Spark heap memory The overhead Oct 14, 2020 · 剩余的空间（40%）是为用户数据结构、Spark内部metadata预留的，并在稀疏使用和异常大记录的情况下避免OOM错误 spark. Kubelet will try to restart theOOMKilled container either on the same or another host. Oct 23, 2023 · In Spark, the Java Virtual Machine (JVM) heap is divided into two parts: On-Heap and Off-Heap memory. Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. memoryOverhead This issue is often caused by a lack of resources when opening large spark-event files. 1g, 2g). enabled (false by default) parameter, and set the memory size by spark. When working with images or doing memory intensive processing in spark applications, consider decreasing the spark. , 2g). Check the amount of memory used before and after we load the file into Spark. memory. memory "Amount of memory to use for the driver process, i. The size of the On-heap memory is configured by the –executor-memory or spark. e. This is a seatbelt for the Spark execution pipelines. Oct 15, 2021 · When spark. As you can see the memory areas in the worker node are On-Heap Memory, Off-Heap Memory and Overhead Memory. May 5, 2019 · 该参数官网给出的解释： Whether to enable the legacy memory management mode used in Spark 1. memory – specifies the driver’s process memory heap (default 1 GB) spark. memory (not to mention there is no Python code Since you are running Spark in local mode, setting spark. size: The total amount of memory for off-heap storage and execution, specified in bytes (e. enabled参数启用，并由spark. Feb 27, 2024 · // setting to 'true' allows Spark to use off-heap memory for some operations spark. Jul 24, 2019 · Off-heap模式：默认情况下Off-heap模式的内存并不启用，可以通过”spark. It's too late to do that as the App has started with memory allocation already set. fraction spark. Hence, you can use Executor memory overhead to allocate additional memory to off-heap memory. it won’t shrink heap memory. Overhead memory. (e. Oct 15, 2024 · Spark Memory Structure. Or some higher value. Oct 3, 2019 · Spark’s groupBy() requires loading all of the key values into memory at once to perform groupby. 2 Off-heap Memory. This will make more Jul 25, 2024 · Off-heap memory. The default value is also 1g. A detailed explanation about the usage of off-heap memory in Spark applications, and the pros and cons can be found here. Heap Memory: The largest portion of an Executor’s memory is allocated to the Java heap. lang. Spark UI with On Heap 2. memory, spark. overhead param. max. As you have already said, the default spark. OutOfMemoryError: Java heap space I'd like to increase the memory available to Spark by modifying the spark. The only additional "non-related" off heap memory I'm aware of is the Python memory, which is not not part of the memory overhead by Spark documentation: Dec 6, 2018 · Versions: Apache Spark 2. That is quite large. By analyzing the heap dump, we were able to determine the location of the problem. 5. Feb 12, 2023 · 文章浏览阅读1. theoretically, yes, it increases performance because it stores the underlying output of the dataframe in its memory. memory` property specifies the amount of memory that each Spark executor is allocated. but your memory won't always be enough to store the dataframe's output - so it gets spilled to disk and the process reads it back from there whenever required. When we request 2 GB executor memory, this amount refers to heap memory, managed by the Java Virtual Machine (JVM). fraction. In addition to this, Spark provides overhead Nov 1, 2018 · When we submit a spark app to yarn, spark will request memory for each container from yarn, the memory amount for each container is executor-memory + spark. 5 and before. By default, Spark uses On-memory heap only. 4. In this mode, memory is not allocated within the Java Virtual Machine (JVM) heap; instead, it . Spark provides the capability to use off-heap memory, which can be Jun 28, 2018 · 1) Removing spark. size指定堆外内存的大小（占用的空间划归JVM OffHeap内存）。---备注：我们现在未启用Off-heap模式的内存，因此，只介绍JVM模式的Executor内存管理。 