Taming Big Data with Big SQL 3.0
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This document discusses Big SQL 3.0, a SQL query engine for analyzing large datasets in Hadoop. Big SQL 3.0 leverages an advanced SQL compiler and native runtime to provide high performance SQL queries without requiring data to be copied. It supports features like stored procedures, functions, and comprehensive security including row and column level access controls. The document provides an overview of Big SQL 3.0's architecture and how it integrates with and utilizes existing Hadoop components to analyze data stored in HDFS and Hive.
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Taming Big Data with Big SQL 3.0
- 1. © 2014 IBM Corporation Taming Big Data with Big SQL Session 3477 Berni Schiefer ([email protected]) Bert Van der Linden ([email protected])
- 2. Please Note IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. 1
- 3. Agenda Internet of Things & Big Data BigInsights Big SQL 3.0 • Architecture • Performance • Best practices 2
- 4. Systems of Insight from Data Emanating from the Internet of Things (IoT) 3
- 5. How Big is the Internet of Things? 4
- 6. 2+ billion people on the Web by end 2011 30 billion RFID tags today (1.3B in 2005) 4.6 billion camera phones world wide 100s of millions of GPS enabled devices sold annually 76 million smart meters in 2009… 200M by 2014 12+ TBs of tweet data every day 25+ TBs of log data every day ?TBsof dataeverydayWhere is Big Data coming from? 5
- 7. Now, they’re installing smart meters. 10 million smart meters read every 15 minutes = 350 billion transactions a year. A major gas and electric utility has 10 million meters. They read the meters once a month. The meters are read once an hour. 6
- 8. Data AVAILABLE to an organization Data an organization can PROCESS The Big Data Conundrum The percentage of available data an enterprise can analyze is decreasing This means enterprises are getting “more naive” over time 7
- 9. Transactional & Application Data Machine Data Social Data Enterprise Content • Volume • Structured • Throughput • Velocity • Structured • Ingestion • Variety • Unstructured • Veracity • Variety • Unstructured • Volume Big Data is All Data and All Paradigms 8
- 10. Big Data Landing zone eco-system 9
- 11. Big Data “Landing zone” eco-system BigSQL 10
- 12. BI / Report ing BI / Reporting Exploration / Visualization Functional App Industry App Predictive Analytics Content Analytics Analytic Applications IBM Big Data Platform Systems Management Application Development Visualization & Discovery Accelerators Information Integration & Governance Stream Computing Data Warehouse InfoSphere BigInsights builds on open source Hadoop capabilities for enterprise class deployments Open source based components Enterprise capabilities Hadoop System Business benefits – Quicker time-to-value due to IBM technology and support – Reduced operational risk – Enhanced business knowledge with flexible analytical platform – Leverages and complements existing software InfoSphere BigInsights Administration & Security Workload Optimization Connectors Advanced Engines Visualization & Exploration Development Tools Open source Hadoop components Big SQL 11
- 13. © 2013 IBM Corporation Announcing BigInsights v3.0 Standard Edition Breadth of capabilities Enterpriseclass Enterprise Edition - Spreadsheet-style tool - Web console - Dashboards - Pre-built applications - Eclipse tooling - RDBMS connectivity - Big SQL 3.0 - Jaql - Platform enhancements - . . . - Accelerators - GPFS – FPO - Adaptive MapReduce - Text analytics - Enterprise Integration - Monitoring and alerts - Big R - InfoSphere Streams* - Watson Explorer* - Cognos BI* - . . . Apache Hadoop 12 * Limited use license included Available for Linux on POWER (Redhat) and Intel x64 Linux (Redhat/SUSE)
- 14. Component BigInsights 3.0 HortonWorks HDP 2.0 MapR 3.1 Pivotal HD 1.1 Cloudera CDH5 Hadoop 2.2 2.2 1.0.3 2.0.5 * 2.3 HBase 0.96.0 0.96.0 0.94.13 0.94.8 0.96.1 Hive 0.12.0 0.12 0.11 0.11.0 0.12.0 Pig 0.12.0 0.12 0.11.0 0.10.1 0.12.0 Zookeeper 3.4.5 3.4.5 3.4.5 3.4.5 3.4.5 Oozie 4.0.0 4.0.0 3.3.2 3.3.2 4.0.0 Avro 1.7.5 X X X 1.7.5 Flume 1.4.0 1.4.0 1.4.0 1.3.1 1.4.0 Sqoop 1.4.4 1.4.4 1.4.4 1.4.2 1.4.4 Current as of April 27, 2014 Common Hadoop core in all Hadoop Distributions 13
