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Advanced Apache Spark Meetup Project Tungsten Nov 12 2015

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Advanced Apache Spark Meetup Project Tungsten Nov 12 2015

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Advanced Apache Spark Meetup Project Tungsten Nov 12 2015

  1. 1. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Project Tungsten Advanced Apache Spark Meetup Chris Fregly Principal Data Solutions Engineer We’re Hiring - Only Nice People! Nov 12, 2015
  2. 2. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Who Am I? 2 Streaming Data Engineer Open Source Committer
 Data Solutions Engineer
 Apache Contributor Principal Data Solutions Engineer IBM Technology Center Founder Advanced Apache Meetup Author Advanced . Due 2016 My Ma’s First Time in California
  3. 3. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Random Slide: More Ma “First Time” Pics 3 In California Using Chopsticks Using “New” iPhone
  4. 4. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Upcoming Meetups and Conferences London Spark Meetup (Oct 12th) Scotland Data Science Meetup (Oct 13th) Dublin Spark Meetup (Oct 15th) Barcelona Spark Meetup (Oct 20th) Madrid Big Data Meetup (Oct 22nd) Paris Spark Meetup (Oct 26th) Amsterdam Spark Summit (Oct 27th) Brussels Spark Meetup (Oct 30th) Zurich Big Data Meetup (Nov 2nd) Geneva Spark Meetup (Nov 5th) San Francisco Datapalooza.io (Nov 10th) 4 San Francisco Advanced Spark (Nov 12th) Oslo Big Data Hadoop Meetup (Nov 18th) Helsinki Spark Meetup (Nov 20th) Stockholm Spark Meetup (Nov 23rd) Copenhagen Spark Meetup (Nov 26th) Budapest Spark Meetup (Nov 27th) Singapore Strata Conference (Dec 1st) San Francisco Advanced Spark (Dec 8th) Mountain View Advanced Spark (Dec 10th) Toronto Spark Meetup (Dec 14th) Washington DC Spark Meetup (Jan 2016)
  5. 5. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Advanced Apache Spark Meetup Meetup Metrics ~1600 Members in just 4 mos! 4th Most Active Spark Meetup!! Meetup Goals   Dig deep into codebase of Spark and related projects   Study integrations of Cassandra, ElasticSearch, Tachyon, S3, BlinkDB, Mesos, YARN, Kafka, R   Surface and share patterns and idioms of these well-designed, distributed, big data components THANKS TO ALL OF YOU!!
  6. 6. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark All Slides and Code Are Available! slideshare.net/cfregly github.com/fluxcapacitor hub.docker.com/r/fluxcapacitor 6
  7. 7. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Themes of this Talk  Filter  Off-Heap  Parallelize  Approximate  Find Similarity  Minimize Seeks  Maximize Scans  Customize for Workload  Tune Performance At Every Layer 7   Be Nice, Collaborate! Like a Mom!!
  8. 8. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Outline ①  Mechanical Sympathy ②  Recap of 100TB GraySort Challenge ③  Project Tungsten Deep Dive 8
  9. 9. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Mechanical Sympathy Hardware and software working together in harmony. - Martin Thompson http://mechanical-sympathy.blogspot.com Whatever your data structure, my array will beat it. - Scott Meyers Every C++ Book, basically 9 Hair Sympathy - Bruce Jenner
  10. 10. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Spark and Mechanical Sympathy 10 Project 
 Tungsten (Spark 1.4-1.6+) GraySort Challenge (Spark 1.1-1.2) Minimize Memory and GC Maximize CPU Cache Locality Saturate Network I/O Saturate Disk I/O
  11. 11. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark AlphaSort Technique: Sort 100 Bytes Recs 11 Value Ptr Key Dereference Not Required! AlphaSort List [(Key, Pointer)] Key is directly available for comparison Naïve List [Pointer] Must dereference key for comparison Ptr Dereference for Key Comparison Key
  12. 12. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU Cache Line and Memory Sympathy Key (10 bytes)+Pointer (*4 bytes)*Compressed OOPs = 14 bytes 12 Key Ptr Not CPU Cache-line Friendly! Ptr Key-Prefix 2x CPU Cache-line Friendly! Key-Prefix (4 bytes) + Pointer (4 bytes) = 8 bytes Key (10 bytes)+Pad (2 bytes)+Pointer (4 bytes)
 = 16 bytes Key Ptr Pad /Pad CPU Cache-line Friendly!
