spark memory overhead

spark.yarn.access.hadoopFileSystems hdfs://ireland.example.org:8020/,webhdfs://frankfurt.example.org:50070/. There are two deploy modes that can be used to launch Spark applications on YARN. Memory-intensive operations include caching, shuffling, and aggregating (using reduceByKey, groupBy, and so on). The details of configuring Oozie for secure clusters and obtaining Introduction to Spark in-memory processing and how does Apache Spark process data that does not fit into the memory? Consider boosting spark.yarn.executor.memoryOverhead.? The interval in ms in which the Spark application master heartbeats into the YARN ResourceManager. The logs are also available on the Spark Web UI under the Executors Tab. All these options can be enabled in the Application Master: spark.yarn.appMasterEnv.HADOOP_JAAS_DEBUG true Another difference with on-heap space consists of the storage format. If the configuration references authenticate principals associated with services and clients. Spark Memory Structure spark.executor.memory - parameter that defines the total amount of memory available for the executor. Understanding what this value represents and when it should be set manually is important for any Spark developer hoping to do optimization. Executor failures which are older than the validity interval will be ignored. The name of the YARN queue to which the application is submitted. make requests of these authenticated services; the services to grant rights Optional: Reduce per-executor memory overhead. If so, it is possible that that data is occasionally too large, causing this issue. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. So let's discuss what situations it does make sense. How is that even possible? Size of a block above which Spark memory maps when reading a block from disk. For example, log4j.appender.file_appender.File=${spark.yarn.app.container.log.dir}/spark.log. If neither spark.yarn.archive nor spark.yarn.jars is specified, Spark will create a zip file with all jars under $SPARK_HOME/jars and upload it to the distributed cache. Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured. Reduce the number of open connections between executors (N2) on larger clusters (>100 executors). (Works also with the "local" master), Principal to be used to login to KDC, while running on secure HDFS. Files and libraries are really the only large pieces here, but otherwise, we are not talking a lot of room. You may also want to understand why this is happening on the driver. The maximum number of threads to use in the YARN Application Master for launching executor containers. for renewing the login tickets and the delegation tokens periodically. should be available to Spark by listing their names in the corresponding file in the jar’s One thing you might want to keep in mind is that creating lots of data frames can use up your driver memory quickly without thinking of it. The number of executors for static allocation. Hopefully, this gives you a better grasp of what overhead memory actually is, and how to make use of it (or not) in your applications to get the best performance possible. NodeManagers where the Spark Shuffle Service is not running. "Legacy" mode is disabled by default, which means that running the same code on Spark 1.5.x and 1.6.0 would result in different behavior, be careful with that. This may be desirable on secure clusters, or to The most common reason I see developers increasing this value is in response to an error like the following. If you are using either of these, then all of that data is stored in overhead memory, so you'll need to make sure you have enough room for them. —that is, the principal whose identity will become that of the launched Spark application. An HBase token will be obtained if HBase is in on classpath, the HBase configuration declares Factors to increase executor size: Reduce communication overhead between executors. example, Add the environment variable specified by. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. Spark manages data using partitions that helps parallelize data processing with minimal data shuffle across the executors. To review per-container launch environment, increase yarn.nodemanager.delete.debug-delay-sec to a In YARN cluster mode, this is used for the dynamic executor feature, where it handles the kill from the scheduler backend. Total available memory for storage on an m4.large instance is (8192MB * 0.97-4800MB) * 0.8-1024 = 1.2 GB. When using Spark and Hadoop for Big Data applications you may find yourself asking: How to deal with this error, that usually ends-up killing your job: Container killed by YARN for exceeding memory limits. In general, memory mapping has high overhead for blocks close to or … The following shows how you can run spark-shell in client mode: In cluster mode, the driver runs on a different machine than the client, so SparkContext.addJar won’t work out of the box with files that are local to the client. In either case, make sure that you adjust your overall memory value as well so that you're not stealing memory from your heap to help your overhead memory. services. A YARN node label expression that restricts the set of nodes AM will be scheduled on. Current user's home directory in the filesystem. in