- 15. What is Big SQL 3.0? Comprehensive SQL functionality • IBM SQL/PL support, including… • Stored procedures (SQL bodied and external) • Functions (SQL bodied and external) • IBM Data Server JDBC and ODBC drivers Leverages advanced IBM SQL compiler/runtime • High performance native (C++) runtime Replaces Map/Reduce • Advanced message passing runtime • Data flows between nodes without requiring persisting intermediate results • Continuous running daemons • Advanced workload management allows resources to remain constrained • Low latency, high throughput… SQL-based Application Big SQL Engine InfoSphere BigInsights Data Sources IBM data server client SQL MPP Run-time CSVCSV SeqSeq ParquetParquet RCRC ORCORCAvroAvro CustomCustomJSONJSON 14
- 16. Big SQL 3.0 – Architecture Head (coordinator) node • Listens to the JDBC/ODBC connections • Compiles and optimizes the query • Coordinates the execution of the query Big SQL worker processes reside on compute nodes (some or all) Worker nodes stream data between each other as needed Workers can spill large data sets to local disk if needed • Allows Big SQL to work with data sets larger than available memory Mgmt Node Big SQL Mgmt Node Hive Metastore Mgmt Node Name Node Mgmt Node Job Tracker••• Compute Node Task Tracker Data Node Compute Node Task Tracker Data Node Compute Node Task Tracker Data Node Compute Node Task Tracker Data Node••• Big SQL Big SQL Big SQL Big SQL GPFS/HDFS 15
- 17. Big SQL 3.0 works with Hadoop All data is Hadoop data • In files in HDFS • SEQ, RC, delimited, Parquet … Never need to copy data to a proprietary representation All data is catalog-ed in the Hive metastore • It is the Hadoop catalog • It is flexible and extensible All Hadoop data is in a Hadoop filesystem • HDFS or GPFS-FPO 16
- 18. Big SQL 3.0 – Architecture (cont.) Big SQL's runtime execution engine is all native code For common table formats a native I/O engine is utilized • e.g. delimited, RC, SEQ, Parquet, … For all others, a java I/O engine is used • Maximizes compatibility with existing tables • Allows for custom file formats and SerDe's All Big SQL built-in functions are native code Customer built UDx's can be developed in C++ or Java • Existing Big SQL UDF's can be used with a slight change in how they are registered Mgmt Node Big SQL Compute Node Task Tracker Data Node Big SQL Big SQL Worker Native I/O Engine Java I/O Engine SerDe I/O Fmt Runtime Java UDFs Native UDFs 17
- 19. Big SQL 3.0 – Enterprise security Users may be authenticated via • Operating system • Lightweight directory access protocol (LDAP) • Kerberos User authorization mechanisms include • Full GRANT/REVOKE based security • Group and role based hierarchical security • Object level, column level, or row level (fine-grained) access controls Auditing • You may define audit policies and track user activity Transport layer security (TLS) • Protect integrity and confidentiality of data between the client and Big SQL 18
- 20. Row Based Access Control - 4 easy steps 2) Create permissions * CREATE PERMISSION BRANCH_A_ACCESS ON BRANCH_TBLCREATE PERMISSION BRANCH_A_ACCESS ON BRANCH_TBLCREATE PERMISSION BRANCH_A_ACCESS ON BRANCH_TBLCREATE PERMISSION BRANCH_A_ACCESS ON BRANCH_TBL FOR ROWS WHERE(VERIFY_ROLE_FOR_USER(SESSION_USER,'BRANCH_A_ROLE'FOR ROWS WHERE(VERIFY_ROLE_FOR_USER(SESSION_USER,'BRANCH_A_ROLE'FOR ROWS WHERE(VERIFY_ROLE_FOR_USER(SESSION_USER,'BRANCH_A_ROLE'FOR ROWS WHERE(VERIFY_ROLE_FOR_USER(SESSION_USER,'BRANCH_A_ROLE') = 1) = 1) = 1) = 1 ANDANDANDAND BRANCH_TBL.BRANCH_NAME = 'Branch_A')BRANCH_TBL.BRANCH_NAME = 'Branch_A')BRANCH_TBL.BRANCH_NAME = 'Branch_A')BRANCH_TBL.BRANCH_NAME = 'Branch_A') ENFORCED FOR ALL ACCESSENFORCED FOR ALL ACCESSENFORCED FOR ALL ACCESSENFORCED FOR ALL ACCESS ENABLEENABLEENABLEENABLE 3) Enable access control * ALTER TABLE BRANCH_TBL ACTIVATE ROW ACCESS CONTROLALTER TABLE BRANCH_TBL ACTIVATE ROW ACCESS CONTROLALTER TABLE BRANCH_TBL ACTIVATE ROW ACCESS CONTROLALTER TABLE BRANCH_TBL ACTIVATE ROW ACCESS CONTROL 4) Select as Branch_A user CONNECT TO TESTDB USER newtonCONNECT TO TESTDB USER newtonCONNECT TO TESTDB USER newtonCONNECT TO TESTDB USER newton SELECT "*" FROM BRANCH_TBLSELECT "*" FROM BRANCH_TBLSELECT "*" FROM BRANCH_TBLSELECT "*" FROM BRANCH_TBL