Integration of Hive and HBase
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Integration of Hive and HBase

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Apache Hive provides SQL-like access to your stored data in Apache Hadoop. Apache HBase stores tabular data in Hadoop and supports update operations. The combination of these two capabilities is often ...

Apache Hive provides SQL-like access to your stored data in Apache Hadoop. Apache HBase stores tabular data in Hadoop and supports update operations. The combination of these two capabilities is often desired, however, the current integration show limitations such as performance issues. In this talk, Enis Soztutar will present an overview of Hive and HBase and discuss new updates/improvements from the community on the integration of these two projects. Various techniques used to reduce data exchange and improve efficiency will also be provided.

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Integration of Hive and HBase Integration of Hive and HBase Presentation Transcript

  • Integration of Apache Hiveand HBaseEnis Soztutarenis [at] apache [dot] org@enissozArchitecting the Future of Big Data © Hortonworks Inc. 2011 Page 1
  • About Me•  User and committer of Hadoop since 2007•  Contributor to Apache Hadoop, HBase, Hive and Gora•  Joined Hortonworks as Member of Technical Staff•  Twitter: @enissoz Architecting the Future of Big Data Page 2 © Hortonworks Inc. 2011
  • Agenda•  Overview of Hive and HBase•  Hive + HBase Features and Improvements•  Future of Hive and HBase•  Q&A Architecting the Future of Big Data Page 3 © Hortonworks Inc. 2011
  • Apache Hive Overview• Apache Hive is a data warehouse system for Hadoop• SQL-like query language called HiveQL• Built for PB scale data• Main purpose is analysis and ad hoc querying• Database / table / partition / bucket – DDL Operations• SQL Types + Complex Types (ARRAY, MAP, etc)• Very extensible• Not for : small data sets, low latency queries, OLTP Architecting the Future of Big Data Page 4 © Hortonworks Inc. 2011
  • Apache Hive Architecture JDBC/ODBC Hive Thrift Hive Web CLI Server Interface Driver M S C Parser Planner l Metastore i e Execution Optimizer n t MapReduce HDFS RDBMS Architecting the Future of Big Data Page 5 © Hortonworks Inc. 2011
  • Overview of Apache HBase• Apache HBase is the Hadoop database• Modeled after Google’s BigTable• A sparse, distributed, persistent multi- dimensional sorted map• The map is indexed by a row key, column key, and a timestamp• Each value in the map is an un-interpreted array of bytes• Low latency random data access Architecting the Future of Big Data Page 6 © Hortonworks Inc. 2011
  • Overview of Apache HBase• Logical view: From: Bigtable: A Distributed Storage System for Structured Data, Chang, et al. Architecting the Future of Big Data Page 7 © Hortonworks Inc. 2011
  • Apache HBase Architecture Client HMaster Zookeeper Region Region Region server server server Region Region Region Region Region Region HDFS Architecting the Future of Big Data Page 8 © Hortonworks Inc. 2011
  • Hive + HBase Features andImprovements Architecting the Future of Big Data Page 9 © Hortonworks Inc. 2011
  • Hive + HBase Motivation• Hive and HBase has different characteristics: High latency Low latency Structured vs. Unstructured Analysts Programmers• Hive datawarehouses on Hadoop are high latency – Long ETL times – Access to real time data• Analyzing HBase data with MapReduce requires custom coding• Hive and SQL are already known by many analysts Architecting the Future of Big Data Page 10 © Hortonworks Inc. 2011
  • Use Case 1: HBase as ETL Data SinkFrom HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebookhttp://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010 Architecting the Future of Big Data Page 11 © Hortonworks Inc. 2011
  • Use Case 2: HBase as Data SourceFrom HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebookhttp://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010 Architecting the Future of Big Data Page 12 © Hortonworks Inc. 2011
  • Use Case 3: Low Latency WarehouseFrom HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebookhttp://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010 Architecting the Future of Big Data Page 13 © Hortonworks Inc. 2011
  • Example: Hive + Hbase (HBase table)hbase(main):001:0> create short_urls, {NAME =>u}, {NAME=>s}hbase(main):014:0> scan short_urlsROW COLUMN+CELL bit.ly/aaaa column=s:hits, value=100 bit.ly/aaaa column=u:url,value=hbase.apache.org/ bit.ly/abcd column=s:hits, value=123 bit.ly/abcd column=u:url,value=example.com/foo Architecting the Future of Big Data Page 14 © Hortonworks Inc. 2011
