© 2014 IBM Corporation

Analyzing SQL on Hadoop

3.03.0

Big!

SQLUday Kale (udayk@us.ibm.com)Big SQL Development, IBM InfoSphere BigInsights

18-Sep-2014

Please Note

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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.

Agenda

Why SQL on Hadoop?

SQL-on-Hadoop landscape

Big SQL 3.0• What is it?• SQL capabilities• Architecture

Conclusion

3

Why SQL for Hadoop?

Hadoop is designed for any data• Doesn't impose any structure• Extremely flexible

At lowest levels is API based• Requires strong programming

expertise

• Steep learning curve• Even simple operations can be

tedious

Yet many, if not most, use cases deal with structured data!• e.g. aging old warehouse data into queriable archive

Why not use SQL in places its strengths shine?• Familiar widely used syntax• Separation of what you want vs. how to get it• Robust ecosystem of tools

Pre-Processing Hub Query-able Archive Exploratory Analysis

Information Integration

Data Warehouse

StreamsReal-time processing

BigInsightsLanding zone

for all data

Data Warehouse

BigInsights Can combine with

unstructured information

Data Warehouse

1 2 3

SQL-on-Hadoop landscape

The SQL-on-Hadoop landscape is changing rapidly!

They all have their different strengths and weaknesses

Many, including Big SQL, draw their basic designs on Hive…

Agenda

Why SQL on Hadoop?

SQL-on-Hadoop landscape

Big SQL 3.0• What is it?• SQL capabilities• Architecture

Conclusion

6

BigInsights Enterprise Edition

Connectivity and Integration

StreamsNetezza

Text processing engine and library

JDBC

Flume

Infrastructure Jaql

Hive

Pig

HBase

MapReduce

HDFS

ZooKeeperLucene

Adaptive MapReduce

Oozie

Text compression

Enhanced security

Flexible scheduler

Optional IBM and partner offerings

Analytics and discovery “Apps”

DB2

BigSheets

Web Crawler

Distrib file copy

DB export

Boardreader

DB import

Ad hoc query

Machine learning

Data processing

. . .

Administrative and development tools

Web console

• Monitor cluster health, jobs, etc. • Add / remove nodes• Start / stop services• Inspect job status• Inspect workflow status• Deploy applications • Launch apps / jobs • Work with distrib file system•Work with spreadsheet interface•Support REST-based API • Create / view alerts • . . .

Big R

Eclipse tools

• Text analytics• MapReduce programming• Jaql, Hive, Pig development• BigSheets plug-in development• Oozie workflow generation

Integrated installer

Open Source IBM IBM

Cognos BI

Big SQL

Accelerator for machine data analysis

Accelerator for social data analysis

Guardium DataStageData Explorer

Sqoop

HCatalogGPFS –FPOGPFS –FPO

Solr

Big SQL 3.0 – At a glance

Application Portability & Integration

Data shared with Hadoop ecosystem

Comprehensive file format support

Superior enablement of IBM software

Enhanced by Third Party software

Performance

Modern MPP runtime

Powerful SQL query rewriter

Cost based optimizer

Optimized for concurrent user throughput

Results not constrained by memory

Federation

Distributed requests to multiple data sources within a single SQL statement

Main data sources supported:DB2 LUW, DB2/z, Teradata, Oracle,

Netezza

Enterprise Features

Advanced security/auditing

Resource and workload management

Self tuning memory management

Comprehensive monitoring

Rich SQLComprehensive SQL Support

IBM SQL PL compatibility

Creating tables in Big SQL

Big SQL syntax is derived from Hive's syntax with extensions

create hadoop table users( id int not null primary key, office_id int null, fname varchar(30) not null, lname varchar(30) not null, salary timestamp(3) null, constraint fk_ofc foreign key (office_id) references office (office_id))row format delimited fields terminated by '|'stored as textfile;