EMP_NO FIRST_NAME BRANCH_NAMEEMP_NO FIRST_NAME BRANCH_NAMEEMP_NO FIRST_NAME BRANCH_NAMEEMP_NO FIRST_NAME BRANCH_NAME -------------------------------------------- ------------------------------------------------ -------------------------------------------- 2 Chris Branch_A2 Chris Branch_A2 Chris Branch_A2 Chris Branch_A 3 Paula Branch_A3 Paula Branch_A3 Paula Branch_A3 Paula Branch_A 5 Pete Branch_A5 Pete Branch_A5 Pete Branch_A5 Pete Branch_A 8 Chrissie Branch_A8 Chrissie Branch_A8 Chrissie Branch_A8 Chrissie Branch_A 4 record(s) selected.4 record(s) selected.4 record(s) selected.4 record(s) selected. Data SELECT "*" FROM BRANCH_TBLSELECT "*" FROM BRANCH_TBLSELECT "*" FROM BRANCH_TBLSELECT "*" FROM BRANCH_TBL EMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARY ---------------------------- ------------------------------------------------ -------------------------------------------- 1 Steve Branch_B1 Steve Branch_B1 Steve Branch_B1 Steve Branch_B 2 Chris Branch_A2 Chris Branch_A2 Chris Branch_A2 Chris Branch_A 3 Paula Branch_A3 Paula Branch_A3 Paula Branch_A3 Paula Branch_A 4 Craig Branch_B4 Craig Branch_B4 Craig Branch_B4 Craig Branch_B 5 Pete Branch_A5 Pete Branch_A5 Pete Branch_A5 Pete Branch_A 6 Stephanie Branch_B6 Stephanie Branch_B6 Stephanie Branch_B6 Stephanie Branch_B 7 Julie Branch_B7 Julie Branch_B7 Julie Branch_B7 Julie Branch_B 8 Chrissie Branch_A8 Chrissie Branch_A8 Chrissie Branch_A8 Chrissie Branch_A 1) Create and grant access and roles * CREATE ROLE BRANCH_A_ROLECREATE ROLE BRANCH_A_ROLECREATE ROLE BRANCH_A_ROLECREATE ROLE BRANCH_A_ROLE GRANT ROLE BRANCH_A_ROLE TO USER newtonGRANT ROLE BRANCH_A_ROLE TO USER newtonGRANT ROLE BRANCH_A_ROLE TO USER newtonGRANT ROLE BRANCH_A_ROLE TO USER newton GRANT SELECT ON BRANCH_TBL TO USER newtonGRANT SELECT ON BRANCH_TBL TO USER newtonGRANT SELECT ON BRANCH_TBL TO USER newtonGRANT SELECT ON BRANCH_TBL TO USER newton * Note: Steps 1, 2, and 3 are done by a user with* Note: Steps 1, 2, and 3 are done by a user with* Note: Steps 1, 2, and 3 are done by a user with* Note: Steps 1, 2, and 3 are done by a user with SECADM authority.SECADM authority.SECADM authority.SECADM authority. 19
- 21. Column Based Access Control 2) Create permissions * CREATE MASK SALARY_MASK ON SAL_TBL FORCREATE MASK SALARY_MASK ON SAL_TBL FORCREATE MASK SALARY_MASK ON SAL_TBL FORCREATE MASK SALARY_MASK ON SAL_TBL FOR COLUMN SALARY RETURNCOLUMN SALARY RETURNCOLUMN SALARY RETURNCOLUMN SALARY RETURN CASE WHEN VERIFY_ROLE_FOR_USER(SESSION_USER,'MANAGER') = 1CASE WHEN VERIFY_ROLE_FOR_USER(SESSION_USER,'MANAGER') = 1CASE WHEN VERIFY_ROLE_FOR_USER(SESSION_USER,'MANAGER') = 1CASE WHEN VERIFY_ROLE_FOR_USER(SESSION_USER,'MANAGER') = 1 THEN SALARYTHEN SALARYTHEN SALARYTHEN SALARY ELSE 0.00ELSE 0.00ELSE 0.00ELSE 0.00 ENDENDENDEND ENABLEENABLEENABLEENABLE 3) Enable access control * ALTER TABLE SAL_TBL ACTIVATE COLUMN ACCESS CONTROLALTER TABLE SAL_TBL ACTIVATE COLUMN ACCESS CONTROLALTER TABLE SAL_TBL ACTIVATE COLUMN ACCESS CONTROLALTER TABLE SAL_TBL ACTIVATE COLUMN ACCESS CONTROL 4b) Select as a MANAGER CONNECT TO TESTDB USER socratesCONNECT TO TESTDB USER socratesCONNECT TO TESTDB USER socratesCONNECT TO TESTDB USER socrates SELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBL EMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARY ---------------------------- ------------------------------------------------ -------------------------------------------- 1 Steve 2500001 Steve 2500001 Steve 2500001 Steve 250000 2 Chris 2000002 Chris 2000002 Chris 2000002 Chris 200000 3 Paula 10000003 Paula 10000003 Paula 10000003 Paula 1000000 3 record(s) selected.3 record(s) selected.3 record(s) selected.3 record(s) selected. Data SELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBL EMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARY ---------------------------- ------------------------------------------------ -------------------------------------------- 1 Steve 2500001 Steve 2500001 Steve 2500001 Steve 250000 2 Chris 2000002 Chris 2000002 Chris 2000002 Chris 200000 3 Paula 10000003 Paula 10000003 Paula 10000003 Paula 1000000 1) Create and grant access and roles * CREATE ROLE MANAGERCREATE ROLE MANAGERCREATE ROLE MANAGERCREATE ROLE MANAGER CREATE ROLE EMPLOYEECREATE ROLE EMPLOYEECREATE ROLE