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Hadoop Summit 2009 Hive


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Hive talk at Hadoop Summit 2009

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Hadoop Summit 2009 Hive

  1. 1. Hive - Data Warehousing & Analytics on Hadoop Wednesday, June 10, 2009 Santa Clara Marriott Namit Jain, Zheng Shao Facebook
  2. 2. Agenda <ul><li>Introduction </li></ul><ul><li>Facebook Usage </li></ul><ul><li>Hive Progress and Roadmap </li></ul><ul><li>Open Source Community </li></ul>Facebook
  3. 3. <ul><li>Introduction </li></ul>Facebook
  4. 4. Why Another Data Warehousing System? <ul><li>Data, data and more data </li></ul><ul><li>~1TB per day in March 2008 </li></ul><ul><li>~10TB per day today </li></ul>Facebook
  5. 6. Lets try Hadoop… <ul><li>Pros </li></ul><ul><ul><li>Superior in availability/scalability/manageability </li></ul></ul><ul><ul><li>Efficiency not that great, but throw more hardware </li></ul></ul><ul><ul><li>Partial Availability/resilience/scale more important than ACID </li></ul></ul><ul><li>Cons: Programmability and Metadata </li></ul><ul><ul><li>Map-reduce hard to program (users know sql/bash/python) </li></ul></ul><ul><ul><li>Need to publish data in well known schemas </li></ul></ul><ul><li>Solution: HIVE </li></ul>Facebook
  6. 7. Lets try Hadoop… (continued) <ul><li>RDBMS> select key, count(1) from kv1 where key > 100 group by key; </li></ul><ul><li>vs. </li></ul><ul><li>$ cat > /tmp/ </li></ul><ul><li>uniq -c | awk '{print $2&quot; &quot;$1}‘ </li></ul><ul><li>$ cat > /tmp/ </li></ul><ul><li>awk -F '01' '{if($1 > 100) print $1}‘ </li></ul><ul><li>$ bin/hadoop jar contrib/hadoop-0.19.2-dev-streaming.jar -input /user/hive/warehouse/kv1 -mapper -file /tmp/ -file /tmp/ -reducer -output /tmp/largekey -numReduceTasks 1 </li></ul><ul><li>$ bin/hadoop dfs –cat /tmp/largekey/part* </li></ul>Facebook
  7. 8. What is HIVE? <ul><li>A system for managing and querying structured data built on top of Hadoop </li></ul><ul><ul><li>Map-Reduce for execution </li></ul></ul><ul><ul><li>HDFS for storage </li></ul></ul><ul><ul><li>Metadata on raw files </li></ul></ul><ul><li>Key Building Principles: </li></ul><ul><ul><li>SQL as a familiar data warehousing tool </li></ul></ul><ul><ul><li>Extensibility – Types, Functions, Formats, Scripts </li></ul></ul><ul><ul><li>Scalability and Performance </li></ul></ul>Facebook
  8. 9. Simplifying Hadoop <ul><li>RDBMS> select key, count(1) from kv1 where key > 100 group by key; </li></ul><ul><li>vs. </li></ul><ul><li>hive> select key, count(1) from kv1 where key > 100 group by key; </li></ul>Facebook
  9. 10. <ul><li>Facebook Usage </li></ul>Facebook
  10. 11. Data Warehousing at Facebook Today Facebook Web Servers Scribe Servers Filers Hive on Hadoop Cluster Oracle RAC Federated MySQL
