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Istanbul Spark Meetup Nov 28 2015

Istanbul Spark Meetup Nov 28 2015

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Power of data. Simplicity of design. Speed of innovation.
IBM Spark
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After Dark 1.6
Istanbul Spark Meetup
Chris Fregly
Principal Data Solutions Engineer
We’re Hiring - Only Nice People!
Nov 28th, 2015
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?
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Streaming Data Engineer
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Data Solutions Engineer

Apache Contributor
Principal Data Solutions Engineer
IBM Technology Center
Founder
Advanced Apache Meetup
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advancedspark.com.
Due 2016
My Ma’s First Time in California
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
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In California
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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
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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
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!

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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 (Nov 10th)
San Francisco Advanced Spark (Nov 12th)
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Oslo Big Data Hadoop Meetup (Nov 19th)
Helsinki Spark Meetup (Nov 20th)
Stockholm Spark Meetup (Nov 23rd)
Copenhagen Spark Meetup (Nov 25th)
Budapest Spark Meetup (Nov 26th)
Istanbul Spark Meetup (Nov 28th)
Singapore Strata Conference (Dec 1st)
Sydney Spark Meetup (Dec 7th)
Melbourne Spark Meetup (Dec 9th)
San Francisco Advanced Spark (Dec 10th)
Toronto Spark Meetup (Dec 14th)
Austin Data Days Conference (Jan 16th)

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Istanbul Spark Meetup Nov 28 2015

  • 1. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc After Dark 1.6 Istanbul Spark Meetup Chris Fregly Principal Data Solutions Engineer We’re Hiring - Only Nice People! Nov 28th, 2015
  • 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 advancedspark.com. Due 2016 My Ma’s First Time in California
  • 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. 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! Top 5 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
  • 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 All Slides and Code Are Available! advancedspark.com slideshare.net/cfregly github.com/fluxcapacitor hub.docker.com/r/fluxcapacitor 5
  • 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 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 (Nov 10th) San Francisco Advanced Spark (Nov 12th) 6 Oslo Big Data Hadoop Meetup (Nov 19th) Helsinki Spark Meetup (Nov 20th) Stockholm Spark Meetup (Nov 23rd) Copenhagen Spark Meetup (Nov 25th) Budapest Spark Meetup (Nov 26th) Istanbul Spark Meetup (Nov 28th) Singapore Strata Conference (Dec 1st) Sydney Spark Meetup (Dec 7th) Melbourne Spark Meetup (Dec 9th) San Francisco Advanced Spark (Dec 10th) Toronto Spark Meetup (Dec 14th) Austin Data Days Conference (Jan 16th)
  • 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 What is “ After Dark”? Spark-based, Advanced Analytics Reference App End-to-End, Scalable, Real-time Big Data Pipeline Demo Spark and Related Open Source Projects 7 github.com/fluxcapacitor
  • 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 Tools of This Talk 8   Kafka   Redis   Docker   Ganglia   Cassandra   Parquet, JSON, ORC, Avro   Apache Zeppelin Notebooks   Spark SQL, DataFrames, Hive   ElasticSearch, Logstash, Kibana   Spark ML, GraphX, Stanford CoreNLP … github.com/fluxcapacitor hub.docker.com/r/fluxcapacitor
  • 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 Themes of this Talk  Filter  Off-Heap  Parallelize  Approximate  Find Similarity  Minimize Seeks  Maximize Scans  Customize Data Structs  Tune Performance At Every Layer 9   Be Nice, Collaborate! Like my Ma!!
  • 10. 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 Live, Interactive Demo! sparkafterdark.com 10
  • 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 Audience Participation Needed!! 11 You -> Audience Instructions   Go to sparkafterdark.com   Click 3 actresses and 3 actors   Wait for us to analyze together! Links To Do This Yourself!   github.com/fluxcapacitor   hub.docker.com/r/fluxcapacitor Data -> Scientist EU Safe Harbor Disclaimer
 This is Totally Anonymous!
