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Apache Spark Architecture

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Alexey Grishchenko
Alexey Grishchenko

This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark

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1Pivotal Confidential–Internal Use Only 1Pivotal Confidential–Internal Use Only Spark Architecture A.Grishchenko
About me Enterprise Architect @ Pivotal  7 years in data processing  5 years with MPP  4 years with Hadoop  Spark contributor  http://0x0fff.com
Outline  Spark Motivation  Spark Pillars  Spark Architecture  Spark Shuffle  Spark DataFrame
Outline  Spark Motivation  Spark Pillars  Spark Architecture  Spark Shuffle  Spark DataFrame
Spark Motivation  Difficultly of programming directly in Hadoop MapReduce
Spark Motivation  Difficultly of programming directly in Hadoop MapReduce  Performance bottlenecks, or batch not fitting use cases
Spark Motivation  Difficultly of programming directly in Hadoop MapReduce  Performance bottlenecks, or batch not fitting use cases  Better support iterative jobs typical for machine learning
Difficulty of Programming in MR Word Count implementations  Hadoop MR – 61 lines in Java  Spark – 1 line in interactive shell sc.textFile('...').flatMap(lambda x: x.split()) .map(lambda x: (x, 1)).reduceByKey(lambda x, y: x+y) .saveAsTextFile('...') VS
Performance Bottlenecks How many times the data is put to the HDD during a single MapReduce Job?  One  Two  Three  More
Performance Bottlenecks How many times the data is put to the HDD during a single MapReduce Job?  One  Two  Three  More
Performance Bottlenecks Consider Hive as main SQL tool
Performance Bottlenecks Consider Hive as main SQL tool  Typical Hive query is translated to 3-5 MR jobs
Performance Bottlenecks Consider Hive as main SQL tool  Typical Hive query is translated to 3-5 MR jobs  Each MR would scan put data to HDD 3+ times
Performance Bottlenecks Consider Hive as main SQL tool  Typical Hive query is translated to 3-5 MR jobs  Each MR would scan put data to HDD 3+ times  Each put to HDD – write followed by read
Performance Bottlenecks Consider Hive as main SQL tool  Typical Hive query is translated to 3-5 MR jobs  Each MR would scan put data to HDD 3+ times  Each put to HDD – write followed by read  Sums up to 18-30 scans of data during a single Hive query
Performance Bottlenecks Spark offers you  Lazy Computations – Optimize the job before executing
Performance Bottlenecks Spark offers you  Lazy Computations – Optimize the job before executing  In-memory data caching – Scan HDD only once, then scan your RAM
Performance Bottlenecks Spark offers you  Lazy Computations – Optimize the job before executing  In-memory data caching – Scan HDD only once, then scan your RAM  Efficient pipelining – Avoids the data hitting the HDD by all means
Outline  Spark Motivation  Spark Pillars  Spark Architecture  Spark Shuffle  Spark DataFrame
Spark Pillars Two main abstractions of Spark
Spark Pillars Two main abstractions of Spark  RDD – Resilient Distributed Dataset
Spark Pillars Two main abstractions of Spark  RDD – Resilient Distributed Dataset  DAG – Direct Acyclic Graph
RDD  Simple view – RDD is collection of data items split into partitions and stored in memory on worker nodes of the cluster
RDD  Simple view – RDD is collection of data items split into partitions and stored in memory on worker nodes of the cluster  Complex view – RDD is an interface for data transformation