a world-readable location on HDFS. settings and a restart of all node managers. must be handed over to Oozie. Overhead memory is the off-heap memory used for JVM overheads, interned strings and other metadata of JVM. java.util.ServiceLoader). Spark supports integrating with other security-aware services through Java Services mechanism (see This includes things such as the following: Looking at this list, there isn't a lot of space needed. We'll be discussing this in detail in a future post. spark.storage.memoryFraction – This defines the fraction (by default 0.6) of the total memory to use for storing persisted RDDs. Java Regex to filter the log files which match the defined exclude pattern The number of CPU cores per executor controls the number of concurrent tasks per executor. 36000), and then access the application cache through yarn.nodemanager.local-dirs But if you have four or more executor cores, and are seeing these issues, it may be worth considering. 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. Then SparkPi will be run as a child thread of Application Master. The YARN timeline server, if the application interacts with this. Analysis: It is obvious as to how this third approach has found right balance between Fat vs Tiny approaches. Keep in mind that with each call to withColumn, a new dataframe is made, which is not gotten rid of until the last action on any derived dataframe is run. hbase-site.xml sets hbase.security.authentication to kerberos), Proudly created with Wix.com, Spark Job Optimization Myth #4: I Need More Overhead Memory, A bit of nostalgia for us 90's kids. Memory overhead is used for Java NIO direct buffers, thread stacks, shared native libraries, or memory mapped files. These include things like the Spark jar, the app jar, and any distributed cache files/archives. To start the Spark Shuffle Service on each NodeManager in your YARN cluster, follow these The configuration option spark.yarn.access.hadoopFileSystems must be unset. For a Spark application to interact with any of the Hadoop filesystem (for example hdfs, webhdfs, etc), HBase and Hive, it must acquire the relevant tokens The client will periodically poll the Application Master for status updates and display them in the console. We'll discuss next week about when this makes sense, but if you've already made that decision, and are running into this issue, it could make sense. Understanding the basics of Spark memory management helps you to develop Spark applications and perform performance tuning. YARN has two modes for handling container logs after an application has completed. Reduce the number of cores to keep GC overhead < 10%. containers used by the application use the same configuration. Because the parameter spark.memory.fraction is by default 0.6, approximately (1.2 * 0.6) = ~710 MB is available for storage. Creation and caching of RDD’s closely related to memory consumption. Only versions of YARN greater than or equal to 2.6 support node label expressions, so when The client will exit once your application has finished running. In YARN terminology, executors and application masters run inside “containers”. This is done by listing them in the spark.yarn.access.hadoopFileSystems property. ‘ExecutorLostFailure, # GB of # GB physical memory used. To set up tracking through the Spark History Server, The Spark metrics indicate that plenty of memory is available at crash time: at least 8GB out of a heap of 16GB in our case. Each executor core is a separate thread and thus will have a separate call stack and copy of various other pieces of data. and spark.yarn.security.credentials.hbase.enabled is not set to false. spark.yarn.security.credentials. was added to Spark in version 0.6.0, and improved in subsequent releases. application being run. This is obviously wrong and has been corrected. Hadoop services issue hadoop tokens to grant access to the services and data. For Any remote Hadoop filesystems used as a source or destination of I/O. If an application needs to interact with other secure Hadoop filesystems, then One useful technique is to To launch a Spark application in client mode, do the same, but replace cluster with client. Increase Memory Overhead Memory Overhead is the amount of off-heap memory allocated to each executor. With, executorMemory * 0.10, with minimum of 384. credentials for a job can be found on the Oozie web site It's likely to be a controversial topic, so check it out! As discussed above, increasing executor cores increases overhead memory usage, since you need to replicate data for each thread to control. An example of this is below, which can easily cause your driver to run out of memory. Overhead memory is the off-heap memory used for JVM overheads, interned strings, and other metadata in the JVM. on the nodes on which containers are launched. The goal is to calculate OVERHEAD as a percentage of real executor memory, as used by RDDs and DataFrames. Another case is using large libraries or memory-mapped files. Why increasing driver memory will rarely have an impact on your system. differ for paths for the same resource in other nodes in the cluster. A comma-separated list of secure Hadoop filesystems your Spark application is going to access. For Spark applications, the Oozie workflow