This document discusses the evolution of SQL on Hadoop. There are two types of SQL engines on Hadoop - Type I engines that compile SQL to MapReduce, and Type II engines that use their own distributed execution engines. Early engines like Hive were Type I and had limitations due to MapReduce. Newer engines like Impala, Presto, Spark SQL, and IBM BigSQL are Type II and can perform SQL queries directly on Hadoop files without MapReduce. IBM BigSQL provides ANSI SQL with performance optimized for big data by leveraging decades of database query optimization experience.

1. © 2013 IBM Corporation1
SQL on Hadoop - 12th Swiss Big Data User Group
Meeting, 3rd of July, 2014, ETH Zurich
Romeo Kienzler
IBM Center of Excellence for Data Science, Cognitive Systems and BigData
(A joint-venture between IBM Research Zurich and IBM Innovation Center DACH)
Source: http://www.kdnuggets.com/2012/04/data-science-history.jpg

2. © 2013 IBM Corporation2
DataScience at present
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Tools (http://blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html)
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SQL (42%)
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R (33%)
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Python (26%)
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Excel (25%)
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Java, Ruby, C++ (17%)
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SPSS, SAS (9%)
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Limitations (Single Node usage)
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Main Memory
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CPU <> Main Memory Bandwidth
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CPU
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Storage <> Main Memory Bandwidth (either Single node or SAN)

3. © 2013 IBM Corporation3
Data Science on Hadoop
SQL (42%)
R (33%)
Python (26%)
Excel (25%)
Java, Ruby, C++ (17%)
SPSS, SAS (9%)
Data Science Hadoop

4. © 2013 IBM Corporation4
SQL on Hadoop
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IBM BigSQL (ANSI 2011 compliant, part of IBM BigInsights)
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HIVE, Presto
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Cloudera Impala
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Lingual
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Shark
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...
SQL Hadoop

5. © 2013 IBM Corporation5
Two types of SQL Engines
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Type I
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Compiler and Optimizer SQL->MapReduce
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Type II
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Brings own distributed execution engine on Data Nodes
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Brings own Task Scheduler
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The Hadoop SQL Ecosystem is evolving very fast

6. © 2013 IBM Corporation6
Hive
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Runs on top of MapReduce
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→ Type I
Source: http://cdn.venublog.com/wp-content/uploads/2013/07/hive-1.jpg

7. © 2013 IBM Corporation7
Lingual
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ANSI SQL Layer on top of Cascading
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Cascading
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Java API do express DAG
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Runs on top of MapReduce
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→ Type I

8. © 2013 IBM Corporation8
Limits of MapReduce
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Disk writes between Map and Reduce
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Slow for computations which depend on previously computed values
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JOINs are very slow and difficult to implement
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Only sequential data access
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Only tuple-wise data access
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Map-Side joins have sort and size constraints
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Reduce-Side joins require secondary sorting of values
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…
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...

9. © 2013 IBM Corporation9
Impala (Type II)
http://blog.cloudera.com/blog/wp-content/uploads/2012/10/impala.png

10. © 2013 IBM Corporation10
Presto (Type II)
https://www.facebook.com/notes/facebook-engineering/presto-interacting-with-petabytes-of-data-at-facebook/10151786197628920

11. © 2013 IBM Corporation11
Spark / Shark (Type II)
Source: http://bighadoop.files.wordpress.com/2014/04/spark-architecture.png

12. © 2013 IBM Corporation12
BigSQL V3.0 (Type II)
Like in Spark, MapReduce has been Kicked out :)
(No JobTracker, No Task Tracker, But HDFS/GPFS remains)

13. © 2013 IBM Corporation13
BigSQL V3.0 – Architecture
Putting the story together….
Big SQL shares a common SQL dialect with DB2
Big SQL shares the same client drivers with DB2

14. © 2013 IBM Corporation14
BigSQL V3.0 – 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
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

