Apache HAWQ (incubating)

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1 HADOOP NATIVE SQL

2 What is HAWQ?

3 Apache HAWQ (incubating) Is an elastic parallel processing SQL engine that runs native in Apache Hadoop to directly access data for advanced analytics.

4 Why HAWQ?

5 Hadoop Native SQL is a business imperative

6 1. Hadoop: the new Data Warehouse Data is moving out of traditional data warehouses and into Apache Hadoop. IT S ABOUT COST IT S ABOUT SCALE IT S ABOUT COLLABORATION IT S ABOUT ANALYTICS IT S ABOUT OPEN SOURCE IT S ABOUT CLOUD IT S ABOUT SQL! SQL continues to me the Most Valuable workload on Hadoop today

7 MASHING BIG DATA WITH BIG MACHINES IS BEAUTIFUL, DESIRABLE, INVESTABLE - IT COULD TRANSFORM GE'S BUSINESS - AND THE ECONOMY. Jeff Immelt, CEO, GE

8 Sophisticated Analytics drive competitive advantage

9 2. The rise of the Data Scientist Data science enables leveraging data assets for competitive advantage. Data science 800% growth in two years[1] Needs tools capable of rich analytics handling of massive data SQL and Machine Learning are two powerful enabling tools Deep ANSI SQL compliance is a requirement for many existing tools IT S ABOUT PREDICTIVE INSIGHTS! [1] source indeed.com

10 Hadoop Native SQL must embrace the Hadoop ecosystem

11 3. Hadoop SQL ecosystem Apache HAWQ Apache Hive Apache Drill Cloudera Impala (incubating) 100% Apache Governance Yes Yes Yes No Native HCatalog Integration Yes Yes No Yes Native Yarn Integration Yes Yes Yes Yes Native Ambari Integration Yes Yes No No Support ACID consistency Yes Yes No No Native Machine Learning Yes No No No Row Level Security Yes No No Yes* Low Latency & Analytic Queries Simple Batch Schema detection Low latency Queries Focus

12 SQL Patterns

13 Scalable Performance drives rapid iteration

14 4. TPC-DS Performance - Impala HAWQ Faster Impala Faster HAWQ Faster on 45 / 60 TPC-DS queries completed* 4.55x mean avg. 12 hrs faster total * Impala supported 74 / 99 queries and 12 crashed mid-run

15 4. TPC-DS Performance - Hive w / Tez HAWQ Faster Impala Faster HAWQ Faster on 46 / 62 TPC-DS queries completed* 3.44x mean avg. 9 hrs faster total * Hive supported 60 / 99 queries and 5 crashed mid-run

16 5. TPC-DS - Standards Support TPC-DS Query 46 SELECT... FROM... WHERE ss_date between ' ' and ' '... Modified to run in Impala SELECT... FROM... WHERE partition key filter ss_sold_date_sk in ( , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,... * Impala required rewriting date ranges to support partition elimination

17 HAWQ Architecture

18 Historical Timeline HAWQ goes Apache Michael Stonebraker develops Postgres at UCB Greenplum forks PostgreSQL Open Source PostgreSQL HAWQ project launched PostgreSQL 7.0 released Hadoop 2.0 Released Postgres adds support for SQL PostgreSQL 8.0 released 2015 Hadoop 1.0 Released

19 Similarities with PostgreSQL PostgreSQL backend/ HAWQ backend/ access/ access/ bootstrap/ bootstrap/ catalog/ catalog/ commands/ cdb/ executar/ commands/ foreign/ executar/ lib/ foreign/ libpq/ gp_libpq_fe/ main/ gpopt/ nodes/ lib/ optimizer/ libgppc/ parser/ libpq/ po/ main/ port/ nodes/ postmaster/ optimizer/ regex/ parser/......

20 High Level Architecture Ambari pxf Yarn pxf hbase pxf pxf pxf pxf HDFS

21 High Level Architecture Session Manager Catalog Parser Query Rewrite Resource Manager Planner libyarn ORCA Dispatch Interconnect Executor Resource Enforcer Storage Manager libhdfs3 PXF

22 HAWQ Ambari Integration

23 Storage Manager Design PostgreSQL HAWQ Single node Distributed design Append Only Local storage HDFS block storage Master Catalog Local Catalog libhdfs3 Metadata dispatch HDFS

24 Data Access HAWQ supports querying unmanaged data via native hcatalog integration pxf external tables HAWQ supports managed transactional tables Managed tables are able to provide transaction isolation. Provide atomicity of data inserts Provide consistent views of the data