EMPLOYEECREATE ROLE EMPLOYEE GRANT SELECT ON SAL_TBL TO USER socratesGRANT SELECT ON SAL_TBL TO USER socratesGRANT SELECT ON SAL_TBL TO USER socratesGRANT SELECT ON SAL_TBL TO USER socrates GRANT SELECT ON SAL_TBL TO USER newtonGRANT SELECT ON SAL_TBL TO USER newtonGRANT SELECT ON SAL_TBL TO USER newtonGRANT SELECT ON SAL_TBL TO USER newton GRANT ROLE MANAGER TO USER socratesGRANT ROLE MANAGER TO USER socratesGRANT ROLE MANAGER TO USER socratesGRANT ROLE MANAGER TO USER socrates GRANT ROLE EMPLOYEE TO USER newtonGRANT ROLE EMPLOYEE TO USER newtonGRANT ROLE EMPLOYEE TO USER newtonGRANT ROLE EMPLOYEE TO USER newton 4a) Select as an EMPLOYEE CONNECT TO TESTDB USER newtonCONNECT TO TESTDB USER newtonCONNECT TO TESTDB USER newtonCONNECT TO TESTDB USER newton SELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBLSELECT "*" FROM SAL_TBL EMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARYEMP_NO FIRST_NAME SALARY ---------------------------- ------------------------------------------------ -------------------------------------------- 1 Steve 01 Steve 01 Steve 01 Steve 0 2 Chris 02 Chris 02 Chris 02 Chris 0 3 Paula 03 Paula 03 Paula 03 Paula 0 3 record(s) selected.3 record(s) selected.3 record(s) selected.3 record(s) selected. * Note: Steps 1, 2, and 3 are done by a user with* Note: Steps 1, 2, and 3 are done by a user with* Note: Steps 1, 2, and 3 are done by a user with* Note: Steps 1, 2, and 3 are done by a user with SECADM authority.SECADM authority.SECADM authority.SECADM authority. 20
- 22. Big SQL 3.0 – Other enterprise features Federation • Join between your Hadoop data and other external relational platforms • Optimizer determines most efficient execution path Open integration across Business Analytic Tools • IBM Optim Data Studio performance tool portfolio • Superior enablement for IBM Software – e.g. Cognos • Enhanced support by 3rd party software – e.g. Microstrategy Mixed workload cluster management • Capacity sharing with the rest of the cluster – Specify %cpu and %memory to dedicate to BigSQL 3.0 • SQL based workload management • Integration with Platform Symphony to manage mixed cluster workloads Support for standard development tools 21
- 23. Workload Management (2) Identify workloads and associate to service classes create workload SALES_WL CURRENT client_appname(‘SalesSys') service class HIGHPRIWORK create workload ITEMCOUNT_WL CURRENT client_appname(‘InventorySys') service class LOWPRIWORK (1) Create service classes create service class BIGDATAWORK create service class HIGHPRIWORK under BIGDATAWORK create service class LOWPRIWORK under BIGDATAWORK (3a) Avoid thrashing by queueing low priority work. create threshold LOW_CONCURRENT for service class LOWPRIWORK under BIGDATAWORK activities enforcement database enable when concurrentdbcoordactivities > 5 and queued activities unbounded continue (3b) Stop high priority job if SLA cannot be met create threshold HIGH_CONCURRENT for service class HEAVYQUERIES under BIGDATAWORK activities enforcement database enable when concurrentdbcoordactivities > 30 and queued activities > 0 stop execution (4a) Stop very long running jobs create threshold LOWPRI_WL_TIMEOUT for service class LOWPRIWORK under BIGDATAWORK activities enforcement database enable when activitytotaltime > 30 minutes stop execution (4b) Stop jobs that return too many rows create threshold TOO_MANY_ROWS_RETURNED for service class HIGHPRIWORK under BIGDATAWORK enforcement database when sqlrowsreturned >30 stop execution (5) Collect data for long running jobs Create threshold LONGRUNINVENTORYACTIVITIES for service class LOWPRIWORK activities enforcement database when activitytotaltime > 15 minutes collect activity data with details continue (6) Reporting system activity create event monitor BIGDATAMONACT for activities write to table
- 24. Using existing standard SQL tools: Eclipse •Using existing SQL tooling against BigData, •Same setup as for existing SQL sources!! •Support for “standard” authentication!! 23
- 25. Using existing standard SQL tools: SQuirrel SQL •Using existing SQL tooling against BigData •Support for authenticating (not supported for Hive, BUT supported by Big SQL!) 24
- 26. Using BigSheets in BigInsights: data discovery •Discovery and analytics in a spreadsheet-like environment.
