Adding to other answers. Is it necessary that spark is installed on all the nodes in the yarn cluster? No, If the spark job is scheduling in YARN(either client or cluster mode). Spark installation is needed in many nodes only for standalone mode. These are the visualizations of spark app deployment modes. Spark Standalone Cluster … Read more
There are two settings that control the number of retries (i.e. the maximum number of ApplicationMaster registration attempts with YARN is considered failed and hence the entire Spark application): spark.yarn.maxAppAttempts – Spark’s own setting. See MAX_APP_ATTEMPTS: private[spark] val MAX_APP_ATTEMPTS = ConfigBuilder(“spark.yarn.maxAppAttempts”) .doc(“Maximum number of AM attempts before failing the app.”) .intConf .createOptional yarn.resourcemanager.am.max-attempts – YARN’s … Read more
In Spark 2.0+ version use spark session variable to set number of executors dynamically (from within program) spark.conf.set(“spark.executor.instances”, 4) spark.conf.set(“spark.executor.cores”, 4) In above case maximum 16 tasks will be executed at any given time. other option is dynamic allocation of executors as below – spark.conf.set(“spark.dynamicAllocation.enabled”, “true”) spark.conf.set(“spark.executor.cores”, 4) spark.conf.set(“spark.dynamicAllocation.minExecutors”,”1″) spark.conf.set(“spark.dynamicAllocation.maxExecutors”,”5″) This was you can let … Read more
It represents a resource (memory) on a single node at a given cluster. A container is supervised by the node manager scheduled by the resource manager One MR task runs in such container(s).
I’m beginning to suspect that stack memory (unlike the JVM heap) seems to be precommitted without becoming resident and over time becomes resident only up to the high water mark of actual stack usage. Yes, at least on linux mmap is lazy unless told otherwise. Anonymous pages are only backed by physical memory once they’re … Read more
This error is almost guaranteed to be caused by memory issues on your executors. I can think of a couple of ways to address these types of problems. 1) You could try to run with more partitions (do a repartition on your dataframe). Memory issues typically arise when one or more partitions contain more data … Read more
mapreduce.map.memory.mb is the upper memory limit that Hadoop allows to be allocated to a mapper, in megabytes. The default is 512. If this limit is exceeded, Hadoop will kill the mapper with an error like this: Container[pid=container_1406552545451_0009_01_000002,containerID=container_234132_0001_01_000001] is running beyond physical memory limits. Current usage: 569.1 MB of 512 MB physical memory used; 970.1 MB … Read more
So in spark you have two different components. There is the driver and the workers. In yarn-cluster mode the driver is running remotely on a data node and the workers are running on separate data nodes. In yarn-client mode the driver is on the machine that started the job and the workers are on the … Read more
I had a very similar problem. I had many executors being lost no matter how much memory we allocated to them. The solution if you’re using yarn was to set –conf spark.yarn.executor.memoryOverhead=600, alternatively if your cluster uses mesos you can try –conf spark.mesos.executor.memoryOverhead=600 instead. In spark 2.3.1+ the configuration option is now –conf spark.yarn.executor.memoryOverhead=600 It … Read more