  13. 13. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Performance Comparison 13
  14. 14. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Similar Trick: Direct Cache Access (DCA) Pull out packet header along side pointer to payload 14
  15. 15. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU Cache Lines: Sequential vs. Random 15
  16. 16. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU Cache Naïve Matrix Multiplication // Dot product of each row & column vector for (i <- 0 until numRowA) for (j <- 0 until numColsB) for (k <- 0 until numColsA) res[ i ][ j ] += matA[ i ][ k ] * matB[ k ][ j ]; 16 Bad: Row-wise traversal, not using CPU cache line,
 ineffective pre-fetching
  17. 17. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU Cache Friendly Matrix Multiplication // Transpose B for (i <- 0 until numRowsB) for (j <- 0 until numColsB) matBT[ i ][ j ] = matB[ j ][ i ]; 
 // Modify dot product calculation for B Transpose for (i <- 0 until numRowsA) for (j <- 0 until numColsB) for (k <- 0 until numColsA) res[ i ][ j ] += matA[ i ][ k ] * matBT[ j ][ k ]; 17 Good: Full CPU cache line,
 effective prefetching OLD: res[ i ][ j ] += matA[ i ][ k ] * matB [ k ] [ j ]; Reference j
 before k
  18. 18. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Instrumenting and Monitoring CPU Use Linux perf command! 18 http://www.brendangregg.com/blog/2015-11-06/java-mixed-mode-flame-graphs.html
  19. 19. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Results of Matrix Multiply Comparison Naïve Matrix Multiply Cache-Friendly Matrix Multiply ~72x ~8x ~3x ~3x ~2x ~7x ~10x perf stat -XX:+PreserveFramePointer -XX:-Inline –event L1-dcache-load-misses,L1-dcache-prefetch-misses,LLC-load-misses, LLC-prefetch-misses,cache-misses,stalled-cycles-frontend ~10x 55 hp 550 hp
  20. 20. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Demo! Compare CPU Naïve & Cache-Friendly Matrix Multiplication 20
  21. 21. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU Cache Naïve Tuple Counters object CacheNaiveTupleIncrement { var tuple = (0,0) … def increment(leftIncrement: Int, rightIncrement: Int) : (Int, Int) = { this.synchronized { tuple = (tuple._1 + leftIncrement, tuple._2 + rightIncrement) tuple } } } 21
  22. 22. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU Cache Naïve Case Class Counters case class MyTuple(left: Int, right: Int) object CacheNaiveCaseClassCounters { var tuple = new MyTuple(0,0) … def increment(leftIncrement: Int, rightIncrement: Int) : MyTuple = { this.synchronized { tuple = new MyTuple(tuple.left + leftIncrement, tuple.right + rightIncrement) tuple } } } 22
  23. 23. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU Cache Friendly Lock-Free Counters object CacheFriendlyLockFreeCounters { // a single Long (8-bytes) will maintain 2 separate Ints (4-bytes each) val tuple = new AtomicLong() … def increment(leftIncrement: Int, rightIncrement: Int) : Long = { var originalLong = 0L var updatedLong = 0L do { originalLong = tuple.get() val originalRightInt = originalLong.toInt // cast originalLong to Int to get right counter val originalLeftInt = (originalLong >>> 32).toInt // shift right to get left counter val updatedRightInt = originalRightInt + rightIncrement // increment right counter val updatedLeftInt = originalLeftInt + leftIncrement // increment left counter updatedLong = updatedLeftInt // update the new long with the left counter updatedLong = updatedLong << 32 // shift the new long left updatedLong += updatedRightInt // update the new long with the right counter } while (tuple.compareAndSet(originalLong, updatedLong) == false) updatedLong } 23 Quiz: Why not @volatile?