- 27. Big SQL 3.0 – Performance Query rewrites • Exhaustive query rewrite capabilities • Leverages additional metadata such as constraints and nullability Optimization • Statistics and heuristic driven query optimization • Query optimizer based upon decades of IBM RDBMS experience Tools and metrics • Highly detailed explain plans and query diagnostic tools • Extensive number of available performance metrics SELECT ITEM_DESC, SUM(QUANTITY_SOLD), AVG(PRICE), AVG(COST) FROM PERIOD, DAILY_SALES, PRODUCT, STORE WHERE PERIOD.PERKEY=DAILY_SALES.PERKEY AND PRODUCT.PRODKEY=DAILY_SALES.PRODKE Y AND STORE.STOREKEY=DAILY_SALES.STOREKEY AND CALENDAR_DATE BETWEEN AND '01/01/2012' AND '04/28/2012' AND STORE_NUMBER='03' AND CATEGORY=72 GROUP BY ITEM_DESC Thread 0 DSS TQA (tq1) AGG (complete) BNO EXT Thread 1 TA (Product) NLJN (Daily Sales) NLJN (Period) NLJN (Store) AGG (partial) TQB (tq1) EXT Thread 2 TA (DS_IX7) EXT Thread 3 TA (PER_IX2) EXT Thread 4 TA (ST_IX1) EXT Access plan generationQuery transformation Access section ~150 query transformations Hundreds or thousands of access plan options Store Product Product Store NLJOIN Daily SalesNLJOIN Period NLJOIN Product NLJOIN Daily Sales NLJOIN Period NLJOIN Store HSJOIN Daily Sales HSJOIN Period HSJOIN Product StoreZZJOIN Daily Sales HSJOIN Period 26
- 28. Statistics are key to performance Table statistics: • Cardinality (count) • Number of Files • Total File Size Column statistics (this applies to column group stats also): • Minimum value • Maximum value • Cardinality (non-nulls) • Distribution (Number of Distinct Values) • Number of null values • Average Length of the column value (for string columns) • Histogram • Frequent Values (MFV) 27
- 29. Performance, Benchmarking, Benchmarketing Performance matters to customers Benchmarking appeals to Engineers to drive product innovation Benchmarketing used to convey performance in a memorable and appealing way SQL over Hadoop is in the “Wild West” of Benchmarketing • 100x claims! Compared to what? Conforming to what rules? The TPC (Transaction Processing Performance Council) is the grand-daddy of all multi-vendor SQL-oriented organizations • Formed in August, 1988 • TPC-H and TPC-DS are the most relevant to SQL over Hadoop – R/W nature of workload not suitable for HDFS Big Data Benchmarking Community (BDBC) formed 28
- 30. Power and Performance of Standard SQL Everyone loves performance numbers, but that's not the whole story • How much work do you have to do to achieve those numbers? A portion of our internal performance numbers are based upon read-only versions of TPC benchmarks Big SQL is capable of executing • All 22 TPC-H queries without modification • All 99 TPC-DS queries without modification SELECT s_name, count(*) AS numwait FROM supplier, lineitem l1, orders, nation WHERE s_suppkey = l1.l_suppkey AND o_orderkey = l1.l_orderkey AND o_orderstatus = 'F' AND l1.l_receiptdate > l1.l_commitdate AND EXISTS ( SELECT * FROM lineitem l2 WHERE l2.l_orderkey = l1.l_orderkey AND l2.l_suppkey <> l1.l_suppkey) AND NOT EXISTS ( SELECT * FROM lineitem l3 WHERE l3.l_orderkey = l1.l_orderkey AND l3.l_suppkey <> l1.l_suppkey AND l3.l_receiptdate > l3.l_commitdate) AND s_nationkey = n_nationkey AND n_name = ':1' GROUP BY s_name ORDER BY numwait desc, s_name SELECT s_name, count(*) AS numwait FROM supplier, lineitem l1, orders, nation WHERE s_suppkey = l1.l_suppkey AND o_orderkey = l1.l_orderkey AND o_orderstatus = 'F' AND l1.l_receiptdate > l1.l_commitdate AND EXISTS ( SELECT * FROM lineitem l2 WHERE l2.l_orderkey = l1.l_orderkey AND l2.l_suppkey <> l1.l_suppkey) AND NOT EXISTS ( SELECT * FROM lineitem l3 WHERE l3.l_orderkey = l1.l_orderkey AND l3.l_suppkey <> l1.l_suppkey AND l3.l_receiptdate > l3.l_commitdate) AND s_nationkey = n_nationkey AND n_name = ':1' GROUP BY s_name ORDER BY numwait desc, s_name JOIN (SELECT s_name, l_orderkey, l_suppkey FROM orders o JOIN (SELECT s_name, l_orderkey, l_suppkey FROM nation n JOIN supplier s ON s.s_nationkey = n.n_nationkey AND n.n_name = 'INDONESIA' JOIN lineitem l ON s.s_suppkey = l.l_suppkey WHERE l.l_receiptdate > l.l_commitdate) l1 ON o.o_orderkey = l1.l_orderkey AND o.o_orderstatus = 'F') l2 ON l2.l_orderkey = t1.l_orderkey) a WHERE (count_suppkey > 1) or ((count_suppkey=1) AND (l_suppkey <> max_suppkey))) l3 ON l3.l_orderkey = t2.l_orderkey) b WHERE (count_suppkey is null) OR ((count_suppkey=1) AND (l_suppkey = max_suppkey))) c GROUP BY s_name ORDER BY numwait DESC, s_name JOIN (SELECT s_name, l_orderkey, l_suppkey FROM orders o JOIN (SELECT s_name, l_orderkey, l_suppkey FROM nation n JOIN supplier s ON s.s_nationkey = n.n_nationkey AND n.n_name = 'INDONESIA' JOIN lineitem l ON s.s_suppkey = l.l_suppkey WHERE