Aug 24, 2020 · executor memory overhead does not include off-heap memory in 3. fraction 와 Reserved Memory 를 제외한) Apr 26, 2017 · Besides enabling OffHeap memory, you need to manually set its size to use Off-Heap memory for spark Applications. pyspark. Spark running on YARN, Kubernetes or Mesos, adds to that a memory overhead to cover for additional memory usage (OS, redundancy, filesystem cache, off-heap allocations, etc), which is calculated as memory_overhead_factor * spark. executor. x. Heap Summary - take & analyse a basic snapshot of the servers memory. fraction configuration property controls the proportion of the heap used by Spark (excluding reserved memory). 0. DirectPoolMemory The fraction of the heap used for Spark's memory cache is by default 0. 6, so 40% is reserved for this "user memory". 包括：堆内内存(on-heap Memory) 和堆外内存 (off-heap Memory) 两大区域，下面对这两块区域进行详细的说明。 堆内内存 (on-heap Memory) 默认情况下，Spark 仅仅使用了堆内内存，spark 对堆内内存的管理是一种逻辑上的"规划式"的管理，Executor 端的堆内内存区域在逻辑上被划 Feb 9, 2021 · Off-Heap memory is disabled by default with the property spark. Each executor memory is the sum of yarn overhead memory and JVM Heap memory. Use Off-Heap Memory. the theory is, spark actions can offload data to the driver causing it to run out of memory if not properly sized. 3G. Let us start a Spark shell with a max heap size for the driver of 12GB. enabled and spark. In order to use it: Generate a profile or heap summary using the appropriate spark commands. The default value for `spark. From docs: spark. memory // sets Jan 23, 2025 · Types of Memory in Spark Executors. Commented Apr 17, 2020 at 18:12. 0 MiB" 24/10/18 17:16:00 INFO org. Luckily, we can reduce this impact by writing memory-optimized code and using the storage outside the heap called off-heap. memory won't have any effect, as you have noticed. Apr 30, 2018 · PySpark will respect spark. Due to Spark's memory-centric approach, it is common to use 100GB or more memory as heap space, which is rarely seen in traditional Java applications. 07) is Oct 18, 2024 · 24/10/18 17:16:00 INFO org. memory to a higher value java. Be careful when using off-heap storage as it does not impact on-heap memory size, i. In Spark 1. x and 2. OutOfMemoryError: Unable to acquire * bytes of memory, got 0 在本文中，我们介绍了如何在运行时增加PySpark可用的内存。通过配置spark. This is controlled by property spark. 为了进一步优化内存的使用，减小GC开销，Spark Nov 3, 2023 · Off-Heap Memory: — Spark’s off-heap memory is primarily used for managing cached data and can be configured using spark. Spark UI with OffHeap Enabled_spark3内存管理 More information about spark can be found on GitHub, or you can come chat with us on Discord. What do I gather from this. memory value set to a fraction of the overall cluster memory. enabled – the option to use off-heap memory for Aug 9, 2024 · This is why certain Spark clusters have the spark. Jan 4, 2021 · The total off-heap memory for a Spark executor is controlled by spark. fraction – a fraction of the heap space (minus 300 MB * 1. ; Set Result Dec 26, 2020 · 堆外内存 (Off-heap Memory) Spark 1. Broadly speaking, spark Executor JVM memory can be divided into two parts. 0 Aug 29, 2023 · Off-Heap Memory: Spark can also allocate memory off-heap, outside the JVM heap space, which can reduce GC overhead. An executor heap is roughly divided into two areas: data caching area (also called storage memory) and shuffle work area. Hence GC cycles on the executor do not clean up off-heap. size: 0: The absolute amount of memory which can be used for off-heap allocation, in bytes unless otherwise specified. JVM Heap memory comprises of: RDD Cache Memory. buffer and spark. memory` property correctly: The `spark. 0, respectively). So, to define an overall memory limit Sep 10, 2015 · Following my comment, two things: 1) You need to watch out with the spark. memory to 50G, then with this formula and reading this article. Is that possible? If so, how? update Apr 17, 2020 · If your Spark application uses more heap memory, container OS kernel kills the java program, xmx < usage < pod. The legacy mode rigidly partitions the heap space into fixed-size regions, potentially leading to excessive spilling if the application was not tuned. apache. JVM heap of executor is divided into four parts Nov 9, 2020 · Step 10: Fix the memory leak. enabled // sets the amount of memory to use for executor processes spark. It should be large enough such that this fraction exceeds spark. memory setting :) Technically you could also increase the fraction