  24. 24. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Demo! Compare CPU Naïve & Cache-Friendly Tuple Counter Sync 24
  25. 25. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Results of Counters Comparison Naïve Tuple Counters Naïve Case Class Counters Cache Friendly Lock-Free Counters ~2x ~1.5x ~3.5x ~2x ~2x ~1.5x ~1.5x ~1.5x
  26. 26. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Profiling Visualizations: Flame Graphs With Java Stack Traces!! 26 Example: Spark Word Count Java Stack Traces are Good! Plateaus
 are Bad!!
  27. 27. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Outline ①  Mechanical Sympathy ②  Recap of 100TB GraySort Challenge ③  Project Tungsten Deep Dive 27
  28. 28. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc 100TB GraySort Challenge Sort 100TB of 100-Byte Records with 10-byte Keys Custom Data Structs & Algos for Sort & Shuffle Saturate Network and Disk I/O Controllers 28
  29. 29. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark 100TB GraySort Challenge Results 29 Performance Goals   Saturate Network I/O   Saturate Disk I/O (2013) (2014)
  30. 30. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Winning Hardware Configuration Compute 206 Workers, 1 Master (AWS EC2 i2.8xlarge) 32 Intel Xeon CPU E5-2670 @ 2.5 Ghz 244 GB RAM, 8 x 800GB SSD, RAID 0 striping, ext4 3 GBps mixed read/write disk I/O per node Network AWS Placement Groups, VPC, Enhanced Networking Single Root I/O Virtualization (SR-IOV) 10 Gbps, low latency, low jitter (iperf: ~9.5 Gbps) 30
  31. 31. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Winning Software Configuration Spark 1.2, OpenJDK 1.7 Disable caching, compression, spec execution, shuffle spill Force NODE_LOCAL task scheduling for optimal data locality HDFS 2.4.1 short-circuit local reads, 2x replication Empirically chose between 4-6 partitions per cpu 206 nodes * 32 cores = 6592 cores 6592 cores * 4 = 26,368 partitions 6592 cores * 6 = 39,552 partitions 6592 cores * 4.25 = 28,000 partitions (empirical best) Range partitioning takes advantage of sequential keyspace Required ~10s of sampling 79 keys from in each partition 31
  32. 32. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark New Sort Shuffle Manager for Spark 1.2 Original “hash-based” New “sort-based” ①  Use less OS resources (socket buffers, file descriptors) ②  TimSort partitions in-memory ③  MergeSort partitions on-disk into a single master file ④  Serve partitions from master file: seek once, sequential scan 32
  33. 33. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Asynchronous Network Module Switch to asyncronous Netty vs. synchronous java.nio Switch to zero-copy epoll Use only kernel-space between disk and network controllers Custom memory management spark.shuffle.blockTransferService=netty Spark-Netty Performance Tuning spark.shuffle.io.preferDirectBuffers=true Reuse off-heap buffers spark.shuffle.io.numConnectionsPerPeer=8 (for example) Increase to saturate hosts with multiple disks (8x800 SSD) 33 Details in SPARK-2468
  34. 34. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Custom Algorithms and Data Structures Optimized for sort & shuffle workloads o.a.s.util.collection.TimSort[K,V] Based on JDK 1.7 TimSort Performs best with partially-sorted runs Optimized for elements of (K,V) pairs Sorts impl of SortDataFormat (ie. KVArraySortDataFormat) o.a.s.util.collection.AppendOnlyMap Open addressing hash, quadratic probing Array of [(K, V), (K, V)] Good memory locality Keys never removed, values only append 34
  35. 35. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Daytona GraySort Challenge Goal Success 1.1 Gbps/node network I/O (Reducers)
 Theoretical max = 1.25 Gbps for 10 GB ethernet 3 GBps/node disk I/O (Mappers) 35 Aggregate 
 Cluster Network I/O! 220 Gbps / 206 nodes ~= 1.1 Gbps per node
