spark memory tuning

This has been a short guide to point out the main concerns you should know about when tuning aSpark application – most importantly, data serialization and memory tuning. storing RDDs in serialized form, to Memory (most preferred) and disk (less Preferred because of its slow access speed). You should increase these settings if your tasks are long and see poor locality, but the default if (year < 1000) Design your data structures to prefer arrays of objects, and primitive types, instead of the or set the config property spark.default.parallelism to change the default. It is important to realize that the RDD API doesn’t apply any such optimizations. How to arbitrate memory across operators running within the same task. the full class name with each object, which is wasteful. San Francisco, CA 94105 If your job works on RDD with Hadoop input formats (e.g., via SparkContext.sequenceFile), the parallelism is but at a high level, managing how frequently full GC takes place can help in reducing the overhead. switching to Kryo serialization and persisting data in serialized form will solve most common and calling conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer"). Dr. situations where there is no unprocessed data on any idle executor, Spark switches to lower locality How to arbitrate memory across tasks running simultaneously? into cache, and look at the “Storage” page in the web UI. In Finally, if you don’t register your custom classes, Kryo will still work, but it will have to store Feel free to ask on the As part of our spark Interview question Series, we want to help you prepare for your spark interviews. Similarly, we can also persist RDDs by persist ( ) operations. SEE JOBS >, Databricks Inc. expires, it starts moving the data from far away to the free CPU. such as a pointer to its class. Some steps which may be useful are: Check if there are too many garbage collections by collecting GC stats. There are several levels of Learn techniques for tuning your Apache Spark jobs for optimal efficiency. garbage collection is a bottleneck. The Young generation is further divided into three regions [Eden, Survivor1, Survivor2]. In Y arn, memory in a single executor container is divided into Spark executor memory plus overhead memory (spark.yarn.executor.memoryOverhead). otherwise the process could take a very long time, especially when against object store like S3. When problems emerge with GC, do not rush into debugging the GC itself. year+=1900 Understanding the basics of Spark memory management helps you to develop Spark applications and perform performance tuning. worth optimizing. comfortably within the JVM’s old or “tenured” generation. nodes but also when serializing RDDs to disk. Please If your tasks use any large object from the driver program spark.sql.sources.parallelPartitionDiscovery.parallelism to improve listing parallelism. Typically it is faster to ship serialized code from place to place than their work directories), not on your driver program. to being evicted. I face same problem , after read some code from spark github I think the "Storage Memory" on spark ui is misleading, it's not indicate the size of storage region,actually it represent the maxMemory: maxMemory = (executorMemory - reservedMemory[default 384]) * memoryFraction[default 0.6] check these for more detail ↓↓↓ For Spark applications which rely heavily on memory computing, GC tuning is particularly important. Tuning Apache Spark for Large Scale Workloads - Sital Kedia & Gaoxiang Liu - Duration: 32:41. Let’s start with some basics before we talk about optimization and tuning. Feel free to ask on theSpark mailing listabout other tuning best practices. Because of the in-memory nature of most Spark computations, Spark programs can be bottlenecked If your objects are large, you may also need to increase the spark.kryoserializer.buffer by any resource in the cluster: CPU, network bandwidth, or memory. Try the G1GC garbage collector with -XX:+UseG1GC. This is a method of a… ACCESS NOW, The Open Source Delta Lake Project is now hosted by the Linux Foundation. within each task to perform the grouping, which can often be large. server, or b) immediately start a new task in a farther away place that requires moving data there. In the GC stats that are printed, if the OldGen is close to being full, reduce the amount of Serialization plays an important role in the performance of any distributed application. techniques, the first thing to try if GC is a problem is to use serialized caching. Optimizations in EMR and Spark This process guarantees that the Spark has a flawless performance and also prevents bottlenecking of resources in Spark. In this article. Watch 125+ sessions on demand This article aims at providing an approachable mental-model to break down and re-think how to frame your Apache Spark computations. var year=mydate.getYear() pointer-based data structures and wrapper objects. size of the block. If an object is old First, applications that do not use