  • 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 Presentation Outline  Spark Core: Tuning & Mechanical Sympathy  Spark SQL: Query Optimizing & Catalyst 12
  • 13. 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 Spark Core: Tuning & Mechanical Sympathy Understand and Acknowledge Mechanical Sympathy Study AlphaSort and 100TB GraySort Challenge Dive Deep into Project Tungsten 13
  • 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 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 14 Hair Sympathy - Bruce Jenner
  • 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 Spark and Mechanical Sympathy 15 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
  • 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 AlphaSort Technique: Sort 100 Bytes Recs 16 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
  • 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 Line and Memory Sympathy Key (10 bytes)+Pointer (*4 bytes)*Compressed OOPs = 14 bytes 17 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!
  • 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 Performance Comparison 18
  • 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 CPU Cache Line Sizes 19 My
 Laptop My
 SoftLayer
 BareMetal
  • 20. 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 Cache Miss/Hit Ratio: Seq vs Random 20
  • 21. 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 Mechanical Sympathy Improving Performance with CPU Cache Line Affinity Matrix Multiplication 21
  • 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 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 ]; 22 Bad: Row-wise traversal, not using CPU cache line,
 ineffective pre-fetching
  • 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 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 ]; 23 Good: Full CPU cache line,
 effective prefetching OLD: res[ i ][ j ] += matA[ i ][ k ] * matB [ k ] [ j ]; Reference j
 before k
  • 24. 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! 24 http://www.brendangregg.com/blog/2015-11-06/java-mixed-mode-flame-graphs.html
  • 25. 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! CPU Cache Line Affinity & Matrix Multiplication 25
  • 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 Results of Matrix Multiplication Cache-Friendly 
 Matrix Multiply 26 Naive 
 Matrix Multiply perf stat –event L1-dcache-load-misses,L1-dcache-prefetch-misses,LLC-load-misses, LLC-prefetch-misses,cache-misses,stalled-cycles-frontend 4% 7% 7% 47% % of Naive
  • 27. 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 Mechanical Sympathy Improving Performance with Lock-Free Thread Synchronization 2-Counter Atomic Increment 27
  • 28. 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 Naïve Case Class 2-Counter Increment case class Counters(left: Int, right: Int) object NaiveCaseClass2CounterIncrement { var counters = new Counters(0,0) … def increment(leftIncrement: Int, rightIncrement: Int) : MyTuple = { this.synchronized { counters = new Counters(counters.left + leftIncrement, counters.right + rightIncrement) counters } } } 28
  • 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 Naïve Tuple 2-Counter Increment object NaiveTuple2CounterIncrement { var counters = (0,0) … def increment(leftIncrement: Int, rightIncrement: Int) : (Int, Int) = { this.synchronized { counters = (counters._1 + leftIncrement, counters._2 + rightIncrement) counters } } } 29
  • 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 Lock-Free AtomicLong 2-Counter Incr object LockFreeAtomicLong2CounterIncrement { // a single Long (8-bytes) will maintain 2 separate Ints (4-bytes each) val counters = new AtomicLong() … def increment(leftIncrement: Int, rightIncrement: Int) : Long = { var originalCounters = 0L var updatedCounters = 0L do { originalCounters = counters.get() … // Store two 32-bit Int into one 64-bit Long // Use >>> 32 and << 32 to set and retrieve each Int from the Long // Retry lock-free, optimistic compareAndSet() until AtomicLong update succeeds ... } while (tuple.compareAndSet(originalCounters, updatedCounters) == false) updatedCounters } } 30 Q: Why not @volatile long? A: JVM Java Memory Model
 does not guarantee atomic
 updates of 64-bit long, double. ** Must use AtomicLong!! **
  • 31. 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! Lock-Free Thread Synchronization & 2-Counter Atomic Increment 31
  • 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 Results of Atomic 2-Counter Increment Naïve Case Class Counters Naïve Tuple Counters Cache Friendly,
 Lock-Free Counters 28% 50% 17% 65% perf stat –event context-switches,L1-dcache-load-misses,L1-dcache-prefetch-misses, LLC-load-misses, LLC-prefetch-misses,cache-misses,stalled-cycles-frontend % of Naïve
  • 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 Profiling Visualizations: Flame Graphs 33 Example: Spark Word Count Java Stack Traces are Good! (-XX:-Inline -XX:+PreserveFramePointer) Plateaus
 are Bad!!