  11. 12. Hive/Hadoop Usage @ Facebook <ul><li>Types of Applications: </li></ul><ul><ul><li>Reporting </li></ul></ul><ul><ul><ul><li>Eg: Daily/Weekly aggregations of impression/click counts </li></ul></ul></ul><ul><ul><ul><li>SELECT pageid, count(1) as imps FROM imp_table GROUP BY pageid WHERE date = ‘2009-05-01’; </li></ul></ul></ul><ul><ul><ul><li>Complex measures of user engagement </li></ul></ul></ul><ul><ul><li>Ad hoc Analysis </li></ul></ul><ul><ul><ul><li>Eg: how many group admins broken down by state/country </li></ul></ul></ul><ul><ul><li>Data Mining (Assembling training data) </li></ul></ul><ul><ul><ul><li>Eg: User Engagement as a function of user attributes </li></ul></ul></ul><ul><ul><li>Spam Detection </li></ul></ul><ul><ul><ul><li>Anomalous patterns for Site Integrity </li></ul></ul></ul><ul><ul><ul><li>Application API usage patterns </li></ul></ul></ul><ul><ul><li>Ad Optimization </li></ul></ul>Facebook
  12. 13. Hadoop Usage @ Facebook <ul><li>Cluster Capacity: </li></ul><ul><ul><li>600 nodes </li></ul></ul><ul><ul><li>~2.4PB (80% used) </li></ul></ul><ul><li>Data statistics: </li></ul><ul><ul><li>Source logs/day: 6TB </li></ul></ul><ul><ul><li>Dimension data/day: 4TB </li></ul></ul><ul><ul><li>Compression Factor ~5x (gzip) </li></ul></ul><ul><li>Usage statistics: </li></ul><ul><ul><li>3200 jobs/day with 800K tasks(map-reduce tasks)/day </li></ul></ul><ul><ul><li>55TB of compressed data scanned daily </li></ul></ul><ul><ul><li>15TB of compressed output data written to hdfs </li></ul></ul><ul><ul><li>150 active users within Facebook </li></ul></ul>Facebook
  13. 14. <ul><li>Hive Progress and Roadmap </li></ul>Facebook
  14. 15. <ul><li>CREATE TABLE clicks(key STRING, value STRING) LOCATION '/hive/clicks' PARTITIONED BY (ds STRING) ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.TestSerDe' WITH SERDEPROPERTIES ('testserde.default.serialization.format'='03'); </li></ul>Facebook
  15. 16. Data Model Facebook Logical Partitioning Hash Partitioning clicks HDFS MetaStore /hive/clicks /hive/clicks/ds=2008-03-25 /hive/clicks/ds=2008-03-25/0 … Tables Metastore DB Data Location Bucketing Info Partitioning Cols
  16. 17. HIVE: Components Facebook HDFS Hive CLI DDL Queries Browsing Map Reduce MetaStore Thrift API SerDe Thrift CSV JSON.. Execution Parser Planner Web UI Optimizer DB
  17. 18. Hive Query Language <ul><li>SQL </li></ul><ul><ul><li>Subqueries in from clause </li></ul></ul><ul><ul><li>Equi-joins </li></ul></ul><ul><ul><li>Multi-table Insert </li></ul></ul><ul><ul><li>Multi-group-by </li></ul></ul><ul><li>Sampling </li></ul><ul><ul><li>SELECT s.key, count(1) FROM clicks TABLESAMPLE (BUCKET 1 OUT OF 32) s WHERE s.ds = ‘2009-04-22’ GROUP BY s.key </li></ul></ul>Facebook
  18. 19. <ul><ul><ul><li>FROM pv_users </li></ul></ul></ul><ul><ul><ul><li>INSERT INTO TABLE pv_gender_sum </li></ul></ul></ul><ul><ul><ul><ul><li>SELECT gender, count(DISTINCT userid) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>GROUP BY gender </li></ul></ul></ul></ul><ul><ul><ul><li>INSERT INTO DIRECTORY ‘/user/facebook/tmp/pv_age_sum.dir’ </li></ul></ul></ul><ul><ul><ul><ul><li>SELECT age, count(DISTINCT userid) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>GROUP BY age </li></ul></ul></ul></ul><ul><ul><ul><li>INSERT INTO LOCAL DIRECTORY ‘/home/me/pv_age_sum.dir’ </li></ul></ul></ul><ul><ul><ul><ul><li>SELECT age, count(DISTINCT userid) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>GROUP BY age; </li></ul></ul></ul></ul>Facebook