Creating tables in Big SQL

Big SQL syntax is derived from Hive's syntax with extensions

create hadoop table users( id int not null primary key, office_id int null, fname varchar(30) not null, lname varchar(30) not null, salary timestamp(3) null, constraint fk_ofc foreign key (office_id) references office (office_id))row format delimited fields terminated by '|'stored as textfile; Hadoop Keyword

• Big SQL requires the HADOOP keyword• Big SQL has internal traditional RDBMS table support

• Stored only at the head node• Does not live on HDFS• Supports full ACID capabilities• Not usable for "big" data

• The HADOOP keyword identifies the table as living on HDFS

Hadoop Keyword• Big SQL requires the HADOOP keyword• Big SQL has internal traditional RDBMS table support

• Stored only at the head node• Does not live on HDFS• Supports full ACID capabilities• Not usable for "big" data

• The HADOOP keyword identifies the table as living on HDFS

Creating tables in Big SQL

Big SQL syntax is derived from Hive's syntax with extensions

create hadoop table users( id int not null primary key, office_id int null, fname varchar(30) not null, lname varchar(30) not null, salary timestamp(3) null, constraint fk_ofc foreign key (office_id) references office (office_id))row format delimited fields terminated by '|'stored as textfile; Nullability Indicators

• Enforced on read• Used by query optimizer for smarter rewrites

Nullability Indicators• Enforced on read• Used by query optimizer for smarter rewrites

Creating tables in Big SQL

Big SQL syntax is derived from Hive's syntax with extensions

create hadoop table users( id int not null primary key, office_id int null, fname varchar(30) not null, lname varchar(30) not null, salary timestamp(3) null, constraint fk_ofc foreign key (office_id) references office (office_id))row format delimited fields terminated by '|'stored as textfile;

Constraints• Unenforced• Useful as documentation and to drive query builders• Used by query optimizer for smarter rewrites

Constraints• Unenforced• Useful as documentation and to drive query builders• Used by query optimizer for smarter rewrites

Table types

Big SQL supports many of the "standard" Hadoop storage formats• Text delimited• Text delimited sequence files• Binary delimited sequence files• Parquet• RC• ORC• Avro

Each has different features/advantages/disadvantages

Custom file formats may be supported as well via custom java classes

Populating Big SQL tables

There are a number of ways to populate tables

Tables can be defined against existing data• All validation is performed at query time

Rows can be directly inserted into tables

• Data is validated and converted to storage format • Only suitable for testing• Produces one physical data file per call to INSERT

create external hadoop table csv_data( c1 int not null primary key, c2 varchar(20) null)row format delimited fields terminated by ','stored as textfilelocation '/user/bob/csv_data'

insert into t1 values (5, 'foo'), (6, 'bar'), (7, 'baz')

Populating Big SQL tables (cont.)

Tables can be populated from other tables

Tables can be created from other tables• Great way to convert between storage types or partition data

insert into top_sellersselect employee_id, rank() over (order by sales) from ( select employee_id, sum(sales) sales from product_sales group by employee_id )limit 10;

create hadoop table partitioned_salespartitioned by (dept_id int not null)stored as rcfileasselect emp_id, prod_id, qty, cost, dept_id from sales

Populating Big SQL tables (cont.)

The LOAD HADOOP is used to populate Hadoop tables from an external data source

• Statement runs on the cluster – cannot access data at the client• Nodes of the cluster ingest data in parallel• Performs data validation during load• Performs data conversion (to storage format) during load

Supports the following sources of data• Any valid Hadoop URL (e.g. hdfs://, sftp://, etc.)

• JDBC data sources (e.g. Oracle, DB2, Netezza, etc.)