  • 34. 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 34
  • 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 100TB GraySort Challenge Results 35 Performance Goals  Saturate Network I/O  Saturate Disk I/O  Maximize Throughput (2013) (2014) EC2 (i2.8xlarge) (2014) 28,000 partitions! 250,000 partitions!! EC2 (i2.8xlarge)
  • 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 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) 36 Q: Why only 206? A: Network is saturated @ 206 Allowed and
 Encouraged
  • 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 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 Overprovision between 4-6 partitions per core 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 co-locates keys and minimize shuffle Required ~10s of sampling 79 keys from in each partition 37 GraySort Challenge Requirement 1000 TB Sort used 250,000 partitions
  • 38. 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 38
  • 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 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) 39 Details in SPARK-2468
  • 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 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 No deletes, only append 40
  • 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 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) 41 Aggregate 
 Cluster Network I/O! 220 Gbps / 206 nodes ~= 1.1 Gbps per node
  • 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 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 42 Many Threads (1 per CPU)
  • 43. 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 43 SPARK-7076 (Spark 1.4)
  • 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 Why is CPU the Bottleneck? CPU is used for serialization, hashing, compression GraySort optimizations improved network & shuffle Network and Disk I/O bandwidth are relatively high More partitioning, pruning, predicate pushdowns Better columnar formats reduce disk I/O bottleneck 44
  • 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 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 45
  • 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 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 46 UnsafeFixedWithAggregationMap TungstenAggregationIterator CodeGenerator GeneratorUnsafeRowJoiner UnsafeSortDataFormat UnsafeShuffleSortDataFormat PackedRecordPointer UnsafeRow UnsafeInMemorySorter UnsafeExternalSorter UnsafeShuffleWriter Mostly Same Join Code, UnsafeProjection UnsafeShuffleManager UnsafeShuffleInMemorySorter UnsafeShuffleExternalSorter Details in SPARK-7075
  • 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 sun.misc.Unsafe 47 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
  • 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 Spark + com.misc.Unsafe 48 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!!
  • 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 Traditional Java Object Row Layout 4-byte String Multi-field Object 49
  • 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 Custom Data Structures for Workload UnsafeRow (Dense Binary Row) TaskMemoryManager (Virtual Memory Address) BytesToBytesMap (Binary, Append-Only Map) 50 Dense, 8-bytes per field (word-aligned) Key Ptr AlphaSort-Style (Key + Pointer) OS-Style Memory Paging
  • 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 UnsafeRow Layout Example 51 Pre-Tungsten Tungsten
  • 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 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] 52 2^13 pages * 2^27 page size = 1 TB RAM per Task
  • 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 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 more 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 Avoids OOMs with spill + external merge sort 53
  • 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 Equality Bitwise comparison on UnsafeRow No need to calculate equals(), hashCode() Row 1 Equals! Row 2 54
  • 55. 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 55
  • 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 Sorting o.a.s.util.collection.unsafe.sort. UnsafeSortDataFormat UnsafeInMemorySorter UnsafeExternalSorter RecordPointerAndKeyPrefix
 UnsafeShuffleWriter AlphaSort-Style Cache Friendly 56 Ptr Key-Prefix 2x CPU Cache-line Friendly! Warning: Using multiple subclasses of SortDataFormat simultaneously will prevent JIT inlining. (Affects sort & shuffle performance.) Supports merging compressed records (if compression CODEC supports it, ie. LZF) Uses format compatible with BytesToBytesMap
  • 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 Spilling More Efficient Spilling Exact data size is known vs. approximate No need to guess or traverse entire object tree Reduces amount of unnecessary spilling External Merge of Compressed Records!! (If compression CODEC supports it - ie. LZF) 57 UnsafeFixedWidthAggregationMap.getPeakMemoryUsedBytes() Exact Memory Byte Count
  • 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 Code Generation Problem Boxing creates excessive objects Expression tree evaluations are costly JVM can’t inline polymorphic impls Lack of polymorphism == poor code design Solution Codegen by-passes virtual functions Defer source code generation to each operator, UDF, UDAF Rewrite and optimize code for overall plan, 8-byte align, etc Use Janino to compile generated code into bytecode More IDE friendly than Scala quasiquotes 58
  • 59. 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 UDF implements Expression.genCode()!
  • 60. 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 60
  • 61. 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 61
  • 62. 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 Project Tungsten Performance Results Query Time Garbage Collection 62 OOM’d on Large Dataset!
