‎05-04-2016 Since you are requesting 15G for each executor, you may want to increase the size of Java Heap space for the Spark executors, as allocated using this parameter: Created So, by setting that to its max value, you probably asked for way, way more heap space than you needed, and more of the physical ram needed to be requested for off-heap. When allocating ExecutorContainer in cluster mode, additional memory is also allocated for things like VM overheads, interned strings, other native overheads, etc. ‎11-17-2017 executor cores = 5 Since Yarn also takes into account the executor memory and overhead, if you increase spark.executor.memory a lot, don't forget to also increase spark.yarn. With 4 cores you can run 4 tasks in parallel, this affects the amount of execution memory being used. The java process is what uses heap memory, the python process uses off heap. So with 12G heap memory running 8 tasks, each gets about 1.5GB with 12GB heap running 4 tasks each gets 3GB memory. Increase heap size to accommodate for memory-intensive tasks. So, finding a sweet spot for the number of partitions is important, usually something relevant with the number of executors and number of cores, like their product*3 would be nice, like this: Going back to Figure 1, decreasing the value of ‘spark.executor.memory’ will help, if you are using Python, since Python will be all off-heap memory and would not use the ram we reserved for heap. First, it is going to read the spark.executor.memoryOverhead parameter and multiply the requested amount of memory by the overhead value (by default, 10%, with a minimum of 384 MB). Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. Remove 10% as YARN overhead, leaving 12GB--executor-memory = 12 Since we have already determined that we can have 6 executors per node the math shows that we can use up to roughly 20GB of memory per executor. (200k in my case). ‎05-04-2016 In addition, the number of partitions is also critical for your applications. Learn how to optimize an Apache Spark cluster configuration for your particular workload. By default, Spark uses On-memory heap only. You can leave a comment or email us at [email protected] 07:07 PM. Learn Spark with this Spark Certification Course by Intellipaat. Big data, The recommendations and configurations here differ a little bit between Spark’s cluster managers (YARN, Mesos, and Spark Standalone), but we’re going to focus only … However, while this is of most significance for performance, it also can result in an error. This tends to grow with the executor size (typically 6-10%). You see more data, means more memory, which may result in spikes, that will go out of memory bounds, triggering the kill of the container from YARN. In this case, the total of Spark executor instance memory plus memory overhead is not enough to handle memory-intensive operations. As a best practice, modify the executor memory value accordingly. Memory-intensive operations include caching, shuffling, and aggregating (using reduceByKey, groupBy, and so on). Notice that here we sacrifice performance and CPU efficiency for reliability, which when your job fails to succeed, makes much sense! 04:44 PM. With 8 partitions, I would want to have 25k images per partition. C) Python / … You need to use `spark.executor.memory` to do so. Formula for that overhead is max (384, .07 * spark.executor.memory) Calculating that overhead - .07 * 21 (Here 21 is calculated as above 63/3) = 1.47 The spark executor memory is shared between these tasks. {resourceName}.amount: 0: Amount of a particular resource type to use per executor process. However small overhead memory is also needed to determine the full memory request to YARN for each executor. Task: A task is a unit of work that can be run on a partition of a distributed dataset and gets executed on a single executor. spark.storage.memoryFraction – This defines the fraction (by default 0.6) of the total memory to use for storing persisted RDDs. Spark's description is as follows: The amount of off-heap memory (in megabytes) to be allocated per executor. Overhead memory is the off-heap memory used for JVM overheads, interned strings, and other metadata in the JVM. 512m, 2g). Having from above 4 executors per node, this is 14 GB per executor. This tends to grow with the executor size (typically 6-10%). Consider boosting spark.yarn.executor.memoryOverhead. offHeap.enabled = false, Created So memory for each executor in each node is 63/3 = 21GB. Spark shell required memory = (Driver Memory + 384 MB) + (Number of executors * (Executor memory + 384 MB)) Here 384 MB is maximum memory (overhead) value that may be utilized by Spark when executing jobs. HALP.” Given the number of parameters that control Spark’s resource utilization, these questions aren’t unfair, but in this section you’ll learn how to squeeze every last bit of juice out of your cluster. --executor-memory 32G --conf spark.executor.memoryOverhead=4000 /* The exact parameter for adjusting overhead