Loading from URL

Data may be loaded from delimited files read via any valid URL• If no URI specified is provided, HDFS is assumed:

Example loading via SFTP:

Just remember LOAD HADOOP executes on the cluster• So file:// will be local to the node chosen to run the statement

LOAD HADOOP USING FILE URL '/user/biadmin/mydir/abc.csv' INTO TABLE T1WITH SOURCE PROPERTIES( 'field.delimiter'=',', 'date.time.format'=''yyyy-MM-dd-HH.mm.ss.S')

LOAD HADOOP USING FILE URL sftp://biadmin:biadmin@myserver.abc.com:22/home/biadmin/mydir'

LOAD HADOOP USING FILE URL file:///path/to/myfile/file.csv'INTO TABLE T1

Loading from JDBC data source

A JDBC URL may be used to load directly from external data source• Tested internally against Oracle, Teradata, DB2, and Netezza

It supports many options to partition the extraction of data • Providing a table and partitioning column• Providing a query and a WHERE clause to use for partitioning

Example usage:

LOAD USING JDBC CONNECTION URL 'jdbc:db2://myhost:50000/SAMPLE' WITH PARAMETERS ( user = 'myuser', password='mypassword' ) FROM TABLE STAFF WHERE "dept=66 and job='Sales'" INTO TABLE staff_sales PARTITION ( dept=66 , job='Sales') APPEND WITH LOAD PROPERTIES (bigsql.load.num.map.tasks = 1) ;

SQL capabilities

Leverage IBM's rich SQL heritage, expertise, and technology• Modern SQL:2011 capabilities• DB2 compatible SQL PL support

– SQL bodied functions– SQL bodied stored procedures– Robust error handling– Application logic/security encapsulation– Flow of control logic

The same SQL you use on your data warehouse should run with few or no modifications

SQL capability highlights

Full support for subqueries• In SELECT, FROM, WHERE and HAVING• Correlated and uncorrelated • Equality, non-equality subqueries • EXISTS, NOT EXISTS, IN, ANY, SOME, etc.

All standard join operations• Standard and ANSI join syntax

• Inner, outer, and full outer joins• Equality, non-equality, cross join support• Multi-value join (WHERE (c1, c2) = …)• UNION, INTERSECT, EXCEPT

SELECT s_name, count(*) AS numwaitFROM supplier, lineitem l1, orders, nationWHERE 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_nameORDER BY numwait desc, s_name;

SELECT s_name, count(*) AS numwaitFROM supplier, lineitem l1, orders, nationWHERE 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_nameORDER BY numwait desc, s_name;

SQL capability highlights (cont.)

Extensive analytic capabilities• Grouping sets with CUBE and ROLLUP• Standard OLAP operations

•

•

• Analytic aggregates

LEAD LAG RANK DENSE_RANK

ROW_NUMBER RATIO_TO_REPORT FIRST_VALUE LAST_VALUE

CORRELATION COVARIANCE STDDEV VARIANCE

REGR_AVGX REGR_AVGY REGR_COUNT REGR_INTERCEPT

REGR_ICPT REGR_R2 REGR_SLOPE REGR_XXX

REGR_SXY REGR_XYY WIDTH_BUCKET VAR_SAMP

VAR_POP STDDEV_POP STDDEV_SAMP COVAR_SAMP

COVAR_POP NTILE

Architected for performance

Architected from the ground up for low latency and high throughput

MapReduce replaced with a modern MPP architecture• Compiler and runtime are native code (not java)• Big SQL worker daemons live directly on cluster

– Continuously running (no startup latency)– Processing happens locally at the data

