Apache Drill - a interactive, ad-hoc query system for large-scale datasets

Talk about the Apache Drill incubator project at the Big Data User Group in Stuttgart, May 2013.

Transcript

1. Apache  Drill   a  interac.ve,  ad-­‐hoc  query  system  for  large-­‐scale  datasets   Michael  Hausenblas,  Chief  Data  Engineer  EMEA,  MapR   Big  Data  User  Group  Stu>gart,  2013-­‐05-­‐16  
2. Which   workloads  do   you   encounter  in   your   environment?   h>p://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  
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. h>p://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   h>p://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   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  
22. 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  
23. 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  
24. 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  
25. 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  
26. Key  features   •  Full  SQL  –  ANSI  SQL  2003   •  Nested  Data  as  first  class  ci[zen   •  Op[onal  Schema   •  Extensibility  Points  …  
27. 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  
28. …  and  Hadoop?   •  HDFS  can  be  a  data  source   •  Complementary  use  cases*   •  …  use  Apache  Drill   –  Find  record  with  specified  condi[on   –  Aggrega[on  under  dynamic  condi[ons   •  …  use  MapReduce   –  Data  mining  with  mul[ple  itera[ons   –  ETL   *)  h>ps://cloud.google.com/files/BigQueryTechnicalWP.pdf    
29. Basic  Demo   h>ps://cwiki.apache.org/confluence/display/DRILL/Demo+HowTo     { "id": "0001", "type": "donut", ”ppu": 0.55, "batters": { "batter”: [ { "id": "1001", "type": "Regular" }, { "id": "1002", "type": "Chocolate" }, … data  source:  donuts.json   query:[ { op:"sequence", do:[ { op: "scan", ref: "donuts", source: "local-logs", selection: {data: "activity"} }, { op: "filter", expr: "donuts.ppu < 2.00" }, … logical  plan:  simple_plan.json   result:  out.json   { "sales" : 700.0, "typeCount" : 1, "quantity" : 700, "ppu" : 1.0 } { "sales" : 109.71, "typeCount" : 2, "quantity" : 159, "ppu" : 0.69 } { "sales" : 184.25, "typeCount" : 2, "quantity" : 335, "ppu" : 0.55 }
30. BE  A  PART  OF  IT!  
31. Status   •  Heavy  development  by  mul[ple  organiza[ons   •  Available   – Logical  plan  (ADSP)   – Reference  interpreter   – Basic  SQL  parser     – Basic  demo  
32. Status   May  2013     •  Full  SQL  support  (+JDBC)   •  Physical  plan   •  In-­‐memory  compressed  data  interfaces   •  Distributed  execu[on  
33. Status   May  2013     •  HBase  and  MySQL  storage  engine   •  WebUI  client  
34. Contribu[ng   Contribu[ons  appreciated  (not  only  code  drops)  …     •  Test  data  &  test  queries   •  Use  case  scenarios  (textual/SQL  queries)   •  Documenta[on   •  Further  schedule   –  Alpha  Q2   –  Beta  Q3  
35. Kudos  to  …   •  Julian  Hyde,  Pentaho     •  Lisen  Mu,  XingCloud   •  Tim  Chen,  Microsow   •  Chris  Merrick,  RJMetrics     •  David  Alves,  UT  Aus[n   •  Sree  Vaadi,  SSS/NGData   •  Jacques  Nadeau,  MapR   •  Ted  Dunning,  MapR  
36. Engage!   •  Follow  @ApacheDrill  on  Twi>er   •  Sign  up  at  mailing  lists  (user  |  dev)     h>p://incubator.apache.org/drill/mailing-­‐lists.html       •  Standing  G+  hangouts  every  Tuesday  at  5pm  GMT   h>p://j.mp/apache-­‐drill-­‐hangouts     •  Keep  an  eye  on  h>p://drill-­‐user.org/