  • 63. 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 Autoscaling Spark Workers (Spark 1.5+) spark-submit Job Submission --max-executors=4 Spark will add Executor JVMs until max is reached SparkContext API addExecutors() & removeExecutors() Scaling up is easy J Scaling down is tricky L Lose RDD cache inside Executor JVM Must rebuild RDD partitions in another Executor JVM Separate External Shuffle Service (Spark 1.2) Enables Executor JVM autoscaling When Executor JVM dies, External Shuffle Service keeps shufflin’ 63
  • 64. 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 Presentation Outline  Spark Core: Tuning & Mechanical Sympathy  Spark SQL: Query Optimizing & Catalyst 64
  • 65. 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 Spark SQL: Query Optimizing & Catalyst Explore DataFrames/Datasets/DataSources, Catalyst Review Partitions, Pruning, Pushdowns, File Formats Create a Custom DataSource API Implementation 65
  • 66. 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 DataFrames Inspired by R and Pandas DataFrames Schema-aware Cross language support SQL, Python, Scala, Java, R Equal performance between all languages DataFrame is container for logical plan Lazy transformations represented as tree Only logical plan is sent from Python -> JVM Only results returned from JVM -> Python Supports existing Hive metastore Small, file-based Hive metastore created by default DataFrame.rdd returns underlying RDD if needed 66 Use DataFrames instead of RDDs!!
  • 67. 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 UDF and UDAF Support 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.registerFunc() Augment DataFrame with new UDF (Scala implicits) o.a.s.sql.functions.scala Don’t forget about Python! python.pyspark.sql.functions.py 67
  • 68. 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 Hive Shark: “Hive on Spark” Spork: “Pig on Spark” Catalyst Optimizer replaces Hive Optimizer Always use HiveContext No Hive? No problem. Spark SQL creates small, file-based Hive metastore Spark 1.5+ supports all Hive versions 0.12+ Separate classloaders for internal vs user Hive spark.sql.hive.metastore.version=1.2.1 spark.sql.hive.metastore.jars=[builtin|maven] 68
  • 69. 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 Catalyst Optimizer DataFrame Abstract Syntax Tree Transformation Subquery Elimination: use aliases to collapse subqueries Constant Folding: replace expression with constant Simplify Filters: remove unnecessary filters Predicate Pushdowns: avoid unnecessary data load Projection Collapsing: avoid unnecessary projections Create Custom Rules Scala Case Classes val newPlan = MyFilterRule(analyzedPlan) 69 Implements oas.sql.catalyst.rules.Rule Apply Rule at any plan stage
  • 70. 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 Parquet Columnar File Format Based on Google Dremel Collaboration with Twitter and Cloudera Self-describing, evolving schema Fast columnar aggregation Supports filter pushdowns Columnar storage format Excellent compression 70 Min/Max Heuristics For Chunk Skipping
  • 71. 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 Types of Compression Run Length Encoding: Repeated data Dictionary Encoding: Fixed set of values Delta, Prefix Encoding: Sorted data 71
  • 72. 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! Demonstrate File Formats, Partition Schemes, and Query Plans 72
  • 73. 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 Partitions Partition Data Access Patterns /genders.parquet/gender=M/… /gender=F/… <-- Use case: access users by gender /gender=U/… Partition Discovery On read, infer partitions from organization of data (ie. gender=F) Dynamic Partitions Upon insert, dynamically create partitions Specify column to for each partition (ie. Gender) SQL: INSERT TABLE genders PARTITION (gender) SELECT … DF: gendersDF.write.format(”parquet").partitionBy(”gender”).save(…) 73
  • 74. 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 Pruning Partition Pruning Filter out rows by partition SELECT id, gender FROM genders where gender = ‘U’ Column Pruning Filter out columns by column filter Extremely useful for columnar storage formats (Parquet, ORC) Skip entire blocks of columns SELECT id, gender FROM genders 74
  • 75. 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 Pushdowns Predicate Pushdowns aka. Filter Pushdowns Predicate returns [true|false] for given function Filter rows deep into the data source Reduce amount of data returned Data Source must implement PrunedFilteredScan def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] 75
