What would the ancient Romans have called Hercules' Club? UnifiedMemoryManager allows for soft boundaries between storage and execution memory (allowing requests for memory in one region to be fulfilled by borrowing memory from the other). User Memory and its completely up to you what would be stored in this Connect and share knowledge within a single location that is structured and easy to search. I created a slightly modified script that creates such a maximum input, it uses a factor of 0.6 and the resulting file can still be processed without an OOM error. An estimation is necessary since this value is not directly exposed in the web interface but can be inferred from the on-disk size (field Shuffle Read shown in the details view of the stage) multiplied by the Memory Expansion Rate: Is it against the law to sell Bitcoin at a flea market? You can set it as a value between 0 and 1, describing what portion of executor JVM memory will be dedicated for caching RDDs. Please go through this nice post to understand more in detail. Using a factor of 0.7 though would create an input that is too big and crash the application again thus validating the thoughts and formulas developed in this section. The first and last change directly contradict the original hypothesis and the other changes make the memory mystery even bigger. UnifiedMemoryManager is given the maximum heap memory to use (for execution and storage) when created (that uses apply factory method which uses getMaxMemory). The executor memory I use is 6 GB though. The best setup for m4.2xlarge instance types might be to just use one large Spark executor with seven cores as one core should always be reserved for the Operating System and other background processes on the node. spark.memory.fraction, --> Memory pool managed by Spark. Storage Memory = spark.memory.storageFraction * Usable Memory = 0.5 * 360MB = 180MB. The memory in the below tests is limited to 900MB . Given our special circumstances, this implies that each line in the file should be 120/200 = 0.6 times shorter. The program that processes this file launches a local Spark executor with three cores and the memory available to it is limited to 900MB as the JVM arguments -Xms900m -Xmx900m are used. The presence of these two metrics indicates that not enough Execution Memory was available during the computation phase so records had to be evicted to disk, a process that is bad for the application performance. The size is the fraction (based on spark.memory.storageFraction configuration property) of the maximum heap memory. I can say from experience that this is fortunately not the case so lets investigate the example from the article above in more detail and see why an OutOfMemory exception occurred. In this case, the available memory can be calculated for instances like DS4 v2 with the following formulas: As already mentioned, the Spark Executor processing the text file uses three cores which results in three tasks trying to load the first three lines of the input into memory at the same time. This variable sets exactly how much of the JVM will be dedicated to the caching and storage of RDDs. acquireExecutionMemoryis part of the MemoryManager abstraction. Shuffle size in memory = Shuffle Read * Memory Expansion Rate. In that case, the Spark Web UI should show two spilling entries (Shuffle spill (disk) and Shuffle spill (memory)) with positive values when viewing the details of a particular shuffle stage by clicking on its Description entry inside the Stage section. It is actually not a property that is explicitly set: Lets say we use two Spark executors and two cores per executor (executor-cores 2) as reflected in the image above. The size of such a String is twice its native size (each character consumes 2 bytes) plus some overhead for headers and fields which amortizes to a Memory Expansion Rate of 2. (-) reverted back to the original input file but made one small change in the application code which now processes it successfully (using .master(local)) You can see 3 main memory regions on the diagram: 1) Reserved Memory : Memory reserved by the system, and its size is UnifiedMemoryManager is createdusing apply factory. Once a app is running the next most likely error you will see is an OOM on a spark executor. Determining the largest record that might lead to an OOM error is much more complicated than in the previous scenario for a typical workload: The line lengths of all input files used (like generated_file_1_gb.txt) were the same so there was no smallest or largest record. To learn more, see our tips on writing great answers. Could not reserve enough space for object heap. The input to the failed Spark application used in the article referred to above is a text file (generated_file_1_gb.txt) that is created by a script similar to this. Making statements based on opinion; back them up with references or personal experience. The sizes for the two most important memory compartments from a developer perspective can be calculated with these formulas: Execution Memory = (1.0 spark.memory.storageFraction) * Usable Memory = 0.5 * 360MB = 180MB In Spark, I'm getting java.lang.OutOfMemoryError: Java heap space error when reading a String of around 1 GB from the HDFS from within a function. Spark Executor OOM: How to set Memory Parameters on Spark To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Spark is an extremely powerful tool for doing in-memory computation but its power comes with some sharp edges. acquireExecutionMemory asserts the invariants. What are the purpose of the extra diodes in this peak detector circuit (LM1815)? This file is 1GB in size and has ten lines, each line simply consists of a line number (starting with zero) that is repeated 100 million times. Show that involves a character cloning his colleagues and making them into videogame characters? If I am making some string inside a function executed by a task, is that in user memory or spark memory?