  36. 36. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Shuffle Performance Tuning Tips Hash Shuffle Manager (Deprecated) spark.shuffle.consolidateFiles (Mapper) o.a.s.shuffle.FileShuffleBlockResolver Intermediate Files Increase spark.shuffle.file.buffer (Reducer) Increase spark.reducer.maxSizeInFlight if memory allows Use Smaller Number of Larger Executors Minimizes intermediate files and overall shuffle More opportunity for PROCESS_LOCAL SQL: BroadcastHashJoin vs. ShuffledHashJoin spark.sql.autoBroadcastJoinThreshold Use DataFrame.explain(true) or EXPLAIN to verify 36 Many Threads (1 per CPU)
  37. 37. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Outline ①  Mechanical Sympathy ②  Recap of 100TB GraySort Challenge ③  Project Tungsten Deep Dive 37
  38. 38. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Project Tungsten Data Struts & Algos Operate Directly on Byte Arrays Maximize CPU Cache Locality, Minimize GC Utilize Dynamic Code Generation 38 SPARK-7076 (Spark 1.4)
  39. 39. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Quick Review of Project Tungsten Jiras 39 SPARK-7076 (Spark 1.4)
  40. 40. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Why is CPU the Bottleneck? CPU is used for serialization, hashing, compression! Network and Disk I/O bandwidth are relatively high GraySort optimizations improved network & shuffle Partitioning, pruning, and predicate pushdowns Binary, compressed, columnar file formats (Parquet) 40
  41. 41. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Yet Another Spark Shuffle Manager! spark.shuffle.manager = hash (Deprecated) < 10,000 reducers Output partition file hashes the key of (K,V) pair Mapper creates an output file per partition Leads to M*P output files for all partitions sort (GraySort Challenge) > 10,000 reducers Default from Spark 1.2-1.5 Mapper creates single output file for all partitions Minimizes OS resources, netty + epoll optimizes network I/O, disk I/O, and memory Uses custom data structures and algorithms for sort-shuffle workload Wins Daytona GraySort Challenge tungsten-sort (Project Tungsten) Default since 1.5 Modification of existing sort-based shuffle Uses com.misc.Unsafe for self-managed memory and garbage collection Maximize CPU utilization and cache locality with AlphaSort-inspired binary data structures/algorithms Perform joins, sorts, and other operators on both serialized and compressed byte buffers 41
  42. 42. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark CPU & Memory Optimizations Custom Managed Memory Reduces GC overhead Both on and off heap Exact size calculations Direct Binary Processing Operate on serialized/compressed arrays Kryo can reorder/sort serialized records LZF can reorder/sort compressed records More CPU Cache-aware Data Structs & Algorithms o.a.s.sql.catalyst.expression.UnsafeRow o.a.s.unsafe.map.BytesToBytesMap Code Generation (default in 1.5) Generate source code from overall query plan 100+ UDFs converted to use code generation 42 UnsafeFixedWithAggregationMap TungstenAggregationIterator CodeGenerator GeneratorUnsafeRowJoiner UnsafeSortDataFormat UnsafeShuffleSortDataFormat PackedRecordPointer UnsafeRow UnsafeInMemorySorter UnsafeExternalSorter UnsafeShuffleWriter Mostly Same Join Code, UnsafeProjection UnsafeShuffleManager UnsafeShuffleInMemorySorter UnsafeShuffleExternalSorter Details in SPARK-7075
  43. 43. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark sun.misc.Unsafe 43 Info addressSize() pageSize() Objects allocateInstance() objectFieldOffset() Classes staticFieldOffset() defineClass() defineAnonymousClass() ensureClassInitialized() Synchronization monitorEnter() tryMonitorEnter() monitorExit() compareAndSwapInt() putOrderedInt() Arrays arrayBaseOffset() arrayIndexScale() Memory allocateMemory() copyMemory() freeMemory() getAddress() – not guaranteed after GC getInt()/putInt() getBoolean()/putBoolean() getByte()/putByte() getShort()/putShort() getLong()/putLong() getFloat()/putFloat() getDouble()/putDouble() getObjectVolatile()/putObjectVolatile() Used by 
