OBIEE, Hadoop and Big Data Analysis (ODTUG KScope'14)

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OBIEE, Hadoop and Big Data Analysis 
Mark Rittman, CTO, Rittman Mead
ODTUG KScope’14, Seattle, June 2014

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About the Speaker
•Mark Rittman, Co-Founder of Rittman Mead
•Oracle ACE Director, specialising in Oracle BI&DW
•14 Years Experience with Oracle Technology
•Regular columnist for Oracle Magazine
•Author of two Oracle Press Oracle BI books
•Oracle Business Intelligence Developers Guide
•Oracle Exalytics Revealed
•Writer for Rittman Mead Blog : 
http://www.rittmanmead.com/blog
•Email : mark.rittman@rittmanmead.com
•Twitter : @markrittman

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About Rittman Mead
•Oracle BI and DW Gold partner
•Winner of five UKOUG Partner of the Year awards in 2013 - including BI
•World leading specialist partner for technical excellence,  
solutions delivery and innovation in Oracle BI
•Approximately 80 consultants worldwide
•All expert in Oracle BI and DW
•Offices in US (Atlanta), Europe, Australia and India
•Skills in broad range of supporting Oracle tools:
‣OBIEE, OBIA
‣ODIEE
‣Essbase, Oracle OLAP
‣GoldenGate
‣Endeca

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New in OBIEE 11.1.1.7 : Hadoop Connectivity through Hive
•OBIEE 11.1.1.7 can now access Hadoop environments, through “Hive”
•Hive is a query environment over Hadoop/MapReduce to support SQL-like queries
•Hive server accepts HiveQL queries via HiveODBC or HiveJDBC, automatically 
creates MapReduce jobs against data previously loaded into the Hive HDFS tables
•Approach used by ODI and OBIEE to gain access to Hadoop data
•Allows Hadoop data to be accessed just like any other data source

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Hadoop, and the Big Data Ecosystem
•Apache Hadoop is one of the most well-known Big Data technologies
‣Family of open-source products used to store, and analyze distributed datasets
‣Hadoop is the enabling framework, automatically parallelises and co-ordinates jobs
‣MapReduce is the programming framework  
for filtering, sorting and aggregating data
‣Map : filter data and pass on to reducers
‣Reduce : sort, group and return results
‣MapReduce jobs can be written in any 
language (Java etc), but it is complicated
•Can be used as an extension of the DW staging layer - cheap processing & storage
•And there may be data stored in Hadoop that our BI users might benefit from

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Oracle’s Big Data Products
•Oracle Big Data Appliance - Engineered System for Big Data Acquisition and Processing
‣Cloudera Distribution of Hadoop
‣Cloudera Manager
‣Open-source R
‣Oracle NoSQL Database Community Edition
‣Oracle Enterprise Linux + Oracle JVM
•Oracle Big Data Connectors
‣Oracle Loader for Hadoop (Hadoop > Oracle RBDMS)
‣Oracle Direct Connector for HDFS (HFDS > Oracle RBDMS)
‣Oracle Data Integration Adapter for Hadoop
‣Oracle R Connector for Hadoop
‣Oracle NoSQL Database (column/key-store DB based on BerkeleyDB)

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BigDataLite Demonstration VM
•Demo / Training VM downloadable from OTN
•Contains Cloudera Hadoop 4.5 + Oracle Big Data Connectors
•Similar to setup on Oracle BDA
•Contains OBIEE enabling technologies:
‣Apache Hive (SQL access over Hadoop)
‣Apache HDFS (file storage)
‣Oracle Direct Connector for HDFS
‣Oracle R Advanced Analytics for Hadoop
•Great way to get started with Hadoop
‣Requires 8GB RAM, modern laptop etc

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HDFS: Low-Cost, Clustered, Fault-Tolerant Storage
•The filesystem behind Hadoop, used to store data for Hadoop analysis
‣Unix-like, uses commands such as ls, mkdir, chown, chmod
•Fault-tolerant, with rapid fault detection and recovery
•High-throughput, with streaming data access and large block sizes
•Designed for data-locality, placing data closed to where it is processed
•Accessed from the command-line, via internet (hdfs://), GUI tools etc
[oracle@bigdatalite mapreduce]$ hadoop fs -mkdir /user/oracle/my_stuff
[oracle@bigdatalite mapreduce]$ hadoop fs -ls /user/oracle
Found 5 items
drwx------ - oracle hadoop 0 2013-04-27 16:48 /user/oracle/.staging
drwxrwxrwx - oracle hadoop 0 2012-09-18 17:02 /user/oracle/moviedemo
drwxrwxrwx - oracle hadoop 0 2012-10-17 15:58 /user/oracle/moviework
drwxr-xr-x - oracle hadoop 0 2013-05-03 17:49 /user/oracle/my_stuff
drwxr-xr-x - oracle hadoop 0 2012-08-10 16:08 /user/oracle/stage

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Hive as the Hadoop “Data Warehouse”
•MapReduce jobs are typically written in Java, but Hive can make this simpler
•Hive is a query environment over Hadoop/MapReduce to support SQL-like queries
•Hive server accepts HiveQL queries via HiveODBC or HiveJDBC, automatically 
creates MapReduce jobs against data previously loaded into the Hive HDFS tables
•Approach used by ODI and OBIEE 
to gain access to Hadoop data
•Allows Hadoop data to be accessed just like  
any other data source (sort of...)

