Shark SQL and Rich Analytics at Scale
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Shark SQL and Rich Analytics at Scale






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    Shark SQL and Rich Analytics at Scale Shark SQL and Rich Analytics at Scale Presentation Transcript

    • Shark: SQL and Rich Analytics at Scale Reynold Xin, Josh Rosen, Matei Zaharia, Michael Franklin, Scott Shenker, Ion Stoica AMPLab, UC Berkeley June 25 @ SIGMOD 2013
    • Challenges Data size growing » Processing has to scale out over large clusters » Faults and stragglers complicate DB design Complexity of analysis increasing » Massive ETL (web crawling) » Machine learning, graph processing » Leads to long running jobs
    • The Rise of MapReduce
    • What’s good about MapReduce? 1.  Scales out to thousands of nodes in a fault- tolerant manner 2.  Good for analyzing semi-structured data and complex analytics 3.  Elasticity (cloud computing) 4.  Dynamic, multi-tenant resource sharing
    • “parallel relational database systems are significantly faster than those that rely on the use of MapReduce for their query engines” “I totally agree.”
    • This Research 1.  Shows MapReduce model can be extended to support SQL efficiently »  Started from a powerful MR-like engine (Spark) »  Extended the engine in various ways 2.  The artifact: Shark, a fast engine on top of MR »  Performant SQL »  Complex analytics in the same engine »  Maintains MR benefits, e.g. fault-tolerance
    • MapReduce Fundamental Properties? Data-parallel operations » Apply the same operations on a defined set of data Fine-grained, deterministic tasks » Enables fault-tolerance & straggler mitigation
    • Why Were Databases Faster? Data representation » Schema-aware, column-oriented, etc » Co-partition & co-location of data Execution strategies » Scheduling/task launching overhead (~20s in Hadoop) » Cost-based optimization » Indexing Lack of mid-query fault tolerance » MR’s pull model costly compared to DBMS “push” See Pavlo 2009, Xin 2013.
    • Why Were Databases Faster? Data representation » Schema-aware, column-oriented, etc » Co-partition & co-location of data Execution strategies » Scheduling/task launching overhead (~20s in Hadoop) » Cost-based optimization » Indexing Lack of mid-query fault tolerance » MR’s pull model costly compared to DBMS “push” See Pavlo 2009, Xin 2013. Not fundamental to “MapReduce” Can be surprisingly cheap
    • Introducing Shark MapReduce-based architecture » Uses Spark as the underlying execution engine » Scales out and tolerate worker failures Performant » Low-latency, interactive queries » (Optionally) in-memory query processing Expressive and flexible » Supports both SQL and complex analytics » Hive compatible (storage, UDFs, types, metadata, etc)
    • Spark Engine Fast MapReduce-like engine » In-memory storage for fast iterative computations » General execution graphs » Designed for low latency (~100ms jobs) Compatible with Hadoop storage APIs » Read/write to any Hadoop-supported systems, including HDFS, Hbase, SequenceFiles, etc Growing open source platform » 17 companies contributing code
    • More Powerful MR Engine General task DAG Pipelines functions within a stage Cache-aware data locality & reuse Partitioning-aware to avoid shuffles join   union   groupBy   map   Stage  3   Stage  1   Stage  2   A:   B:   C:   D:   E:   F:   G:   =  previously  computed  partition  
    • Client CLI JDBC Hive Architecture Meta store Hadoop Storage (HDFS, S3, …) Driver SQL Parser Query Optimizer Physical Plan Execution MapReduce
    • Client CLI JDBC Shark Architecture Meta store Hadoop Storage (HDFS, S3, …) Driver SQL Parser Spark Cache Mgr. Physical Plan Execution Query Optimizer
    • Extending Spark for SQL Columnar memory store Dynamic query optimization Miscellaneous other optimizations (distributed top-K, partition statistics & pruning a.k.a. coarse- grained indexes, co-partitioned joins, …)
    • Columnar Memory Store Simply caching records as JVM objects is inefficient (huge overhead in MR’s record-oriented model) Shark employs column-oriented storage, a partition of columns is one MapReduce “record”. 1   Column  Storage   2   3   john   mike   sally   4.1   3.5   6.4   Row  Storage   1   john   4.1   2   mike   3.5   3   sally   6.4   Benefit: compact representation, CPU efficient compression, cache locality.
    • How do we optimize:  SELECT * FROM table1 a JOIN table2 b ON a.key=b.key WHERE my_crazy_udf(b.field1, b.field2) = true; Hard to estimate cardinality!
    • Partial DAG Execution (PDE) Lack of statistics for fresh data and the prevalent use of UDFs necessitate dynamic approaches to query optimization. PDE allows dynamic alternation of query plans based on statistics collected at run-time.
    • Shuffle Join Stage 3Stage 2 Stage 1 Join Result Stage 1 Stage 2 Join Result Map Join (Broadcast Join) minimizes network traffic
    • PDE Statistics Gather customizable statistics at per-partition granularities while materializing map output. » partition sizes, record counts (skew detection) » “heavy hitters” » approximate histograms Can alter query plan based on such statistics » map join vs shuffle join » symmetric vs non-symmetric hash join » skew handling
    • Complex Analytics Integration Unified system for SQL, machine learning Both share the same set of workers and caches def logRegress(points: RDD[Point]): Vector { var w = Vector(D, _ => 2 * rand.nextDouble - 1) for (i <- 1 to ITERATIONS) { val gradient = { p => val denom = 1 + exp(-p.y * (w dot p.x)) (1 / denom - 1) * p.y * p.x }.reduce(_ + _) w -= gradient } w } val users = sql2rdd("SELECT * FROM user u JOIN comment c ON c.uid=u.uid") val features = users.mapRows { row => new Vector(extractFeature1(row.getInt("age")), extractFeature2(row.getStr("country")), ...)} val trainedVector = logRegress(features.cache())
    • Pavlo Benchmark Selection 0 22.5 45 67.5 90 Shark Shark5(disk) Hive 1.1 0 150 300 450 600 Aggregation 1K5Groups 32 Hive Shark5(disk) Shark Shark5Copartitioned 0 500 1000 1500 2000 Runtime5(seconds)
    • Machine Learning Performance K"Means(Clustering 0 36 72 108 144 180 157 4.1 Logistic(Regression 0 24 48 72 96 120 110 0.96 Shark Hadoop Runtime per iteration (secs)
    • Real Warehouse Benchmark 0 25 50 75 100 Q1 Q2 Q3 Q4 Runtime0(seconds) Shark Shark0(disk) Hive 1.1 0.8 0.7 1.0 1.7TB Real Warehouse Data on 100 EC2 nodes
    • New Benchmark Impala Impala&(mem) Redshift Shark&(disk) Shark&(mem) 0 5 10 15 20 Runtime&(seconds)
    • Other benefits of MapReduce Elasticity » Query processing can scale up and down dynamically StragglerTolerance Schema-on-read & Easier ETL Engineering » MR handles task scheduling / dispatch / launch » Simpler query processing code base (~10k LOC)
    • Berkeley Data Analytics Stack Spark Shark SQL HDFS / Hadoop Storage Mesos Resource Manager Spark Streaming GraphX MLBase
    • Community 3000 people attended online training 800 meetup members 17 companies contributing
    • Conclusion Leveraging a modern MapReduce engine and techniques from databases, Shark supports both SQL and complex analytics efficiently, while maintaining fault-tolerance. Growing open source community » Users observe similar speedups in real use cases » »
    • MapReduce DBMSs Shark