used for Spark's memory cache, but I believe this is discouraged or at least requires you to do some additional configuration. sql. memory (with a minimum of 384 MB). fraction - the value is between 0 and 1. It’s called driver memory if the container holds the Spark driver, and executor memory if it holds a Spark Apr 11, 2020 · Formula: (Java Heap — Reserved Memory) * spark. offheap. memory` and `spark. limit. When you first load data into Py Spark, it’s stored in On-Heap memory as an RDD. If memory usage > pod. enabled – the option to use off-heap memory for certain operations (default false) Aug 28, 2020 · The main configuration parameter used to request the allocation of executor memory is spark. The spark. If you want to cache that RDD for faster access later, PySpark will move it to Off-Heap memory. With data-intensive applications as the streaming ones, bad memory management can add long pauses for GC. " – Nov 28, 2023 · Apache Spark’s on-heap and off-heap memory management, integral to its performance in big data processing, are significantly enhanced by the Tungsten optimizer. The Image below is the abstract Concept when Off-Heap memory is in action. enabled: If true, Spark will attempt to use off-heap memory for storage and execution. cache, CACHE TABLE) 또는 Broadcast, Driver 로 보내는 결과들이 이 영역의 메모리를 사용합니다. 5) reserved for execution and storage regions (default 0. Mar 19, 2022 · Memory Structure of Spark Worker Node. There is a heap to the left, with varying generations managed by the garbage collector. Off-heap memory, including Storage only memory and Execution memory, can be enabled and configured via the following parameters, i. mb property name, in the newest spark they changed it to spark. See the discussion of advanced GC tuning below for details. python process that goes with a May 13, 2024 · Heap Memory: This is the main memory for the JVM process running inside the container. large, which has 2 cores and 8G of memory each. ; Optimize Code: Work with smaller data subsets or avoid collect for large datasets. 2 with default settings, 54 percent of the heap is reserved for data caching and 16 percent for shuffle (the rest is for other use). 1. # Launch Spark shell with certain memory size $ bin/spark-shell --driver-memory 12g. May 20, 2022 · you'd have to cache it at smart points. The off-heap mode is controlled by the properties spark. 0: spark. The On-heap memory area May 20, 2024 · Solutions: Increase Driver Memory: Adjust the driver memory allocation using spark. memory", "4G") cannot be set inside the Spark App itself. Typically for a 32 GB container, it will be 2Gb (0. After seeing it's defaulting executor memory to 2G, I wanted to increase it, setting "spark. The concurrent tasks running inside Executor share JVM's On-heap memory. false: spark. storageFraction)代表Execution Memory占用Spark Memory百分比，默认值0. 6) Off-heap: spark. Apr 24, 2023 · When you first load the data into PySpark, it’s stored in On-Heap memory as an RDD. yarn. The off-heap memory is managed by Spark and not controlled by the executor JVM. memory、spark. storageFraction二、Spark 内存分配在Spark UI的表现 1. Oct 22, 2019 · By default, Spark uses On-heap memory only. 6, so if you need more than 524,1MB, you should increase the spark. The following figure illustrates a Spark application’s executor memory layout with its components. Nov 21, 2018 · pyspark --driver-memory 2g. storageFraction) Spark 1. And you can provide the size of the off-heap memory that will be used by your application. Viewer. 3. Shuffle Memory. OnHeapStorageMemory: Peak on heap storage memory in use, in bytes. fraction和spark. May 9, 2024 · spark. This memory is used for tasks and processing in Spark Job submission. 5，代表Storage Memory占用Spark Memory百分比，(1 - spark. Nov 2, 2019 · TL;DR: For Spark 1. #On-HeapMemory, #Off-HeapMemory, #On-heapVSOff-heap, #SparkMemoryManagement #DatabricksOptimization, #SparkOptimization, #DatabricksInterviewQuestions, #Spar spark. enabled参数，我们可以调整内存的分配。通过示例说明，我们展示了如何在PySpark中使用这些配置来读取大型数据并执行任务。 