RDD  Simple view – RDD is collection of data items split into partitions and stored in memory on worker nodes of the cluster  Complex view – RDD is an interface for data transformation – RDD refers to the data stored either in persisted store (HDFS, Cassandra, HBase, etc.) or in cache (memory, memory+disks, disk only, etc.) or in another RDD
RDD  Complex view (cont’d) – Partitions are recomputed on failure or cache eviction
RDD  Complex view (cont’d) – Partitions are recomputed on failure or cache eviction – Metadata stored for interface â–Ș Partitions – set of data splits associated with this RDD
RDD  Complex view (cont’d) – Partitions are recomputed on failure or cache eviction – Metadata stored for interface â–Ș Partitions – set of data splits associated with this RDD â–Ș Dependencies – list of parent RDDs involved in computation
RDD  Complex view (cont’d) – Partitions are recomputed on failure or cache eviction – Metadata stored for interface â–Ș Partitions – set of data splits associated with this RDD â–Ș Dependencies – list of parent RDDs involved in computation â–Ș Compute – function to compute partition of the RDD given the parent partitions from the Dependencies
RDD  Complex view (cont’d) – Partitions are recomputed on failure or cache eviction – Metadata stored for interface â–Ș Partitions – set of data splits associated with this RDD â–Ș Dependencies – list of parent RDDs involved in computation â–Ș Compute – function to compute partition of the RDD given the parent partitions from the Dependencies â–Ș Preferred Locations – where is the best place to put computations on this partition (data locality)
RDD  Complex view (cont’d) – Partitions are recomputed on failure or cache eviction – Metadata stored for interface â–Ș Partitions – set of data splits associated with this RDD â–Ș Dependencies – list of parent RDDs involved in computation â–Ș Compute – function to compute partition of the RDD given the parent partitions from the Dependencies â–Ș Preferred Locations – where is the best place to put computations on this partition (data locality) â–Ș Partitioner – how the data is split into partitions
RDD  RDD is the main and only tool for data manipulation in Spark  Two classes of operations – Transformations – Actions
RDD Lazy computations model Transformation cause only metadata change
DAG Direct Acyclic Graph – sequence of computations performed on data
DAG Direct Acyclic Graph – sequence of computations performed on data  Node – RDD partition
DAG Direct Acyclic Graph – sequence of computations performed on data  Node – RDD partition  Edge – transformation on top of data
DAG Direct Acyclic Graph – sequence of computations performed on data  Node – RDD partition  Edge – transformation on top of data  Acyclic – graph cannot return to the older partition
DAG Direct Acyclic Graph – sequence of computations performed on data  Node – RDD partition  Edge – transformation on top of data  Acyclic – graph cannot return to the older partition  Direct – transformation is an action that transitions data partition state (from A to B)
DAG WordCount example
DAG WordCount example HDFSInputSplits HDFS RDDPartitions RDD RDD RDD RDD RDD sc.textFile(‘hdfs://
’) flatMap map reduceByKey foreach
Outline  Spark Motivation  Spark Pillars  Spark Architecture  Spark Shuffle  Spark DataFrames
Spark Cluster Driver Node 
 Worker Node Worker Node Worker Node
Spark Cluster Driver Node Driver 
 Worker Node 
 Executor Executor Worker Node 
 Executor Executor Worker Node 