Am I doing something wrong here? Each active task gets the same chunk of Execution Memory (360MB), thus if its not applied, you can set this grammatically and try to see the effect. This is the memory pool that remains after the allocation of Spark Shuffle Partition Number = Shuffle size on disk (= Shuffle Read) / 150. spark.memory.fraction). UnifiedMemoryManager uses the fraction (based on spark.memory.fraction configuration property) of the "usable" memory for the maximum heap memory. This can be set as above on either the command line or in the SparkConf object. How does a tailplane provide downforce if it has the same AoA as the main wing? YARN will be responsible for resource allocations and each Spark executor will run inside a YARN container. In the end, acquireExecutionMemory requests the ExecutionMemoryPool to acquire memory of numBytes bytes (with the maybeGrowExecutionPool and the maximum size of execution pool functions). Apache Spark 1.2.1 standalone cluster giving java heap space error, how to solve java.lang.OutOfMemoryError: Java heap space when train word2vec model in Spark, Spark: Exception in thread "dag-scheduler-event-loop" java.lang.OutOfMemoryError: Java heap space, Spark ALS: Running out of java heap space: java.lang.OutOfMemoryError: Java heap space, Spark memory fraction vs Young Generation/Old Generation java heap split. How is this Container Memory determined? Thanks for contributing an answer to Stack Overflow! Assigning just one core to the Spark executor will prevent the Out Of Memory exception as shown in the following picture: Now there is only one active task that can use all Execution Memory and each record fits comfortably into the available space since 200MB < < 360MB. hard coded, 2) User Memory (in Spark 1.6 Java Heap Reserved Memory) * (1.0 Therefore each Spark executor has 0.9 * 12GB available (equivalent to the JVM Heap sizes in the images above) and the various memory compartments inside it could now be calculated based on the formulas introduced in the first part of this article. Then the Container Memory is
(-) switched to the DataFrame API instead of the RDD API which again crashes the application with an OOM Error If all else fails you may just need additional ram on each worker. Find centralized, trusted content and collaborate around the technologies you use most. How does one show this complex expression equals a natural number? These have become obsolete and using them will not have an effect unless the user explicitly requests the old static strategy by setting spark.memory.useLegacyMode to true. maxOnHeapStorageMemory is the difference between Maximum Heap Memory and the memory used in the on-heap execution memory pool. RAM and how, Spark makes completely no accounting on what you do there I am using Spark 1.6.1 and compiling with Spark 1.6 core library. Memory, and it is completely up to you to use it in a way you like. The remaining memory space (of the maximum heap memory) is used for the on-heap execution memory. UnifiedMemoryManager makes sure that the driver's system memory is at least 1.5 of the Reserved System Memory. This dynamic memory management strategy has been in use since Spark 1.6, previous releases drew a static boundary between Storage and Execution Memory that had to be specified before run time via the configuration properties spark.shuffle.memoryFraction, spark.storage.memoryFraction, and spark.storage.unrollFraction. (-) created a second input file that is twice the disk size of the original (generated_file_1_gb.txt) but will be processed successfully by ProcessFile.scala Things become a bit easier again when Spark is deployed without YARN in StandAlone Mode as is the case with services like Azure Databricks: Only one Spark executor will run per node and the cores will be fully used. This defeats the whole point of using Spark of course since there is no parallelism, all records are now processed consecutively. The on-disk-size of each line is easy to calculate, it is one byte for the line number multiplied by 100 million or ~100MB.
Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Finding the maximum would be much harder if not practically impossible when transformations and aggregations occur. With the formulas developed above, we can estimate the largest record size which would not crash the original version of the application (which uses .master(local)): We have around 120MB per task available so any record can only consume up to 120MB of memory. Can anyone Identify the make, model and year of this car? If in user memory, I should decrease the value of spark.memory.fraction, no?
Things become even more complicated in a distributed environment. Try Unravel Today for Free or Book a Demo, Memory mysteries I recently read an excellent blog series about Apache Spark but one article caught my attention as its author states: Lets try to figure out what happens with the application when the source file . I played around with the Python script that created the original input file here and Execution Memory is used for objects and computations that are typically short-lived like the intermediate buffers of shuffle operation whereas Storage Memory is used for long-lived data that might be reused in downstream computations. Sum of Convergent Series for Problem Like Schrdingers Cat. All rights reserved. UnifiedMemoryManager considers 300MB (300 * 1024 * 1024 bytes) as a reserved system memory while calculating the maximum heap memory. Container Memory = yarn.scheduler.maximum-allocation-mb / Number of Spark executors per node = 24GB / 2 = 12GB. How to encourage melee combat when ranged is a stronger option, Extract 2D quad mesh from 3D hexahedral mesh. In your case somehow seems like memory fraction setting was not applied.