  • 76. 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 DataSources API Relations (o.a.s.sql.sources.interfaces.scala) BaseRelation (abstract class): Provides schema of data TableScan (impl): Read all data from source PrunedFilteredScan (impl): Column pruning & predicate pushdowns InsertableRelation (impl): Insert/overwrite data based on SaveMode RelationProvider (trait/interface): Handle options, BaseRelation factory Execution (o.a.s.sql.execution.commands.scala) RunnableCommand (trait/interface): Common commands like EXPLAIN ExplainCommand(impl: case class) CacheTableCommand(impl: case class) Filters (o.a.s.sql.sources.filters.scala) Filter (abstract class): Handles all predicates/filters supported by this source EqualTo (impl) GreaterThan (impl) StringStartsWith (impl) 76
  • 77. 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 Native Spark SQL DataSources 77
  • 78. 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 Cartesian vs. Inner Join 78
  • 79. 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 Broadcast vs. Normal Shuffle 79
  • 80. 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 Partitioned and Unpartitioned Join 80
  • 81. 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 Both Partitioned Join 81
  • 82. 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 Visualizing the Query Plan 82 Effectiveness of Filter CPU Cache 
 Friendly Binary Format Cost-based Join Optimization Similar to MapReduce Map-side Join Peak Memory for Joins and Aggs
  • 83. 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 JSON Data Source DataFrame val ratingsDF = sqlContext.read.format("json") .load("file:/root/pipeline/datasets/dating/ratings.json.bz2") -- or – val ratingsDF = sqlContext.read.json
 ("file:/root/pipeline/datasets/dating/ratings.json.bz2") SQL Code CREATE TABLE genders USING json OPTIONS (path "file:/root/pipeline/datasets/dating/genders.json.bz2") 83 json() convenience method
  • 84. 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 JDBC Data Source Add Driver to Spark JVM System Classpath $ export SPARK_CLASSPATH=<jdbc-driver.jar> DataFrame val jdbcConfig = Map("driver" -> "org.postgresql.Driver", "url" -> "jdbc:postgresql:hostname:port/database", "dbtable" -> ”schema.tablename") df.read.format("jdbc").options(jdbcConfig).load() SQL CREATE TABLE genders USING jdbc 
 OPTIONS (url, dbtable, driver, …) 84
  • 85. 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 Parquet Data Source Configuration spark.sql.parquet.filterPushdown=true spark.sql.parquet.mergeSchema=false (unless your schema is evolving) spark.sql.parquet.cacheMetadata=true (requires sqlContext.refreshTable()) spark.sql.parquet.compression.codec=[uncompressed,snappy,gzip,lzo] DataFrames val gendersDF = sqlContext.read.format("parquet") .load("file:/root/pipeline/datasets/dating/genders.parquet") gendersDF.write.format("parquet").partitionBy("gender") .save("file:/root/pipeline/datasets/dating/genders.parquet") SQL CREATE TABLE genders USING parquet OPTIONS (path "file:/root/pipeline/datasets/dating/genders.parquet") 85
  • 86. 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 ORC Data Source Configuration spark.sql.orc.filterPushdown=true DataFrames val gendersDF = sqlContext.read.format("orc") .load("file:/root/pipeline/datasets/dating/genders") gendersDF.write.format("orc").partitionBy("gender") .save("file:/root/pipeline/datasets/dating/genders") SQL CREATE TABLE genders USING orc OPTIONS (path "file:/root/pipeline/datasets/dating/genders") 86
  • 87. 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 Third-Party Spark SQL DataSources 87 spark-packages.org
  • 88. 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 CSV DataSource (Databricks) Github https://github.com/databricks/spark-csv Maven com.databricks:spark-csv_2.10:1.2.0 Code val gendersCsvDF = sqlContext.read .format("com.databricks.spark.csv") .load("file:/root/pipeline/datasets/dating/gender.csv.bz2") .toDF("id", "gender") 88 toDF() is required if CSV does not contain header
  • 89. 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 ElasticSearch DataSource (Elastic.co) Github https://github.com/elastic/elasticsearch-hadoop Maven org.elasticsearch:elasticsearch-spark_2.10:2.1.0 Code val esConfig = Map("pushdown" -> "true", "es.nodes" -> "<hostname>", 