25 HCatalog Access SELECT * FROM hcatalog.ops.weblogs WHERE ts between and ;

26 HCatalog Access SELECT * FROM hcatalog.ops.weblogs WHERE ts between and ; PXF in-memory: pg_exttable pg_class... disk heap: pg_class... PXF PXF HCAT weblogs: id double date timestamp... HIVE

27 PXF Design Master / agent process model Exposed as external tables in HAWQ Extensible design Fragmenter Accessor Resolver pxf master pxf HDFS HIVE HBASE... pxf pxf agents

28 Concurrent Transactional Inserts Files in hdfs Catalog metadata /hawq_data/.../ segno eof

29 Concurrent Transactional Inserts Files in hdfs Catalog metadata /hawq_data/.../ 0 <- session 1 inserts segno eof (mvcc)

30 Concurrent Transactional Inserts Files in hdfs Catalog metadata /hawq_data/.../ 0 <- session 1 inserts 1 <- session 2 inserts 2... segno eof (mvcc) (mvcc)

31 Concurrent Transactional Inserts Files in hdfs Catalog metadata /hawq_data/.../ 0 <- abort / truncate 1 <- commit 2... segno eof (mvcc) (mvcc) HAWQ relies on HDFS Truncate support (HDFS-3107) to truncate aborted inserts so that later sessions can insert atomically

32 HAWQ Metadata management Master Catalog Stores all the system metadata Based on PostgreSQL style catalog representation Query Annotation Metadata is needed at query execution time on the workers The most efficient method of providing metadata is to dispatch it with the query Supports master mirroring for fault tolerance Provides for fully transactional DDL operations Achieved by walking the plan prior to dispatch and annotating with query metadada Local Catalog Cache Each worker has native understand of all bootstrap types Data dispatched with the query is added to a local cache for the duration of a query. Each worker is effectively stateless and receives the needed metadata at execution time.

33 HAWQ Distributed Query Engine Motion 2 phase aggregation Dispatch explain select * from a join b on (a.i=b.j); QUERY PLAN Gather Motion 2:1 -> Hash Join Hash Cond: a.i = b.j -> Seq Scan on a -> Hash -> Redistribute Motion 2:2 Hash Key: b.j -> Seq Scan on b GATHER Motion: Data from all nodes is brought to 1 location REDISTRIBUTE Motion: Data is hash partitioned between virtual segments BROADCAST Motion: Data is broadcast to all virtual segments

34 HAWQ Distributed Query Engine Motion Join A copartitioned join GATHER Join / \ A B 2 phase aggregation Join B redistributed join GATHER Join / \ A REDISTRIBUTE B Pipelines Join C broadcast join GATHER Join / \ A BROADCAST B

35 HAWQ Distributed Query Engine Motion 2 phase aggregation Pipelines explain select count(*) from b group by j; QUERY PLAN Gather Motion 2:1 -> HashAggregate Group By: b.j -> Redistribute Motion 2:2 Hash Key: b.j -> HashAggregate Group By: b.j -> Seq Scan on b Similar in concept to COMBINE/REDUCE in Hadoop Local aggregation occurs on the data processed by each virtual segment 2nd phase aggregation occurs after GATHER/REDISTRIBUTE to accumulate partial aggregations from individual virtual segments

36 HAWQ Distributed Query Engine Motion 2 phase aggregation Pipelines Each Executor node operates on a pull based model Several nodes may be active at any time Most nodes are non-blocking Optimized such that inactive executor nodes do not occupy resources.

37 Resource Manager Design Yarn HAWQ RM HAWQ Dispatch Provisions containers to Yarn Applications Requests resources from Yarn when needed Allocates HAWQ virtual segments to a query Provides multitenant Resource Management across applications Returns resources to Yarn when unused Assigns HDFS blocks to HAWQ virtual segments Provides Low latency allocation of HAWQ containers to queries Determines how many resources to allocate to a query Allocates resources within Yarn containers to individual chunks of a distributed query plan Support for different scheduling policies fair scheduler capacity scheduler

38 Resource Manager Design Yarn HAWQ RM HAWQ Dispatch Ambari pxf Yarn pxf hbase pxf pxf pxf pxf HDFS

39 Resource Manager Design Yarn HAWQ RM Q1 Q1 Q1 Q2 HAWQ Dispatch Q1 Q1 Q1

40 Resource Manager Design Yarn HAWQ RM Q1 HAWQ Dispatch Q1 Q1 Q1 Q1 HDFS Q1

41 HAWQ Extensibility User Defined Functions User Defined Aggregates User Defined Operators User Defined Types Supports multiple languages

42 HAWQ Machine Learning Apache MADlib (incubating) Leverages robust extensibility Provides in database machine learning capabilities Supports Clustering Regression Classification Topic Modeling and much more

43 Questions? Website Wiki Github mirror Bug reporting HADOOP NATIVE SQL

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