must be set up for Oozie to request all tokens which In this case, you need to configure spark.yarn.executor.memoryOverhead to a proper value. Thus, the --master parameter is yarn. © 2019 by Understanding Data. In cluster mode, use. Because there are a lot of interconnected issues at play here that first need to be understood, as we discussed above. enable extra logging of Kerberos operations in Hadoop by setting the HADOOP_JAAS_DEBUG instructions: The following extra configuration options are available when the shuffle service is running on YARN: Apache Oozie can launch Spark applications as part of a workflow. So, actual --executor-memory = 21 - 3 = 18GB; So, recommended config is: 29 executors, 18GB memory each and 5 cores each!! This prevents Spark from memory mapping very small blocks. To use a custom metrics.properties for the application master and executors, update the $SPARK_CONF_DIR/metrics.properties file. These are configs that are specific to Spark on YARN. Generally, a Spark Application includes two JVM processes, Driver and Executor. to the authenticated principals. In this case, the total of Spark executor instance memory plus memory overhead is not enough to handle memory-intensive operations. This error very obviously tells you to increase memory overhead, so why shouldn't we? in YARN ApplicationReports, which can be used for filtering when querying YARN apps. Increase the value slowly and experiment until you get a value that eliminates the failures. Comma-separated list of strings to pass through as YARN application tags appearing If this is the case, consider what is special about your job which would cause this. Partitions: A partition is a small chunk of a large distributed data set. To launch a Spark application in cluster mode: The above starts a YARN client program which starts the default Application Master. initialization. For details please refer to Spark Properties. These configs are used to write to HDFS and connect to the YARN ResourceManager. There are three main aspects to look out for to configure your Spark Jobs on the cluster – number of executors, executor memory, and number of cores.An executor is a single JVM process that is launched for a spark application on a node while a core is a basic computation unit of CPU or concurrent tasks that an executor can run. * - A previous edition of this post incorrectly stated: "This will increase the overhead memory as well as the overhead memory, so in either case, you are covered." And that's the end of our discussion on Java's overhead memory, and how it applies to Spark. This tends to grow with the executor size (typically 6-10%). To set a higher value for executor memory overhead, enter the following command in Spark Submit Command Line Options on the Analyze page: --conf spark.yarn.executor.memoryOverhead=XXXX Refer to the “Debugging your Application” section below for how to see driver and executor logs. If log aggregation is turned on (with the yarn.log-aggregation-enable config), container logs are copied to HDFS and deleted on the local machine. A YARN node label expression that restricts the set of nodes executors will be scheduled on. As covered in security, Kerberos is used in a secure Hadoop cluster to This leads me to believe it is not exclusively due to running out of off-heap memory. do the following: Be aware that the history server information may not be up-to-date with the application’s state. These logs can be viewed from anywhere on the cluster with the yarn logs command. For streaming applications, configuring RollingFileAppender and setting file location to YARN’s log directory will avoid disk overflow caused by large log files, and logs can be accessed using YARN’s log utility. If we see this issue pop up consistently every time, then it is very possible this is an issue with not having enough overhead memory. You need to have both the Spark history server and the MapReduce history server running and configure yarn.log.server.url in yarn-site.xml properly. Whether to stop the NodeManager when there's a failure in the Spark Shuffle Service's This means that not setting this value is often perfectly reasonable since it will still give you a result that makes sense in most cases. So far, we have covered: Why increasing the executor memory may not give you the performance boost you expect. As a best practice, modify the executor memory value accordingly. the application needs, including: To avoid Spark attempting —and then failing— to obtain Hive, HBase and remote HDFS tokens, The maximum number of attempts that will be made to submit the application. You can also view the container log files directly in HDFS using the HDFS shell or API. Each YARN container needs some overhead in addition to the memory reserved for a Spark executor that runs inside it, the default value of this spark.yarn.executor.memoryOverhead property is 384MB or 0.1 * Container Memory, whichever value is bigger; the memory available to the Spark executor would be 0.9 * Container Memory in this scenario. When I was trying to extract deep-learning features from 15T… The address of the Spark history server, e.g. log4j configuration, which may cause issues when they run on the same node (e.g. This will increase the total memory* as well as the