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Hive: A data warehouse on Hadoop

Hive: A data warehouse on Hadoop. Based on Facebook Team’s paper. Motivation. Yahoo worked on Pig to facilitate application deployment on Hadoop. Their need mainly was focused on unstructured data

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Hive: A data warehouse on Hadoop

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  1. Hive: A data warehouse on Hadoop Based on Facebook Team’s paper

  2. Motivation • Yahoo worked on Pig to facilitate application deployment on Hadoop. • Their need mainly was focused on unstructured data • Simultaneously Facebook started working on deploying warehouse solutions on Hadoop that resulted in Hive. • The size of data being collected and analyzed in industry for business intelligence (BI) is growing rapidly making traditional warehousing solution prohibitively expensive. • Hive attempts to the leverage the extensive SQL skilled workforce to do big data analytics

  3. Hadoop MR • MR is very low level and requires customers to write custom programs. • HIVE supports queries expressed in SQL-like language called HiveQL which are compiled into MR jobs that are executed on Hadoop. • Hive also allows MR scripts • It also includes MetaStore that contains schemas and statistics that are useful for data explorations, query optimization and query compilation. • At Facebook Hive warehouse contains tens of thousands of tables, stores over 700TB and is used for reporting and ad-hoc analyses by 200 Fb users (numbers at the time the paper was written)

  4. Data model • Hive structures data into well-understood database concepts such as: tables, rows, cols, partitions • It supports primitive types: integers, floats, doubles, and strings • Hive also supports: • associative arrays: map<key-type, value-type> • Lists: list<element type> • Structs: struct<file name: file type…> • SerDe: serialize and deserialized API is used to move data in and out of tables

  5. Query Language (HiveQL) • Subset of SQL • Meta-data queries • Limited equality and join predicates • No inserts on existing tables (to preserve worm property) • Can overwrite an entire table

  6. Wordcount in Hive FROM ( MAP doctext USING 'python wc_mapper.py' AS (word, cnt) FROM docs CLUSTER BY word ) a REDUCE word, cnt USING 'pythonwc_reduce.py';

  7. Session/tmstamp example FROM ( FROM session_table SELECT sessionid, tstamp, data DISTRIBUTE BY sessionid SORT BY tstamp ) a REDUCE sessionid, tstamp, data USING 'session_reducer.sh';

  8. Data Storage • Tables are logical data units; table metadata associates the data in the table to hdfs directories. • Hdfs namespace: tables (hdfs directory), partition (hdfs subdirectory), buckets (subdirectories within partition) • /user/hive/warehouse/test_table is a hdfs directory

  9. Hive architecture (from the paper)

  10. Architecture • Metastore: stores system catalog • Driver: manages life cycle of HiveQL query as it moves thru’ HIVE; also manages session handle and session statistics • Query compiler: Compiles HiveQL into a directed acyclic graph of map/reduce tasks • Execution engines: The component executes the tasks in proper dependency order; interacts with Hadoop • HiveServer: provides Thrift interface and JDBC/ODBC for integrating other applications. • Client components: CLI, web interface, jdbc/odbcinteface • Extensibility interface include SerDe, User Defined Functions and User Defined Aggregate Function.

  11. Sample Query Plan

  12. Hive Usage in Facebook • Hive and Hadoop are extensively used in Facbook for different kinds of operations. • 700 TB = 2.1Petabyte after replication! • Think of other application models that can leverage Hadoop MR.

  13. General approach to learning Hive • Similar to RDBMS • DDL: Data definition language • Schema definition, create table, meta data defn. • DML: Data Manipulation Language • Load table • HiveQL: Query language • Select, insert etc. • Underlying structure is either HDFS/hadoop or HBase

  14. Demo on Amazon aws Goal: • Understand amazon aws • Essential services among the many services provided • Hive setup and query • Demo URL: http://docs.aws.amazon.com/gettingstarted/latest/emr/getting-started-emr-tutorial.html

  15. Discussion of the Demo Problem • Data: Apache collected web logs with IP address of the client (want to know who accessed your website? ) • Goal: web log is unstructured; big data; collect and clean the data and load using Hive; query the data using Hive • Deserialize and Serialize into and from Hive.. So the data is called SerDe • Count the line • See the first line of data • Count the number of accesses from a particular IP

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