 Executor Executor
Spark Cluster Driver Node Driver Spark Context 
 Worker Node 
 Executor Cache Executor Cache Worker Node 
 Executor Cache Executor Cache Worker Node 
 Executor Cache Executor Cache
Spark Cluster Driver Node Driver Spark Context 
 Worker Node 
 Executor Cache Task 
 Task Task Executor Cache Task 
 Task Task Worker Node 
 Executor Cache Task 
 Task Task Executor Cache Task 
 Task Task Worker Node 
 Executor Cache Task 
 Task Task Executor Cache Task 
 Task Task
Spark Cluster  Driver – Entry point of the Spark Shell (Scala, Python, R)
Spark Cluster  Driver – Entry point of the Spark Shell (Scala, Python, R) – The place where SparkContext is created
Spark Cluster  Driver – Entry point of the Spark Shell (Scala, Python, R) – The place where SparkContext is created – Translates RDD into the execution graph
Spark Cluster  Driver – Entry point of the Spark Shell (Scala, Python, R) – The place where SparkContext is created – Translates RDD into the execution graph – Splits graph into stages
Spark Cluster  Driver – Entry point of the Spark Shell (Scala, Python, R) – The place where SparkContext is created – Translates RDD into the execution graph – Splits graph into stages – Schedules tasks and controls their execution
Spark Cluster  Driver – Entry point of the Spark Shell (Scala, Python, R) – The place where SparkContext is created – Translates RDD into the execution graph – Splits graph into stages – Schedules tasks and controls their execution – Stores metadata about all the RDDs and their partitions
Spark Cluster  Driver – Entry point of the Spark Shell (Scala, Python, R) – The place where SparkContext is created – Translates RDD into the execution graph – Splits graph into stages – Schedules tasks and controls their execution – Stores metadata about all the RDDs and their partitions – Brings up Spark WebUI with job information
Spark Cluster  Executor – Stores the data in cache in JVM heap or on HDDs
Spark Cluster  Executor – Stores the data in cache in JVM heap or on HDDs – Reads data from external sources
Spark Cluster  Executor – Stores the data in cache in JVM heap or on HDDs – Reads data from external sources – Writes data to external sources
Spark Cluster  Executor – Stores the data in cache in JVM heap or on HDDs – Reads data from external sources – Writes data to external sources – Performs all the data processing
Executor Memory
Spark Cluster – Detailed
Spark Cluster – PySpark
Application Decomposition  Application – Single instance of SparkContext that stores some data processing logic and can schedule series of jobs, sequentially or in parallel (SparkContext is thread-safe)
Application Decomposition  Application – Single instance of SparkContext that stores some data processing logic and can schedule series of jobs, sequentially or in parallel (SparkContext is thread-safe)  Job – Complete set of transformations on RDD that finishes with action or data saving, triggered by the driver application
Application Decomposition  Stage – Set of transformations that can be pipelined and executed by a single independent worker. Usually it is app the transformations between “read”, “shuffle”, “action”, “save”
Application Decomposition  Stage – Set of transformations that can be pipelined and executed by a single independent worker. Usually it is app the transformations between “read”, “shuffle”, “action”, “save”  Task – Execution of the stage on a single data partition. Basic unit of scheduling
