HUG Stockholm—Apache Drill

I talked about Apache Drill at the HUG Stockholm, see http://www.meetup.com/stockholm-hug/events/129201812/ for details. Thanks to Spotify there is also a recording of the session available: http://youtu.be/p__e4hmY1II

Transcript

1. Apache  Drill   Interac/ve,  analy/cs  for  large-­‐scale  datasets   Michael  Hausenblas,  Chief  Data  Engineer  EMEA,  MapR     HUG  Stockholm,  2013-­‐09-­‐12  
2. Which   workloads  do   you   encounter  in   your   environment?   hJp://www.flickr.com/photos/kevinomara/2866648330/  licensed  under  CC  BY-­‐NC-­‐ND  2.0  
3. Batch  processing   …  for  recurring  tasks  such  as  large-­‐scale  data  mining,  ETL   offloading/data-­‐warehousing  à  for  the  batch  layer  in  Lambda   architecture   Apache  Pig   Cascalog  
4. OLTP   …  user-­‐facing  eCommerce  transac[ons,  real-­‐[me  messaging  at   scale  (FB),  [me-­‐series  processing,  etc.  à  for  the  serving  layer  in   Lambda  architecture  
5. Stream  processing   …  in  order  to  handle  stream  sources  such  as  social  media  feeds   or  sensor  data  (mobile  phones,  RFID,  weather  sta[ons,  etc.)  à   for  the  speed  layer  in  Lambda  architecture    
6. Search/Informa[on  Retrieval   …  retrieval  of  items  from  unstructured  documents  (plain   text,  etc.),  semi-­‐structured  data  formats  (JSON,  etc.),  as   well  as  data  stores  (MongoDB,  CouchDB,  etc.)  
7. hJp://www.flickr.com/photos/9479603@N02/4144121838/    licensed  under  CC  BY-­‐NC-­‐ND  2.0   But  what  about   interac/ve   ad-­‐hoc  query     at  scale?  
8. Impala Interac[ve  Query  (?)   low-­‐latency  
9. Use  Case:  Marke[ng  Campaign   •  Jane,  a  marke[ng  analyst   •  Determine  target  segments   •  Data  from  different  sources    
10. Use  Case:  Logis[cs   •  Supplier  tracking  and  performance   •  Queries   – Shipments  from  supplier  ‘ACM’  in  last  24h   – Shipments  in  region  ‘US’  not  from  ‘ACM’   SUPPLIER_ID   NAME   REGION   ACM   ACME  Corp   US   GAL   GotALot  Inc   US   BAP   Bits  and  Pieces  Ltd   Europe   ZUP   Zu  Pli   Asia   { "shipment": 100123, "supplier": "ACM", “timestamp": "2013-02-01", "description": ”first delivery today” }, { "shipment": 100124, "supplier": "BAP", "timestamp": "2013-02-02", "description": "hope you enjoy it” } …
11. Use  Case:  Crime  Detec[on   •  Online  purchases   •  Fraud,  bilking,  etc.   •  Batch-­‐generated  overview   •  Modes   – Explora[ve   – Alerts  
12. Requirements   •  Support  for  different  data  sources   •  Support  for  different  query  interfaces   •  Low-­‐latency/real-­‐[me   •  Ad-­‐hoc  queries   •  Scalable,  reliable  
13. And now for something completely different …
14. Google’s  Dremel   hJp://research.google.com/pubs/pub36632.html       Sergey  Melnik,  Andrey  Gubarev,  Jing  Jing  Long,  Geoffrey  Romer,  Shiva  Shivakumar,  Ma@  Tolton,   Theo  Vassilakis,  Proc.  of  the  36th  Int'l  Conf  on  Very  Large  Data  Bases  (2010),  pp.  330-­‐339   Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of data, and has thousands of users at Google. … “ “ Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of data, and has thousands of users at Google. …
15. Google’s  Dremel   multi-level execution trees   columnar data layout  
16. Google’s  Dremel   nested data + schema   column-striped representation   map nested data to tables  
17. Google’s  Dremel   experiments: datasets & query performance  
18. Back to Apache Drill …
19. Apache  Drill–key  facts   •  Inspired  by  Google’s  Dremel   •  Standard  SQL  2003  support   •  Plug-­‐able  data  sources   •  Nested  data  is  a  first-­‐class  ci[zen   •  Schema  is  op/onal   •  Community  driven,  open,  100’s  involved  
20. High-­‐level  Architecture  
21. Principled  Query  Execu[on   •  Source  query—what  we  want  to  do  (analyst   friendly)   •  Logical  Plan—  what  we  want  to  do  (language   agnos[c,  computer  friendly)   •  Physical  Plan—how  we  want  to  do  it  (the  best   way  we  can  tell)   •  Execu/on  Plan—where  we  want  to  do  it  
22. Principled  Query  Execu[on   Source   Query   Parser   Logical   Plan   Op[mizer   Physical   Plan   Execu[on   SQL  2003     DrQL   MongoQL   DSL   scanner  API   Topology   CF   etc.   query: [ { @id: "log", op: "sequence", do: [ { op: "scan", source: “logs” }, { op: "filter", condition: "x > 3” }, parser  API  
23. Wire-­‐level  Architecture   •  Each  node:  Drillbit  -­‐  maximize  data  locality   •  Co-­‐ordina[on,  query  planning,  execu[on,  etc,  are  distributed   •  Any  node  can  act  as  endpoint  for  a  query—foreman   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node  