l.l_receiptdate > l.l_commitdate) l1 ON o.o_orderkey = l1.l_orderkey AND o.o_orderstatus = 'F') l2 ON l2.l_orderkey = t1.l_orderkey) a WHERE (count_suppkey > 1) or ((count_suppkey=1) AND (l_suppkey <> max_suppkey))) l3 ON l3.l_orderkey = t2.l_orderkey) b WHERE (count_suppkey is null) OR ((count_suppkey=1) AND (l_suppkey = max_suppkey))) c GROUP BY s_name ORDER BY numwait DESC, s_name SELECT s_name, count(1) AS numwait FROM (SELECT s_name FROM (SELECT s_name, t2.l_orderkey, l_suppkey, count_suppkey, max_suppkey FROM (SELECT l_orderkey, count(distinct l_suppkey) as count_suppkey, max(l_suppkey) as max_suppkey FROM lineitem WHERE l_receiptdate > l_commitdate GROUP BY l_orderkey) t2 RIGHT OUTER JOIN (SELECT s_name, l_orderkey, l_suppkey FROM (SELECT s_name, t1.l_orderkey, l_suppkey, count_suppkey, max_suppkey FROM (SELECT l_orderkey, count(distinct l_suppkey) as count_suppkey, max(l_suppkey) as max_suppkey FROM lineitem GROUP BY l_orderkey) t1 SELECT s_name, count(1) AS numwait FROM (SELECT s_name FROM (SELECT s_name, t2.l_orderkey, l_suppkey, count_suppkey, max_suppkey FROM (SELECT l_orderkey, count(distinct l_suppkey) as count_suppkey, max(l_suppkey) as max_suppkey FROM lineitem WHERE l_receiptdate > l_commitdate GROUP BY l_orderkey) t2 RIGHT OUTER JOIN (SELECT s_name, l_orderkey, l_suppkey FROM (SELECT s_name, t1.l_orderkey, l_suppkey, count_suppkey, max_suppkey FROM (SELECT l_orderkey, count(distinct l_suppkey) as count_suppkey, max(l_suppkey) as max_suppkey FROM lineitem GROUP BY l_orderkey) t1 Original Query Re-written for Hive 29
- 31. 30 Comparing Big SQL and Hive 0.12 for Ad-Hoc Queries *Based on IBM internal tests comparing IBM Infosphere Biginsights 3.0 Big SQL with Hive 0.12 executing the "1TB Classic BI Workload" in a controlled laboratory environment. The 1TB Classic BI Workload is a workload derived from the TPC-H Benchmark Standard, running at 1TB scale factor. It is materially equivalent with the exception that no update functions are performed. TPC Benchmark and TPC-H are trademarks of the Transaction Processing Performance Council (TPC). Configuration: Cluster of 9 System x3650HD servers, each with 64GB RAM and 9x2TB HDDs running Redhat Linux 6.3. Results may not be typical and will vary based on actual workload, configuration, applications, queries and other variables in a production environment. Results as of April 22, 2014 Big SQL is up to 41x faster than Hive 0.12 Big SQL is up to 41x faster than Hive 0.12
- 32. 31 Comparing Big SQL and Hive 0.12 for Decision Support Queries * Based on IBM internal tests comparing IBM Infosphere Biginsights 3.0 Big SQL with Hive 0.12 executing the "1TB Modern BI Workload" in a controlled laboratory environment. The 1TB Modern BI Workload is a workload derived from the TPC-DS Benchmark Standard, running at 1TB scale factor. It is materially equivalent with the exception that no updates are performed, and only 43 out of 99 queries are executed. The test measured sequential query execution of all 43 queries for which Hive syntax was publically available. TPC Benchmark and TPC-DS are trademarks of the Transaction Processing Performance Council (TPC). Configuration: Cluster of 9 System x3650HD servers, each with 64GB RAM and 9x2TB HDDs running Redhat Linux 6.3. Results may not be typical and will vary based on actual workload, configuration, applications, queries and other variables in a production environment. Results as of April 22, 2014 Big SQL is 10x faster than Hive 0.12 (Total elapsed time) Big SQL is 10x faster than Hive 0.12 (Total elapsed time)
- 33. How many times Faster is Big SQL than Hive 0.12? * Based on IBM internal tests comparing IBM Infosphere Biginsights 3.0 Big SQL with Hive 0.12 executing the "1TB Modern BI Workload" in a controlled laboratory environment. The 1TB Modern BI Workload is a workload derived from the TPC-DS Benchmark Standard, running at 1TB scale factor. It is materially equivalent with the exception that no updats are performed, and only 43 out of 99 queries are executed. The test measured sequential query execution of all 43 queries for which Hive syntax was publically available. TPC Benchmark and TPC-DS are trademarks of the Transaction Processing Performance Council (TPC). Configuration: Cluster of 9 System x3650HD servers, each with 64GB RAM and 9x2TB HDDs running Redhat Linux 6.3. Results may not be typical and will vary based on actual workload, configuration, applications, queries and other variables in a production environment. Results as of April 22, 2014 Max Speedup of 74x Max Speedup of 74x 32 Queries sorted by speed up ratio (worst to best) Avg Speedup of 20x Avg Speedup of 20x
