Impala: A Modern SQL Engine for Hadoop. James Kinley, Solutions Architect Cloudera, Inc.

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1 Impala: A Mdern SQL Engine fr Hadp James Kinley, Slutins Architect Cludera, Inc.

2 Agenda Gals User view f Impala Impala internals Cmparing Impala t ther systems

3 Impala Overview: Gals General-purpse SQL query engine: wrks fr bth analytical and transactinal wrklads will supprt queries that take frm millisecnds t hurs Runs directly within Hadp: reads widely used Hadp file frmats talks t widely used Hadp strage managers runs n same ndes that run Hadp prcesses High perfrmance: C++ instead f Java runtime cde generatin (LLVM) cmpletely new executin engine that desn't build n MapReduce

4 User View f Impala: Overview Runs as a distributed service in cluster: ne Impala daemn n each nde with data User submits query via ODBC/JDBC t any f the daemns Query is distributed t all ndes with relevant data If any nde fails, the query fails Impala uses Hive's metadata interface: cnnects t Hive's metastre Supprted file frmats: uncmpressed / lz-cmpressed text files sequence files and RCFile with snappy/gzip cmpressin Avr data files Parquet clumnar frmat (mre n that later)

5 User View f Impala: SQL SQL supprt: patterned after Hive's versin f SQL essentially SQL-92, minus crrelated subqueries INSERT INTO... SELECT... nly equi-jins: n nn-equi jins, n crss prducts ORDER BY requires LIMIT Limited DDL supprt (CREATE TABLE) Functinal limitatins: n custm UDFs, file frmats, r SerDes n beynd SQL (buckets, samples, transfrms, arrays, structs, maps, xpath, jsn) nly hash jins; jined table has t fit in memry: beta: f single nde (bradcast) GA: aggregate memry f all executing ndes (partitined)

6 User View f Impala: Apache HBase HBase functinality: uses Hive's mapping f HBase table int metastre table predicates n rwkey clumns are mapped int start/stp rw predicates n ther clumns are mapped int SingleClumnValueFilters HBase functinal limitatins: n nested-lp jins all data stred as text

7 Impala Architecture Tw binaries: impalad and statestred Impala daemn (impalad) handles client requests and all internal requests related t query executin exprts Thrift services fr these tw rles State stre daemn (statestred) prvides name service and metadata distributin als exprts a Thrift service

8 Impala Architecture Query executin phases request arrives via ODBC/JDBC planner turns request int cllectins f plan fragments (tree f query peratrs) crdinatr initiates executin n remte impalad's During executin intermediate results are streamed between executrs query results are streamed back t client subject t limitatins impsed t blcking peratrs (tp-n, aggregatin)

9 Query Planning: Overview Java frnt-end 2-phase planning prcess: single-nde plan: left-deep tree f plan peratrs partitining f peratr tree int plan fragments fr parallel executin Parallelizatin f plan peratrs: all query peratrs are fully distributed jins: either bradcast r partitined: decisin is cst based aggregatin: fully parallel pre-aggregatin and merge aggregatin tp-n: initial stage dne in parallel Jin rder = FROM clause rder (sn t be cst-based)

10 Query Planning: Example Plan peratrs: Scan, HashJin, HashAggregatin, Unin, TpN, Exchange Example query: SELECT t1.custid, SUM(t2.revenue) AS revenue FROM LargeHdfsTable t1 JOIN LargeHdfsTable t2 ON (t1.id1 = t2.id) JOIN SmallHbaseTable t3 ON (t1.id2 = t3.id) WHERE t3.categry = 'Online' GROUP BY t1.custid ORDER BY revenue DESC LIMIT 10

11 Query Planning: Example Single-nde plan:

12 Query Planning: Distributed Plans Gals: maximize scan lcality, minimize data mvement full distributin f all query peratrs Parallel jins: bradcast jin: jin is cllcated with left input, right-hand side is bradcast t each nde executing the jin (preferred fr small right-hand side input) partitined jin: bth tables are hash-partitined n jin clumns (preferred fr large jins) cst-based decisin based n clumn stats and estimated cst f data transfers

13 Query Planning: Distributed Plans Parallel aggregatin: pre-aggregatin where data is first materialized merge aggregatin partitined by gruping clumns Parallel tp-n: initial tp-n where data is first materialized final tp-n in single-nde plan fragment

14 Query Planning: Distributed Plans

15 Impala Architecture: Query Executin Request arrives via ODBC/JDBC SQL App ODBC Hive Metastre HDFS NN Statestre SQL request Query Planner Query Crdinatr Query Executr Query Planner Query Crdinatr Query Executr Query Planner Query Crdinatr Query Executr HDFS DN HBase HDFS DN HBase HDFS DN HBase