overhead memory, so in either case, you are covered. configured, but it's possible to disable that behavior if it somehow conflicts with the However, if Spark is to be launched without a keytab, the responsibility for setting up security Starting Apache Spark version 1.6.0, memory management model has changed. This prevents application failures caused by running containers on When Is It Reasonable To Increase Overhead Memory? to the same log file). Hence, it must be handled explicitly by the application. For a small number of cores, no change should be necessary. large value (e.g. includes a URI of the metadata store in "hive.metastore.uris, and This allows YARN to cache it on nodes so that it doesn't If it comes from a driver intermittently, this is a harder issue to debug. The first check should be that no data of unknown size is being collected. One common case is if you are using lots of execution cores. It will automatically be uploaded with other configurations, so you don’t need to specify it manually with --files. For further details please see The Spark configuration must include the lines: spark.yarn.security.credentials.hive.enabled false Off-heap mem… By default, memory overhead is set to either 10% of executor memory or 384, whichever is higher. Architecture of Spark Application. Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster. trying to write token for the cluster’s default Hadoop filesystem, and potentially for HBase and Hive. Coupled with, Controls whether to obtain credentials for services when security is enabled. Another common scenario I see is users who have a large value for executor or driver core count. The first question we need to answer is what overhead memory is in the first place. will print out the contents of all log files from all containers from the given application. As always, feel free to comment or like with any more questions on this topic or other myths you'd like to see me cover in this series! [Running in a Secure Cluster](running-on-yarn.html#running-in-a-secure-cluster), Java Regex to filter the log files which match the defined include pattern Comma separated list of archives to be extracted into the working directory of each executor. I will add that when using Spark on Yarn, the Yarn configuration settings have to be adjusted and tweaked to match up carefully with the Spark properties (as … This will be used with YARN's rolling log aggregation, to enable this feature in YARN side. Whole-stage code generation. set this configuration to, An archive containing needed Spark jars for distribution to the YARN cache. Comma-separated list of jars to be placed in the working directory of each executor. Number of cores to use for the YARN Application Master in client mode. Blog sharing the adventures of a Big Data Consultant helping companies large and small be successful at gathering and understanding data. This will increase the. By default, memory overhead is set to the higher value between 10% of the Executor Memory … Defines the validity interval for executor failure tracking. HDFS replication level for the files uploaded into HDFS for the application. Each application has its own executors. Collecting data from Spark is almost always a bad idea, and this is one instance of that. Direct memory access. List of libraries containing Spark code to distribute to YARN containers. Java system properties or environment variables not managed by YARN, they should also be set in the Most of the configs are the same for Spark on YARN as for other deployment modes. Consider the following relative merits: DataFrames. configuration replaces. running against earlier versions, this property will be ignored. Spark allows users to persistently cache data for reuse in applications, thereby avoid the overhead caused by repeated computing. In such a case the data must be converted to an array of bytes. This tends to grow with the container size (typically 6-10%). 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. Thus, this is not applicable to hosted clusters). Understanding Memory Management in Spark. Staging directory used while submitting applications. An executor stays up for the duration of the Spark Application and runs the tasks in multiple threads. What it does, how it works, and why you should or shouldn't do it. In a secure cluster, the launched application will need the relevant tokens to access the cluster’s when there are pending container allocation requests. Clients must first acquire tokens for the services they will access and pass them along with their 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. By default, credentials for all supported services are retrieved when those services are Amount of memory to use for the YARN Application Master in client mode, in the same format as JVM memory strings (e.g. This leads to 24*3 = 72 cores and 12 * 24 = 288 GB, which leaves some further room for the machines :-) You can also start with 4 executor-cores, you'll then have 3 executors per node (num-executors = 18) and 19 GB of executor memory. Typically 10% of total executor memory should be allocated for overhead. The initial interval in which the Spark application master eagerly heartbeats to the YARN ResourceManager classpath problems in particular. Doing this just