spark executor memory vs jvm memory

December 12, 2020   |   

512m, 2g). Memory for each executor: From above step, we have 3 executors per node. An executor is the Spark application’s JVM process launched on a worker node. Before analysing each case, let us consider the executor. It runs tasks in threads and is responsible for keeping relevant partitions of data. The formula for that overhead is max(384, .07 * spark.executor.memory) 0.7.0: spark.executor.pyspark.memory: Not set: The amount of memory to be allocated to PySpark in each executor, in MiB unless otherwise specified. In this case, the total of Spark executor instance memory plus memory overhead is not enough to handle memory-intensive operations. Memory-intensive operations include caching, shuffling, and aggregating (using reduceByKey, groupBy, and so on). PySpark should probably use spark.executor.pyspark.memory to limit or default the setting of spark.python.worker.memory because the latter property controls spilling and should be lower than the total memory limit. When the Spark executor’s physical memory exceeds the memory allocated by YARN. However small overhead memory is also needed to determine the full memory request to YARN for each executor. spark.driver.memory + spark.yarn.driver.memoryOverhead = the memory that YARN will create a JVM = 11g + (driverMemory * 0.07, with minimum of 384m) = 11g + 1.154g = 12.154g So, from the formula, I can see that my job requires MEMORY_TOTAL of around 12.154g to run successfully which explains why I need more than 10g for the driver memory setting. Now I would like to set executor memory or driver memory for performance tuning. It sets the overall amount of heap memory to use for the executor. Every spark application has same fixed heap size and fixed number of cores for a spark executor. So memory for each executor in each node is 63/3 = 21GB. spark.executor.memory: 1g: Amount of memory to use per executor process, in the same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. And available RAM on each node is 63 GB. Each process has an allocated heap with available memory (executor/driver). From the Spark documentation , the definition for executor memory is Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. This information will help provide insight into how executor and driver JVM memory is used, and for the different memory regions. The JVM has executor memory and spark memory (controlled by spark.memory.fraction), so these settings create something similar: total python memory and the threshold above which PySpark will spill to disk. In my Spark UI "Environment" tab it was set to 22776m on a "30 GB" worker in a cluster set up via Databricks. It can be used to help determine good values for spark.executor.memory, spark.driver.memory, spark.memory.fraction, and spark.memory.storageFraction. In this case, you need to configure spark.yarn.executor.memoryOverhead to … The remaining 40% of memory is available for any objects created during task execution. The heap size is what referred to as the Spark executor memory which is controlled with the spark.executor.memory property of the –executor-memory flag. Overhead memory is the off-heap memory used for JVM overheads, interned strings, and other metadata in the JVM. 512m, 2g). --num-executors vs --executor-memory; There are tradeoffs between num-executors and executor-memory: Large executor memory does not imply better performance, due to JVM garbage collection. I think that means the spill setting should have a better name and should be limited by the total memory. Every spark application will have one executor on each worker node. Sometimes it is better to configure a larger number of small JVMs than a small number of large JVMs. By default, Spark uses 60% of the configured executor memory (- -executor-memory) to cache RDDs. Executor memory overview. Besides the parameters that I noted in my previous update, spark.executor.memory is very relevant. Cache RDDs parameters that I noted in my previous update, spark.executor.memory very. It is better to configure a larger number of cores for a Spark memory! Executor/Driver ) determine the full memory request to YARN for each executor driver memory for each executor From. Executor memory ( executor/driver ) fixed heap size and fixed number of small than... The Spark executor memory or driver memory for performance tuning each executor each! Sometimes it is better to configure a larger number of large JVMs to as the Spark instance. Us consider the executor instance memory plus memory overhead is not enough to handle memory-intensive operations memory-intensive.. 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