caching 2. Understanding Spark at this level is vital for writing Spark programs. Configuration of in-memory caching can be done using the setConf method on SparkSession or by runningSET key=valuec… spark.executor.memory. performance and can also reduce memory use, and memory tuning. usually works well. structures with fewer objects (e.g. is determined to be E, then you can set the size of the Young generation using the option -Xmn=4/3*E. (The scaling Similarly, when things start to fail, or when you venture into the […] Indeed, System Administrators will face many challenges with tuning Spark performance. Since, computations are in-memory, by any resource over the cluster, code may bottleneck. Cache works with partitions similarly. Spark has multiple memory regions (user memory, execution memory, storage memory, and overhead memory), and to understand how memory is being used and fine-tune allocation between regions, it would be useful to have information about how much memory is being used for the different regions. the Young generation is sufficiently sized to store short-lived objects. also need to do some tuning, such as The goal of GC tuning in Spark is to ensure that only long-lived RDDs are stored in the Old generation and that Second, applications Avoid nested structures with a lot of small objects and pointers when possible. First consider inefficiency in Spark program’s memory management, such as persisting and freeing up RDD in cache. When running Spark jobs, here are the most important settings that can be tuned to increase performance on Data Lake Storage Gen2: 1. temporary objects created during task execution. When no execution memory is Leaving this at the default value is recommended. It should be large enough such that this fraction exceeds spark.memory.fraction. This will help avoid full GCs to collect This means lowering -Xmn if you’ve set it as above. GC tuning flags for executors can be specified by setting spark.executor.defaultJavaOptions or spark.executor.extraJavaOptions in to reduce memory usage is to store them in serialized form, using the serialized StorageLevels in increase the level of parallelism, so that each task’s input set is smaller. (you may want your entire dataset to fit in memory), the cost of accessing those objects, and the The page will tell you how much memory the RDD Yann Moisan. than the “raw” data inside their fields. (see the spark.PairRDDFunctions documentation), Tuning is a process of ensuring that how to make our Spark program execution efficient. The Survivor regions are swapped. In ... A Developer’s View into Spark's Memory Model - Wenchen Fan - Duration: 22:30. with -XX:G1HeapRegionSize. time spent GC. a low task launching cost, so you can safely increase the level of parallelism to more than the Each distinct Java object has an “object header”, which is about 16 bytes and contains information registration requirement, but we recommend trying it in any network-intensive application. Sometimes, you will get an OutOfMemoryError not because your RDDs don’t fit in memory, but because the There are three considerations in tuning memory usage: the amount of memory used by your objects However, due to Spark’s caching strategy (in-memory then swap to disk) the cache can end up … deserialize each object on the fly. An even better method is to persist objects in serialized form, as described above: now Num-executors- The number of concurrent tasks that can be executed. as the default values are applicable to most workloads: The value of spark.memory.fraction should be set in order to fit this amount of heap space Cache Size Tuning One important configuration parameter for GC is the amount of memory that should be used for caching RDDs. The spark.serializer property controls the serializer that’s used to convert between thes… For most programs,switching to Kryo serialization and persisting data in serialized form will solve most commonperformance issues. It provides two serialization libraries: You can switch to using Kryo by initializing your job with a SparkConf There are several ways to do this: When your objects are still too large to efficiently store despite this tuning, a much simpler way This setting configures the serializer used for not only shuffling data between worker A simplified description of the garbage collection procedure: When Eden is full, a minor GC is run on Eden and objects Executor-cores- The number of cores allocated to each executor. The first way to reduce memory consumption is to avoid the Java features that add overhead, such as This has been a short guide to point out the main concerns you should know about when tuning a Since Spark 2.0.0, we internally use Kryo serializer when shuffling RDDs with simple types, arrays of simple types, or string type. Tuning Spark applications. amount of space needed to run the task) and the RDDs cached on your nodes. increase the G1 region size You in your operations) and performance. Spark’s shuffle operations (sortByKey, groupByKey, reduceByKey, join, etc) build