 "es.port" -> "<port>") df.write.format("org.elasticsearch.spark.sql”).mode(SaveMode.Overwrite) .options(esConfig).save("<index>/<document-type>") 89
  • 90. 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 Elasticsearch Tips Change id field to not_analyzed to avoid indexing Use term filter to build and cache the query Perform multiple aggregations in a single request Adapt scoring function to current trends at query time 90
  • 91. 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 AWS Redshift Data Source (Databricks) Github https://github.com/databricks/spark-redshift Maven com.databricks:spark-redshift:0.5.0 Code val df: DataFrame = sqlContext.read .format("com.databricks.spark.redshift") .option("url", "jdbc:redshift://<hostname>:<port>/<database>…") .option("query", "select x, count(*) my_table group by x") .option("tempdir", "s3n://tmpdir") .load(...) 91 UNLOAD and copy to tmp bucket in S3 enables parallel reads
  • 92. 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 DB2 and BigSQL DataSources (IBM) Coming Soon! 92
  • 93. 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 Cassandra DataSource (DataStax) Github https://github.com/datastax/spark-cassandra-connector Maven com.datastax.spark:spark-cassandra-connector_2.10:1.5.0-M1 Code ratingsDF.write .format("org.apache.spark.sql.cassandra") .mode(SaveMode.Append) .options(Map("keyspace"->"<keyspace>", "table"->"<table>")).save(…) 93
  • 94. 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 Cassandra Pushdown Support spark-cassandra-connector/…/o.a.s.sql.cassandra.PredicatePushDown.scala Pushdown Predicate Rules 1. Only push down no-partition key column predicates with =, >, <, >=, <= predicate 2. Only push down primary key column predicates with = or IN predicate. 3. If there are regular columns in the pushdown predicates, they should have at least one EQ expression on an indexed column and no IN predicates. 4. All partition column predicates must be included in the predicates to be pushed down, only the last part of the partition key can be an IN predicate. For each partition column, only one predicate is allowed. 5. For cluster column predicates, only last predicate can be non-EQ predicate including IN predicate, and preceding column predicates must be EQ predicates. If there is only one cluster column predicate, the predicates could be any non-IN predicate. 6. There is no pushdown predicates if there is any OR condition or NOT IN condition. 7. We're not allowed to push down multiple predicates for the same column if any of them is equality or IN predicate. 94
  • 95. 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 Cassandra DataSource (?) By-pass CQL optimized for transactional data Instead, do bulk reads/writes directly on SSTables Similar to 5 year old Netflix Open Source project Aegisthus Promotes Cassandra to first-class Analytics Option Potentially only part of DataStax Enterprise?! Please mail a nasty letter to your local DataStax office 95
  • 96. 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 Rumor of REST DataSource (Databricks) Coming Soon? Ask Michael Armbrust Spark SQL Lead @ Databricks 96
  • 97. 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 DataSource (Me and You!) Coming Right Now! 97 DEMO ALERT!!
  • 98. 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 Create a Custom DataSource Study Existing Native & Third-Party Data Sources Native Spark JDBC (o.a.s.sql.execution.datasources.jdbc) class JDBCRelation extends BaseRelation with PrunedFilteredScan with InsertableRelation Third-Party DataStax Cassandra (o.a.s.sql.cassandra) class CassandraSourceRelation extends BaseRelation with PrunedFilteredScan with InsertableRelation! 98
  • 99. 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! Create a Custom DataSource 99
  • 100. 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 Contribute a Custom Data Source spark-packages.org Managed by Contains links to external github projects Ratings and comments Declare supported Spark version per package Kind of like a package manager Custom Maven Repo ----> Examples https://github.com/databricks/spark-csv https://github.com/datastax/spark-cassandra-connector 100