 Tungsten
  44. 44. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Spark + com.misc.Unsafe 44 org.apache.spark.sql.execution. aggregate.SortBasedAggregate aggregate.TungstenAggregate aggregate.AggregationIterator aggregate.udaf aggregate.utils SparkPlanner rowFormatConverters UnsafeFixedWidthAggregationMap UnsafeExternalSorter UnsafeExternalRowSorter UnsafeKeyValueSorter UnsafeKVExternalSorter local.ConvertToUnsafeNode local.ConvertToSafeNode local.HashJoinNode local.ProjectNode local.LocalNode local.BinaryHashJoinNode local.NestedLoopJoinNode joins.HashJoin joins.HashSemiJoin joins.HashedRelation joins.BroadcastHashJoin joins.ShuffledHashOuterJoin (not yet converted) joins.BroadcastHashOuterJoin joins.BroadcastLeftSemiJoinHash joins.BroadcastNestedLoopJoin joins.SortMergeJoin joins.LeftSemiJoinBNL joins.SortMergerOuterJoin Exchange SparkPlan UnsafeRowSerializer SortPrefixUtils sort basicOperators aggregate.SortBasedAggregationIterator aggregate.TungstenAggregationIterator datasources.WriterContainer datasources.json.JacksonParser datasources.jdbc.JDBCRDD org.apache.spark. unsafe.Platform unsafe.KVIterator unsafe.array.LongArray unsafe.array.ByteArrayMethods unsafe.array.BitSet unsafe.bitset.BitSetMethods unsafe.hash.Murmur3_x86_32 unsafe.map.BytesToBytesMap unsafe.map.HashMapGrowthStrategy unsafe.memory.TaskMemoryManager unsafe.memory.ExecutorMemoryManager unsafe.memory.MemoryLocation unsafe.memory.UnsafeMemoryAllocator unsafe.memory.MemoryAllocator (trait/interface) unsafe.memory.MemoryBlock unsafe.memory.HeapMemoryAllocator unsafe.memory.ExecutorMemoryManager unsafe.sort.RecordComparator unsafe.sort.PrefixComparator unsafe.sort.PrefixComparators unsafe.sort.UnsafeSorterSpillWriter serializer.DummySerializationInstance shuffle.unsafe.UnsafeShuffleManager shuffle.unsafe.UnsafeShuffleSortDataFormat shuffle.unsafe.SpillInfo shuffle.unsafe.UnsafeShuffleWriter shuffle.unsafe.UnsafeShuffleExternalSorter shuffle.unsafe.PackedRecordPointer shuffle.ShuffleMemoryManager util.collection.unsafe.sort.UnsafeSorterSpillMerger util.collection.unsafe.sort.UnsafeSorterSpillReader util.collection.unsafe.sort.UnsafeSorterSpillWriter util.collection.unsafe.sort.UnsafeShuffleInMemorySorter util.collection.unsafe.sort.UnsafeInMemorySorter util.collection.unsafe.sort.RecordPointerAndKeyPrefix util.collection.unsafe.sort.UnsafeSorterIterator network.shuffle.ExternalShuffleBlockResolver scheduler.Task rdd.SqlNewHadoopRDD executor.Executor org.apache.spark.sql.catalyst.expressions. regexpExpressions BoundAttribute SortOrder SpecializedGetters ExpressionEvalHelper UnsafeArrayData UnsafeReaders UnsafeMapData Projection LiteralGeneartor UnsafeRow JoinedRow SpecializedGetters InputFileName SpecificMutableRow codegen.CodeGenerator codegen.GenerateProjection codegen.GenerateUnsafeRowJoiner codegen.GenerateSafeProjection codegen.GenerateUnsafeProjection codegen.BufferHolder codegen.UnsafeRowWriter codegen.UnsafeArrayWriter complexTypeCreator rows literals misc stringExpressions Over 200 source files affected!!
  45. 45. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Traditional Java Object Row Layout 4-byte String Multi-field Object 45
  46. 46. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Custom Data Structures for Workload UnsafeRow (Dense Binary Row) TaskMemoryManager (Virtual Memory Address) BytesToBytesMap (Dense Binary HashMap) 46 Dense, 8-bytes per field (word-aligned) Key Ptr AlphaSort-Style (Key + Pointer) OS-Style Memory Paging
  47. 47. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark UnsafeRow Layout Example 47 Pre-Tungsten Tungsten
  48. 48. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Custom Memory Management o.a.s.memory.
