifier.verify(AbstractVerifier.java:128)
at org.apache.http.conn.ssl.SSLSocketFactory.connectSocket(SSLSocketFactory.java:397)
at org.apache.http.impl.conn.DefaultClientConnectionOperator.openConnection(DefaultClientConnectionOperator.java:148)
at org.apache.http.impl.conn.AbstractPoolEntry.open(AbstractPoolEntry.java:149)
at org.apache.http.impl.conn.AbstractPooledConnAdapter.open(AbstractPooledConnAdapter.java:121)
at org.apache.http.impl.client.DefaultRequestDirector.tryConnect(DefaultRequestDirector.java:573)
at org.apache.http.impl.client.DefaultRequestDirector.execute(DefaultRequestDirector.java:425)
at org.apache.http.impl.client.AbstractHttpClient.execute(AbstractHttpClient.java:820)
at org.apache.http.impl.client.AbstractHttpClient.execute(AbstractHttpClient.java:754)
at org.jets3t.service.impl.rest.httpclient.RestStorageService.performRequest(RestStorageService.java:334)
at org.jets3t.service.impl.rest.httpclient.RestStorageService.performRequest(RestStorageService.java:281)
at org.jets3t.service.impl.rest.httpclient.RestStorageService.performRestHead(RestStorageService.java:942)
at org.jets3t.service.impl.rest.httpclient.RestStorageService.getObjectImpl(RestStorageService.java:2148)
at org.jets3t.service.impl.rest.httpclient.RestStorageService.getObjectDetailsImpl(RestStorageService.java:2075)
at org.jets3t.service.StorageService.getObjectDetails(StorageService.java:1093)
at org.jets3t.service.StorageService.getObjectDetails(StorageService.java:548)
at org.apache.hadoop.fs.s3native.Jets3tNativeFileSystemStore.retrieveMetadata(Jets3tNativeFileSystemStore.java:172)
at sun.reflect.GeneratedMethodAccessor18.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:190)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:103)
at org.apache.hadoop.fs.s3native.$Proxy8.retrieveMetadata(Unknown Source)
at org.apache.hadoop.fs.s3native.NativeS3FileSystem.getFileStatus(NativeS3FileSystem.java:414)
at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1398)
at org.apache.hadoop.fs.s3native.NativeS3FileSystem.create(NativeS3FileSystem.java:341)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:906)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:887)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:784)
I did some research and found out that this authentication can be disabled in low-security situation, by setting environment variable:
com.amazonaws.sdk.disableCertChecking=true
but I can only set it with spark-submit.sh --conf, which only affects driver, while most of the errors are on workers.
Is there a way to propagate them to workers?
Thanks a lot.
Spark Configuration Spark properties control most application parameters and can be set by using a SparkConf object, or through Java system properties. Environment variables can be used to set per-machine settings, such as the IP address, through the conf/spark-env.sh script on each node.
If cluster/application is not enabled dynamic allocation and if you set --conf spark. executor. instances=1 then it will launch only 1 executor. Apart from executor, you will see AM/driver in the Executor tab Spark UI.
Determine the memory resources available for the Spark application. Multiply the cluster RAM size by the YARN utilization percentage. Provides 5 GB RAM for available drivers and 50 GB RAM available for worker nodes. Discount 1 core per worker node to determine the executor core instances.
The number of executors for a spark application can be specified inside the SparkConf or via the flag –num-executors from command-line. Cluster Manager : An external service for acquiring resources on the cluster (e.g. standalone manager, Mesos, YARN).
For spark 2.4 , @Amit Kushwaha 's method doesn't work.
I have tested:
spark-submit --conf spark.executorEnv.DEBUG=1 --conf spark.appMasterEnv.DEBUG=1 --conf spark.yarn.appMasterEnv.DEBUG=1 --conf spark.yarn.executorEnv.DEBUG=1 main.py
spark-submit --deploy-mode=client --conf spark.executorEnv.DEBUG=1 --conf spark.appMasterEnv.DEBUG=1 --conf spark.yarn.appMasterEnv.DEBUG=1 --conf spark.yarn.executorEnv.DEBUG=1 main.py
None of above can set enviroment variable into executor system(aka. can not read by os.environ.get('DEBUG')
) .
submit:
spark-submit --conf DEBUG=1 main.py
get variable:
DEBUG = spark.conf.get('DEBUG')
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