SQL on Hadoop: Defining the New Generation of Analytic SQL Databases
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SQL on Hadoop: Defining the New Generation of Analytic SQL Databases
- 1. SQL on Hadoop Defining the New Genera/on of Analy/c Databases Strata Conference, February 2013
- 2. Speaker Bio: Carl Steinbach Currently: Engineer at Citus Data PMC Chair, Committer -- Apache Hive Project @cwsteinbach on Twitter Formerly: Cloudera, Informatica, NetApp, Oracle 2
- 3. This is going to sound strange, but… I used to think databases were boring 3
- 4. Why? Undergrad at MIT 1997-2001 Number of Database Classes: 0 Number of Database Faculty Members: 0 My Conclusion: Databases are a Dead Field 4
- 5. Things Changed Over the Next Couple of Years I got a job! Database Group Formed at MIT (2003) - Mike Stonebraker - Sam Madden New Class: 6.830 Database Systems (2005) 5
- 6. What Changed? Web-scale Data New DB Research: Columnar Storage, NoSQL MPP Analytic Databases Gained Market Traction GFS (’03) and MapReduce (‘04) Papers Apache Hadoop – v0.1.0 released in 2006 6
- 7. What’s Good About Hadoop? Commodity Storage Scale-out Flexibility MapReduce Multi-structured Data 7
- 8. What’s Bad About Hadoop? MapReduce! No Schemas! Missing Features Optimizer, Indexes, Views Incompatibility with Existing Tools BI, ETL, IDEs 8
- 9. Apache Hive Solved Many of These Problems User Client HiveServer2 Hive MetaStore Hive CLI SQL to MapReduce Table to Files SQL Queries Catalog Metadata Compiler Table to Format ETL, BI, SQL IDE Rule Based Op/mizer Hive ODBC/JDBC MR Plan Execu/on Coordinator Map/Reduce Map/Reduce Map/Reduce Hive Operators Hive Operators Hive Operators Hive SerDes Hive SerDes Hive SerDes HDFS HDFS HDFS datanode datanode datanode 9
- 10. But Other Problems Remained MapReduce: Latency Overhead Many Missing Features: • ANSI SQL • Cost Based Optimizer • UDFs • Data Types • Security • … 10
- 11. One Solution: Separate MPP DB Cluster MPP Database Cluster MPP Master Node Global Query Executor MPP Worker Node MPP Worker Node MPP Worker Node MPP Worker Node Local Query Local Query Local Query Local Query Executor Executor Executor Executor Hadoop Cluster HDFS HDFS HDFS HDFS datanode datanode datanode datanode 11
- 12. One Solution: Separate MPP DB Cluster MPP Master Node Global Query Executor MPP Worker Node MPP Worker Node MPP Worker Node MPP Worker Node Local Query Local Query Local Query Local Query Executor Executor Executor Executor Pull Data to Work IO Bo]leneck HDFS HDFS HDFS HDFS datanode datanode datanode datanode 12
- 13. Better Solution: A New Architecture for SQL on Hadoop MPP Master Node Global Query Push Executor Work to Data Maintain Local Query Local Query Local Query Local Query Executor Executor Executor Executor Data Locality HDFS HDFS HDFS HDFS datanode datanode datanode datanode 13
- 14. The New Architecture in Detail: CitusDB CitusDB Master Node Hadoop Metadata Metadata PostgreSQL Tools HDFS Distributed Query ODBC/JDBC Planner NameNode Clients Distributed Query Executor Local Query Planner Local Query Planner Local Query Planner Local Query Executor Local Query Executor Local Query Executor Foreign Data Wrappers Foreign Data Wrappers Foreign Data Wrappers HDFS HDFS HDFS datanode datanode datanode 14