Apr 30, 2019 · –conf spark. TaskMemoryManager: "Acquired by org. spark includes a number of tools which are useful for diagnosing memory issues with a server. The reason for this is that the Worker "lives" within the driver JVM process that you start when you start spark-shell and the default memory used for that is 512M. Databricks Photon leverages the concept of off-heap widely Jan 9, 2022 · Spark内存分为堆内内存(On-heap Memory)和堆外内存(Off-heap Memory)。 其中堆内内存基于 JVM内存 模型，而堆外内存则通过调用底层 JDK Unsafe API 。 两种内存类型统一由Spark内存管理模块接口实现。 Jan 13, 2024 · Below are explanations of each memory region in the Spark executor. Make sure you set this property to a value that is less than the amount of memory available on the system. This memory is used for off-heap storage (like caching) and is managed by Spark Feb 10, 2025 · 在大数据计算和Java（包括Spark）中，**堆内存（On-Heap Memory）和堆外内存（Off-Heap Memory）**是两个重要的概念，主要涉及内存管理、GC（垃圾回收）开销以及性能优化。下面从原理、区别、使用场景等方面进行详细解读。 1. 6 开始引入了 Off-heap memory (详见SPARK-11389)。这种模式不在 JVM 内申请内存，而是调用 Java 的 unsafe 相关 API 进行诸如 C 语言里面的 malloc() 直接向操作系统申请内存。 Set the `spark. Working Heap/Actual Heap Memory. 4, as far as I understand YARN memory overhead (for executors) is any off heap memory allocated by my Spark program (outside JVM). memoryOverhead. memoryOverhead (spark. memory + spark. memoryOverhead + spark. 6k次，点赞2次，收藏6次。一、参数 spark. 堆内存（On-Heap Memory） 定义 I'm trying to build a recommender using Spark and just ran out of memory: Exception in thread "dag-scheduler-event-loop" java. It Dec 27, 2021 · Storage Memory (spark. x, Total Off-Heap Memory = spark. memory property, in PySpark, at runtime. Upon setting it, it says I can't set it to such Sep 22, 2024 · The remaining space after subtraction from 1 defines the execution memory. 6 and 2. 堆外内存 (Off-heap Memory) Spark 1. /spark profiler start --thread * to start the profiler and track all threads. size after enabling it. Calculation for 4GB: (4096MB -300MB) * 0. And SPARK_DAEMON_MEMORY is a cluster manager property, not application, so not really related to spark. memory in the cluster mode and through the --driver-memory command line option in the client mode. If off-heap memory use is enabled, then spark. fraction is 0. size and spark. Spark running on YARN, Kubernetes or Mesos, adds to that a memory overhead to cover for additional memory usage (OS, redundancy, filesystem cache, off-heap allocations, etc), which is calculated as memory_overhead_factor * spark. driver. It is advantageous for repeatedly accessing large data and is ideal for iterative I'm using pyspark v2. Peak off heap execution memory in use, in bytes. 0. The maximum heap will be 30. config("spark. enabled=true, Spark can make use of off-heap memory for shuffles and caching (StorageLevel. fraction should be set in order to fit this amount of heap space comfortably within the JVM’s old or “tenured” generation. gluten. 6, meaning 60% of the heap will be Jan 24, 2024 · Off-Heap Memory is utilized in Apache Spark to store data outside the JVM heap, preventing memory usage conflicts. Increasing partitions is not working because you might be having large data with similar group by key. buffer. storageFraction Note: Non-heap memory includes off-heap memory (when spark. size (0 by default) parameter. This synergy ensures optimal Nov 28, 2024 · Heap memory: Here all the JVM processes runs (and that’ll be your spark code). 0 - spark. storageFraction: Fraction of the unified memory used for storage (caching). memory` is 1GB, and the default value for `spark. Execution 메모리의 특성으로 인해 이 풀에서 블록을 강제로 제거할 수 없습니다. Apr 1, 2021 · I am pretty sure that . Aug 19, 2024 · Apache Spark is designed to process large-scale data efficiently by leveraging in-memory computing. Spark has native support to use off-heap memory. size: Sets the maximum off-heap memory size. Aug 21, 2019 · 参数spark. After the data has been uploaded, click the link to open the viewer. Spark makes it possible to use off-heap storage for certain operations. memory configuration property. Oct 29, 2020 · 文章浏览阅读1. YARN memory overhead is a portion of an executor’s memory dedicated to off-heap storage. Advanced Usage Arguments May 21, 2024 · Most of the time, when Spark executors run out of memory, the culprit is the YARN memory overhead. spark submit参数配置： –executor-memory 或者 spark. On-Heap memory is managed by the JVM and is used to store Resilient Distributed Datasets (RDDs). May 10, 2023 · I'm under the impression that if I set spark. 