  • 101. 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 Hive JDBC ODBC ThriftServer Allow BI Tools to Query and Process Spark Data Register Permanent Table CREATE TABLE ratings(fromuserid INT, touserid INT, rating INT) USING org.apache.spark.sql.json OPTIONS (path "datasets/dating/ratings.json.bz2") Register Temp Table ratingsDF.registerTempTable("ratings_temp") Configuration spark.sql.thriftServer.incrementalCollect=true spark.driver.maxResultSize > 10gb (default) Configuration Multi-session mode is default Separate SQL configuration & temporary function registry Temp tables can be shared across sessions optionspark.sql.hive.thriftServer.singleSession=true 101
  • 102. 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! Query and Process Spark Data from Beeline and/or Tableau 102
  • 103. 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 IBM Spark Technology Center San Francisco, California Find me: LinkedIn, Twitter, Github 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 103
  • 104. 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 More Relevant Links http://meetup.com/Advanced-Apache-Spark-Meetup http://advancedspark.com http://github.com/fluxcapacitor/pipeline http://hub.docker.com/r/fluxcapacitor/pipeline http://sortbenchmark.org/ApacheSpark2014.pd https://databricks.com/blog/2014/11/05/spark-officially-sets-a-new-record-in-large-sca le-sorting.html http://0x0fff.com/spark-architecture-shuffle/ http://www.cs.berkeley.edu/~kubitron/courses/cs262a-F13/projects/reports/project16_ report.pdf http://stackoverflow.com/questions/763262/how-does-one-write-code-that-best-utilize s-the-cpu-cache-to-improve-performance http://www.aristeia.com/TalkNotes/ACCU2011_CPUCaches.pdf http://mishadoff.com/blog/java-magic-part-4-sun-dot-misc-dot-unsafe/ http://docs.scala-lang.org/overviews/quasiquotes/intro.html http://lwn.net/Articles/252125/ (Memory Part 2: CPU Caches) http://lwn.net/Articles/255364/ (Memory Part 5: What Programmers Can Do) https://www.safaribooksonline.com/library/view/java-performance-the/9781449363512/ ch04.html http://web.eece.maine.edu/~vweaver/projects/perf_events/perf_event_open.html http://www.brendangregg.com/perf.html https://perf.wiki.kernel.org/index.php/Tutorial http://techblog.netflix.com/2015/07/java-in-flames.html http://techblog.netflix.com/2015/04/introducing-vector-netflixs-on-host.html http://www.brendangregg.com/FlameGraphs/cpuflamegraphs.html#Java http://sortbenchmark.org/ApacheSpark2014.pdf https://databricks.com/blog/2014/11/05/spark-officially-sets-a-new-record-in-large-sca le-sorting.html http://0x0fff.com/spark-architecture-shuffle/ http://www.cs.berkeley.edu/~kubitron/courses/cs262a-F13/projects/reports/project16_ report.pdf http://stackoverflow.com/questions/763262/how-does-one-write-code-that-best-utilize s-the-cpu-cache-to-improve-performance http://www.aristeia.com/TalkNotes/ACCU2011_CPUCaches.pdf http://mishadoff.com/blog/java-magic-part-4-sun-dot-misc-dot-unsafe/ http://docs.scala-lang.org/overviews/quasiquotes/intro.html 104 http://lwn.net/Articles/252125/ <-- Memory Part 2: CPU Caches http://lwn.net/Articles/255364/ <-- Memory Part 5: What Programmers Can Do http://antirez.com/news/75 http://esumitra.github.io/algebird-boston-spark/#/ https://github.com/fluxcapacitor/pipeline http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/ http://spark.apache.org/docs/latest/ml-guide.html http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html (part 1) http://techblog.netflix.com/2012/06/netflix-recommendations-beyond-5-stars.html (part 2) http://www.brendangregg.com/blog/2015-11-06/java-mixed-mode-flame-graphs.html
  • 105. 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 What’s Next? 105 After Dark 1.7
  • 106. 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 What’s Next? Autoscaling Docker/Spark Workers Completely Docker-based Docker Compose, Google Kubernetes Lots of Demos and Examples More Zeppelin & IPython/Jupyter notebooks More advanced analytics use cases Performance Tuning and Profiling Work closely with Netflix & Databricks Identify & fix Spark performance bottlenecks 106
  • 107. 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 (Nov 10th) San Francisco Advanced Spark (Nov 12th) 107 Oslo Big Data Hadoop Meetup (Nov 19th) Helsinki Spark Meetup (Nov 20th) Stockholm Spark Meetup (Nov 23rd) Copenhagen Spark Meetup (Nov 25th) Budapest Spark Meetup (Nov 26th) Istanbul Spark Meetup (Nov 28th) Singapore Strata Conference (Dec 1st) Sydney Spark Meetup (Dec 7th) Melbourne Spark Meetup (Dec 9th) San Francisco Advanced Spark (Dec 10th) Toronto Spark Meetup (Dec 14th) Austin Data Days Conference (Jan 16th)
  • 108. Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark