20080529dublinpt3
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20080529dublinpt3

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One in a series of presentations given at the IBM Cloud Computing Center in Dublin.

One in a series of presentations given at the IBM Cloud Computing Center in Dublin.

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20080529dublinpt3 20080529dublinpt3 Presentation Transcript

  • HIVE data warehousing using Hadoop Facebook Data Team
  • Motivation Structured log and dimension data – Well known schemas, different serialization formats (binary/text) – Rich data structures – nesting/maps/lists Query language over structured data – SQL helps in easier adoption by business analysts + reduced learning curve for everyone – Developers love streaming and direct access to map-reduce – Query Language brings together SQL and Streaming Data Management – Tables/Partitions for easy data addressability – Abstractions allow optimizations: Organize data for large joins/sampling Add indices/manage compression/replication transparently
  • What is HIVE? Mgmt. Web UI Map Reduce HDFS Hive CLI Browsing Queries DDL Thrift API Parser Execution Planner Hive QL SerDe Thrift Jute JSON.. MetaStore
  • Dealing with Structured Data Type system – Primitive types – Recursively build up using Composition/Maps/Lists Generic (De)Serialization Interface (SerDe) – To recursively list schema – To recursively access fields within a row object Serialization families implement interface – Thrift (Binary and Delimited Text), RecordIO, JSON/PADS(?) XPath like field expressions – profiles.network[@is_primary=1].id Inbuilt DDL – Define schema over delimited text files – Leverages Thrift DDL
  • Data Model #Partitions=32 Schema Sort-key=uid uid Library Hash clicks Partitioning views IP Logical Partitioning userId … AdId /hive/clicks /hive/clicks/ds=2008-03-25 Tables Dimensions /hive/clicks/ds=2008-03-25/0 HDFS MetaStore
  • MetaStore Stores Table/Partition properties: – Table schema and SerDe library – Table Location on HDFS – Logical Partitioning keys and types – Sort column – Mapping from columns to well known Dimensions Thrift API – Current clients in Php (Web Interface), Python (CLI), Java (Query Engine), Perl (Tests) Stores all properties in text files
  • Hive CLI Implemented in Python – uses MetaStore Thrift API DDL: – create table/drop table/rename table – alter table add column etc. Browsing: – show tables – describe table – cat table Loading Data – load data inpath <path1, …> into table <tablename/partition-spec>] [bucketed <N> ways by <dimension>] Queries – Issue queries in Hive QL.
  • Hive Query Language Philosophy – SQL like constructs + Hadoop Streaming Query Operators in initial version – Projections – Equijoins and Cogroups – Group by – Sampling Output of these operators can be: – passed to Streaming mappers/reducers – can be stored in another Hive Table – can be output to HDFS files
  • Hive Query Language Package these capabilities into a more formal SQL like query language in next version Introduce other important constructs: – Views – Multi table inserts – Order bys – Select distincts – SQL like column expressions – A bunch of other builtin functions Still work in progress
  • Query Language - Examples Multi table inserts FROM ad_impressions_stg imps INSERT INTO ad_legals/ds=2008-03-08 select imps.* where imps.legal = 1 INSERT INTO ad_non_legals/ds=2008-03-08 select imps.* where imps.legal = 0 Joins FROM ad_impressions imps, ad_dimensions ads INSERT INTO ad_legals_joined select imps.*, ads.campaignid JOIN ON(imps.adid, ads.adid) WHERE imps.legal = 1
  • Query Language - Examples Group By FROM ad_legals_joined imps INSERT INTO hdfs://hadoop001:9000/user/ads/adid_uu_summary select imps.adid, count_distinct(imps.uid) group by(imps.adid) INSERT INTO hdfs://hadoop001:9000/user/ads/campaignid_uu_summary select imps.campaign_id, count_distinct(imps.uid) group by(imps.campaignid)
  • Query Language – HadoopStreaming APPLY ON TABLE CREATE OPERATOR filter_legal using ‘exec://filter_legal.py’ (ts date, adid long, uid long) FROM (APPLY filter_legal ON TABLE ad_impression) INSERT INTO ad_legals where ts >= ‘2008-03-11’ and ts < ‘2008-03-12’ APPLY can also be applied after JOIN as reducer script FROM ad_impressions imps, ad_dimensions ads INSERT INTO ad_legals_joined select imps.*, ads.campaignid JOIN ON(imps.adid, ads.adid) APPLY filter_legal BEFORE OUTPUT
  • Query Language – Views Used for expressing – Union alls – APPLY operators Example CREATE VIEW actions SELECT photo_views.* UNION ALL SELECT video_views.* UNION ALL SELECT profile_views.* …
  • Hive Usage in Facebook Applications: – Summarization Eg: Daily/Weekly aggregations of impression/click counts – Ad hoc Analysis Eg: how many group admins broken down by state/country – Data Mining (Assembling training data) Eg: User Engagement as a function of user attributes Usage statistics: – Total Users: ~40 (about 25% of engineering !) – Hive Data (compressed): 22 TB total, ~200GB incoming per day – Jobs over last 7 days: Total Jobs: 3514, Projections:821, Joins: 152, Aggregates: 800, Loaders: 600 * Aggregates biased because of multi-stage map-reduce
  • Conclusion Release to Open Source in 3-4 months People: – Suresh Anthony (suresh@facebook.com) – Jeff Hammerbacher (jeffh@) – Joydeep Sarma (jssarma@) – Ashish Thusoo (athusoo@) – Pete Wyckoff (pwyckoff@)