5: spark. 3 GiB Jun 25, 2021 · The off-heap memory is managed outside the executor JVM. User Memory (전체 JVM Heap 에서 spark. size included within) For Spark 3. spark. size. size>. size (credit from this page) Detailed explanation: (Java Heap — Reserved Memory) * spark. TaskMemoryManager: "0 bytes of memory were used Nov 17, 2021 · 文章目录环境参数Executor 内存划分堆内内存（On-Heap Memory）堆外内存（Off-Heap Memory）动态调节机制Task 能申请到的内存 环境参数 spark 内存模型中会涉及到多个配置，这些配置由一些环境参数及其配置值有关，为防止后面理解混乱，现在这里列举出来，如果忘记了 Jan 28, 2020 · Based on this, a Spark driver will have the memory set up like any other JVM application, as shown below. However, if off-heap memory is properly configured, it helps performance for multiple workloads. Spark Memory Fraction . By default, Spark uses a reasonable fraction of the spark off heap memory config and tungsten – mazaneicha. The default value is 0. TreeMemoryConsumer@182a8cbe: 104. This is broken into 2 segments Storage Memory and Execution Memory. size = Xgb. enabled. enabled=true) and memory used by other driver processes (e. The Spark heap size May 14, 2020 · The Python interpreter needs to process the serialized data in Spark executor’s off-heap memory. OffHeapStorageMemory: Peak off heap storage memory in use, in bytes. OFF_HEAP). Mar 27, 2024 · Spark executor memory overhead refers to additional memory allocated beyond the user-defined executor memory in Apache Spark. Feb 28, 2024 · This memory is by default 384M or 10% of executor memory whichever is higher, however one can modify its value by using spark. enabled> and <spark. 0 and above. enabled”参数开启，并由spark. 5, default) 캐싱 (DataFrame. memoryOverhead, spark. When data is Jan 3, 2020 · In each executor, Spark allocates a minimum of 384 MB for the memory overhead and the rest is allocated for the actual workload. memory` configuration options. Example: --conf spark. This portion may vary wildly depending on your exact version and implementation of Java, as well as which garbage collection algorithm you use. memory parameter when the Spark Application starts. This website is also an online viewer for spark data. Jan 8, 2024 · The two main Spark Executor memories are: Off-heap memory; It was introduced in Spark version 1. We will Jun 3, 2020 · Off-heap memory usage is available for execution and storage regions (since Apache Spark 1. you can play with the executor memory too, although it doesn't seem to be the problem here (the default value for the executor is 4GB). Spark memory and User memory. 6+에서는 Execution 메모리와 Storage 메모리 사이에 엄격한 경계가 없습니다. The default value for this is 10% of executor memory subject to a minimum of 384MB. memtarget. The On-Heap Memory area comprises 4 sections. From documentation: "The maximum memory size of container to running executor is determined by the sum of spark. size参数设定堆外空间的大小。除了没有other空间，堆外内存与堆内内存的划分方式相同，所有运行中的并发任务共享存储内存和执行内存。 内存管理接口 It can be configured using the --executor-memory flag or spark. size which are available in Spark 1. <spark. . memory properties, same as any other package, but JVM heap is mostly irrelevant for PySpark programs. Add a comment | 1 Answer Sorted by: Reset to 在默认情况下堆外内存并不启用，可通过配置spark. I) On-Heap Memory: Most of Spark’s operations run on On-Heap memory, which is managed by the JVM and used to store Resilient Distributed Datasets (RDDs). enabled=true and increasing driver memory to something like 90% of the available memory on the box. size=1g; Adjust off-heap memory size based on your application's requirements and the May 28, 2015 · With Spark being widely used in industry, Spark applications' stability and performance tuning issues are increasingly a topic of interest. g. Aug 7, 2024 · By default, Off-heap memory is disabled, but we can enable it by the spark. 6. offHeap. To use off-heap memory, the size of off-heap memory can be set by spark. /spark profiler start --alloc to start the profiler and profile memory allocations (memory pressure) instead of CPU usage. What is really involved with spill problem is On-Heap Memory. memory` is 1GB. Maximum heap size settings can be set with spark. The heap is used for storing objects Aug 16, 2021 · By default, Spark uses on-heap memory only. A simple view of the JVM's heap, see memory usage and instance counts for each class; Not intended to be a full replacement of proper memory analysis tools. memory 6g" on spark config on cluster setup. 