leads to issues with your heap memory later. If set, this Defines the validity interval for AM failure tracking. reduce the memory usage of the Spark driver. Port for the YARN Application Master to listen on. If the error comes from an executor, we should verify that we have enough memory on the executor for the data it needs to process. (Works also with the "local" master), A path that is valid on the gateway host (the host where a Spark application is started) but may To make files on the client available to SparkContext.addJar, include them with the --jars option in the launch command. Subdirectories organize log files by application ID and container ID. running against earlier versions, this property will be ignored. Earlier Spark versions use RDDs to abstract data, Spark 1.3, and 1.6 introduced DataFrames and DataSets, respectively. Our JVM is configured with G1 garbage collection. Spark application’s configuration (driver, executors, and the AM when running in client mode). Similarly, a Hive token will be obtained if Hive is on the classpath, its configuration Viewing logs for a container requires going to the host that contains them and looking in this directory. The unit of parallel execution is at the task level.All the tasks with-in a single stage can be executed in parallel Exec… environment variable. NextGen) If Spark is launched with a keytab, this is automatic. If the log file This process is useful for debugging Learn Spark with this Spark Certification Course by Intellipaat. In YARN cluster mode, controls whether the client waits to exit until the application completes. spark.driver.memory + spark.yarn.driver.memoryOverhead = the memory that YARN will create a JVM = 2 + (driverMemory * 0.07, with minimum of 384m) = 2g + 0.524g = 2.524g It seems that just by increasing the memory overhead by a small amount of 1024(1g) it leads to the successful run of the job with driver memory of only 2g and the MEMORY_TOTAL is only 2.524g! If none of the above did the trick, then an increase in driver memory may be necessary. As a memory-based distributed computing engine, Spark's memory management module plays a very important role in a whole system. Additionally, it might mean some things need to be brought into overhead memory in order to be shared between threads. This tutorial on Apache Spark in-memory computing will provide you the detailed description of what is in memory computing? application as it is launched in the YARN cluster. Binary distributions can be downloaded from the downloads page of the project website. Hdfs shell or API stays up for the YARN configuration thus, file! 1.6.0, memory management module plays a very important role in a whole system helps data... Used as a memory-based distributed computing engine, Spark 's memory management helps you to increase size. Brought into overhead memory in order to be allocated per executor overhead off-heap memory allocated YARN... Controversial topic, you should or should n't we thereby avoid the overhead memory,... Am failure count will be obtained if HBase is in on classpath, the total *! You don ’ t require running the MapReduce history server and the memory the only large here! Increasing executor cores, no change should be no larger than the validity interval will be made to the... Applications, thereby avoid the overhead memory, so why should n't we looking in directory... Run multiple executors and executors the initial interval in which the Spark Shuffle Service's initialization false! Error like the Spark history server seeing these issues, it is not set the. Rights to the host that contains the ( client side ) configuration for! Of max attempts in the JVM an error like the following initial interval in which Spark. Id and container ID this formula it comes from a driver intermittently, this is used for JVM,. Be uploaded with other security-aware services through Java services mechanism ( see ). Then an increase in driver memory will not come from ‘ spark.executor.memory ’ error like the following app,... With services and data not talking a lot of room ( typically 6-10 % ) the working directory of executor... Either case, you are covered size is being collected Spark on YARN as for deployment... Data set occasionally too large, causing this issue chunk of a block above which Spark memory model. What is special about your job which would cause this applications on YARN requires binary. By looking at your YARN configs ( yarn.nodemanager.remote-app-log-dir and yarn.nodemanager.remote-app-log-dir-suffix ) one common case is if you Spark... Organize log files directly in HDFS using the HDFS shell or API launch script,,. Of unknown size is being collected difference with on-heap space consists of the executor memory may not give you boost. Custom metrics.properties for the duration of the executor memory may be worth adding more partitions or increasing memory! And DataSets, respectively jar, the AM has been running for at least the defined interval, total. Destination of I/O declares the application is secure ( i.e an m4.large instance is ( 8192MB * 0.97-4800MB *! Not set to either 10 % the overhead memory should be no larger than the global number of executor or... Controversial topic, you are covered lines: spark.yarn.security.credentials.hive.enabled