- 15. The New Architecture in Detail: CitusDB CitusDB Master Node Metadata Sync Hadoop Metadata Metadata PostgreSQL Tools HDFS Distributed Query ODBC/JDBC Planner NameNode Clients Distributed Query Executor Local Query Planner Local Query Planner Local Query Planner Local Query Executor Local Query Executor Local Query Executor Foreign Data Wrappers Foreign Data Wrappers Foreign Data Wrappers HDFS HDFS HDFS datanode datanode datanode Step 1) The CitusDB Master Node retrieves file system metadata from the Hadoop NameNode. 15
- 16. The New Architecture in Detail: CitusDB CitusDB Master Node Hadoop Metadata Metadata PostgreSQL Tools User Query HDFS Distributed Query ODBC/JDBC Planner NameNode Clients Distributed Query Executor Local Query Planner Local Query Planner Local Query Planner Local Query Executor Local Query Executor Local Query Executor Foreign Data Wrappers Foreign Data Wrappers Foreign Data Wrappers HDFS HDFS HDFS datanode datanode datanode Step 2) The user submits a SQL query to the CitusDB master node using the PostgreSQL CLI or a JDBC/ODBC app. 16
- 17. The New Architecture in Detail: CitusDB CitusDB Master Node Hadoop Metadata Metadata PostgreSQL Tools HDFS Distributed Query ODBC/JDBC Planner NameNode Clients Distributed Query Executor Local Queries Local Query Planner Local Query Planner Local Query Planner Local Query Executor Local Query Executor Local Query Executor Foreign Data Wrappers Foreign Data Wrappers Foreign Data Wrappers HDFS HDFS HDFS datanode datanode datanode Step 3) The Master Node generates an op/mized global query plan and sends fragment queries to the workers. 17
- 18. The New Architecture in Detail: CitusDB CitusDB Master Node Hadoop Metadata Metadata PostgreSQL Tools HDFS Distributed Query ODBC/JDBC Planner NameNode Clients Distributed Query Executor Local Results Local Query Planner Local Query Planner Local Query Planner Local Query Executor Local Query Executor Local Query Executor Foreign Data Wrappers Foreign Data Wrappers Foreign Data Wrappers HDFS HDFS HDFS datanode datanode datanode Step 4) The CitusDB worker processes running on each DataNode process the fragment queries 18 and send par/al result sets back to the Master Node.
- 19. The New Architecture in Detail: CitusDB CitusDB Master Node Hadoop Metadata Metadata PostgreSQL Tools Query Results HDFS Distributed Query ODBC/JDBC Planner NameNode Clients Distributed Query Executor Local Query Planner Local Query Planner Local Query Planner Local Query Executor Local Query Executor Local Query Executor Foreign Data Wrappers Foreign Data Wrappers Foreign Data Wrappers HDFS HDFS HDFS datanode datanode datanode Step 5) The Master Node merges the par/al result sets and returns the final result to the user. 19
- 20. CitusDB: Standing on the Shoulders of Giants + Mature, Battle-tested Proven Scalability Enterprise Class Features Cost Effectiveness Has an Elephant Mascot Has an Elephant Mascot 20