memory will vary based on which Spark version you … So less concurrent tasks, less overhead space. Number of executors per node = 30/10 = 3; Memory per executor = 64GB/3 = 21GB; Counting off heap overhead = 7% of 21GB = 3GB. Balancing the data across partitions, is always a good thing to do, for performance issues, and for avoiding spikes in the memory trace, which once it overpasses the memoryOverhead, it will result in your container be killed by YARN. 04/15/2020; 7 minutes to read; E; j; K; In this article. This is obviously just a rough approximation. Deep Learning, asked Jul 17, 2019 in Big Data Hadoop & Spark by Aarav (11.5k points) The value of the spark.yarn.executor.memory overhead property is added to the executor memory to determine the full memory request to YARN for each executor. To set a higher value for executor memory overhead, enter the following command in Spark Submit Command Line Options on the Workbench page: --conf spark.yarn.executor.memoryOverhead=XXXX In general, I had this figure in mind: The first thing to do, is to boost ‘spark.yarn.executor.memoryOverhead’, which I set to 4096. You may not need that much, but you may need more off-heap, since there is the Python piece running. So, by decreasing this value, you reserve less space for the heap, thus you get more space for the off-heap operations (we want that, since Python will operate there). Let’s start with some basic definitions of the terms used in handling Spark applications. The executor memory overhead value increases with the executor size (approximately by 6-10%). To know more about Spark configuration, please refer below link: ‎05-04-2016 Btw. spark.driver/executor.memory + spark.driver/executor.memoryOverhead < yarn.nodemanager.resource.memory-mb Distribution of Executors, Cores and Memory for a Spark Application , An executor is a process that is launched for a Spark application on a worker node. Executor overhead memory defaults to 10% of your executor size or 384MB (whichever is greater). executormemoryOverhead. But what’s the trade-off here? You see, the RDD is distributed across your cluster. If I'm allocating 8GB for memoryOverhead, then OVERHEAD = 567 MB !! Each executor memory is the sum of yarn overhead memory and JVM Heap memory. 4) Per node we have 64 - 8 = 56 GB. What is being stored in this container that it needs 8GB per container? There isn’t a good way to see python memory. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. spark.yarn.driver.memoryOverhead It controls the executor heap size, but JVMs can also use some memory off heap, for example for interned Strings and direct byte buffers. If this is used, you must also specify the spark.executor.resource. Limiting Python's address space allows Python to participate in memory management. Alert: Welcome to the Unified Cloudera Community. Spark manages data using partitions that helps parallelize data processing with minimal data shuffle across the executors. As mentioned before, the more the partitions, the less data each partition will have. The formula for that overhead is max(384, .07 * spark.executor.memory) Calculating that overhead: .07 * 21 (Here 21 is calculated as above 63/3) = 1.47 If you want to contribute, please email us. By default, Spark uses On-memory heap only. Basically you took memory away from java process to give to the python process and seems to have worked for you. Set ‘spark.executor.memory’ to 12G, from 8G. Except from the fact your partitions might become too tiny (if they are too many for your current dataset), a large number of partitions means a large number of output files (yes, the number of partitions is equal to the number of part-xxxxx files you will get in the output directory), and usually if the the partitions are too many, the output files are small, which is OK, but the problem appears with the metadata HDFS has to housekeep, which puts pressure in HDFS and decreases its performance. {resourceName}.discoveryScript for the executor to find the resource on startup. 05:16 PM, Thanks. 04:55 PM, you may be interested by this article: http://www.wdong.org/wordpress/blog/2015/01/08/spark-on-yarn-where-have-all-my-memory-gone/, The link seems to be dead at the moment (here is a cached version: http://m.blog.csdn.net/article/details?id=50387104), Created 3 cores * 4 executors mean that potentially 12 threads are trying to read from HDFS per machine. Created Spark Memory Structure spark.executor.memory - parameter that defines the total amount of memory available for the executor. When the Spark executor’s physical memory exceeds the memory allocated by YARN. Consider boosting spark.yarn.executor.memoryOverhead.? ‘spark.executor.memory’ is for JVM heap only. Available memory is 63G. www.learn4master.com/algorithms/memoryoverhead-issue-in-spark You want your data to be balanced, for performance reasons usually, since as with every distributed/parallel computing job, you want all your nodes/threads to have the same amount of work. The