  • Example: Hive + HBase (Hive table)CREATE TABLE short_urls( short_url string, url string, hit_count int)STORED BYorg.apache.hadoop.hive.hbase.HBaseStorageHandlerWITH SERDEPROPERTIES("hbase.columns.mapping" = ":key, u:url, s:hits")TBLPROPERTIES("hbase.table.name" = ”short_urls"); Architecting the Future of Big Data Page 15 © Hortonworks Inc. 2011
  • Storage Handler• Hive defines HiveStorageHandler class for different storage backends: HBase/ Cassandra / MongoDB/ etc• Storage Handler has hooks for –  Getting input / output formats –  Meta data operations hook: CREATE TABLE, DROP TABLE, etc• Storage Handler is a table level concept –  Does not support Hive partitions, and buckets Architecting the Future of Big Data Page 16 © Hortonworks Inc. 2011
  • Apache Hive + HBase Architecture Hive Thrift Hive Web CLI Server Interface Driver M S Parser Planner C l Metastore i Execution Optimizer e n t StorageHandler MapReduce HBase HDFS RDBMS Architecting the Future of Big Data Page 17 © Hortonworks Inc. 2011
  • Hive + HBase Integration• For Input/OutputFormat, getSplits(), etc underlying HBase classes are used• Column selection and certain filters can be pushed down• HBase tables can be used with other(Hadoop native) tables and SQL constructs• Hive DDL operations are converted to HBase DDL operations via the client hook. – All operations are performed by the client – No two phase commit Architecting the Future of Big Data Page 18 © Hortonworks Inc. 2011
  • Schema / Type MappingArchitecting the Future of Big Data Page 19© Hortonworks Inc. 2011
  • Schema Mapping•  Hive table + columns + column types <=> HBase table + column families (+ column qualifiers)•  Every field in Hive table is mapped in order to either – The table key (using :key as selector) – A column family (cf:) -> MAP fields in Hive – A column (cf:cq)•  Hive table does not need to include all columns in HBase•  CREATE TABLE short_urls( short_url string, url string, hit_count int, props, map<string,string> ) WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key, u:url, s:hits, p:") Architecting the Future of Big Data Page 20 © Hortonworks Inc. 2011
  • Type Mapping• Recently added to Hive (0.9.0)• Previously all types were being converted to strings in HBase• Hive has: – Primitive types: INT, STRING, BINARY, DATE, etc – ARRAY<Type> – MAP<PrimitiveType, Type> – STRUCT<a:INT, b:STRING, c:STRING>• HBase does not have types – Bytes.toBytes() Architecting the Future of Big Data Page 21 © Hortonworks Inc. 2011
  • Type Mapping• Table level property "hbase.table.default.storage.type” = “binary”• Type mapping can be given per column after # – Any prefix of “binary” , eg u:url#b – Any prefix of “string” , eg u:url#s – The dash char “-” , eg u:url#-CREATE TABLE short_urls( short_url string, url string, hit_count int, props, map<string,string>)WITH SERDEPROPERTIES("hbase.columns.mapping" = ":key#b,u:url#b,s:hits#b,p:#s") Architecting the Future of Big Data Page 22 © Hortonworks Inc. 2011
  • Type Mapping• If the type is not a primitive or Map, it is converted to a JSON string and serialized• Still a few rough edges for schema and type mapping: – No Hive BINARY support in HBase mapping – No mapping of HBase timestamp (can only provide put timestamp) – No arbitrary mapping of Structs / Arrays into HBase schema Architecting the Future of Big Data Page 23 © Hortonworks Inc. 2011
  • Bulk Load• Steps to bulk load: – Sample source data for range partitioning – Save sampling results to a file – Run CLUSTER BY query using HiveHFileOutputFormat and TotalOrderPartitioner – Import Hfiles into HBase table• Ideal setup should be SET hive.hbase.bulk=true INSERT OVERWRITE TABLE web_table SELECT …. Architecting the Future of Big Data Page 24 © Hortonworks Inc. 2011
  • Filter PushdownArchitecting the Future of Big Data Page 25© Hortonworks Inc. 2011
  • Filter Pushdown• Idea is to pass down filter expressions to the storage layer to minimize scanned data• To access indexes at HDFS or HBase• Example: CREATE EXTERNAL TABLE users (userid LONG, email STRING, … ) STORED BY org.apache.hadoop.hive.hbase.HBaseStorageHandler’ WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,…") SELECT ... FROM users WHERE userid > 1000000 and email LIKE‘%@gmail.com’;-> scan.setStartRow(Bytes.toBytes(1000000)) Architecting the Future of Big Data Page 26 © Hortonworks Inc. 2011
