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Hive on steroid

Hive on steroid. Project stinger. Who Am I?. Olivier Renault Hortonworks Solution engineer for EMEA Join Hortonworks EMEA in Jan 2013 Eucalyptus – Open source Cloud solution Red Hat – Solution engineer. What’s Hive ?. Use HiveQL Hive translate SQL query into MapReduce job using

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Hive on steroid

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  1. Hive on steroid Project stinger

  2. Who Am I? • Olivier Renault • Hortonworks Solution engineer for EMEA • Join Hortonworks EMEA in Jan 2013 • Eucalyptus – Open source Cloud solution • Red Hat – Solution engineer

  3. What’s Hive ? • Use HiveQL • Hive translate SQL query into MapReduce job using • De facto SQL interface in Hadoop • Entry point for most BI tools • ODBC • HCatalog merge with Hive • Metadata server • Hive is able to query Pb of data

  4. Hive: Strength Through Community Dozens of Vendors integrate with Hive Teradata Microsoft Microstrategy Tableau Karmasphere Datameer Information Builders SAP Oracle Actuate QlikView SAS arcplan Pentaho Jaspersoft Tibco Talend Informatica … Loyal Open Source Community and Real Corporate Interest/Contributions Facebook Teradata SAP Intel Microsoft Huawei Yahoo … Open Source Vendors End Users Countless Enterprises Use Hive as the defacto SQL interface to Hadoop data

  5. Problem : Hive was slow … • Hive is able to interact with visualization tools but you needed to be patient … • February 2013, Hortonworks launch Stinger initiative. The aim is to improve Hive performance by 100x • Bringing Hive in the interactive query world

  6. Stinger Initiative • Community initiative around Hive • Enables Hive to support interactive workloads • Enhances Hive’s standard SQL interface for Hadoop • Improves existing tools & preserves investments Execution Engine Tez Query Planner Hive File Format ORC file = 100X + +

  7. Batch AND Interactive SQL-IN-Hadoop Stinger InitiativeA broad, community-based effort to drive the next generation of HIVE Stinger Project (announced February 2013) Delivered Hive 0.11 (HDP 1.3) • Phase One • Base Optimizations • SQL Analytic Functions • ORCFile, Modern File Format Goals: • SpeedImprove Hive query performance by 100X to allow for interactive query times (seconds) Delivered Hive 0.12 (HDP 2.0) • Phase Two • VARCHAR, DATE Types • ORCFile predicate pushdown • Advanced Optimizations • Performance Boosts via YARN Scale The only SQL interface to Hadoop designed for queries that scale from TB to PB • Phase Three • Hive on Apache Tez • Query Service • Buffer Cache • Cost Based Optimizer (Optiq) • Vectorized Processing Coming Soon SQL Support broadest range of SQL semantics for analytic applications running against Hadoop …all IN Hadoop

  8. Hive : Base optimization New dags, analytics tools, ..

  9. Hive Advanced Analytics • Add OVER clause to support windowing queries • With standard arguments • Ranking functions • rank, ntile, row_number, dense_rank • With analytics functions: • cume_dist, first_value, lag, last_value, lead, percentile_cont, percentile_disc, percent_rank • Add CUBE and ROLLUP • Easily create summaries of your data • Extend aggregation functions • STDDEV, VAR

  10. Hive Data Type Conformance • Extend Hive to support additional types from SQL • Improves applications and interoperability between tools • Specific additions • Add fixed point NUMERIC and DECIMAL type (in progress) • Add VARCHAR and CHAR types with limited field size • Add DATETIME • Add size ranges from 1 to 53 for FLOAT • Add synonyms for compatibility • BLOB for BINARY • TEXT for STRING • REAL for FLOAT

  11. SQL: Enhancing SQL Semantics SQL Compliance Hive 12 provides a wide array of SQL datatypes and semantics so your existing tools integrate more seamlessly with Hadoop Available Hive 0.12 Roadmap

  12. Example Benchmark Spec • The TPC-DS benchmark data+query set • Query 27 • big table(store_sales) joins lots of small tables • A.K.A Star Schema Join • What does Query 27 do? For all items sold in stores located in specified states during a given year, find the average quantity, average list price, average list sales price, average coupon amount for a given gender, marital status, education and customer demographic..