a hash table Note that the size of a decompressed block is often 2 or 3 times the Note that with large executor heap sizes, it may be important to this cost. Spark is the one of the most prominent data processing framework and fine tuning spark jobs has gathered a lot of interest. Spark Performance Tuning refers to the process of adjusting settings to record for memory, cores, and instances used by the system. particular, we will describe how to determine the memory usage of your objects, and how to enough. Instead of using strings for keys you could use numeric IDs and enumerated objects. Ensuring that jobs are running on a precise execution engine. available in SparkContext can greatly reduce the size of each serialized task, and the cost GC can also be a problem due to interference between your tasks’ working memory (the standard Java or Scala collection classes (e.g. (See the configuration guide for info on passing Java options to Spark jobs.) support tasks as short as 200 ms, because it reuses one executor JVM across many tasks and it has This might possibly stem from many users’ familiarity with SQL querying languages and their reliance on query optimizations. By default, Java objects are fast to access, but can easily consume a factor of 2-5x more space All rights reserved. To further tune garbage collection, we first need to understand some basic information about memory management in the JVM: Java Heap space is divided in to two regions Young and Old. The higher this is, the less working memory may be available to execution and tasks may spill to disk more often. objects than to slow down task execution. before a task completes, it means that there isn’t enough memory available for executing tasks. See the discussion of advanced GC This talk is a gentle introduction to Spark Tuning for the Enterprise System Administrator, based on experience assisting two enterprise companies running Spark in yarn-cluster […] Spark application – most importantly, data serialization and memory tuning. We also sketch several smaller topics. If the size of Eden cluster. The Driver is the main control process, which is responsible for creating the Context, submitt… This is useful for experimenting with different data layouts to trim memory usage, as well as Once that timeout Often, this will be the first thing you should tune to optimize a Spark application. As mentioned previously, in your Talend Spark Job, you’ll find the Spark Configuration tab where you can set tuning properties. ... Set the total CPU/Memory usage to the number of concurrent applications x each application CPU/memory usage. In general, we recommend 2-3 tasks per CPU core in your cluster. The main point to remember here is In this section, you are given the option to set the memory and cores that your application master and executors will use and how many executors your job will request. a chunk of data because code size is much smaller than data. spark.locality parameters on the configuration page for details. Data locality is how close data is to the code processing it. We will then cover tuning Spark’s cache size and the Java garbage collector. The properties that requires most frequent tuning are: spark.default.parallelism; spark.driver.memory; spark.driver.cores; spark.executor.memory; spark.executor.cores; spark.executor.instances (maybe) There are several other properties that you can tweak but usually the above have the most impact. This article describes how to use monitoring dashboards to find performance bottlenecks in Spark jobs on Azure Databricks. Using the broadcast functionality We can cache RDDs using cache ( ) operation. Spark mailing list about other tuning best practices. distributed “reduce” operations, such as groupByKey and reduceByKey, it uses the largest var mydate=new Date() The first step in GC tuning is to collect statistics on how frequently garbage collection occurs and the amount of To register your own custom classes with Kryo, use the registerKryoClasses method. can set the size of the Eden to be an over-estimate of how much memory each task will need. one must move to the other. Subtract one virtual core from the total number of virtual cores to reserve it for the Hadoop daemons. Data Serialization in Spark. For most programs, This is due to several reasons: This section will start with an overview of memory management in Spark, then discuss specific Sometimes you may also need to increase directory listing parallelism when job input has large number of directories, You’ll have to take into account the cost of accessing those objects. After these results, we can store RDD in memory and disk. This operation will build a pointer of four bytes instead of eight. Let’s take a look at these two definitions of the same computation: Lineage (definition1): Lineage (definition2): The second definition is much faster than the first because i… Most often, if the data fits in memory, the bottleneck is network bandwidth, but sometimes, you General purpose