16 Impala Architecture: Query Executin Planner turns request int cllectins f plan fragments Crdinatr initiates executin n remte impalad's SQL App ODBC Hive Metastre HDFS NN Statestre Query Planner Query Crdinatr Query Executr Query Planner Query Crdinatr Query Executr Query Planner Query Crdinatr Query Executr HDFS DN HBase HDFS DN HBase HDFS DN HBase

17 Impala Architecture: Query Executin Intermediate results are streamed between impalad s Query results are streamed back t client SQL App ODBC Hive Metastre HDFS NN Statestre query results Query Planner Query Crdinatr Query Executr Query Planner Query Crdinatr Query Executr Query Planner Query Crdinatr Query Executr HDFS DN HBase HDFS DN HBase HDFS DN HBase

18 Metadata Handling Utilizes Hive's metastre Caches metadata: n synchrnus metastre API calls during query executin Beta: impalad's read metadata frm metastre at startup Pst-GA: metadata distributin thrugh statestre

19 Impala Executin Engine Written in C++ Runtime cde generatin fr "big lps Internal in-memry tuple frmat puts fixed-width data at fixed ffsets Uses intrinsics/special cpu instructins fr text parsing, crc32 cmputatin, etc.

20 Impala Executin Engine Mre n runtime cde generatin example f "big lp": insert batch f rws int hash table knwn at query cmpile time: # f tuples in batch, tuple layut, clumn types, etc. generate at cmpile time: lp that inlines all functin calls, cntains n dead cde, minimizes branches cde generated using llvm Abstract Syntax Tree t Intermediate Representatin (IR) IR ptimizatin (cmpiler ptimizatins) IR t machine cde generatin

21 Impala's Statestre Central system state repsitry Sft-state name service (membership) Pst-GA: metadata Pst-GA: ther scheduling-relevant r diagnstic state all data can be recnstructed frm the rest f the system cluster cntinues t functin when statestre fails, but per-nde state becmes increasingly stale Sends peridic heartbeats pushes new data checks fr liveness

22 Cmparing Impala t Dremel What is Dremel: clumnar strage fr data with nested structures distributed scalable aggregatin n tp f that Clumnar strage in Hadp: jint prject between Cludera and Twitter: new clumnar frmat: Parquet; derived frm Dug Cutting's Trevni stres data in apprpriate native/binary types can als stre nested structures similar t Dremel's ClumnIO Distributed aggregatin: Impala Impala plus Parquet: a superset f the published versin f Dremel (which didn't supprt jins)

23 Cmparing Impala t Hive Hive: MapReduce as an executin engine High latency, lw thrughput queries Fault-tlerance mdel based n MapReduce's n-disk checkpinting; materializes all intermediate results Java runtime allws fr easy late-binding f functinality: file frmats and UDFs. Extensive layering impses high runtime verhead Impala: direct, prcess-t-prcess data exchange n fault tlerance an executin engine designed fr lw runtime verhead

24 Cmparing Impala t Hive Impala's perfrmance advantage ver Hive: n hard numbers, but: Impala can get full disk thrughput (~100MB/sec/ disk): I/O-bund wrklads ften faster by 3-4x queries that require multiple map-reduce phases in Hive see a higher speedup queries that run against in-memry data see a higher speedup (bserved up t 100x)

25 Impala Radmap: GA (April 2013) Imprved query executin partitined jins Further perfrmance imprvements Guidelines fr prductin deplyment: lad balancing acrss impalad s resurce islatin within MR cluster

26 Impala Radmap: 2013 Additinal SQL: UDFs SQL authrizatin and DDL ORDER BY withut LIMIT windw functins supprt fr structured data types Imprved HBase supprt: cmpsite keys, cmplex types in clumns, index nested-lp jins, INSERT/UPDATE/DELETE

27 Impala Radmap: 2013 Runtime ptimizatins: straggler handling jin rder ptimizatin imprved cache management data cllcatin fr imprved jin perfrmance Better metadata handling: autmatic metadata distributin thrugh statestre Resurce management: gal: run explratry and prductin wrklads in same cluster, against same data, withut impacting prductin jbs

28 Try it ut! Beta versin available since 24/10/12 Latest versin is 0.6 (as f 26/02); 0.7 will cntain Parquet supprt We have packages fr: RHEL6.2/5.7 Ubuntu and SLES11 Debian6 We're targeting GA fr April 2013 Questins/cmments? impala-user@cludera.rg kinley@cludera.cm,

Impala. A Modern, Open Source SQL Engine for Hadoop. Yogesh Chockalingam

Impala. A Modern, Open Source SQL Engine for Hadoop. Yogesh Chockalingam Impala A Modern, Open Source SQL Engine for Hadoop Yogesh Chockalingam Agenda Introduction Architecture Front End Back End Evaluation Comparison with Spark SQL Introduction Why not use Hive or HBase?