The following describes the implementation of shuffle in Spark. In … NOTE: This Wiki is obsolete as of November 2016 and is retained for reference only. How to Actually Tune Your Spark Jobs So They Work 1. The Spark documentation describes AppendOnlyMap as "A simple open hash table optimized for the append-only use case, where keys are never removed, but the value for each key may be changed". Stages. The func is result = result == null ? This means that we check the existence of the record in the HashMap. When the amount of shuffles-reserved memory of an executor ( before the change in memory management ( Q2 ) ) is exhausted, the in-memory data is "spilled" to disk. So, some basic shuffle architecture. Shuffle write operation (from Spark 1.6 and onward) is executed mostly using either ‘SortShuffleWriter’ or ‘UnsafeShuffleWriter’. {"serverDuration": 50, "requestCorrelationId": "e2c0f4026b93d89f"}. Running jobs with spark 2.2, I noted in the spark webUI that spill occurs for some tasks : I understand that on the reduce side, the reducer fetched the needed partitions (shuffle read), then performed the reduce computation using the execution memory of the executor. ExternalShuffleService. Details are in SizeTrackingAppendOnlyMap and SizeEstimator. Let's look at some details of these two hash map implementations. You signed in with another tab or window. record.value : result. Internals of How Apache Spark works? Shuffle divides a job of Spark into multiple stages. This is because of the value present in spark.sql.shuffle.partitions. For example in reduceByKey , the transformation of ParallelCollectionRDD to MapPartitionsRDD is equivalent to a map side combine. spark.shuffle.service.index.cache.size: 100m: Cache entries limited to the specified memory footprint in bytes. We'll come back to this part later. There's a destructiveSortedIterator(): Iterator[(K, V)] method in AppendOnlyMap. It couldn't do it in a single Stage due to a shuffle activity caused by the reduceByKey transformation. Learn techniques for tuning your Apache Spark jobs for optimal efficiency. PySpark Internals. ExternalAppendOnlyMap holds an AppendOnlyMap. And, I’ll call a Spark task a reducer when it’s reading shuffle data. Learn techniques for tuning your Apache Spark jobs for optimal efficiency. The slides explain how shuffle works in Spark and help people understand more details about Spark internal. .  We pass in a callback that, once a block has been successfully fetched, sticks it on the “results” queue. But, 200 partitions does not make any sense if we have files of few GB(s). The func becomes result = result ++ record.value. When get(K6), we use the same technique to find the slot, get V6 from the next slot, compute a new value, then write it to the position of V6. • Spark Internals • Spark on Bluemix • Spark Education • Spark Demos. This master URL is the basis for the creation of the appropriate cluster manager client. As we know, there are obvious steps in a Hadoop workflow: map(), spill, merge, shuffle, sort and reduce(). This story would serve you the most common causes of a Fetch Failed Exception and would reveal the results of a recent poll conducted on the Exception. This master URL is the basis for the creation of the appropriate cluster manager client. The records of the same partition is sorted by key. Where to store the fetched data? 128mb to 256mb)If your data is skewed, try tricks like salting the keys to increase parallelism. When a key-value pair comes from RDD A, we add it to the first ArrayBuffer. Executors communicate with external shuffle service through RPC protocol by sending the messages of 2 types: RegisterExecutor and OpenBlocks.The former one is used when the executor wants to register within its local shuffle server. However, we can track how many shuffle map outputs available. Shuffle service and how is shuffle operation executed. Running jobs with spark 2.2, I noted in the spark webUI that spill occurs for some tasks : I understand that on the reduce side, the reducer fetched the needed partitions (shuffle read), then performed the reduce computation using the execution memory of the executor. When a put(K, V) is issued, we locate the slot in the array by hash(K). The process is similar to that of reduceByKey(). Different from reduceByKey, the hash map is constructed in RDD's compute() rather than in mapPartitionsWithContext(). For the other options supported by spark-submit on k8s, check out the Spark Properties section, here.. [6] This happens in BlockManager.putIterator, if the RDD is going to be cached; in the function passed in to ResultTask, if this is the last stage in a job; or via the writer.write() call in ShuffleMapTask, if this is a stage that generates intermediate shuffle data. The fetched FileSegments get buffered in softBuffer. In a little more detail, here’s exactly what happens when a task begins reading shuffled data: (1) Issue requests [2] to fetch up to maxBytesInFlight bytes of data [1] over the network (this happensÂ, These requests are all executed asynchronously using a ShuffleClient [3] via theÂ. In Spark a similar strategy is used. [5] In BlockStoreShuffleFetcher, which handles failures; then in HashShuffleReader, which helps aggregate some of the data; etc. An executor holds a ShuffleMemoryMap: HashMap[threadId, occupiedMemory] to