• Message passing allows data to flow directly between nodes

Operations occur in memory with the ability to spill to disk

• Supports aggregations and sorts larger than available RAM

InfoSphere BigInsights

Big SQL

SQL MPP Runtime

Data Sources

Parquet CSV Seq RC

Avro ORC JSON Custom

SQL-basedApplication

IBM Data Server Client

Big SQL 3.0 – Architecture

23

Management Node

Big SQLMaster Node

Management Node

Big SQLScheduler

Big SQLWorker Node

JavaI/O

FMP

NativeI/O

FMP

HDFS Data Node

MRTask Tracker

Other ServiceHDFS

Data HDFSData HDFS

Data

TempData

UDF FMP

Compute Node

Database Service

Hive Metastore

Hive Server

Big SQLWorker Node

JavaI/O

FMP

NativeI/O

FMP

HDFS Data Node

MRTask Tracker

Other ServiceHDFS

Data HDFSData HDFS

Data

TempData

UDF FMP

Compute Node

Big SQLWorker Node

JavaI/O

FMP

NativeI/O

FMP

HDFS Data Node

MRTask Tracker

Other ServiceHDFS

Data HDFSData HDFS

Data

TempData

UDF FMP

Compute Node

DDLFMP

UDF FMP

*FMP = Fenced mode process

Scheduler Service

The Scheduler is the main RDBMS↔Hadoop service interface

Interfaces with Hive metastore for table metadata• Compiler ask it for some "hadoop" metadata, such as partitioning

columns

Acts like the MapReduce job tracker for Big SQL• Big SQL provides query predicates for scheduler to perform

partition elimination• Determines splits for each “table” involved in the query• Schedules splits on available Big SQL nodes

(favoring scheduling locally to the data)• Serves work (splits) to I/O engines• Coordinates “commits” after INSERTs

Management Node

Big SQLMaster Node

Big SQLScheduler

DDLFMP

UDF FMP

Mgmt Node

Database Service

Hive Metastore

Big SQLWorker Node

JavaI/O

FMP

NativeI/O

FMP

HDFS Data Node

MRTask TrackerUDF

FMP

I/O Processing

Native I/O FMP• The high-speed interface for common file formats• Delimited, Parquet, RC, Avro, and Sequencefile

Java I/O FMP• Handles all other formats via standard Hadoop/Hive API’s

Both perform multi-threaded direct I/O on local data

The database engine understands storage format capabilities• Projection list is pushed into I/O format• Predicates are pushed as close to the data as

possible (into storage format, if possible)• Predicates that cannot be pushed down are

evaluated within the database engine

The database engine is only aware of which nodesneed to read

• Scheduler directs the readers to their portion of work

Big SQLWorker Node

JavaI/O

FMP

NativeI/O

FMP

HDFS Data Node

MRTask Tracker

Other ServiceHDFS

Data HDFSData HDFS

Data

TempData

UDF FMP

Compute Node

Conclusion

Today, it seems, performance numbers are the name of the game

But in reality there is so much more…• How rich is the SQL?• How difficult is it to (re-)use your existing SQL?• How secure is your data?• Is your data still open for other uses on Hadoop?• Can your queries span your enterprise?• Can other Hadoop workloads co-exist in harmony?• …

With Big SQL 3.0 performance doesn't mean compromise

Questions?

Useful links• BigInsights Information Center• Big SQL technology preview (cloud demo)• BigInsights Trial VM Image• “IBM Analytics for Hadoop” Big Data Service on Bluemix

https://ace.ng.bluemix.net/#/solutions/solution=big_data

• Big SQL Tutorial and videos

https://developer.ibm.com/hadoop/docs/tutorials/big-sql-hadoop-tutorial/

https://developer.ibm.com/hadoop/videos/category/sql-on-hadoop-live-events/

http://www.ibm.com/support/knowledgecenter/SSPT3X_3.0.0/com.ibm.swg.im.infosphere.biginsights.tut.doc/doc/tut_intro_bigsql.html

https://developer.ibm.com/bluemix/2014/08/26/hands-on-with-hadoop-in-minutes/

• BigInsights Quickstart Edition (includes Big SQL)– Tutorial Video:

https://developer.ibm.com/hadoop/videos/getting-started-biginsights-quick-start-edition-single-node/

BACKUP SLIDES

PerformanceQuery 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.PRODKEY 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

Access plan generationQuery transformation

Dozens of 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

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

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 10x faster than Hive 0.12

(total workload elapsed time)

Big SQL 10x faster than Hive 0.12

(total workload elapsed time)

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

32

Queries sorted by speed up ratio (worst to best)

Max Speedup 74xMax Speedup 74x

Avg Speedup 20xAvg Speedup 20x