As reflected in the picture above, the JVM heap size is limited to 900MB and default values for both spark.memory. Why apply a value of 1? A good side effect of this costly spilling is that the memory expansion rate can be easily approximated by dividing the value for Shuffle spill (memory) by Shuffle spill (disk) since both metrics are based on the same records and denote how much space they take up in-memory versus on-disk, therefore: The memory size of this object can then be directly determined by passing a reference to SizeEstimator.estimate, a version of this function that can be used outside of Spark can be found here. UnifiedMemoryManager is given the size of the on-heap storage memory (region) when created. acquireExecutionMemory selects the execution and storage pools, the storage region size and the maximum memory for the given MemoryMode. When was automatic Spark RDD partition cache eviction implemented? maxOnHeapStorageMemoryis part of the MemoryManager abstraction. The most common cause for an executor OOMing is that the application is trying to cache or load too much information into memory. Data Imbalance: what would be an ideal number(ratio) of newly added class's data? Otherwise, getMaxMemory throws an IllegalArgumentException: UnifiedMemoryManager makes sure that the executor memory (spark.executor.memory) is at least the Reserved System Memory. Copyright 2022 Unravel Data. Suppose we run on AWS/EMR and use a cluster of m4.2xlarge instance types, then every node has eight vCPUs (four physical CPUs) and 32GB memory according to https://aws.amazon.com/ec2/instance-types/. Visualizations will be useful for illuminating this mystery, the following pictures show Sparks memory compartments when running ProcessFile.scala on my MacBook: According to the system spec, my MacBook has four physical cores that amount to eight vCores. The example application does not cache any data so Execution Memory will eat up all of Storage Memory but this is still not enough: We can finally see the root cause for the application failure and the culprit is not the total input size but the individual record size: Each record consists of 100 million numbers (0 to 9) from which a java.lang.String is created. It seems as though decreasing that value had no effect. Announcing the Stacks Editor Beta release! Since the application was initializd with .master(local), three out of those eight virtual cores will participate in the processing. If you have a job that will require very little shuffle memory but will utilize a lot of cached RDDs increase this variable (example: Caching an RDD then performing aggregates on it.). Asking for help, clarification, or responding to other answers. Going distributed: Spark inside YARN containers, Memory and partitions in real life workloads, https://aws.amazon.com/ec2/instance-types/, https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hadoop-task-config.html, Tuning Spark applications: Detect and fix common issues with Spark driver. This results in an OOM error after a few seconds so this little experiment seems to validate the initial hypothesis that we cant process datasets bigger than the memory limits. Container Memory = (Instance Memory * 0.97 4800) The intermediate values needed for the last formula might be hard to determine in practice in which case the following alternative calculation can be used; it only uses values that are directly provided by the Web UI: The Shuffle Read Size per task for the largest shuffle stage should be around 150MB so the number of shuffle partitions would be equal to the value of Shuffle Read divided by it: Total Available On-Heap Memory for Storage, Access private members in Scala in Spark shell, Learning Jobs and Partitions Using take Action, Spark Standalone - Using ZooKeeper for High-Availability of Master, Spark's Hello World using Spark shell and Scala, Your first complete Spark application (using Scala and sbt), Using Spark SQL to update data in Hive using ORC files, Developing Custom SparkListener to monitor DAGScheduler in Scala, Working with Datasets from JDBC Data Sources (and PostgreSQL). And this supposition is true: It would be bad if Spark could only process input that is smaller than the available memory in a distributed environment, it implies that an input of 15 Terabytes in size could only be processed when the number of Spark executors multiplied by the amount of memory given to each executor equals at least 15TB. So How should we do boxplots with small samples? 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. In the twin paradox or twins paradox what do the clocks of the twin and the distant star he visits show when he's at the star? Naively we could think that a file bigger than available memory will fail the processing with OOM memory error.
Record Memory Size = Record size (disk) * Memory Expansion Rate "spark.memory.fraction" seems to have no effect, Design patterns for asynchronous API communication. Once an application succeeds, it might be useful to determine the average memory expansion rate for performance reasons as this could influence the choice of the number of (shuffle) partitions: One of the clearest indications that more partitions should be used is the presence of spilled tasks during a shuffle stage. spark.executor.memory = (0.8 * Container Memory). Should I remove older low level jobs/education from my CV at this point? boundary in your code might cause OOM error. The reverse does not hold true though, execution is never evicted by storage.
= 100MB * 2 = 200MB UnifiedMemoryManager takes the following to be created: While being created, UnifiedMemoryManager asserts the invariants.
To increase the user memory, I even decreased spark.memory.fraction to just 0.3, but I am still getting the same error.
Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, print all the configurations which were applied while running the job to see the parameter were set correctly like this logger.info(sparkContext.getConf.getAll.mkString("\n"), I checked that but it didn't contain spark.memory.fraction. 3) Spark Memory (Java Heap Reserved Memory) * Blamed in front of coworkers for "skipping hierarchy", Sets with both additive and multiplicative gaps. Further fraction properties are used. UnifiedMemoryManager asserts the following: Sum of the pool size of the on-heap ExecutionMemoryPool and on-heap StorageMemoryPool is exactly the maximum heap memory, Sum of the pool size of the off-heap ExecutionMemoryPool and off-heap StorageMemoryPool is exactly the maximum off-heap memory. Execution Memory per Task = (Usable Memory Storage Memory) / spark.executor.cores = (360MB 0MB) / 3 = 360MB / 3 = 120MB. UnifiedMemoryManager is a MemoryManager (with the onHeapExecutionMemory being the Maximum Heap Memory with the onHeapStorageRegionSize taken out). Not respecting this Additional memory properties have to be taken into acccount since YARN needs some resources for itself: Out of the 32GB node memory in total of an m4.2xlarge instance, 24GB can be used for containers/Spark executors by default (property yarn.nodemanager.resource.memory-mb) and the largest container/executor could use all of this memory (property yarn.scheduler.maximum-allocation-mb), these values are taken from https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hadoop-task-config.html.