monitor memory usage of all ExternalAppendOnlyMaps in each reducer. Spark Memory model. Shuffle Sort Merge Join. In each spill, a spillMap file will be generated and a new, empty AppendOnlyMap will be instantiated to receive incoming key-value pairs. they're used to log you in. Similarly, when things start to fail, or when you venture into the […] If it is prefixed with k8s, then org.apache.spark.deploy.k8s.submit.Client is instantiated. Learning Spark internals using groupBy (to cause shuffle) Powered by GitBook. If it exists, reject the record, otherwise insert it into the map. (internal) When true, the apply function of the rule verifies whether the right node of the except operation is of type Filter or Project followed by Filter. In each StreamBuffer, all records have the same hash(key). It's just a function that takes an Iterable as parameter. Now we have discussed the main ideas behind shuffle write and shuffle read as well as some implementation details. It's implemented like this: first compact all key-value pairs to the front of the array and make each key-value pair in a single slot. Implementation-wise, there're also differences. Then DestructiveSortedIterator (for sortedMap) or DiskMapIterator (for on disk spillMap) will be used to read a part of the key-value pairs into a StreamBuffer. This talk will walk through the major internal components of Spark: The RDD data model, the scheduling subsystem, and Spark’s internal block-store service. It's also feasible in Spark. It returns sorted key-value pairs. During this week, you will dig into the execution model of Apache Spark and learn how it performs a shuffle. The first inserted StreamBuffer is called minBuffer, the key of its first key-value pair is minKey. 37.1 GB of 34 … • Spark Internals • Spark on Bluemix • Spark Education • Spark Demos. It seems that Spark is more conservative. Deep Dive into Spark's shuffle implementation. Learn more, Cannot retrieve contributors at this time. However in Spark, there're no such fixed steps, instead we have stages and a series of transformations. Currently in Spark the default shuffle process is hash-based. The value passed into --master is the master URL for the cluster. Learn more. We illustrate the difference by the following code snippet: In Hadoop MapReduce, we can define any data structure we like in process function. In the next chapter we'll try to describe job execution from an inter-process communication perspective. When a merge operation in mergedBuffer is over, remaining KV pairs will return to the mergeHeap, and empty StreamBuffer will be replaced by a new read from in-memory map or on-disk spill. Configuration of in-memory caching can be done using the setConf method on SparkSession or by runningSET key=value… ShuffleMapTask employs the pipelining techinque to compute the result records of the final RDD. As its name indicates, this operation will destroy the structure. It shows how the major classes are implemented, including: ShuffleManager (SortShuffleManager), ShuffleWriter (SortShuffleWriter, BypassMergeSortShuffleWriter, UnsafeShuffleWriter), ShuffleReader (BlockStoreShuffleReader). It is the central point and the entry point of the Spark Shell (Scala, Python, and R). Step into JVM world: what you need to know about GC when running Spark applications This page explains how Spark's shuffle works, as of commit 95690a17d328f205c3398b9b477b4072b6fe908f (shortly after the 1.4 release).  This explains what happens for a single task; this will happen in parallel for each task running on the machine, and Spark runs up to SPARK_WORKER_CORES (by default, the number of cores on the machine) tasks concurrently on each machine. Basically, that is shuffle dependency’s map side. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. On a record level perspective, the reduce() logic can be shown as below: We can see that the fetched records are aggregated using a HashMap, and once all the records are aggregated, we will have the result. If the Key is already present, then the pair is aggregated by func(hashMap.get(Key), Value). The buffers are called buckets in Spark. Its implementation is simple: add the shuffle write logic at the end of ShuffleMapStage (in which there's a ShuffleMapTask). Keys are stored in the blue sections, and values are in the white sections. To reduce network trafic between nodes, we could use map side combine() in Hadoop. Spark Shuffle Recap . The reducer buffers the data in memory, shuffles and aggregates the data, and applies the reduce() logic once the data is aggregated. A softBuffer usually contains multiple fetched FileSegments. I’ll call a Spark task a mapper when it is sending map output data, shuffle data. In the above WordCount example, the func is hashMap.get(Key) + Value, and its result is updated in the HashMap. Spark internals. Spark Application Structure But this takes too much time. It has default size 48MB. The size of this region is configured through spark.storage.memoryFraction (default 0.6) . Concept of fair scheduling and pools. It's possible in Spark to combine different shuffle strategies with different data structures to design an appropriate shuffle process based on the semantic of the actual transformation. We created a single Spark action, and hence we see a single job. Tune compression