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Information about Presto

Published on April 22, 2014

Author: chenchunss


Presto Thursday, 24 April, 14

Content • Background • Architecture • Key points for low query latency • TPCH benchmark test • What we do • Reference Thursday, 24 April, 14

Background • 300+PB data stored in Hadoop/HDFS-based clusters • More queries and get results faster improves analysts, data scientists, and engineers productivity • MapReduce and Hive are designed for large-scale, reliable computation • External projects too nascent or did not meet our requirements for flexibility and scale Thursday, 24 April, 14

Architecture Thursday, 24 April, 14

Key points for low latency • In memory parallel computing • Pipeline • Data local computation • Data cache • Dynamic compile part of the plan to byte code • Careful use of memory and data structure • BlinkDB liked approximate queries • Traditional SQL optimize • GC control Thursday, 24 April, 14

Compile flow Thursday, 24 April, 14

In memory parallel computing select c1.rank, count(*) from c1 join c2 on = where > 10 group by c1.rank limit 10; Thursday, 24 April, 14

In memory parallel computing Thursday, 24 April, 14

In memory parallel computing Thursday, 24 April, 14

In memory parallel computing • PlanDistribution=Source – InputSplit[] splits = inputFormat.getSplits(jobConf , 0); • PlanDistribution=Hash – Hash Shuffle – Fixed Workers – query.initial-hash-partitions Thursday, 24 April, 14

SplitRunner thread number task.shard.max-threads=availableProcessors() * 4 Pipeline - TaskExecutor Thursday, 24 April, 14

Pipeline - Operator process flow Page(max page size: 1MB, max rows: 16 * 1024 ) Thursday, 24 April, 14

Pipeline - ExchangeOperator Thursday, 24 April, 14

Data local computation • Select acceptable nodes (as least 10 nodes by default) – Nodes has the same address – If not enough, add nodes in the same rack – If not enough, randomly select nodes in other racks • Select the node with the smallest number of assignments (pending tasks) Thursday, 24 April, 14

Data cache • Google Guava LoadingCache • Cached Objects – HiveMeta database table partition – Byte Code Class FilterAndProjectOperatorFactoryFactory, ScanFilterAndProjectOperatorFactoryFactory – functions Thursday, 24 April, 14

Dynamic compile plan to byte code • Presto dynamic compile FilterAndProjectOperator and ScanFilterAndProjectOperator to byte code which lets the JIT optimize and generate native machine code • How much does it speed up ? • ScanFilterAndProjectOperator Thursday, 24 April, 14

Careful use mem & data structure • Slice – Unsafe#copyMemory – 20% ~ 30% speed up for ORCFile write performance • ThreadLocalRandom – ThreadLocal seed instead of AtomicLong – 100% speed up • ListenableFuture – Async Callback Thursday, 24 April, 14

Approximate queries • approx_avg, approx_distinct, approx_percentile • +50% speed up Thursday, 24 April, 14

Traditional SQL optimize • ImplementSampleAsFilter • LimitPushDown • MaterializeSamplePullUp • MergeProjections • PredicatePushDown • PruneRedundantProjections • PruneUnreferencedOutputs • SetFlatteningOptimizer • SimplifyExpressions • UnaliasSymbolReferences Thursday, 24 April, 14

GC control • A JDK 1.7 BUG • When code cache fills up, there is a chance that JIT might stop compile byte code to native code. • By forcing classes to unload from the perm gen, we let the code cache evictor make room before the cache fills up. • System.gc() Thursday, 24 April, 14

TPCH benchmark test • Run presto-main/src/test/java/com/facebook/ presto/benchmark/ • A part of the result as below Thursday, 24 April, 14

What we do • Support kerberos authentication • Implicit type coercion • Support reading lzo compressed tables • Implement useful functions • Fix planning issue when using DISTICT aggregations in HAVING clause • mt-0.60 Thursday, 24 April, 14

Reference • • engineering/presto-interacting-with-petabytes-of- data-at-facebook/10151786197628920 • from_search=1 • code-reading-15-in-japan-presto Thursday, 24 April, 14

Thanks Thursday, 24 April, 14

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