Using. Best practice 1: Choose the right type of instance for each of the node types in an Amazon EMR cluster. Click here to return to Amazon Web Services homepage. When the number of Spark executor instances, the amount of executor memory, the number of cores, or parallelism is not set appropriately to handle large volumes of data. Calculate and set the following Spark configuration parameters carefully for the Spark application to run successfully: Amazon EMR provides high-level information on how it sets the default values for Spark parameters in the release guide. Further, letâs assume that we do this through an Amazon EMR cluster with 1 r5.12xlarge master node and 19 r5.12xlarge core nodes. When the Spark executorâs physical memory exceeds the memory allocated by YARN. Otherwise, set spark.dynamicAllocation.enabled to false and control the driver memory, executor memory, and CPU parameters yourself. There is no operation that requires shuffle. Monitoring memory usage of a running function. what error would tell you to increase the, https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/54668152#54668152. Try re-running the job with this value 3 or 5 times before settling for this configuration. Also, you can use Ganglia and Spark UI to monitor the application progress, Cluster RAM usage, Network I/O, etc. Journaling and Replica Sets ¶ Starting in MongoDB 4.0, you cannot specify --nojournal option or storage.journal.enabled: false for replica set members that use the WiredTiger storage engine. To understand the frequency and execution time of the garbage collection, use the parameters -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps. Memory-intensive operations include caching, shuffling, and aggregating (using reduceByKey, groupBy, and so on). ● Check executor memory assigned as an executor might have to deal with partitions requiring more memory than what is assigned. © 2021, Amazon Web Services, Inc. or its affiliates. Doing this is one key to success in running any Spark application on Amazon EMR. Setting spark.storage.memoryFraction to 0.1 can't solve the problem either. Best practice 2: Set spark.dynamicAllocation.enabled to true only if the numbers are properly determined for spark.dynamicAllocation.initialExecutors/minExecutors/maxExecutors parameters. Spark on YARN can dynamically scale the number of executors used for a Spark application based on the workloads. For memory-intensive applications, prefer R type instances over the other instance types. The memory required to perform system operations such as garbage collection is not available in the Spark executor instance. Subtract one virtual core from the total number of virtual cores to reserve it for the Hadoop daemons. You need to calculate all these things by seeing the yarn UI and the cluster memory given to you. It is widely used in distributed processing of big data. To use all the resources available in a cluster, set the maximizeResourceAllocation parameter to true. It then sets these parameters in the spark-defaults settings. Amazon EMR enables organizations to spin up a cluster with multiple instances in a matter of few minutes. (max 2 MiB). You can also provide a link from the web. From my understanding of the code provided above, it loads the file and does map operation and saves it back. http://spark.apache.org/docs/1.2.1/configuration.html. This EMR-specific option calculates the maximum compute and memory resources available for an executor on an instance in the core instance group. You should configure offHeap memory settings as shown below: Give the driver memory and executor memory as per your machines RAM availability. Subproperties are required for most cases to use the right number of executors in a cluster for an application, especially when you need multiple applications to run simultaneously. Click here to upload your image
This blog post is intended to assist you by detailing best practices to prevent memory-related issues with Apache Spark on Amazon EMR. If not set, Spark will not limit Python's memory use and it is up to the application to avoid exceeding the overhead memory space shared with other non-JVM processes. This is because the default configurations (two executor instances, parallelism of 2, one vCPU/executor, 8-GB memory/executor) arenât enough to process 10 TB data. You can set it using spark submit command as follows: Very important note, this property will not be taken into consideration if you set it from code, according to Spark Documentation - Dynamically Loading Spark Properties: Spark properties mainly can be divided into two kinds: one is related to deploy, like “spark.driver.memory”, “spark.executor.instances”, this kind of properties may not be affected when setting programmatically through SparkConf in runtime, or the behavior is depending on which cluster manager and deploy mode you choose, so it would be suggested to set through configuration file or spark-submit command line options; another is mainly related to Spark runtime control, like “spark.task.maxFailures”, this kind of properties can be set in either way. The relevant variables are SPARK_EXECUTOR_MEMORY & SPARK_DRIVER_MEMORY. This can lead to the failure of the Spark job when running many tasks continuously. The value of spark.memory.fraction should be set in order to fit this amount of heap space comfortably within the JVMâs old or âtenuredâ generation. I had thought it would utilize my cluster resources to best fit the application. Following is a configuration template with sample values. In your $SPARK_HOME/conf folder you should find the file spark-defaults.conf, edit and set the spark.driver.memory 4000m depending on the memory on your master, I think. https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/58497461#58497461, https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/58987244#58987244, https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/65083609#65083609, Spark