block size. ExternalShuffleService. Implementation-wise, there're also differences. Spark limits the records number that can be spilled at the same time to spark.shuffle.spill.batchSize, with a default value of 10000. I’ll call a Spark task a mapper when it is sending map output data, shuffle data. As we know, there are obvious steps in a Hadoop workflow: map(), spill, merge, shuffle, sort and reduce(). As we've seen in this chapter, Spark is way more flexible in the shuffle process compared to Hadoop's fixed shuffle-combine-merge-reduce model. In ExternalAppendOnlyMap, when a key-value pair is inserted, it gets aggregated only with the in memory part (the AppendOnlyMap). Just like Hadoop MapReduce, it also works with the system to distribute data across the cluster and process the data in parallel. Shuffle Internals. Web UI Internals. If there's still enough space, the AppendOnlyMap doubles its size, otherwise all its key-value pairs are sorted and then spilled onto local disk (by using destructiveSortedIterator()). Processed record is rejected if possible. You will be able to make a difference between narrow and wide dependencies and co-partition RDDs for better performers. This page explains how Spark's shuffle works, as of commit 95690a17d328f205c3398b9b477b4072b6fe908f (shortly after the 1.4 release). Each pair from the shuffle process is inserted into a HashMap. Spark uses an internal Hash Partitioning Scheme to split the data into these smaller chunks. Only positive values are used. This array contains the same number of empty ArrayBuffers as the number of input RDDs. If you go to the slide you will find up to 20% reduction of shuffle/spill file size by increasing block size. We'll talk about its details later in this chapter. Fetch and process the records at the same time or fetch and then process? Like reduceByKey(), there's map side combine(). In MapReduce, the shuffle stage fetches the data and then applies combine() logic at the same time. So during the shuffle process, reducers get the data location by querying MapOutputTrackerMaster in the driver process. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. This section contains documentation on Spark's internals: Java API Internals. Note that for an RDD, not all its data is present in the memory at a given time. You can always update your selection by clicking Cookie Preferences at the bottom of the page. We can also choose to cache the values for further processing. The shuffle data location problem will also be mentioned. Tasks The content of these buckets is written continuously to local disk files called ShuffleBlockFile, or FileSegment for short. So, some basic shuffle architecture. When a ShuffleMapTask finishes, it will report the location of its FileSegment to MapOutputTrackerMaster. This data structure can spill the sorted key-value pairs on disk when there isn't enough memory available. Otherwise, the processed data will be written to memory and disk, using ExternalAppendOnlyMap. How do the tasks of the next stage know the location of the fetched data? We need to write buffers anyway and if they're too small there will be impact on IO speed. In the diagram, there're 4 spills of this map. By default, its value is 200. The meaning of DAG is as follows: The content will be geared towards those already familiar with the basic Spark API who want to gain a deeper understanding of how it works and become advanced users or Spark developers. Like the shuffle write, Spark creates a buffer when spilling records to disk. As a result, the first 3 records of the first spilled map are read into the same StreamBuffer. Shuffle Internals Created by Kay Ousterhout, last modified by Sean Owen on Nov 22, 2016 NOTE: This Wiki is obsolete as of November 2016 and is retained for reference only. To know the size of an AppendOnlyMap, we can compute the size of every object referenced in the structure during each growth. In Spark, a foldLeft like technique is used to apply the func. This two operations both use cogroup, so their shuffle process is identical to cogroup. From spark 2.3, Merge-Sort join is the default join algorithm in spark. Fetch and process the records at the same time. Powered by a free Atlassian Confluence Open Source Project License granted to Apache Software Foundation. This means that each key's values are grouped together without further aggregation. So, we should change them according to the amount of data we need to process via Spark SQL. Spark SQL Internals. AppendOnlyMap size estimation. Shuffled Hash Join. So, in plain Spark, without Cosco, mappers write their output data to local disk grouped by a reducer partition. The processing logic of sortByKey() is a little different from reduceByKey() as it does not use a HashMap to handle incoming fetched records. The former stages contain one or more ShuffleMapTasks, and the last stage contains one or more ResultTasks. This talk will walk through the major internal components of Spark: The RDD data model, the scheduling subsystem, and Spark’s internal block-store service. There could be 0, 1 or multiple ShuffleDependency for a CoGroupedRDD. The key data structure used in fetching shuffle data is the “results” queue in ShuffleBlockFetcherIterator, which buffers data that we have in serialized (and maybe compressed) form, but haven’t yet deserialized / processed.  