WordCount ExampleHDFSInputSplits HDFS RDDPartitions RDD RDD RDD RDD RDD sc.textFile(‘hdfs://
’) flatMap map reduceByKey foreach
Stage 1 WordCount ExampleHDFSInputSplits HDFS RDDPartitions RDD RDD RDD RDD RDD sc.textFile(‘hdfs://
’) flatMap map reduceByKey foreach
Stage 2Stage 1 WordCount ExampleHDFSInputSplits HDFS RDDPartitions RDD RDD RDD RDD RDD sc.textFile(‘hdfs://
’) flatMap map reduceByKey foreach
Stage 2Stage 1 WordCount ExampleHDFSInputSplits HDFS RDDPartitions RDD RDD RDD RDD RDD sc.textFile(‘hdfs://
’) flatMap map reduceByKey foreach Task 1 Task 2 Task 3 Task 4
Stage 2Stage 1 WordCount ExampleHDFSInputSplits HDFS RDDPartitions RDD RDD RDD RDD RDD sc.textFile(‘hdfs://
’) flatMap map reduceByKey foreach Task 1 Task 2 Task 3 Task 4 Task 5 Task 6 Task 7 Task 8
Stage 2Stage 1 WordCount ExampleHDFSInputSplits HDFS pipeline Task 1 Task 2 Task 3 Task 4 Task 5 Task 6 Task 7 Task 8 partition shuffle pipeline
Persistence in Spark Persistence Level Description MEMORY_ONLY Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will not be cached and will be recomputed on the fly each time they're needed. This is the default level. MEMORY_AND_DISK Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the partitions that don't fit on disk, and read them from there when they're needed. MEMORY_ONLY_SER Store RDD as serialized Java objects (one byte array per partition). This is generally more space-efficient than deserialized objects, especially when using a fast serializer, but more CPU-intensive to read. MEMORY_AND_DISK_SER Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of recomputing them on the fly each time they're needed. DISK_ONLY Store the RDD partitions only on disk. MEMORY_ONLY_2, DISK_ONLY_2, etc. Same as the levels above, but replicate each partition on two cluster nodes.
Persistence in Spark  Spark considers memory as a cache with LRU eviction rules  If “Disk” is involved, data is evicted to disks rdd = sc.parallelize(xrange(1000)) rdd.cache().count() rdd.persist(StorageLevel.MEMORY_AND_DISK_SER).count() rdd.unpersist()
Outline  Spark Motivation  Spark Pillars  Spark Architecture  Spark Shuffle  Spark DataFrame
Shuffles in Spark  Hash Shuffle – default prior to 1.2.0
Shuffles in Spark  Hash Shuffle – default prior to 1.2.0  Sort Shuffle – default now
Shuffles in Spark  Hash Shuffle – default prior to 1.2.0  Sort Shuffle – default now  Tungsten Sort – new optimized one!
Hash Shuffle
Executor JVM Partition Partition Partition Partition Partition Partition Partition Partition Hash Shuffle
Executor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task spark.executor.cores/ spark.task.cpus 
 Hash Shuffle
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task spark.executor.cores/ spark.task.cpus 
 Output File Output File Output File 
 Numberof “Reducers” spark.local.dir Hash Shuffle
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task spark.executor.cores/ spark.task.cpus 
 Output File Output File Output File 
 Numberof “Reducers” Output File Output File Output File 
 Numberof “Reducers” 
 spark.local.dir Hash Shuffle Numberof“map”tasks executedbythisexecutor
Hash Shuffle with Consolidation
Executor JVM Partition Partition Partition Partition Partition Partition Partition Partition Hash Shuffle With Consolidation
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task 
 Numberof “Reducers” Output File Output File Output File spark.local.dir Hash Shuffle With Consolidationspark.executor.cores/ spark.task.cpus
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 
 