- 34. Big SQL 3.0 Best Practices Ensure you have a homogenous and balanced cluster • Utilize IBM reference architecture Choose an optimized file format (if possible) • ORC or Parquet Choose appropriate data types • Use the smallest and most precise datatype available Define informational constraints • Primary key, foreign key, check constraints Ensure you have good statistics • Current and comprehensive Use the full power of SQL available to you • Don’t constrain yourself to Hive syntax/capability 33
- 35. BigInsights Big SQL 3.0: Summary Big SQL provides rich, robust, standards-based SQL support for data stored in BigInsights • Uses IBM common client ODBC/JDBC drivers Big SQL fully integrates with SQL applications and tools • Existing queries run with no or few modifications* • Existing JDBC and ODBC compliant tools can be leveraged Big SQL provides faster and more reliable performance • Big SQL uses more efficient access paths to the data • Queries processed by Big SQL no longer need to use MapReduce • Big SQL is optimized to more efficiently move data over the network Big SQL provides and enterprise grade data management • Security, Auditing, workload management … 34
- 36. Questions?
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- 39. Legal Disclaimer • © IBM Corporation 2014. All Rights Reserved. • The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM’s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. 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- 41. What is Hadoop? Hadoop is not a piece of software, you can't install "hadoop" It is an ecosystem of software that work together • Hadoop Core (API's) • HDFS (File system) • MapReduce (Data processing framework) • Hive (SQL access) • HBase (NoSQL database) • Sqoop (Data movement) • Oozie (Job workflow) • …. There are is a LOT of "Hadoop" software However, there is one common component they all build on: HDFS… 40
- 42. HDFS configuration (shared-nothing cluster) NN DN Local disks DN Local disks DN Local disks DN Local disks DN Local disks DN Local disks DN Local disks DN Local disks DN Local disks DN Local disks NN = NameNode, which manages all the metadata DD = DataNode, which reads/writes the file data 41
- 43. HDFS Driving principals • Files are stored across the entire cluster • Programs are brought to the data, not the data to the program Distributed file system (DFS) stores blocks across the whole cluster • Blocks of a single file are distributed across the cluster • A given block is typically replicated for resiliency • Just like a regular file system, the contents of a file is up to the application 10110100 10100100 11100111 11100101 00111010 01010010 11001001 01010011 00010100 10111010 11101011 11011011 01010110 10010101 00101010 10101110 01001101 01110100 Logical File 1 2 3 4 Blocks 1 Cluster 1 1 2 2 2 3 3 34 4 4 42
- 44. Hadoop I/O Hadoop (HDFS) doesn't dictate file content/structure • It is just a filesystem! • It provides standard API's to list directories, open files, delete files, etc. • In particular it allows your task to ask "where does each block live?" Hadoop provides a framework for creating "splittable" data sources • A data source is typically file(s), but not necessarily • A large input is "split" into pieces, each piece to be processed in parallel • Each split indicates the host(s) on which that split can be found • For files, a split typically refers to an HDFS block, but not necessarily 10110100 10100100 11100111 11100101 00111010 01010010 11001001 01010011 00010100 10111010 11101011 11011011 01010110 10010101 1 2 3 Logical File Splits 1 Cluster 32 App App App Results 43
- 45. InputFormat This splitting process is encapsulated in the InputFormat interface • Hadoop has a large library of InputFormat's for various purposes • You can create and provided your own as well An InputFormat does the following • Configured with a set of name/value pair properties • When configured you can ask it for a list of InputSplit's – Each input split has… – A list of hosts on which the data for the split is recommended to be processed (optional) – A size in bytes (optional) • Given an InputSplit, an InputFormat can produce a RecordReader A RecordReader does the following • Acts as an input stream to read the contents of the split • Produces a stream of records • There is no fixed definition of a record – it depends upon the input type Let's look at an example of an InputFormat… 44
