I'm running a job on a local Spark cluster (pyspark). When I run it with a small dataset it works fine, but once it's large, I get an error. I'm wondering 1. How to find logs from the scheduler process that appears to be crashing, and 2. more generally, what might be going on and how to debug the problem. Thanks in advance. Happy to provide more info.
Here's the error (from what I understand to be the driver logs):
block-manager-ask-thread-pool-224 ERROR BlockManagerMasterEndpoint: Fail to know th
e executor driver is alive or not.
org.apache.spark.SparkException: Exception thrown in awaitResult:
at...
...
<stacktrace>
...
Caused by: org.apache.spark.rpc.RpcEndpointNotFoundException: Cannot find endpoint: spark://CoarseGrainedScheduler#<host:port>
and then immediately below that
block-manager-ask-thread-pool-224 WARN BlockManagerMasterEndpoint: Error trying to remove shuffle 25. The executor driver may have been lost.
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply from <host:port>
What I know about my job... I'm using Pyspark and running Spark standalone, using a local cluster with 72 workers (the machine has 96 cores). Here's my config:
spark:
master: "local[72]"
files:
maxPartitionBytes: 67108864
sql:
files:
maxPartitionBytes: 67108864
driver:
memory: "50g"
maxResultSize: "2g"
supervise: true
cores: 72
log:
dfsDir: <my/logs/dir>
persistToDfs:
enabled: true
loglevel: "WARN"
logConf: true
I've set SPARK_LOG_DIR and SPARK_WORKER_LOG_DIR to attempt to see scheduler logs, but I still only see driver (worker?) logs as far as I can tell, with the above error. I'm monitoring memory usage and it doesn't seem like my machine is memory-constrained, but I can't be sure I'm checking at the right moments. The machine has about 1TB of memory and tens of terabytes of free disk space.
Thanks in advance!
Related
When I submit my spark program, it fails at the end but with a ExitCode:0 as shown in the picture.
The program should write a table on hive and despite the failure, the table was created successfully.
But I can't figure out the origin of the error. Can you help please.
Yarn logs -appID gives the following output here
I finally solved my problem. In fact today I suprisingly got another error from the same program saying
ERROR CoarseGrainedExecutorBackend: Executor self-exiting due to : Driver X:37478 disassociated! Shutting down.
And I found so many solutions talking about memory or timeOut.
What did the trick is just that I forgot to close my sparkSession (spark.close()).
I am getting some transient excepting in using spark-streaming with Amazon Kinesis with storage level "MEMORY_AND_DISK_2". We are using Spark 2.2.0 with emr-5.9.0.
19/05/22 01:56:16 ERROR TransportRequestHandler: Error opening block StreamChunkId{streamId=438690479801, chunkIndex=0} for request from /10.1.100.56:38074
org.apache.spark.storage.BlockNotFoundException: Block broadcast_13287_piece0 not found
I have checked that are no lost nodes in EMR cluster. And HDFS utilization percentage is 35%
When attempting to call H2OContext.getOrCreate with a valid SparkContext, randomly we keep seeing failures to deploy:
17/04/21 17:21:32 ERROR TaskSchedulerImpl: Lost executor 0 on 172.17.0.4: Remote RPC client disassociated. Likely due to containers exceeding thresholds, or network issues. Check driver logs for WARN messages.
17/04/21 17:21:38 ERROR LiveListenerBus: Listener ExecutorAddNotSupportedListener threw an exception
java.lang.IllegalArgumentException: Executor without H2O instance discovered, killing the cloud!
at org.apache.spark.listeners.ExecutorAddNotSupportedListener.onExecutorAdded(H2OSparkListener.scala:27)
at org.apache.spark.scheduler.SparkListenerBus$class.doPostEvent(SparkListenerBus.scala:61)
at org.apache.spark.scheduler.LiveListenerBus.doPostEvent(LiveListenerBus.scala:36)
at org.apache.spark.scheduler.LiveListenerBus.doPostEvent(LiveListenerBus.scala:36)
at org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:63)
at org.apache.spark.scheduler.LiveListenerBus.postToAll(LiveListenerBus.scala:36)
at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(LiveListenerBus.scala:94)
at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:79)
at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:79)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(LiveListenerBus.scala:78)
at org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1252)
at org.apache.spark.scheduler.LiveListenerBus$$anon$1.run(LiveListenerBus.scala:77)
The H2OContext.getOrCreate causes the error:
Context.spark_session = SparkSession.builder.getOrCreate()
Context.h2o_context = H2OContext.getOrCreate(Context.spark_session)
Any thoughts from the H2O Crew?
this is a known behaviour of Sparkling Water internal backend at the moment. To avoid this, the external Sparkling Water backend can be used. More information about this can be found here https://github.com/h2oai/sparkling-water/blob/master/doc/backends.md
I'm currently working on this JIRA which should eliminate the behaviour above as well. It's work in progress, this JIRA https://0xdata.atlassian.net/browse/SW-369 can be tracked to get the status of the task.
I'm new to spark and I'm trying to develop my first application. I'm only trying to count lines in a file but I got the error:
2015-11-28 10:21:34 WARN TaskSchedulerImpl:71 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
I have enough cores and enough memory. I read that can be a firewall problem but I'm getting this error both on my server and on my macbook and for sure on the macbook there is no firewall. If I open the UI it says that the application is WAITING and apparently the application is getting no cores at all:
Application ID Name Cores Memory per Node State
app-20151128102116-0002 (kill) New app 0 1024.0 MB WAITING
My code is very simple:
SparkConf sparkConf = new SparkConf().setAppName(new String("New app"));
sparkConf.setMaster("spark://MacBook-Air.local:7077");
JavaRDD<String> textFile = sc.textFile("/Users/mattiazeni/Desktop/test.csv.bz2");
if(logger.isInfoEnabled()){
logger.info(textFile.count());
}
if I try to run the same program from the shell in scala it works great.
Any suggestion?
Check that workers are running - there should be at least one worker listed on the Spark UI at http://:8080
If not are running, use /sbin/start-slaves.sh (or start-all.sh)
I've the follow errors/warns:
1) WARN AkkaRpcEndpointRef: Error sending message [message = Heartbeat(2,[Lscala.Tuple2;#58149ee3,BlockManagerId(2, 192.168.0.171, 49714))] in 1 attempts
java.util.concurrent.TimeoutException: Futures timed out after [120 seconds]
2) ERROR CoarseGrainedExecutorBackend: Driver 192.168.0.131:41837 disassociated! Shutting down.
I'm running a Spark (v. 1.4.0) app in a cluster of 4 machines in which the driver has less memory (4 GB) of the workers (8 Gb each one). Is it possible that the driver produces the error due to its workload?
The driver was not able to respond to the executors since it was under stress during the computation.
The problem was solved simply by adding mroe RAM to the driver.