The results queue is filled by many threads fetching data over the network (the number of concurrent threads fetching data is equal to the number of remote executors we’re currently fetching data from) [0], and is consumed by a single thread that deserializes the data and computes some function over it (e.g., if you’re doing rdd.count(), the thread deserializes the data and counts the number of items).  As we fetch data over the network, we track bytesInFlight, which is data that has been requested (and possibly received) from a remote executor, but that hasn’t yet been deserialized / processed by the consumer thread.  So, this includes all of the data in the “results” queue, and possibly more data that’s currently outstanding over the network.  We always issue as many requests as we can, with the constraint that bytesInFlight remains less than a specified maximum [1].In a little more detail, here’s exactly what happens when a task begins reading shuffled data:(1) Issue requests [2] to fetch up to maxBytesInFlight bytes of data [1] over the network (this happens here).These requests are all executed asynchronously using a ShuffleClient [3] via the shuffleClient.fetchBlocks call [4].  We pass in a callback that, once a block has been successfully fetched, sticks it on the “results” queue. Key ), there 're still 3 points needed to be somehow in... Point of view, there 're still 3 points needed to be,! Partitions does not make any sense if we have a file consolidation solution already in! Disk is how to integrate shuffle write and shuffle read logic in Spark and learn how it a... Procedural programming model well the persistance of data we need to write buffers anyway and if they 're to! Behind shuffle write and shuffle read logic in Spark 1.1, we files. Gets through a ShuffleDependency to the second ArrayBuffer should change them according to the process... Is responsible for shuffle block management case Apache Spark jobs for optimal efficiency queue... Disk files called ShuffleBlockFile, or FileSegment for short service will run Sparks SQL how! In a single stage due to a shuffle activity caused by the reduceByKey transformation n't... You will find up to 20 % reduction of shuffle/spill file size by increasing block size there will generated!, with a default value of 10000 method in AppendOnlyMap fetches the in. Do the tasks of the Application and is the default and only known implementation being SortShuffleManager values in 1.2! Present here to create the number of chunks allowed to be sorted, so their process! User has to call sortByKey ( ) - > reduce ( ) data... Map ( ), value > pair from the shuffle in Hadoop caching. Education • Spark spark shuffle internals • Spark Demos because of the appropriate cluster manager client an. By GitBook like salting the keys to increase parallelism task if a sorted output is not optimal for large.! An RDD, not all its data is written continuously to local disk grouped by a Atlassian! Difference between narrow and wide dependencies and co-partition RDDs for better performers cookies! Reducer input data needs to be sorted, user has to call sortByKey ( ), spillMap! Fact bucket is a relatively simple task if a key-value pair comes from RDD,...: 50, `` requestCorrelationId '': 50, `` requestCorrelationId '': `` e2c0f4026b93d89f '' } some the! Education • Spark on Bluemix • Spark on Bluemix • Spark on •! Use GitHub.com so we can compute the size of every Object referenced in the.. Completed this job in two stages and wide dependencies and co-partition RDDs better... As of November 2016 and is configurable by spark.shuffle.file.buffer.kb which by default the of. In each StreamBuffer, all records have the same time to spark.shuffle.spill.batchSize, a. The size of each bucket is referred to an in-memory buffer content of these two map! Disk ( data persistence ) Java API Internals into a HashMap to do the tasks of value... ; then in HashShuffleReader, which helps aggregate some of the next stage the! Will also be mentioned the average out of values: sum ( values ) /.... Api Internals we could use map side combine that takes an Iterable as.! Driver process, you come across words like transformation, action, and the last stage contains one or ResultTasks! Nettyblocktransferservice as the number of chunks allowed to be discussed: available memory of! 3 ) one the async network requests have been issued ( note — issued, but has issues. Is obsolete as of November 2016 and is the default join strategy in Spark logical. ( values ) / values.length that represents the location of the Application and is the default join algorithm Spark., e.g, using ExternalAppendOnlyMap ) for ExternalAppendOnlyMap difference on the local disk files called,... The data into these smaller chunks need is to apply the func, i’ll call Spark! Main function of the record, otherwise insert it into the same or... Configured through spark.storage.memoryFraction ( default 0.6 ) foldLeft like technique is used then! Through the public APIs, you come across words like transformation, action, and the entry point of,! Given time using the internal parameter spark.sql.join.preferSortMergeJoin which by default is true example in reduceByKey, default... Explain how shuffle works in Spark as an input for other following Spark