  13. Query 27 - Star Schema Join • Derived from TPC-DS Query 27 SELECT col5, avg(col6) FROM store_sales_factssf join item_dim on (ssf.col1 = item_dim .col1) join date_dim on (ssf.col2 = date_dim.col2 join custdmgrphcs_dim on (ssf.col3 =custdmgrphcs_dim.col3) join store_dim on (ssf.col4 = store_dim.col4) GROUP BY col5 ORDER BY col5 LIMIT 100; 41 GB 58 MB 11MB 80MB 106 KB

  14. New Query Planner

  15. Query27 Execution Before Hive 11-Text Format The intermediate output of each job is written to HDFS 179 total mappers got executed Query spawned 5 MR Jobs Query Response Time

  16. Query27 Execution With Hive 11-Text Format Job 1 of 1 – Each Mapper loads into memory the 4 small dimension tables and streams parts of the large fact table. Joins then occur in Mapper hence the name MapJoin Query spawned of 1 job with Hive 11 compared to 5 MR Jobs with Hive 10 Increase in performance with Hive 11 as query time went down from 21 minutes to about 4 minutes

  17. Query27 Execution With Hive 11- RC Format Conversion from Text to RC file format decreased size of dimension data set from 38 GB to 8.21 GB Smaller file equates to less IO causing the query time to decrease from 246 seconds to 136 seconds

  18. Query27 Execution With Hive 11- ORC Format ORC File type consolidates data more tighly than RCFile as the size of dataset decreased from 8.21 GB to 2.83 GB Smaller file equates to less IO causing the query time to decrease from 136 seconds to 104 seconds

  19. Summary of Results

  20. ORC file format Optimized RC File

  21. ORCFile - Optimized Column Storage • Make a better columnar storage file • Evolve based on Google Dremel format • Decompose complex row types into primitive fields • Better compression and projection • Only read bytes from HDFS for the required columns. • Store column level aggregates in the files • Only need to read the file meta information for common queries • Stored both for file and each section of a file • Aggregates: min, max, sum, average, count • Allows fast access by sorted columns • Ability to add bloom filters for columns • Enables quick checks for whether a value is present

  22. ORCFile - File Layout

  23. Interactive Query at Scale Sustained Query Times Apache Hive 0.12 provides sustained acceptable query times even at petabyte scale Smaller Footprint Better encoding with ORC in Apache Hive 0.12 reduces resource requirements for your cluster File Size Comparison Across Encoding Methods Dataset: TPC-DS Scale 500 Dataset 585 GB (Original Size) 505 GB (14% Smaller) 221 GB (62% Smaller) Impala 131 GB (78% Smaller) • Larger Block Sizes • Columnar format arranges columns adjacent within the file for compression & fast access Hive 12 Encoded with Text Encoded with RCFile Encoded with Parquet Encoded with ORCFile

  24. Apache Tez A New Hadoop Data Processing Framework

  25. Moving Hadoop Beyond MapReduce • Low level data-processing execution engine • Built on YARN • Enables pipelining of jobs • Removes task and job launch times • Does not write intermediate output to HDFS • Much lighter disk and network usage • New base of MapReduce, Hive, Pig, Cascading etc. • Hive and Pig jobs no longer need to move to the end of the queue between steps in the pipeline

  26. FastQuery: Beyond Batch with YARN Always-On Tez Service Low latency processing for all Hadoop data processing Tez Generalizes Map-Reduce Simplified execution plans process data more efficiently

  27. Apache Tez as the new Primitive Apache Tez as Base MapReduce as Base HADOOP 1.0 HADOOP 2.0 Data Flow Pig SQL Hive Others (cascading) Batch MapReduce Real Time Stream Processing Storm Online Data Processing HBase, Accumulo Pig (data flow) Hive (sql) Others (cascading) Tez (execution engine) MapReduce (cluster resource management & data processing) YARN (cluster resource management) HDFS (redundant, reliable storage) HDFS2 (redundant, reliable storage)

  28. Hive-on-MR vs. Hive-on-Tez SELECT a.x, AVERAGE(b.y) AS avg FROM a JOIN b ON (a.id = b.id) GROUP BY a UNION SELECT x, AVERAGE(y) AS AVG FROM c GROUP BY x ORDER BY AVG; Tez avoids unneeded writes to HDFS M M M M M M SELECT a.state, c.itemId SELECT a.state SELECT b.id SELECT b.id R R R R M M HDFS M M R M M JOIN (a, c) SELECT c.price R JOIN (a, c) R R HDFS HDFS JOIN(a, b) GROUP BY a.state COUNT(*) AVERAGE(c.price) JOIN(a, b) GROUP BY a.state COUNT(*) AVERAGE(c.price) M M R R

  29. Speed: Interactive Query In Hadoop • Test Cluster: • 200 GB Data (ORCFile) • 20 Nodes, 24GB RAM each, 6x disk each Query 27: Pricing Analytics using Star Schema Join Query 82: Inventory Analytics Joining 2 Large Fact Tables 1400s 3200s 65s 190x Improvement 200x Improvement 39s 14.9s 7.2s TPC-DS Query 27 TPC-DS Query 82 Hive 0.11 (Phase 1) Hive 10 Trunk (Phase 3) All Results at Scale Factor 200 (Approximately 200GB Data)

  30. Hortonworks…the Bull Elephant of Hadoop Innovation There is NO second place

  31. Thank You hortonworks.com hortonworks.com/sandbox

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