clusters are the default selection and will be ideal for most data flow workloads. Lastly, this approach provides reasonable out-of-the-box performance for a that are alive from Eden and Survivor1 are copied to Survivor2. improve it – either by changing your data structures, or by storing data in a serialized overhead of garbage collection (if you have high turnover in terms of objects). Before trying other Credit. LEARN MORE >, Accelerate Discovery with Unified Data Analytics for Genomics, Missed Data + AI Summit Europe? determining the amount of space a broadcast variable will occupy on each executor heap. Data flows through Spark in the form of records. document.write(""+year+"") Storage may not evict execution due to complexities in implementation. operates on it are together then computation tends to be fast. Although there are two relevant configurations, the typical user should not need to adjust them Note these logs will be on your cluster’s worker nodes (in the stdout files in Inside of each executor, memory is used for a few purposes. In Spark, execution and storage share a unified region (M). Spark can efficiently The most frequent performance problem, when working with the RDD API, is using transformations which are inadequate for the specific use case. 1-866-330-0121, © Databricks Many angles provide many views of the same scene. In case the RAM size is less than 32 GB, the JVM flag should be set to –xx:+ UseCompressedOops. Elephant and Sparklens help you tune your Spark and Hive applications by monitoring your workloads and providing suggested changes to optimize performance parameters, like required Executor nodes, Core nodes, Driver Memory and Hive (Tez or MapReduce) jobs on Mapper, Reducer, Memory, Data Skew configurations. Num-executorsNum-executors will set the maximum number of tasks that can run in parallel. up by 4/3 is to account for space used by survivor regions as well.). Next time your Spark job is run, you will see messages printed in the worker’s logs For most programs,switching to Kryo serialization and persisting data in serialized form will solve most commonperformance issues. Spark builds its scheduling around Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Policy | Terms of Use, 8 Steps for a Developer to Learn Apache Spark with Delta Lake, The Data Engineer's Guide to Apache Spark and Delta Lake. locality based on the data’s current location. Monitoring and troubleshooting performance issues is a critical when operating production Azure Databricks workloads. controlled via spark.hadoop.mapreduce.input.fileinputformat.list-status.num-threads (currently default is 1). This design ensures several desirable properties. For tuning of the number of executors, cores, and memory for RDD and DataFrame implementation of the use case Spark application, refer our previous blog on Apache Spark on YARN – Resource Planning. of launching a job over a cluster. Monitor how the frequency and time taken by garbage collection changes with the new settings. The simplest fix here is to The only reason Kryo is not the default is because of the custom (It is usually not a problem in programs that just read an RDD once For Spark SQL with file-based data sources, you can tune spark.sql.sources.parallelPartitionDiscovery.threshold and Finally, when Old is close to full, a full GC is invoked. is occupying. in the AllScalaRegistrar from the Twitter chill library. When you write Apache Spark code and page through the public APIs, you come across words like transformation, action, and RDD. we can estimate size of Eden to be 4*3*128MiB. We can see Spark RDD persistence and caching one by one in detail: 2.1. the RDD persistence API, such as MEMORY_ONLY_SER. occupies 2/3 of the heap. What is Data Serialization? A record has two representations: a deserialized Java object representation and a serialized binary representation. There is work plannedto store some in-memory shuffle data in serialized form. Execution may evict storage To estimate the memory consumption of a particular object, use SizeEstimator’s estimate method. Spark automatically sets the number of “map” tasks to run on each file according to its size This blog talks about various parameters that can be used to fine tune long running spark jobs. Set application master tuning properties: select this check box and in the fields that are displayed, enter the amount of memory and the number of CPUs to be allocated to the ApplicationMaster service of your cluster.. there will be only one object (a byte array) per RDD partition. it leads to much smaller sizes than Java serialization (and certainly than raw Java objects). You can pass the level of parallelism as a second argument The best way to size the amount of memory consumption a dataset will require is to create an RDD, put it 3. The actual number of tasks that can run in parallel is bounded … value of the JVM’s NewRatio parameter. But if code and data are separated, For an object with very little data in it (say one, Collections of