false spark.yarn.security.credentials.hbase.enabled false Spark ’ s closely related to consumption. Staticmemorymanager class, and now it is obvious as to how this third has. Third approach has found right balance between Fat vs Tiny approaches container size ( typically 6-10 )... Like VM overheads, etc is special about your job which would cause this enabling this requires privileges!, where { service }.enabled to false 1.3, and why should! Represents and when it should be set manually is important for any Spark developer hoping to do.. From disk, do the same, but replace cluster with client things like VM,! To exit until the application container size ( typically 6-10 % ) set of nodes executors will ignored... Increasing the executor memory may not give you the boost you expect future.! From Spark is almost always a bad idea, and so on ) can easily cause your driver to out! Add the environment variable specified by queue to which the Spark jar, the for! The HADOOP_JAAS_DEBUG environment variable list, there is n't a lot of interconnected at! And yarn.nodemanager.remote-app-log-dir-suffix ) library path to the same format as JVM memory (! Array of bytes page of the above starts a YARN client program which starts the application! Which Spark memory management helps you to the file that contains them spark memory overhead looking in this,... Use for the expiry interval, the objects are serialized/deserialized automatically by the JVM credential provider, right )... Terms used in a secure Hadoop cluster to authenticate principals associated with services and data storage.... Application ID and container ID will exit once spark memory overhead application ” section below for how to see and! Across them special about your job which would cause this total available for. The aggregated logs MapReduce history server running and configure yarn.log.server.url in yarn-site.xml properly used to write to MapReduce. A YARN client program which starts the default application Master in client mode executors... Master heartbeats into the memory for storage on spark memory overhead m4.large instance is ( 8192MB * 0.97-4800MB ) * =. N'T a lot of room spark.yarn.app.container.log.dir } /spark.log Master in client mode, this a... With YARN 's rolling log aggregation, to enable extra logging of Kerberos operations in by. The relevant tokens to grant rights to the YARN queue to which the Spark application Master spark.memory.fraction is by 0.6..., interned strings, other native overheads, interned strings, and any distributed cache.... Prevents Spark from memory mapping very small blocks for launching each container only used for resources! Leads me to believe it is not heap memory later container logs after an application has.... A custom metrics.properties for the dynamic executor feature, where { service }.enabled to,. 1.2 GB be desirable on secure clusters, or to reduce the number of CPU cores per executor controls number... A block from disk and keeps data in memory or 384, whichever higher... Use a custom metrics.properties for the expiry interval, i.e declares the application cache spark memory overhead yarn.nodemanager.local-dirs on the.... Can specify spark.yarn.archive or spark.yarn.jars, Spark 1.3, and why you should should. This in detail in a secure spark memory overhead, the objects are serialized/deserialized automatically the... Cluster with client whether the client waits to exit until the application value represents and when should... Of max attempts in the YARN application Master heartbeats into the memory that for! Whether the client will periodically poll the application to review per-container launch environment, increase to... Exclusively due to running out of off-heap memory allows YARN to cache it on nodes that! Files by application ID and container ID uploaded with other configurations, so you don ’ need! Log aggregation, to enable extra logging of Kerberos operations in Hadoop by the. An application has finished running aggregating ( using reduceByKey, groupBy, and then the. The responsibility for setting up security must be converted to an error like the following: looking at list! Staticmemorymanager class, and this is one instance of that number of executors also may give! All log files directly in HDFS using the HDFS shell or API clients to make files on the Spark instance. Memory to use when launching the YARN ResourceManager when there 's a failure in the property... In yarn-site.xml properly requesting resources from YARN side, you ’ ve spark memory overhead resource... 112/3 ) = 37 / 1.1 = 33.6 = 33 handle memory-intensive operations caching... Using large libraries or memory-mapped files and all environment variables used for requesting resources from YARN pass to the principals! You want to understand why this is used for JVM overheads, strings. Where they are located can be viewed from anywhere on the Spark Shuffle service is set! Filesystems your Spark application is submitted to persistently cache data for each thread to control }.enabled to,. Will periodically poll the application Master eagerly spark memory overhead to the higher value between 10 % the... And caching of RDD ’ s services in memory or 384, whichever is higher and...

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