15. © 2013 IBM Corporation15
BigSQL V3.0 – Performance
You are substantially faster if you don't use MapReduce
IBM BigInsights v3.0, with Big SQL
3.0, is the only Hadoop distribution
to successfully run ALL 99 TPC-DS
queries and ALL 22 TPC-H queries
without modification. Source:
http://www.ibmbigdatahub.com/blog/big-deal-about-
infosphere-biginsights-v30-big-sql

16. © 2013 IBM Corporation16
BigSQL V3.0 – Query Federation
Head Node
Big SQL
Compute Node
Task Tracker Data Node Big
SQL
Compute Node
Task Tracker Data Node
Big
SQL
Compute Node
Task Tracker Data Node
Big
SQL
Compute Node
Task Tracker Data Node
Big
SQL

17. © 2013 IBM Corporation17
BigSQL V1.0 – Demo (small)
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32 GB Data, ~650.000.000 rows (small, Innovation Center Zurich)
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3 TB Data, ~ 60.937.500.000 rows (middle, Innovation Center Zurich)
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0.7 PB Data, ~ 1.421875×10¹³ rows (large, Innovation Center Hursley)
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32 GB Data, ~650.000.000 rows (small, Innovation Center Zurich)
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3 TB Data, ~ 60.937.500.000 rows (middle, Innovation Center Zurich)
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0.7 PB Data, ~ 1.421875×10¹³ rows (large, Innovation Center Hursley)

18. © 2013 IBM Corporation18
BigSQL V1.0 – Demo (small)
CREATE EXTERNAL TABLE trace (
hour integer, employeeid integer,
departmentid integer, clientid integer,
date string, timestamp string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LINES
TERMINATED BY 'n' STORED AS TEXTFILE LOCATION
'/user/biadmin/32Gtest';

19. © 2013 IBM Corporation19
BigSQL V1.0 – Demo (small)

20. © 2013 IBM Corporation20
BigSQL V1.0 – Demo (small)

21. © 2013 IBM Corporation21
BigSQL V1.0 – Demo (small)
[bivm.ibm.com][biadmin] 1> select count(*) from trace1;
+----------+
| |
+----------+
| 11416740 |
+----------+
1 row in results(first row: 39.78s; total: 39.78s)

22. © 2013 IBM Corporation22
BigSQL V1.0 – Demo (small)
select count(hour), hour from trace group by hour order by hour
30 rows in results(first row: 37.98s; total: 37.99s)

23. © 2013 IBM Corporation23
BigSQL V1.0 – Demo (small)
[bivm.ibm.com][biadmin] 1> select count(*) from trace1 t3 inner
join trace2 t4 on t3.hour=t4.hour;
+--------+
| |
+--------+
| 477340 |
+--------+
1 row in results(first row: 32.24s; total: 32.25s)

24. © 2013 IBM Corporation24
BigSQL V3.0 – Demo (small)
CREATE HADOOP TABLE trace3 (
hour int, employeeid int,
departmentid int,clientid int,
date varchar(30), timestamp varchar(30) )
row format delimited
fields terminated by '|'
stored as textfile;

25. © 2013 IBM Corporation25
BigSQL V3.0 – Demo (small)
[bivm.ibm.com][biadmin] 1> select count(*) from trace3;
+----------+
| 1 |
+----------+
| 12014733 |
+----------+
1 row in results(first row: 2.94s; total: 2.95s)

26. © 2013 IBM Corporation26
BigSQL V3.0 – Demo (small)
[bivm.ibm.com][biadmin] 1> select count(*) from trace3 t3 inner
join trace4 t4 on t3.hour=t4.hour;
+--------+
| 1 |
+--------+
| 504360 |
+--------+
1 row in results(first row: 0.79s; total: 0.80s)

27. © 2013 IBM Corporation27
BigSQL V3.0 – Demo (small)
[bivm.ibm.com][biadmin] 1> select count(hour), hour from trace3
group by hour order by hour;
29 rows in results(first row: 1.88s; total: 1.89s)

28. © 2013 IBM Corporation28
Questions?
http://www.ibm.com/software/data/bigdata/
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ibm.co/quickstart
Twitter: @RomeoKienzler, @IBMEcosystem_DE, @IBM_ISV_Alps