- 21. Leveraging PostgreSQL Performance Cost-based Query Optimizer postgres=# EXPLAIN SELECT customer.c_custkey, sum((lineitem.l_extendedprice * (1::numeric -‐ lineitem.l_discount))) …. -‐> Sort (cost=282459.19..282599.52 rows=56134 width=182) Sort Key: customer.c_custkey, customer.c_name Sort Method: external merge Disk: 17192kB …. -‐> Hash Join (cost=39666.61..257246.25 rows=56134 width=16) Hash Cond: (lineitem.l_orderkey = orders.o_orderkey) -‐> Seq Scan on lineitem_102022 lineitem (cost=0.00..190571.11) 21
- 22. Leveraging PostgreSQL Features: More than 300 Built-in Functions QUOTE_LITERAL REGR_SLOPE COS GREATEST QUOTE_IDENT SET_BYTE STRING_TO_ARRAY ENUM_RANGE EXTRACT REGR_SXY REGR_R2 XMLFOREST CONVERT_TO NTH_VALUE DIV OVERLAPS LAG LAG DATE_TRUNC SIN BTRIM FLOOR PI FORMAT TO_DATE TRANSACTION_TIMESTAMP LOWER SQRT TRUNC ARRAY_AGG LOWER_INC REGR_SYY CONCAT RTRIM STRIP LTRIM CHAR_LENGTH IS FALSE ARRAY_FILL REGR_AVGY XMLAGG BETWEEN CURRENT_TIMESTAMP BROADCAST JUSTIFY_DAYS IS DISTINCT UPPER BOX ARRAY_LENGTH ISCLOSED VAR_POP TIMEOFDAY COVAR_POP CURRVAL REPEAT VAR_SAMP OCTET_LENGTH LN NETMASK LOCALTIME UPPER QUERY_TO_XML STATEMENT_TIMESTAMP TO_CHAR FIRST_VALUE LPAD CASE GET_BIT TAN TRUNC LOWER_INF REGR_AVGX BOOL_AND IS NOT UNKNOWN ARRAY_APPEND ISNULL REGR_COUNT DATE_PART CORR ENUM_LAST XMLCOMMENT SCHEMA_TO_XML SET_MASKLEN ARRAY_TO_STRING XPATH_EXISTS NUMNODE REGEXP_MATCHES COALESCE NOW EXTRACT RADIUS SPLIT_PART CONVERT_FROM ENUM_FIRST ISOPEN UPPER_INC MOD REPLACE XPATH BIT_AND REGR_COUNT TRANSLATE AREA EVERY AT TIME ZONE RADIANS NOW SQRT ATAN2 IS TRUE RANDOM SUM MIN NOT LIKE REGEXP_REPLACE RPAD CEILING TRIM TO_HEX LOG DECODE NOW WIDTH STDDEV_POP GET_BYTE DATE_TRUNC BOOL_OR REGR_SXX ROUND LSEG XML_IS_WELL_FORMED VARIANCE CUME_DIST PATH COVAR_SAMP STRING_AGG LASTVAL UNNEST OVERLAY PERCENT_RANK HOSTMASK PCLOSE HEIGHT ANY POINT IN ARRAY_DIMS MASKLEN DENSE_RANK LOCALTIMESTAMP JUSTIFY_INTERVAL CURRENT_DATE CURSOR_TO_XML LIKE SETVAL LENGTH POWER UPPER_INF GENERATE_SUBSCRIPTS POSITION LAST_VALUE INITCAP IS NOT TRUE XMLAGG PG_SLEEP VAR_POP STRPOS SIGN FORMAT GENERATE_SERIES STDDEV_SAMP DENSE_RANK COT SUBSTR REVERSE REGR_INTERCEPT SIMILAR TO DATABASE_TO_XML ARRAY_CAT STDDEV IS NOT FALSE DIAMETER NOTNULL HOST TO_ASCII ABS ROW_TO_JSON ROW_NUMBER SUBSTRING SETSEED ISFINITE SOME SET_BIT ARRAY_NDIMS REGEXP_SPLIT_TO_ARRAY TO_TIMESTAMP NOT MD5 22
- 23. Leveraging PostgreSQL Features Extensible, Rich Type System Pluggable Format Handlers Security Internationalization Connectivity: ODBC, JDBC Ecosystem Add-Ons: PostGIS, XML/JSON, Fuzzy Search, Language Bindings (.NET, Python, etc) 23
- 24. Where are We Headed? Distributed. SQL. Anywhere. CitusDB Master Node Metadata Distributed Query Planner Distributed Query Executor Local Query Planner Local Query Planner Local Query Planner Local Query Executor Local Query Executor Local Query Executor Foreign Data Wrapper Foreign Data Wrapper Foreign Data Wrapper HDFS mongod RDBMS Hadoop Datanode MongoDB Shard RDBMS server 24
- 25. Defining the New Generation of Distributed Analytic Databases SQL à Ease of Use, Increased Productivity Real-time responsiveness à Faster Data Locality à Proven Scalability Schema-on-Read à Flexibility, Lower Cost 25
- 26. Where Are We At? CitusDB SQL on Hadoop is in Open Beta Download our Binary Packages Or Use Our EC2 AMI http://citusdata.com/docs/sql-on-hadoop 26
- 28. For questions and more information: info@citusdata.com (650) 566-9010 28