24. Wire-­‐level  Architecture   •  Curator/Zookeeper  for  ephemeral  cluster  membership  info   •  Distributed  cache  (Hazelcast)  for  metadata,  locality   informa[on,  etc.   Curator/Zk   Distributed  Cache   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Distributed  Cache   Distributed  Cache   Distributed  Cache  
25. Wire-­‐level  Architecture   •  Origina[ng  Drillbit  acts  as  foreman:  manages  query  execu[on,   scheduling,  locality  informa[on,  etc.   •  Streaming  data  communica/on  avoiding  SerDe   Curator/Zk   Distributed  Cache   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Storage   Process   Drillbit   node   Distributed  Cache   Distributed  Cache   Distributed  Cache  
26. Wire-­‐level  Architecture   Foreman  turns  into   root  of  the  mul[-­‐level   execu[on  tree,  leafs   ac[vate  their  storage   engine  interface.   node   node   node   Curator/Zk  
27. On  the  shoulders  of  giants  …   •  Jackson  for  JSON  SerDe  for  metadata   •  Typesafe  HOCON  for  configura[on  and  module  management   •  NeWy4  as  core  RPC  engine,  protobuf  for  communica[on   •  Vanilla  Java,  Larray  and  NeWy  ByteBuf  for  off-­‐heap  large  data  structures   •  Hazelcast  for  distributed  cache   •  Neqlix  Curator  on  top  of  Zookeeper  for  service  registry   •  Op/q  for  SQL  parsing  and  cost  op[miza[on   •  Parquet  (hJp://parquet.io)/  ORC     •  Janino  for  expression  compila[on     •  ASM  for  ByteCode  manipula[on   •  Yammer  Metrics  for  metrics   •  Guava  extensively   •  Carrot  HPC  for  primi[ve  collec[ons  
28. Key  features   •  Full  SQL  –  ANSI  SQL  2003   •  Nested  Data  as  first  class  ci[zen   •  Op[onal  Schema   •  Extensibility  Points  …  
29. Extensibility  Points   •  Source  query  à  parser  API   •  Custom  operators,  UDF  à  logical  plan   •  Serving  tree,  CF,  topology  à  physical  plan/op[mizer   •  Data  sources  &formats  à  scanner  API   Source   Query   Parser   Logical   Plan   Op[mizer   Physical   Plan   Execu[on  
30. User  Interfaces   •  API—DrillClient     – Encapsulates  endpoint  discovery   – Supports  logical  and  physical  plan  submission,   query  cancella[on,  query  status   – Supports  streaming  return  results   •  JDBC  driver,  conver[ng  JDBC  into  DrillClient   communica[on.       •  REST  proxy  for  DrillClient  
31. User  Interfaces  
32. LET’S  GET  OUR  HANDS  DIRTY…  
33. Demo   •  Install     •  Prepara/on     •  Usage   $  wget   hJp://people.apache.org/~jacques/apache-­‐drill-­‐1.0.0-­‐m1.rc3/ apache-­‐drill-­‐1.0.0-­‐m1-­‐binary-­‐release.tar.gz   $  tar  -­‐zxf  apache-­‐drill-­‐1.0.0-­‐m1-­‐binary-­‐release.tar.gz   $  export  JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.7.0_11.jdk/ Contents/Home   $  export  DRILL_LOG_DIR=$PWD   $  ./bin/drillbit.sh  start   $  ./bin/sqlline  -­‐u  jdbc:drill:schema=parquet-­‐local  -­‐n  admin  -­‐p  admin  
34. Useful  Resources   •  Geyng  Started  guide   hJps://github.com/vrtx/incubator-­‐drill/blob/ geyng_started/docs/geyng_started.rst     •  Demo  HowTo   hJps://cwiki.apache.org/confluence/display/DRILL/ Demo+HowTo     •  How  to  build/install  Apache  Drill  on  Ubuntu  13.04   hJp://www.confusedcoders.com/bigdata/apache-­‐ drill/how-­‐to-­‐build-­‐apache-­‐drill-­‐on-­‐ubuntu-­‐13-­‐04    
35. BE  A  PART  OF  IT!  
36. Status   •  Heavy  development  by  mul[ple  organiza[ons   (MapR,  Pentaho,  Microso{,  Thoughtworks,   XingCloud,  etc.)   •  Currently  more  than  100k  LOC   •  Alpha  available  via   hJp://people.apache.org/~jacques/apache-­‐ drill-­‐1.0.0-­‐m1.rc3/    
37. Kudos  to  …   •  Julian  Hyde,  Pentaho     •  Lisen  Mu,  XingCloud   •  Tim  Chen,  Microso{   •  Chris  Merrick,  RJMetrics     •  David  Alves,  UT  Aus[n   •  Sree  Vaadi,  SSS   •  Srihari  Srinivasan,   ThoughtWorks   •  Alexandre  Beche,  CERN   •  Jason  Altekruse,  MapR     hJp://incubator.apache.org/drill/team.html     •  Ben  Becker,  MapR   •  Jacques  Nadeau,  MapR   •  Ted  Dunning,  MapR   •  Keys  Botzum,  MapR   •  Jason  Frantz   •  Ellen  Friedman   •  Chris  Wensel,  Concurrent   •  Gera  Shegalov,  Oracle   •  Ryan  Rawson,  Ohm  Data    
38. Contribu[ng   Contribu[ons  appreciated—not  only  code  drops  …     •  Test  data  &  test  queries   •  Use  case  scenarios  (textual/SQL  queries)   •  Documenta[on    
39. Engage!   •  Follow  @ApacheDrill  on  TwiJer   •  Sign  up  at  mailing  lists  (user  |  dev)     hJp://incubator.apache.org/drill/mailing-­‐lists.html       •  Standing  G+  hangouts  every  Tuesday  at  18:00  CET   hJp://j.mp/apache-­‐drill-­‐hangouts     •  Keep  an  eye  on  hJp://drill-­‐user.org/