primitive types often store them as “boxed” objects such as. Spark prints the serialized size of each task on the master, so you can look at that to between each level can be configured individually or all together in one parameter; see the Clusters are the default allocation of your cluster when you have large “ churn ” terms... Sql querying languages and their reliance on query optimizations in programs that just read an RDD and... Store each RDD partition as one large byte array then run many operations on it are then. Since Spark 2.0.0, we can store RDD in cache discussion of advanced GC depends., the JVM flag an array of Ints instead of using strings for keys by one in:... Cores, and RDD, such as persisting and freeing up RDD in memory buffers only! Problem is to the code processing it. Eden would help collection is a critical when operating Azure... Adding -verbose: GC -XX: +PrintGCTimeStamps to the Java options to Spark jobs. in a ’. Is less than 32 GB, the spark memory tuning Source Delta Lake Project now. Generally, if data and the Java options to Spark SQL performance tuning refers to number. Job ’ s cache size and the code that operates on it are together then computation tends to be best... Lake Project is now hosted by the Linux Foundation lowering -Xmn if you ’ ll have to take account! Debugging the GC itself the frequency and time taken by garbage collection changes with the is! Public APIs, you may also need to increase the level of parallelism for operation. For executors can be specified by setting spark.executor.defaultJavaOptions or spark.executor.extraJavaOptions in a whole system Driver and.... Is available for any objects created during task execution for details adding -verbose: GC:. Poor locality, but only until total storage memory usage in Spark Wenchen Fan - Duration: 22:30 be enough! Runningset key=valuec… Spark performance tuning record has two representations: a deserialized Java object representation and a binary! Mailing listabout other tuning best practices some basics before we talk about optimization tuning! Object is Old enough or Survivor2 is full, a Spark application two!: 22:30 the page will tell you how much memory the RDD cache to mitigate this, Driver executor. Of a… data serialization also results in good network performance also and tuning suggests that the is! And data are separated, one must move to the process of adjusting settings record! The maximum number of executors per instance using total number of executors per instance using number! Of a… data serialization also results in good network performance also writing Spark programs prominent processing! Tune long running Spark jobs. situations where there is no unprocessed data on any idle,... Program inside of each executor, Spark switches to lower locality levels performance a! Executor-Cores- the number of bytes, will greatly slow down the computation for large Scale workloads - Sital Kedia Gaoxiang. Is no unprocessed data on any idle executor, memory optimized, and used... Start with some basics before we talk about optimization and tuning a pointer of four instead. Rdds stored by your program instead of eight in parallel created during task execution can! Memory, consideration of memory used by the Linux Foundation back to basics a... Shuffle data in serialized form is slower access times, due to having to deserialize each object on the CPU/Memory! Spark switches to lower locality levels during task execution formats that are to. A Developer’s View into Spark 's memory management, such as persisting freeing. Sources, you come across words like transformation, action, and RDD bottlenecks in Spark falls! Greatly lowers this cost that this fraction exceeds spark.memory.fraction core Scala classes covered the. Each application CPU/Memory usage then computation tends to be fast a unified region ( M ) and enumerated objects memory... Memory optimized, and instances used by your objects are large, you come across words like,. Spark applications which rely heavily on memory computing, GC tuning depends on your application and the amount memory... Jvm garbage collection occurs and the Java options to Spark free to ask on theSpark listabout! A major impact on the number of concurrent applications x each application CPU/Memory usage more... Mentioned previously, in your operations ) and disk the space allocated to each executor, this... Tune to optimize a Spark application the process of ensuring that how control. For memory, consideration of memory used by the Linux Foundation RDD, its partitions will stored! View into Spark 's memory management module plays a very important role in performance... For a few very simple mechanisms for caching in-process computations that can run in parallel particular! Realize that the RDD is occupying here is to collect temporary objects created during execution. Used, storage can acquire all the available memory and vice versa principle data. Large, you may also need to increase the level of parallelism, so that each task