- 46. InputFormat example - TextInputFormat Purpose • Reads input file(s) line by line, each read produces one line of text Configuration • Configured with the names of one or more (HDFS) files to process Splits • Each split it produces represents a single HDFS block of a file RecordReader • When opened, finds the first newline of the block it is to read • Each read produces the next available line of text in the block • May read into the next block of text to ensure the last line is fully read – Even if the block is physically located on another host!! 101101 001010 010011 100111 111001 010011 101001 010010 110010 010101 101101 001010 010011 100111 111001 010011 101001 010010 110010 010101 1 2 3 Text File (logical) Splits Readers Records (lines of text)45
- 47. Hadoop MapReduce MapReduce is a way of writing parallel processing programs Built around InputFormat's (and OutputFormat's) Programs are written in two pieces: Map and Reduce Programs are submitted to the MapReduce job scheduler: JobTracker • The JobTracker asks for the InputFormat splits • For each split, tries to schedule the processing on a host on which the split lives • Hosts are chosen based upon available processing resources Program is shipped to a host and given a split to process Output of the program is written back to HDFS 46
- 48. MapReduce - Mappers Mappers • Small program (typically), distributed across the cluster, local to data • Handed a portion of the input data (called a split) • Each mapper parses, filters, and/or transforms its input • Produces grouped <key,value> pairs 10110100 10100100 11100111 11100101 00111010 01010010 11001001 01010011 00010100 10111010 11101011 11011011 01010110 10010101 00101010 10101110 01001101 01110100 Logical Input File 1 2 3 4 1 map sort 2 map sort 3 map sort 4 map sort reduce reduce copy merge merge 10110100 10100100 11100111 11100101 00111010 01010010 11001001 10110100 10100100 11100111 11100101 00111010 01010010 11001001 Logical Output File Logical Output File To DFS To DFS Map Phase 47
- 49. MapReduce – The Shuffle The shuffle is transparently orchestrated by MapReduce The output of each mapper is locally grouped together by key One node is chosen to process data for each unique key Shuffle 10110100 10100100 11100111 11100101 00111010 01010010 11001001 01010011 00010100 10111010 11101011 11011011 01010110 10010101 00101010 10101110 01001101 01110100 1 2 3 4 1 map sort 2 map sort 3 map sort 4 map sort reduce reduce copy merge merge 10110100 10100100 11100111 11100101 00111010 01010010 11001001 10110100 10100100 11100111 11100101 00111010 01010010 11001001 Logical Output File Logical Output File To DFS To DFS 48
- 50. MapReduce – Reduce Phase Reducers • Small programs (typically) that aggregate all of the values for the key that they are responsible for • Each reducer writes output to its own file Reduce Phase 10110100 10100100 11100111 11100101 00111010 01010010 11001001 01010011 00010100 10111010 11101011 11011011 01010110 10010101 00101010 10101110 01001101 01110100 1 2 3 4 1 map sort 2 map sort 3 map sort 4 map sort reduce reduce copy merge merge 10110100 10100100 11100111 11100101 00111010 01010010 11001001 10110100 10100100 11100111 11100101 00111010 01010010 11001001 Logical Output File Logical Output File To DFS To DFS 49
- 51. Joins in MapReduce Hadoop is used to group data together at the same reducer based upon the join key • Mappers read blocks from each “table” in the join • The <key> is the value of the join key, the <value> is the record to be joined • Reducer receives a mix of records from each table with the same join key • Reducers produce the results of the join reduce dept 1 reduce dept 2 reduce dept 3 1011010 0101001 0011100 1111110 0101001 1010111 0111010 1 1 map 2 map 2 1 map employees 1011010 0101001 0011110 0111011 1 depts select e.fname, e.lname, d.dept_name from employees e, depts d where e.salary > 30000 and d.dept_id = e.dept_id select e.fname, e.lname, d.dept_name from employees e, depts d where e.salary > 30000 and d.dept_id = e.dept_id 50
- 52. Joins in MapReduce (cont.) For N-way joins involving different join keys, multiple jobs are used reduce dept 1 reduce dept 2 reduce dept 3 1011010 0101001 0011100 1111110 0101001 1010111 0111010 1 1 map 2 map 2 1 map employees 1011010 0101001 0011110 0111011 1 select e.fname, e.lname, d.dept_name, p.phone_type, p.phone_number from employees e, depts d, emp_phones p where e.salary > 30000 and d.dept_id = e.dept_id and p.emp_id = e.emp_id select e.fname, e.lname, d.dept_name, p.phone_type, p.phone_number from employees e, depts d, emp_phones p where e.salary > 30000 and d.dept_id = e.dept_id and p.emp_id = e.emp_id depts 1011010 0101001 0011100 1111110 0101001 1010111 0111010 1 2 1011010 0101001 0011110 0111011 1 1011010 0101001 0011100 1111110 0101001 1010111 0111010 1 2 1011010 0101001 0011100 1111110 0101001 1010111 0111010 1 2 emp_phones (temp files) 1 map 2 map 1 map 1 map 2 map 1 map 2 map reduce dept 1 reduce emp_id 1 reduce emp_id 2 reduce emp_id N results results results 51