 TaskMemoryManager & MemoryConsumer Memory management: virtual memory allocation, pageing Off-heap: direct 64-bit address On-heap: 13-bit page num + 27-bit page offset o.a.s.shuffle.sort. PackedRecordPointer 64-bit word (24-bit partition key, (13-bit page num, 27-bit page offset)) o.a.s.unsafe.types. UTF8String Primitive Array[Byte] 48 2^13 pages * 2^27 page size = 1 TB RAM per Task
  49. 49. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark UnsafeFixedWidthAggregationMap Aggregations o.a.s.sql.execution.
 UnsafeFixedWidthAggregationMap Uses BytesToBytesMap In-place updates of serialized data No object creation on hot-path Improved external agg support No OOM’s for large, single key aggs o.a.s.sql.catalyst.expression.codegen. GenerateUnsafeRowJoiner Combine 2 UnsafeRows into 1 o.a.s.sql.execution.aggregate. TungstenAggregate & TungstenAggregationIterator Operates directly on serialized, binary UnsafeRow 2 Steps: hash-based agg (grouping), then sort-based agg Supports spilling and external merge sorting 49
  50. 50. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Equality Bitwise comparison on UnsafeRow No need to calculate equals(), hashCode() Row 1 Equals! Row 2 50
  51. 51. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Joins Surprisingly, not many code changes o.a.s.sql.catalyst.expressions. UnsafeProjection Converts InternalRow to UnsafeRow 51
  52. 52. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Sorting o.a.s.util.collection.unsafe.sort. UnsafeSortDataFormat UnsafeInMemorySorter UnsafeExternalSorter RecordPointerAndKeyPrefix
 UnsafeShuffleWriter AlphaSort-Style Cache Friendly 52 Ptr Key-Prefix 2x CPU Cache-line Friendly! Using multiple subclasses of SortDataFormat simultaneously will prevent JIT inlining. This affects sort & shuffle performance. Supports merging compressed records if compression CODEC supports it (LZF)
  53. 53. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Spilling Efficient Spilling Exact data size is known No need to maintain heuristics & approximations Controls amount of spilling Spill merge on compressed, binary records! If compression CODEC supports it 53 UnsafeFixedWidthAggregationMap.getPeakMemoryUsedBytes() Exact Peak Memory for Spark Jobs
  54. 54. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Code Generation Problem Boxing causes excessive object creation Expensive expression tree evals per row JVM can’t inline polymorphic impls Solution Codegen by-passes virtual function calls Defer source code generation to each operator, UDF, UDAF Use Scala quasiquote macros for Scala AST source code gen Rewrite and optimize code for overall plan, 8-byte align, etc Use Janino to compile generated source code into bytecode 54
  55. 55. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc IBM | spark.tc Spark SQL UDF Code Generation 100+ UDFs now generating code More to come in Spark 1.6+ Details in SPARK-8159, SPARK-9571 Each Implements Expression.genCode()!
  56. 56. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Creating a Custom UDF with Codegen Study existing implementations https://github.com/apache/spark/pull/7214/files Extend base trait o.a.s.sql.catalyst.expressions.Expression.genCode() Register the function o.a.s.sql.catalyst.analysis.FunctionRegistry.registerFunction() Augment DataFrame with new UDF (Scala implicits) o.a.s.sql.functions.scala Don’t forget about Python! python.pyspark.sql.functions.py 56
  57. 57. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Who Benefits from Project Tungsten? Users of DataFrames All Spark SQL Queries Catalyst All RDDs Serialization, Compression, and Aggregations 57
  58. 58. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Performance Results Query Time Garbage Collection 58 OOM’d on Large Dataset!
  59. 59. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark Thank You!!! Chris Fregly @cfregly IBM Spark Technology Center San Francisco, California Relevant Links advancedspark.com Signup for the book & global meetup! github.com/fluxcapacitor/pipeline Clone, contribute, and commit code! hub.docker.com/r/fluxcapacitor/pipeline/wiki Run all demos in your own environment with Docker! 59
  60. 60. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark

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