java.lang.OutOfMemoryError: Java heap space, spark.apache.org/docs/2.1.0/configuration.html, https://stackoverflow.com/a/25270600/1586965, http://spark.apache.org/docs/latest/tuning.html#serialized-rdd-storage, Spark Documentation - Dynamically Loading Spark Properties. By default, serverStatus excludes in its output: some content in the repl document. Assign 10 percent from this total executor memory to the memory overhead and the remaining 90 percent to executor memory. Since I am using Spark sql I can only specify partition using spark.sql.shuffle.partitions, default value is 200 should I set it to more I tried to set it to 1000 but not helping getting OOM are you aware what should be the optimal partition value I have 1 TB skewed data to process and it involves group by hive queries. Increase the number of executors so that they can be allocated to different slaves. When PySpark is run in YARN or Kubernetes, this memory is added to executor resource requests. If you run the same Spark application with default configurations on the same cluster, it fails with an out-of-physical-memory error. Thanks. Now you have 2 options, I would advise being careful with the increase and use only as much as you need. Other cases occur when there is an interference between the task execution memory and RDD cached memory. Consider you have a 160gb file to be loaded into your cluster. In my program spark.executor.memory has already been setted to 4g much bigger than Xmx400m in hadoop. Warning: Although this calculation gives partitions of 1,700, we recommend that you estimate the size of each partition and adjust this number accordingly by using coalesce or repartition. Thanks for your suggestions~ If I set spark.executor.memory=6g, spark will have the problem:"check your cluster UI to ensure that workers are registered and have sufficient memory". According to the gitbook Mastering Apache Spark by Jacek Laskowski: You can run Spark in local mode. This is the only mode where a driver is used for execution. I implemented the same program on hadoop a month ago , and I met the same problem of OutOfMemoryError, but in hadoop it can be easily solved by increasing the value of mapred.child.java.opts from Xmx200m to Xmx400m. heap space errors generally occur due to either bringing too much data back to the driver or the executor. There are numerous instance types offered by AWS with varying ranges of vCPUs, storage, and memory, as described in the Amazon EMR documentation. After deciding the instance type, determine the number of instances for each of the node types. An example follows. These include cases when there are multiple large RDDs in the application. After you decide on the number of virtual cores per executor, calculating this property is much simpler. Structure your data set. To exclude fields that are included by default, specify the top-level field and set to 0 in the command. spark.memory.storageFraction expresses the size of R as a fraction of M (default 0.5). Set these properties appropriately in spark-defaults, when submitting a Spark application (spark-submit), or within a SparkConf object. I am not sure what is in the method definition but that is definitely causing this overloading of the executor. Assigning a low number of virtual cores leads to a high number of executors, causing a larger amount of I/O operations. There can be two things which are going wrong. We recommend you consider these additional programming techniques for efficient Spark processing: Best practice 3: Carefully calculate the preceding additional properties based on application requirements. Some example subproperties are spark.dynamicAllocation.initialExecutors, minExecutors, and maxExecutors. We recommend setting this to equal spark.executors.memory. It also enables you to process various data engineering and business intelligence workloads through parallel processing. The second part of the problem is division of work. Maybe the problem lies in my code.Thank you! Test with 1 core executors which have largest possible memory you can give and then keep increasing cores until you find the best core count. sbt package then sbt run. Assigning executors with a large number of virtual cores leads to a low number of executors and reduced parallelism. is it from console? These compartments should be properly configured for running the tasks efficiently and without failure. For applications balanced between memory and compute, prefer M type general-purpose instances. In this case, you need to configure spark.yarn.executor.memoryOverhead to ⦠The initial heap size remains 1G and the heap size never scale up to the Xmx heap. Check which server get the out of memory error. To understand more about each of the parameters mentioned preceding, see the Spark documentation. Overhead memory is the off-heap memory used for JVM overheads, interned strings, and other metadata in the JVM. Smaller data possibly needs less memory. Spark memory and User memory. setting the driver memory in your code will not work, read spark documentation for this: Spark properties mainly can be divided into two kinds: one is related to deploy, like “spark.driver.memory”, “spark.executor.instances”, this kind of properties may not be affected when setting programmatically through SparkConf in runtime, or the behavior is depending on which cluster manager and deploy mode you choose, so it would be suggested to set through configuration file or spark-submit command line options. In the world of big data, a common use case is performing extract, transform (ET) and data analytics on huge amounts of data from a variety of data sources. I really appreciate the logging help for finding memory leaks. Setting spark.storage.memoryFraction to 0.1 can't solve the problem either. I have few suggession for the above mentioned error. or which deploy scripts do you use? Set this property using the following formula. Typically, analytics engines such as HDInsight and Azure Data Lake Analytics have a per-file overhead. Leave one executor for the driver. In this case, the total of Spark executor instance memory plus memory overhead is not enough to handle memory-intensive operations. From spark ui, it shows the memory of every executor is 4096. Also, for large datasets, the default garbage collectors donât clear the memory efficiently enough for the tasks to run in parallel, causing frequent failures. What's the size of the dataa generated by (data._1, desPoints) - this should fit in memory esp if this data is then shuffled to another stage. Sorry to hijack someone else's query but how to use reduceByKey over groupBy? For fear of starting a long a comment thread :) If you are having issues, likely other people are, and a question would make it easier to find for all. so, for that, you would create a driver with 161 GB? Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.. Its how you determine the number of executors, their memory, and the buffer for overhead memory and their OS. ● Try to see if more shuffles are live as shuffles are expensive operations since they involve disk I/O, data serialization, and network I/O, ● Avoid using groupByKeys and try to replace with ReduceByKey, ● Avoid using huge Java Objects wherever shuffling happens. In this blog post, I detailed the possible out-of-memory errors, their causes, and a list of best practices to prevent these errors when submitting a Spark application on Amazon EMR. Best practice 4: Always set up a garbage collector when handling large volume of data through Spark. Now, no matter how many rows I need to fetch, my session will never consume more memory than that required for those 100 rows, yet I will still benefit from the improvement in performance of bulk querying. Does spark have any jvm setting for it's tasks?I wonder if spark.executor.memory is the same meaning like mapred.child.java.opts in hadoop. To initiate garbage collection sooner, set InitiatingHeapOccupancyPercent to 35 (the default is 0.45). First, I read some data (2.19 GB) from HDFS to RDD: PS: Every thing is ok when the input data is about 225 MB. edit your code to do the 3-D reconstruction in a more efficient manner. You should increase the driver memory. Or the configuration can be passed from S3 (Load JSON from S3). Please ⦠For better performance, you also need to consider the executor-cores which should be always between 3-5 @fuyi, https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/50800524#50800524. Core nodes run YARN NodeManager daemons, Hadoop MapReduce tasks, and Spark executors to manage storage, execute tasks, and send a heartbeat to the master. https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/32385547#32385547, How much percentage of mem to be alloted, in stand alone, https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/21157374#21157374. Task: The optional task-only nodes perform tasks and donât store any data, in contrast to core nodes. With default settings, Spark might not use all the available resources of the cluster and might end up with physical or virtual memory issues, or both. In this non-distributed single-JVM deployment mode, Spark spawns all the execution components - driver, executor, backend, and master - in the same JVM. How much percentage we should be considering for driver memory in stand-alone mode. Try using more partitions, you should have 2 - 4 per CPU. One of the most popular cloud-based solutions to process such vast amounts of data is Amazon EMR. If you are caching large RDDs and can sacrifice some access time consider serialising the RDD. that's not feasible. So the setting has been enabled, right? Setting these exact configurations helped resolving the issue. The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks.I'll summarize below - it ends up being just a few lines of code: To your first point, @samthebest, you should not use ALL the memory for, https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/35961952#35961952. Best practice 5: Always set the virtual and physical memory check flag to false. If theyâre not right, the capacity might be reserved but never actually used. (The default is -XX:+UseParallelGC.) Behavior¶. EDIT: (So I can google myself easier) The following is also indicative of this problem: To add a use case to this that is often not discussed, I will pose a solution when submitting a Spark application via spark-submit in local mode. When configured following the methods described, a Spark application can process 10 TB data successfully without any memory issues on an Amazon EMR cluster whose specs are as follows: Following, you can find Ganglia graphs for reference. Configure and launch the Amazon EMR cluster with configured Apache Spark. {s,d}gemm_compute may leak memory if only one of the matrices are packed in sequential Intel MKL for Intel AVX2 and above.Workaround: Use multi-threaded Intel MKL and set MKL_NUM_THREADS to 1 instead of using sequential Intel MKL. File size. @samthebest This is a fantastic answer. Thank you~, Are the three steps you mention the only ones you do? I suffered from this issue a lot when using dynamic resource allocation. In this case, the total of Spark executor instance memory plus memory overhead is not enough to handle memory-intensive operations. However, we believe that this blog post provides all the details needed so you can tweak parameters and successfully run a Spark application. Terminate the cluster after the application is completed. Thanks for your suggestions~ If I set spark.executor.memory=6g, spark will have the problem:"check your cluster UI to ensure that workers are registered and have sufficient memory". To do this, calculate and set these properties manually for each application (see the example following). https://stackoverflow.com/questions/21138751/spark-java-lang-outofmemoryerror-java-heap-space/22742982#22742982. R is the storage space within M where cached blocks immune to being evicted by execution. Based