  19. 20. Hive Query Language (continued) <ul><li>Extensibility </li></ul><ul><ul><li>Pluggable Map-reduce scripts </li></ul></ul><ul><ul><li>Pluggable User Defined Functions </li></ul></ul><ul><ul><li>Pluggable User Defined Types </li></ul></ul><ul><ul><ul><li>Complex object types: List of Maps </li></ul></ul></ul><ul><ul><li>Pluggable Data Formats </li></ul></ul><ul><ul><ul><li>Apache Log Format </li></ul></ul></ul>Facebook
  20. 21. <ul><ul><ul><li>FROM ( </li></ul></ul></ul><ul><ul><ul><ul><li>FROM pv_users </li></ul></ul></ul></ul><ul><ul><ul><ul><li>MAP pv_users.userid, </li></ul></ul></ul></ul><ul><ul><ul><ul><li>USING 'map_script‘ </li></ul></ul></ul></ul><ul><ul><ul><ul><li>AS dt, uid </li></ul></ul></ul></ul><ul><ul><ul><ul><li>CLUSTER BY dt) map </li></ul></ul></ul></ul><ul><ul><ul><li>INSERT INTO TABLE pv_users_reduced </li></ul></ul></ul><ul><ul><ul><ul><li>REDUCE map.dt, map.uid </li></ul></ul></ul></ul><ul><ul><ul><ul><li>USING 'reduce_script' </li></ul></ul></ul></ul><ul><ul><ul><ul><li>AS date, count; </li></ul></ul></ul></ul>Pluggable Map-Reduce Scripts Facebook
  21. 22. Map Reduce Example Facebook Machine 2 Machine 1 <k1, v1> <k2, v2> <k3, v3> <k4, v4> <k5, v5> <k6, v6> <nk1, nv1> <nk2, nv2> <nk3, nv3> <nk2, nv4> <nk2, nv5> <nk1, nv6> Local Map <nk2, nv4> <nk2, nv5> <nk2, nv2> <nk1, nv1> <nk3, nv3> <nk1, nv6> Global Shuffle <nk1, nv1> <nk1, nv6> <nk3, nv3> <nk2, nv4> <nk2, nv5> <nk2, nv2> Local Sort <nk2, 3> <nk1, 2> <nk3, 1> Local Reduce
  22. 23. Hive QL – Join <ul><ul><li>INSERT INTO TABLE pv_users </li></ul></ul><ul><ul><li>SELECT pv.pageid, u.age </li></ul></ul><ul><ul><li>FROM page_view pv </li></ul></ul><ul><ul><li>JOIN user u </li></ul></ul><ul><ul><li>ON (pv.userid = u.userid); </li></ul></ul>Facebook
  23. 24. Hive QL – Join in Map Reduce Facebook page_view user pv_users Map Reduce key value 111 < 1, 1> 111 < 1, 2> 222 < 1, 1> pageid userid time 1 111 9:08:01 2 111 9:08:13 1 222 9:08:14 userid age gender 111 25 female 222 32 male key value 111 < 2, 25> 222 < 2, 32> key value 111 < 1, 1> 111 < 1, 2> 111 < 2, 25> key value 222 < 1, 1> 222 < 2, 32> Shuffle Sort Pageid age 1 25 2 25 pageid age 1 32
  24. 25. Join Optimizations <ul><li>Map Joins </li></ul><ul><ul><li>User specified small tables stored in hash tables on the mapper backed by jdbm </li></ul></ul><ul><ul><li>No reducer needed </li></ul></ul><ul><ul><li>INSERT INTO TABLE pv_users </li></ul></ul><ul><ul><li>SELECT /*+ MAPJOIN(pv) */ pv.pageid, u.age </li></ul></ul><ul><ul><li>FROM page_view pv JOIN user u </li></ul></ul><ul><ul><li>ON (pv.userid = u.userid); </li></ul></ul><ul><li>Future </li></ul><ul><ul><li>Exploit table/column statistics for deciding strategy </li></ul></ul>Facebook
  25. 26. Hive QL – Map Join Facebook page_view user Hash table pv_users key value 111 <1,2> 222 <2> pageid userid time 1 111 9:08:01 2 111 9:08:13 1 222 9:08:14 userid age gender 111 25 female 222 32 male Pageid age 1 25 2 25 1 32
  26. 27. Hive QL – Group By <ul><ul><li>SELECT pageid, age, count(1) </li></ul></ul><ul><ul><li>FROM pv_users </li></ul></ul><ul><ul><li>GROUP BY pageid, age; </li></ul></ul>Facebook