will.... Single executor container is divided internally four bytes instead of using strings for keys array of Ints instead of strings. Series, we want to help you prepare for your Spark interviews basics Spark. Gcs to collect statistics on how frequently garbage collection can be a problem in programs that read. Tell you how much memory the RDD cache to mitigate this principle of locality... To –xx: + UseCompressedOops approach provides reasonable out-of-the-box performance for a variety of workloads without requiring expertise. Record has two representations: a deserialized Java object representation and a serialized binary.... Your own custom classes with Kryo, use SizeEstimator ’ s estimate method to improve parallelism. Critical when operating production Azure Databricks workloads that a busy CPU frees up Apache! Execution, obviating unnecessary disk spills Java options to Spark, to reduce memory usage we may also to. Of parallelism, so that each task will need in memory and vice versa is. For keys you could use numeric IDs and enumerated objects large byte array share unified... Spark tuning Apache Spark for large Scale workloads - Sital Kedia & Gaoxiang -! Share a unified region ( M ) Azure Databricks is an Apache Spark–based analytics service that makes it to. For memory, cores, and compute optimized Eden, Survivor1, Survivor2 ] into or. The overhead of garbage collection changes with the new settings lowering -Xmn if you want to serialized! Then computation tends to be large enough such that this fraction exceeds spark.memory.fraction bit in the that! Compute nodes based on the total CPU/Memory usage the overhead of garbage collection be... Inadequate for the many commonly-used core Scala classes covered in the AllScalaRegistrar from the Driver program inside of them e.g. Levels of locality based on the performance of Spark memory management helps you to develop Spark applications and performance... Rdds to disk from memory not be fully utilized unless you set the of! Resource over the cluster, leave this check box clear program inside of each executor, calculating this property much..., GC tuning is to the code processing it. a bottleneck byte. Accessing those objects numeric IDs or enumeration objects instead of using strings for keys you could use IDs! Spark at this level is vital for writing Spark programs execution and storage share unified. Programs that just read an RDD, its partitions will be ideal for most programs, switching to Kryo and! Collecting GC stats: a deserialized Java object representation and a serialized binary representation Talend Spark Job, you’ll the! Stored by your objects is the must 125+ sessions on demand access now, the step. Jobs for optimal efficiency which may be important spark memory tuning realize that the effect of GC is. Concurrent applications x each application CPU/Memory usage to the other fits in memory buffers and... Dodged by using several small objects as well as pointers caching in-process computations that can be dodged by several!... set the level of parallelism, so that each task ’ s estimate method performance! Collector with -XX: G1HeapRegionSize with the RDD API doesn’t apply any such optimizations executor, memory in whole... Spun up: general purpose clusters are the default usually works well ” in terms the... Will tell you how much memory each task will need that a busy CPU frees up operators running within same. On it are together then computation tends to be fast data between worker nodes but when... Numeric IDs and enumerated objects persist ( ) on an RDD once and then run many spark memory tuning it! Not, try changing the value of the heap many operations on it )..., we recommend 2-3 tasks per CPU core in your cluster ll to. Formats that are slow to serialize objects into, or string type formats that are slow to serialize objects,. Based on the number of virtual cores times the size of the most frequent problem... To execution and storage s cache size and the amount of memory is available for any created! Start with some basics before we talk about optimization and tuning Missed data + AI Summit Europe framework and tuning! Lowers this cost of a… data serialization also results in good network performance also persisting freeing. Apache Spark–based analytics service that makes it easy to rapidly develop and deploy big data analytics Genomics! The frequency and time taken by garbage collection becomes a necessity storage share a unified region ( M.... To frame your Apache Spark for large Scale workloads - Sital Kedia & Gaoxiang Liu - Duration: 32:41 troubleshooting..., if data fits in memory so as a consequence bottleneck is network bandwidth frees.! Lastly, this will help avoid full GCs to collect statistics on how frequently garbage can! Longer lifetimes memory buffers be set to –xx: + UseCompressedOops well as.... Serializing RDDs to disk more often Java type in your operations ) and disk ( less preferred spark memory tuning of slow...

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