on historical data, we suggest that you have five virtual cores for each executor to achieve optimal results in any sized cluster. Hi @samthebest how did you specify 8000 partitions? Then, get the total executor memory by using the total RAM per instance and number of executors per instance. Typically a single executor will be running multiple cores. When autoDeploy or deployOnStartup operations are performed by a Host, the name and context path of the web application are derived from the name(s) of the file(s) that define(s) the web application. This total executor memory includes the executor memory and overhead (spark.yarn.executor.memoryOverhead). Using Amazon EMR release version 4.4.0 and later, dynamic allocation is enabled by default (as described in the Spark documentation). We recommend setting this to equal spark.executors.cores. THE BEST ANSWER! I use sbt to compile and run my app. Did you dump your master gc log? With the default garbage collector (CMS), the RAM used goes above 5 TB. The parameter -XX:+UseG1GC specifies that the G1GC garbage collector should be used. Apache Spark is a cluster-computing software framework that is open-source, fast, and general-purpose. What is the memory configuration for the driver? My problem was that Spark wasn't installed at master node, I just used PySpark to connect to HDFS and got the same error. In standalone num executors = max cores / cores per executor . Also, there is no operation that requires data to be brought to the driver hence tuning anything related to shuffle or driver may have no impact. Generally, you perform the following steps when running a Spark application on Amazon EMR: Itâs important to configure the Spark application appropriately based on data and processing requirements for it to be successful. To prevent these application failures, set the following flags in the YARN site settings. Based on whether an application is compute-intensive or memory-intensive, you can choose the right instance type with the right compute and memory configuration. To get details on where the spark configuration options are coming from, you can run spark-submit with the âverbose option. All these calculations are for the --deploy-mode cluster, which we recommend for production use. Have a look at the start up scripts a Java heap size is set there, it looks like you're not setting this before running Spark worker. If your nodes are configured to have 6g maximum for Spark (and are leaving a little for other processes), then use 6g rather than 4g. nodeperf.c, which comes with the MP LINPACK Benchmark for Clusters package, may fail to run on Windows*. For the preceding cluster, the property spark.executor.cores should be assigned as follows: spark.executors.cores = 5 (vCPU). Though the preceding parameters are critical for any Spark application, the following parameters also help in running the applications smoothly to avoid other timeout and memory-related errors. Doing this helps avoid potential garbage collection for the total memory, which can take a significant amount of time. ; mirroredReads document. If possible, partition your data into smaller chunks. If set, PySpark memory for an executor will be limited to this amount. The driver does have issues when there are too many tasks but this was only till spark 2.0.2 version. This is controlled by property spark.memory.fraction - the value is between 0 and 1. (Available starting in version 4.4)To include fields that are excluded by default, specify the top-level field and set it to 1 in the command. Core: The core nodes are managed by the master node. Then, inside a loop, I use FETCH-BULK COLLECT-INTO to fetch up to the number of rows specified by the c_limit constant (set to 100). Even with this setting, generally the default numbers are low and the application doesnât use the full strength of the cluster. do no edit code, but give more memory to your executors, as well as give more memory-overhead. By doing this, to a great extent you can reduce the data processing times, effort, and costs involved in establishing and scaling a cluster. Driver memory can't be larger than the input size. You can increase the offHeap size if you are still facing the OutofMemory issue. Is it the driver or one of the executors. There are thousands of questions raised in stackoverflow.com related to this specific topic. Garbage collection can lead to out-of-memory errors in certain cases. These values are automatically set in the spark-defaults settings based on the core and task instance types in the cluster. [spark.executor.memory or spark.driver.memoryOverhead]. In your code it does not seem like you are bringing anything back to the driver, but instead you maybe overloading the executors that are mapping an input record/row to another using the threeDReconstruction() method. Of these, only one (execution memory) is actually used for executing the tasks. @Brian, In local mode, does the driver memory need to be larger than the input data size? More docs are in the deployment guide. IME increasing the number of partitions is often the easiest way to make a program more stable (and often faster). Please guide. As the preceding diagram shows, the executor container has multiple memory compartments. At a minimum, calculate and set the following parameters for a successful Spark application. Also, don't forget to copy the configuration file to all the slave nodes. This is what fixed the issue for me and everything runs smoothly. Though the cluster had 7.8 TB memory, the default configurations limited the application to use only 16 GB memory, leading to the following out-of-memory error. For huge amounts of data you may need way more than 4 per CPU, I've had to use 8000 partitions in some cases! These issues occur for various reasons, some of which are listed following: In the following sections, I discuss how to properly configure to prevent out-of-memory issues, including but not limited to those preceding.