  • Filter Decomposition• Optimizer pushes down the predicates to the query plan• Storage handlers can negotiate with the Hive optimizer to decompose the filter x > 3 AND upper(y) = XYZ’• Handle x > 3, send upper(y) = ’XYZ’ as residual for Hive• Works with:key = 3, key > 3, etckey > 3 AND key < 100• Only works against constant expressions Architecting the Future of Big Data Page 27 © Hortonworks Inc. 2011
  • Security AspectsTowards fully secure deploymentsArchitecting the Future of Big Data Page 28© Hortonworks Inc. 2011
  • Security – Big Picture• Security becomes more important to support enterprise level and multi tenant applications• 5 Different Components to ensure / impose security – HDFS – MapReduce – HBase – Zookeeper – Hive• Each component has: – Authentication – Authorization Architecting the Future of Big Data Page 29 © Hortonworks Inc. 2011
  • HBase Security – Closer look• Released with HBase 0.92• Fully optional module, disabled by default• Needs an underlying secure Hadoop release• SecureRPCEngine: optional engine enforcing SASL authentication – Kerberos – DIGEST-MD5 based tokens – TokenProvider coprocessor• Access control is implemented as a Coprocessor: AccessController• Stores and distributes ACL data via Zookeeper – Sensitive data is only accessible by HBase daemons – Client does not need to authenticate to zk Architecting the Future of Big Data Page 30 © Hortonworks Inc. 2011
  • Hive Security – Closer look• Hive has different deployment options, security considerations should take into account different deployments• Authentication is only supported at Metastore, not on HiveServer, web interface, JDBC• Authorization is enforced at the query layer (Driver)• Pluggable authorization providers. Default one stores global/ table/partition/column permissions in MetastoreGRANT ALTER ON TABLE web_table TO USER bob;CREATE ROLE db_readerGRANT SELECT, SHOW_DATABASE ON DATABASE mydb TOROLE db_reader Architecting the Future of Big Data Page 31 © Hortonworks Inc. 2011
  • Hive Deployment Option 1 Client CLI Driver M Authorization S C Parser Planner l Authentication i Metastore e Execution Optimizer n t A/A A/A MapReduce HBase A12n/A11N A12n/A11N HDFS RDBMS Architecting the Future of Big Data Page 32 © Hortonworks Inc. 2011
  • Hive Deployment Option 2 Client CLI Driver M Authorization S C Parser Planner l Authentication i e Metastore n Execution Optimizer t A/A A/A MapReduce HBase A12n/A11N A12n/A11N HDFS RDBMS Architecting the Future of Big Data Page 33 © Hortonworks Inc. 2011
  • Hive Deployment Option 3 Client JDBC/ODBC Hive Thrift Hive Web CLI Server Interface M Driver Authorization S C Parser Planner l Authentication i Metastore e Execution Optimizer n t A/A A/A MapReduce HBase A12n/A11N HDFS A12n/A11N RDBMS Architecting the Future of Big Data Page 34 © Hortonworks Inc. 2011
  • Hive + HBase + Hadoop Security• Regardless of Hive’s own security, for Hive to work on secure Hadoop and HBase, we should: – Obtain delegation tokens for Hadoop and HBase jobs – Ensure to obey the storage level (HDFS, HBase) permission checks – In HiveServer deployments, authenticate and impersonate the user• Delegation tokens for Hadoop are already working• Obtaining HBase delegation tokens are released in Hive 0.9.0 Architecting the Future of Big Data Page 35 © Hortonworks Inc. 2011
  • Future of Hive + HBase• Improve on schema / type mapping• Fully secure Hive deployment options• HBase bulk import improvements• Sortable signed numeric types in HBase• Filter pushdown: non key column filters• Hive random access support for HBase – https://cwiki.apache.org/HCATALOG/random-access- framework.html Architecting the Future of Big Data Page 36 © Hortonworks Inc. 2011
  • References• Security – https://issues.apache.org/jira/browse/HIVE-2764 – https://issues.apache.org/jira/browse/HBASE-5371 – https://issues.apache.org/jira/browse/HCATALOG-245 – https://issues.apache.org/jira/browse/HCATALOG-260 – https://issues.apache.org/jira/browse/HCATALOG-244 – https://cwiki.apache.org/confluence/display/HCATALOG/Hcat+Security +Design• Type mapping / Filter Pushdown – https://issues.apache.org/jira/browse/HIVE-1634 – https://issues.apache.org/jira/browse/HIVE-1226 – https://issues.apache.org/jira/browse/HIVE-1643 – https://issues.apache.org/jira/browse/HIVE-2815 – https://issues.apache.org/jira/browse/HIVE-1643 Architecting the Future of Big Data Page 37 © Hortonworks Inc. 2011
  • Other Resources• Hadoop Summit – June 13-14 – San Jose, California – www.Hadoopsummit.org• Hadoop Training and Certification – Developing Solutions Using Apache Hadoop – Administering Apache Hadoop – Online classes available US, India, EMEA – http://hortonworks.com/training/ © Hortonworks Inc. 2012 Page 38
  • ThanksQuestions? Architecting the Future of Big Data Page 39 © Hortonworks Inc. 2011