reason adjusting the heap helped is because you are running pyspark. Set ‘spark.executor.memory’ to 12G, from 8G. This memory is set using spark.executor.memoryOverhead configuration (or deprecated spark.yarn.executor.memoryOverhead). It's never too late to learn to be a master. @Henry : I think that equation uses the executor memory (in your case, 15G) and outputs the overhead value. To find out the max value of that, I had to increase it to the next power of 2, until the cluster denied me to submit the job. The goal is to calculate OVERHEAD as a percentage of real executor memory, as used by RDDs and DataFrames. Click to share on Facebook (Opens in new window), Click to share on Google+ (Opens in new window), Click to share on Twitter (Opens in new window), Click to share on Reddit (Opens in new window), Click to share on Pocket (Opens in new window), Click to email this to a friend (Opens in new window), Start, Restart and Stop Apache web server on Linux, Adding Multiple Columns to Spark DataFrames, Move Hive Table from One Cluster to Another, use spark to calculate moving average for time series data, Five ways to implement Singleton pattern in Java, A Spark program using Scopt to Parse Arguments, Convert infix notation to reverse polish notation (Java). 1 view. Optional: Reduce per-executor memory overhead. Architecture of Spark Application. so memory per each executor will be 63/3 = 21G. So, the more partitions you have, the smaller their sizes are. What is spark executor memory overhead? Caching Memory Memory overhead is the amount of off-heap memory allocated to each executor . Java, Partitions: A partition is a small chunk of a large distributed data set. Data Mining, That starts both a python process and a java process. In this case, you need to configure spark.yarn.executor.memoryOverhead to a proper value. In practice, we see fewer cases of Python taking too much memory because it doesn't know to run garbage collection. spark.executor.memory. However, Scala seems to do the trick. The dataset had 200k partitions and our cluster was of version Spark 1.6.2. spark.executor.pyspark.memory: Not set: The amount of memory to be allocated to PySpark in each executor… You can also have multiple Spark configs in DSS to manage different workloads. spark.storage.memoryFraction – This defines the fraction (by default 0.6) of the total memory to use for storing persisted RDDs. This though is not 100 percent true as we also should calculate in it, the memory overhead that each executor will have. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. Learn Spark with this Spark Certification Course by Intellipaat. Think about it like this (taken from slides): The solution to this is to use repartition(), which promises that it will balance the data across partitions. The java process is what uses heap memory, the python process uses off heap. max executors = 60 For scientists to find answers, we need DNA from the whole family. spark.driver/executor.memory + spark.driver/executor.memoryOverhead < yarn.nodemanager.resource.memory-mb As also discussed in the SO question above, upgrading to Spark 2.0.0, might resolve errors like this: Another important factor, is the cores number; a decrease in that will result in holding less tasks in memory at one time, than with the maximum number of cores. et al. Python, Just an FYI, Spark 2.1.1 doesn't allow setting the heap space in `extraJavaOptions`: Find answers, ask questions, and share your expertise. If I have 200k images and 4 partitions, then the ideal thing is to have 50k(=200k/4) images per partition. 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. Mainly executor side errors are due to YARN Memory overhead (if spark is running on YARN). What changes were proposed in this pull request? Created Physical memory limit for Spark executors is computed as spark.executor.memory + spark.executor.memoryOverhead (spark.yarn.executor.memoryOverhead before Spark 2.3). If for example, you had 4 partitions, with the first 3 having 20k images each and the last one, the 4th, having 180k images, then what will (likely) happen is that the first three will finish much earlier than the 4th, which will have to process much more images (x9) and in overall, our job will have to wait for that 4th chunk of data to be processed, thus, in overall, our job will be much slower than if the data were balanced along the partitions. Check your email addresses your search results by suggesting possible matches as you type: http //www.learn4master.com/algorithms/memoryoverhead-issue-in-spark... Some kind of Structure in your data is balanced across the executors = 567 MB! Spark manages using... //Gsamaras.Wordpress.Com/Code/Memoryoverhead-Issue-In-Spark/, URL for this post: http: //www.learn4master.com/algorithms/memoryoverhead-issue-in-spark physical memory exceeds memory. 'M allocating 8GB for memoryOverhead, then overhead = max ( SPECIFIED_MEMORY 0.07... 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