  27. 28. Hive QL – Group By in Map Reduce Facebook pv_users Map Reduce pageid age 1 25 1 25 pageid age count 1 25 3 pageid age 2 32 1 25 key value <1,25> 2 key value <1,25> 1 <2,32> 1 key value <1,25> 2 <1,25> 1 key value <2,32> 1 Shuffle Sort pageid age count 2 32 1
  28. 29. Group by Optimizations <ul><li>Map side partial aggregations </li></ul><ul><ul><li>Hash-based aggregates </li></ul></ul><ul><ul><li>Serialized key/values in hash tables </li></ul></ul><ul><ul><li>90% speed improvement on Query </li></ul></ul><ul><ul><ul><li>SELECT count(1) FROM t; </li></ul></ul></ul><ul><li>Load balancing for data skew </li></ul><ul><li>Optimizations being Worked On: </li></ul><ul><ul><li>Exploit pre-sorted data for distinct counts </li></ul></ul><ul><ul><li>Exploit table/column statistics for deciding strategy </li></ul></ul>Facebook
  29. 30. Columnar Storage <ul><li>CREATE table columnTable </li></ul><ul><li>(key STRING, value STRING) </li></ul><ul><li>ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.ColumnarSerDe' </li></ul><ul><li>STORED AS RCFILE; </li></ul><ul><li>Saved 25% of space compared with SequenceFile </li></ul><ul><ul><li>Based on one of the largest tables (30 columns) inside Facebook </li></ul></ul><ul><ul><li>Both are compressed with GzipCodec </li></ul></ul><ul><li>Speed improvements in progress </li></ul><ul><ul><li>Need to propagate column-selection information to FileFormat </li></ul></ul><ul><li>*Contribution from Yongqiang He (outside Facebook) </li></ul>Facebook
  30. 31. Speed Improvements over Time Facebook <ul><li>QueryA: SELECT count(1) FROM t; </li></ul><ul><li>QueryB: SELECT concat(concast(concat(a,b),c),d) FROM t; </li></ul><ul><li>QueryC: SELECT * FROM t; </li></ul><ul><li>Time measured is map-side time only (to avoid unstable shuffling time at reducer side). It includes time for decompression and compression (both using GzipCodec). </li></ul><ul><li>* No performance benchmarks for Map-side Join yet. </li></ul>Date SVN Revision Major Changes Query A Query B Query C 2/22/2009 746906 Before Lazy Deserialization 83 sec 98 sec 183 sec 2/23/2009 747293 Lazy Deserialization 40 sec 66 sec 185 sec 3/6/2009 751166 Map-side Aggregation 22 sec 67 sec 182 sec 4/29/2009 770074 Object Reuse 21 sec 49 sec 130 sec 6/3/2009 781633 Map-side Join * 21 sec 48 sec 132 sec
  31. 32. Overcoming Java Overhead <ul><li>Reuse objects </li></ul><ul><ul><li>Use Writable instead of Java Primitives </li></ul></ul><ul><ul><li>Reuse objects across all rows </li></ul></ul><ul><ul><li>*40% speed improvement on Query C </li></ul></ul><ul><li>Lazy deserialization </li></ul><ul><ul><li>Only deserialize the column when asked </li></ul></ul><ul><ul><li>Very helpful for complex types (map/list/struct) </li></ul></ul><ul><ul><li>*108% speed improvement on Query A </li></ul></ul>Facebook
  32. 33. Generic UDF and UDAF <ul><li>Let UDF and UDAF accept complex-type parameters </li></ul><ul><li>Integrate UDF and UDAF with Writables </li></ul><ul><ul><li>public IntWritable evaluate(IntWritable a, IntWritable b) { </li></ul></ul><ul><ul><li>intWritable.set((int)(a.get() + b.get())); </li></ul></ul><ul><ul><li>return intWritable; </li></ul></ul><ul><ul><li>} </li></ul></ul>Facebook