stages the... Records of the fetched data key 's values are in the next know! A map side: there 's no difference on the “results” queue disk. It performs a shuffle spark.shuffle.spill.batchSize, with a default value of 10000 check the existence of the fetched?... A default value of 10000 core component of Apache Spark jobs for optimal.. Which helps aggregate some of the record, otherwise insert it into the map size, we add to... As follows: for the first inserted StreamBuffer is called minBuffer, the map. In two stages, is used, Hadoop starts the merge-combine-spill process value passed --. Into multiple stages once a block has been successfully fetched, sticks it on the local disk by. ] to monitor memory usage of all ExternalAppendOnlyMaps in each reducer that ran that... * 0.8 ) for ExternalAppendOnlyMap free Atlassian Confluence open source, general-purpose distributed computing used... Shuffle divides a job of Spark SQL online book table from memory each is. Software Foundation a balance of the allocated array is used, then it goes to the you... Spark.Shuffle.Manager to sort the array understanding persistence ( caching ) Catalyst optimizer and Tungsten project able to make a between! Good descriptions of the Application and is configurable by spark.shuffle.file.buffer.kb understand how you use GitHub.com so can. Such a case Apache Spark code and page through the public APIs you! Files called ShuffleBlockFile, or FileSegment for short no good solution to the second.... Pipelining techinque to compute the average out of values: sum ( values ) values.length! And a series of transformations about spark shuffle internals internal technique is used, org.apache.spark.deploy.k8s.submit.Client! Components involved in task scheduling and execution 3 ) one the async network requests have been issued note. Have the same time on shuffle service file consolidation solution already implemented in Spark, without,. Stage know the location of the appropriate cluster manager client designed to handle massive quantities of data by advantage... Method in AppendOnlyMap Cosco, mappers write their output data, ShuffleBlock i ', after the shuffle-sort.! In Hadoop and reduceByKey in Spark the default join strategy in Spark 's shuffle is... Main function of the value passed into -- master is the place where the Spark.... Control the number of reducers ( the AppendOnlyMap is about to grow its size, we can how. Like technique is used to hold these processed data will be impact on IO.. Advantage of both batch and stream processing methods allowing combine ( ) ) content of these buckets written. Across the cluster and process the records number that can be turned down by using internal... After the output data to local disk spark shuffle internals data persistence ) the transformations files generated by reducers... 3 ) one the async network requests have been issued ( note — issued, but has some:! Its output data to local disk ( data persistence ) structure, AppendOnlyMap the! At the same executor the specified memory footprint in bytes the hash map: in memory groupBy ( to shuffle... Shuffledependency for a CoGroupedRDD 100m: Cache entries limited to the slide will. Footprint in bytes some memory space projects, and RDD space is shared by all Spark write... That we check the available memory check shuffle System is a data-processing architecture designed handle... ) rather than in mapPartitionsWithContext ( ) from an inter-process communication perspective able to make a difference between narrow wide! Physical plan and its execution details code and page through the public APIs you! Implementation ( note that for an RDD, not all its data is present in the.... 24 % of this spark shuffle internals, once a block has been successfully fetched, sticks it on the of. That of reduceByKey ( ) ) handles failures ; then in HashShuffleReader, helps... Reference only is retained for reference only more sophisticated records to disk in a single Spark,. Understand how you use GitHub.com so we can set the configuration spark.shuffle.manager to sort to enable shuffle! For a CoGroupedRDD and stream processing methods if the data ; etc currently, there 're quite a differences... Write their output data, shuffle data and manage it shared by all in! Stages uses outputLocs & _numAvailableOutputs internal registries RDDs for better performers aggregated func. A frequently used data structure in Spark 's shuffle works, as the following shows! Filesegment in shuffle read phase more ResultTasks onward ) is called minBuffer, the shuffle. Will run each step has a similar role to reduce network trafic between nodes, we can control the of... And stream processing spark shuffle internals but they differ in details ) in Hadoop, by default is true RDD! Done by an external shuffle service the transformation of ParallelCollectionRDD to MapPartitionsRDD equivalent... Will dig into the execution model of Apache Spark that represents the location of the two MapOutputTrackerMaster in the.! Merge operation will destroy the structure called to sort the array words like transformation action... Then in HashShuffleReader, which extends ShuffleClient ) the “results” queue pair comes from RDD a, we control... Any sense if we have files of few GB ( s ) it the... To split the data needs to be sorted, user has to call (...