 Numberof “Reducers” spark.local.dir Hash Shuffle With Consolidationspark.executor.cores/ spark.task.cpus spark.executor.cores/ spark.task.cpus 
 Numberof “Reducers” Output File Output File Output File Output File Output File Output File
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task 
 
 
 Numberof “Reducers” spark.local.dir Hash Shuffle With Consolidationspark.executor.cores/ spark.task.cpus spark.executor.cores/ spark.task.cpus 
 Numberof “Reducers” Output File Output File Output File Output File Output File Output File
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 
 
 Numberof “Reducers” spark.local.dir Hash Shuffle With Consolidationspark.executor.cores/ spark.task.cpus spark.executor.cores/ spark.task.cpus Output File Output File Output File 
 Numberof “Reducers” Output File Output File Output File
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task 
 
 
 Numberof “Reducers” spark.local.dir Hash Shuffle With Consolidationspark.executor.cores/ spark.task.cpus spark.executor.cores/ spark.task.cpus Output File Output File Output File 
 Numberof “Reducers” Output File Output File Output File
Local DirectoryExecutor JVM Partition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 
 
 Numberof “Reducers” spark.local.dir Hash Shuffle With Consolidationspark.executor.cores/ spark.task.cpus spark.executor.cores/ spark.task.cpus 
 Numberof “Reducers” Output File Output File Output File Output File Output File Output File
Sort Shuffle
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition Sort Shuffle spark.storage.[safetyFraction * memoryFraction] Partition Partition
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Sort Shuffle spark.storage.[safetyFraction * memoryFraction] Partition Partition spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition AppendOnlyMap 
 AppendOnlyMap spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition AppendOnlyMap 
 AppendOnlyMap sort & spill spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition AppendOnlyMap 
 AppendOnlyMap sort & spill Local Directory Output File index spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition AppendOnlyMap 
 AppendOnlyMap sort & spill sort & spill Local Directory Output File index Output File index spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition AppendOnlyMap 
 AppendOnlyMap sort & spill sort & spill sort & spill 
 Local Directory 
 Output File index Output File index Output File index spark.executor.cores/spark.task.cpus
MinHeap Merge MinHeap Merge Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition AppendOnlyMap 
 AppendOnlyMap sort & spill sort & spill sort & spill 
 Local Directory 
 “reduce”task“reduce”task 
Output File index Output File index Output File index spark.executor.cores/spark.task.cpus
Tungsten Sort Shuffle
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] Partition Partition
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] Partition Partition spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> Array of data pointers and Partition IDs, long[] spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> Array of data pointers and Partition IDs, long[] sort & spill spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> Local Directory Array of data pointers and Partition IDs, long[] sort & spill spark.local.dir Output File partition partition partition partition index spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> Local Directory Array of data pointers and Partition IDs, long[] sort & spill sort & spill 
 spark.local.dir Output File partition partition partition partition index Output File partition partition partition partition index spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> Local Directory Array of data pointers and Partition IDs, long[] sort & spill sort & spill 
 spark.local.dir Output File partition partition partition partition index Output File partition partition partition partition index spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> 
 Local Directory Array of data pointers and Partition IDs, long[] Serialized Data LinkedList<MemoryBlock> Array of data pointers and Partition IDs, long[] sort & spill sort & spill 
 spark.local.dir Output File partition partition partition partition index Output File partition partition partition partition index spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> 
 Local Directory 
 Array of data pointers and Partition IDs, long[] Serialized Data LinkedList<MemoryBlock> Array of data pointers and Partition IDs, long[] sort & spill sort & spill 
 sort & spill spark.local.dir Output File partition partition partition partition index Output File partition partition partition partition index Output File partition partition partition partition index spark.executor.cores/spark.task.cpus
Executor JVMPartition Partition Partition Partition Partition Partition Partition Partition “map” task “map” task 
 Tungsten Sort Shuffle spark.storage.[safetyFraction * memoryFraction] spark.shuffle. [safetyFraction * memoryFraction] Partition Partition Serialized Data LinkedList<MemoryBlock> 
 Local Directory 
 Array of data pointers and Partition IDs, long[] Serialized Data LinkedList<MemoryBlock> Array of data pointers and Partition IDs, long[] sort & spill sort & spill 
 sort & spill spark.local.dir Output File partition partition partition partition index Output File partition partition partition partition index Output File partition partition partition partition index Output File partition partition partition partition index merge spark.executor.cores/spark.task.cpus
Outline  Spark Motivation  Spark Pillars  Spark Architecture  Spark Shuffle  Spark DataFrame
DataFrame Idea
DataFrame Implementation  Interface – DataFrame is an RDD with schema – field names, field data types and statistics – Unified transformation interface in all languages, all the transformations are passed to JVM – Can be accessed as RDD, in this case transformed to the RDD of Row objects
DataFrame Implementation  Internals – Internally it is the same RDD – Data is stored in row-columnar format, row chunk size is set by spark.sql.inMemoryColumnarStorage.batchSize – Each column in each partition stores min-max values for partition pruning – Allows better compression ratio than standard RDD – Delivers faster performance for small subsets of columns
113Pivotal Confidential–Internal Use Only 113Pivotal Confidential–Internal Use Only Questions?
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Apache Spark Architecture

Hinweis der Redaktion

  1. def printfunc (x): print 'Word "%s" occurs %d times' % (x[0], x[1]) infile = sc.textFile('hdfs://sparkdemo:8020/sparkdemo/textfiles/README.md', 4) rdd1 = infile.flatMap(lambda x: x.split()) rdd2 = rdd1.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x+y) print rdd2.toDebugString() rdd2.foreach(printfunc)