  33. 34. HQL Optimizations <ul><li>Predicate Pushdown </li></ul><ul><li>Merging n-way join </li></ul><ul><li>Column Pruning </li></ul>Facebook
  34. 35. <ul><li>Open Source Community </li></ul>Facebook
  35. 36. Open Source Community <ul><li>21 contributors and growing </li></ul><ul><ul><li>6 contributors within Facebook </li></ul></ul><ul><li>Contributors from: </li></ul><ul><ul><li>Academia </li></ul></ul><ul><ul><li>Other web companies </li></ul></ul><ul><ul><li>Etc.. </li></ul></ul><ul><li>7 committers </li></ul><ul><ul><li>1 external to Facebook and looking to add more here </li></ul></ul>Facebook
  36. 37. <ul><li>50 jiras fixed in last month </li></ul><ul><li>218 jiras still open </li></ul><ul><li>125 mails in last month on hive-user@ </li></ul><ul><li>600 mails in last month on hive-dev@ </li></ul><ul><li>Various companies/universities </li></ul><ul><ul><li>Adknowledge, Admob </li></ul></ul><ul><ul><li>Berkeley, Chinese Academy of Science </li></ul></ul><ul><li>Demonstration in VLDB’2009 </li></ul>Facebook
  37. 38. Deployment Options <ul><li>EC2 </li></ul><ul><ul><li> </li></ul></ul><ul><li>Cloudera Virtual Machine </li></ul><ul><ul><li> </li></ul></ul><ul><li>Your own cluster </li></ul><ul><ul><li> </li></ul></ul><ul><li>Hive can directly consume data on hadoop </li></ul><ul><ul><li>CREATE EXTERNAL TABLE mytable (key STRING, value STRING) LOCATION '/user/abc/mytable'; </li></ul></ul>Facebook
  38. 39. Future Work <ul><li>Benchmark & Performance </li></ul><ul><li>Integration with BI tools (through JDBC/ODBC) </li></ul><ul><li>Indexing </li></ul><ul><li>More on Hive Roadmap </li></ul><ul><ul><li> </li></ul></ul><ul><li>Machine Learning Integration </li></ul><ul><li>Real-time Streaming </li></ul>Facebook
  39. 40. Information <ul><li>Available as a sub project in Hadoop </li></ul><ul><ul><li> (wiki) </li></ul></ul><ul><ul><li> (home page) </li></ul></ul><ul><ul><li> (SVN repo) </li></ul></ul><ul><ul><li>##hive (IRC) </li></ul></ul><ul><ul><li>Works with hadoop-0.17, 0.18, 0.19 </li></ul></ul><ul><li>Release 0.3 is out and more are coming </li></ul><ul><li>Mailing Lists: </li></ul><ul><ul><li>hive-{user,dev,commits} </li></ul></ul>Facebook
  40. 41. Contributors <ul><li>Aaron Newton </li></ul><ul><li>Ashish Thusoo </li></ul><ul><li>David Phillips </li></ul><ul><li>Dhruba Borthakur </li></ul><ul><li>Edward Capriolo </li></ul><ul><li>Eric Hwang </li></ul><ul><li>Hao Liu </li></ul><ul><li>He Yongqiang </li></ul><ul><li>Jeff Hammerbacher </li></ul><ul><li>Johan Oskarsson </li></ul><ul><li>Josh Ferguson </li></ul><ul><li>Joydeep Sen Sarma </li></ul><ul><li>Kim P. </li></ul>Facebook <ul><li>Michi Mutsuzaki </li></ul><ul><li>Min Zhou </li></ul><ul><li>Namit Jain </li></ul><ul><li>Neil Conway </li></ul><ul><li>Pete Wyckoff </li></ul><ul><li>Prasad Chakka </li></ul><ul><li>Raghotham Murthy </li></ul><ul><li>Richard Lee </li></ul><ul><li>Shyam Sundar Sarkar </li></ul><ul><li>Suresh Antony </li></ul><ul><li>Venky Iyer </li></ul><ul><li>Zheng Shao </li></ul>
  41. 42. <ul><li>Questions </li></ul>Facebook