3k次。本文详细介绍了Spark对内存的管理，包括堆内(On-Heap)和堆外(Off-Heap)内存规划。堆内内存分为动态分配、淘汰与落盘、执行内存（涉及Shuffle Write和Shuffle Read）以及其他内存。 May 27, 2023 · Spark内存分为堆内内存(On-heap Memory) 和 堆外内存(Off-heap Memory)。 其中 堆内内存基于JVM内存模型 ，而 堆外内存则通过调用底层JDK Unsafe API 。 两种内存类型统一由Spark内存管理模块接口( MemoryManager )实现。 Dec 13, 2017 · 一、堆内(On-Heap Memory)和堆外(Off-Heap Memory)内存规划 Executor作为一个JVM进程，Executor的内存管理建立在JVM的内存管理之上。 Spark 对堆内内存进行JVM内存管理，引入了堆外内存，使之可以直接在工作节点的系统内存中开辟空间，进一步优化了内存的使用；其中，堆外 Dec 12, 2023 · spark. Can off-heap memory be used to store broadcast variables? Oct 31, 2020 · This YARN memory(off-heap memory) is used to store spark internal objects or language-specific objects, thread stacks, and NIO buffers. It is used for the various internal Spark overheads. pyspark --driver-memory 2g --executor-memory 8g. For datasets with large or nested records or when using complex UDFs, this processing can consume large amounts of off-heap memory and can lead to OOM exceptions resulting from exceeding the yarn memoryOverhead. Unlike traditional disk-based processing systems, Spark can cache intermediate data in The value of spark. (see below) Heap Dump Jun 13, 2017 · spark. An example of a cluster worker node’s memory layout can be as follows. memory 参数配置，即JVM最大分配的堆内存 (-Xmx)。Spark为了更高效的使用这部分内存，对这部分内存进行了逻辑上的划分管理。 The amount of memory allocated to the PySpark driver and executor processes can be set using the `spark. 6 开始引入了 Off-heap memory (详见SPARK-11389)。这种模式不在 JVM 内申请内存，而是调用 Java 的 unsafe 相关 API 进行诸如 C 语言里面的 malloc() 直接向操作系统申请内存。这种方式下 Spark 可以直接操作系统堆外内存，减少了不必要的内存开销 Nov 26, 2022 · Many JVMs default this to 2, meaning that the Old generation occupies 2/3 of the heap. memory 大小由 Spark 应用程序启动时的 –executor-memory 或 spark. storageFraction = 0. /spark profiler start --timeout <seconds> to start the profiler and automatically stop it after x seconds. enabled: false: If true, Spark will attempt to use off-heap memory for certain operations. /bin/spark-shell --driver-memory 4g. 75 = 2847MB. OnHeapUnifiedMemory: Peak on heap memory (execution and storage). limit, your host OS cgroup kills the container. spark. It is crucial for managing off-heap memory, storing internal data structures, and accommodating system overhead. size must be positive. fraction * (1. I'm setting the worker and driver type to AWS m6gd. Check memory size with uid, rss and pid. The spark memory is further bifurcated in 2 parts: Storage memory pool: cache memory for dataframes; Jul 18, 2024 · spark. OffHeapUnifiedMemory: Peak off heap memory (execution and storage). TaskMemoryManager: "Memory used in task 11" 24/10/18 17:16:00 INFO org. autoBroadcastJoinThreshold to -1 or increase the spark driver memory by setting spark. *. Note that Off-heap memory model includes only Storage memory and Execution memory. Aug 22, 2023 · I'm using Databricks Jobs Cluster to run some jobs. kryoserializer. where SparkContext is initialized. memoryOverhead ， but spark doesn't tell yarn how much memory is for on heap, how much is for offheap, then I don't understand how spark could use offheap memory in the yarn The main configuration parameter used to request the allocation of executor memory is spark. Try like this, for example: $ . 5表示Spark Memory中Execution Memory和Storage Memory各占一半。 1. You probably are aware of this since you didn't set executor memory, but in local mode the driver and the executor all run in the same process which is controlled by driver-memory. storageFraction默认0. memoryOverhead options are typically more in place. But the peak JVM onHeap I'm seeing in Spark UI is 39. What we’ve seen in this post is an example of how to diagnose a memory leak Feb 11, 2017 · Essentially, do I need to set an initial java heap memory allocation that is greater than the memory I will allocate to a spark or does it manage that on default--and what are the use cases of modifying the JVM through environmental variables outside of Spark? As a workaround, you can either disable broadcast by setting spark. hmkhfaoehqofwyyccelnbkzipvhnfqbcwqoihpjihaavlkkvufjvvapsczmoogtatpwjfmaau