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How Hive Provides SQL Access over Hadoop
•Hive uses a RBDMS metastore to hold 
table and column definitions in schemas
•Hive tables then map onto HDFS-stored files
‣Managed tables
‣External tables
•Oracle-like query optimizer, compiler, 
executor
•JDBC and OBDC drivers, 
plus CLI etc
Hive Driver 
(Compile  
Optimize, Execute)
Managed Tables
/user/hive/warehouse/
External Tables
/user/oracle/
/user/movies/data/
HDFS
HDFS or local files  
loaded into Hive HDFS 
area, using HiveQL 
CREATE TABLE 
command
HDFS files loaded into HDFS 
using external process, then 
mapped into Hive using 
CREATE EXTERNAL TABLE 
command
Metastore

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Transforming HiveQL Queries into MapReduce Jobs
•HiveQL queries are automatically translated into Java MapReduce jobs
•Selection and filtering part becomes Map tasks
•Aggregation part becomes the Reduce tasks
SELECT a, sum(b) 
FROM myTable 
WHERE a<100 
GROUP BY a
Map 
Task
Map 
Task
Map 
Task
Reduce 
Task
Reduce 
Task
Result

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Leveraging Hadoop with OBIEE 11g 
Demonstration of Hive

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•Where many organisations are going:
•Traditional DW at core of strategy
•Making increasing use of low-cost,  
cloud/big data tech for storage /  
pre-processing
•Access to non-traditional data sources, 
usually via ETL in to the DW
•Federated data access through 
OBIEE connectivity & metadata layer
DW 2013: The Mixed Architecture with Federated Queries

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Oracle Business Analytics and Big Data Sources
•OBIEE 11g can also make use of big data sources
‣OBIEE 11.1.1.7+ supports Hive/Hadoop as a data source
‣Oracle R Enterprise can expose R models through DB functions, columns
‣Oracle Exalytics has InfiniBand connectivity to Oracle BDA
•Endeca Information Discovery can analyze unstructured and semi-structured sources
‣Increasingly tighter-integration between 
OBIEE and Endeca

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OBIEE 11g and Hadoop/Big Data Access
•Two main scenarios for OBIEE 11g accessing “big data” sources
1. Through the data warehouse - no different to any other data provided through the DW
2. Directly - through OBIEE 11.1.1.7+ Hadoop/Hive connectivity
1 2

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Importing Hadoop/Hive Metadata into RPD
•HiveODBC driver has to be installed into Windows environment, so that  
BI Administration tool can connect to Hive and return table metadata
•Import as ODBC datasource, change physical DB type to Apache Hadoop afterwards
•Note that OBIEE queries cannot span >1 Hive schema (no table prefixes)
1
2
3

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Set up ODBC Connection at the OBIEE Server
•OBIEE 11.1.1.7+ ships with HiveODBC drivers, need to use 7.x versions though (only Linux
supported)
•Configure the ODBC connection in odbc.ini, name needs to match RPD ODBC name
•BI Server should then be able to connect to the Hive server, and Hadoop/MapReduce
[ODBC Data Sources] 
AnalyticsWeb=Oracle BI Server 
Cluster=Oracle BI Server 
SSL_Sample=Oracle BI Server 
bigdatalite=Oracle 7.1 Apache Hive Wire Protocol
[bigdatalite] 
Driver=/u01/app/Middleware/Oracle_BI1/common/ODBC/ 
Merant/7.0.1/lib/ARhive27.so 
Description=Oracle 7.1 Apache Hive Wire Protocol 
ArraySize=16384 
Database=default 
DefaultLongDataBuffLen=1024 
EnableLongDataBuffLen=1024 
EnableDescribeParam=0 
Hostname=bigdatalite 
LoginTimeout=30 
MaxVarcharSize=2000 
PortNumber=10000 
RemoveColumnQualifiers=0 
StringDescribeType=12 
TransactionMode=0 
UseCurrentSchema=0

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Demonstration of OBIEE 11.1.1.7 accessing Hadoop 
through Hive Connectivity

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Dealing with Hadoop / Hive Latency Option 1 : Exalytics
•Hadoop access through Hive can be slow - due to inherent latency in Hive
•Hive queries use MapReduce in the background to query Hadoop
•Spins-up Java VM on each query
•Generates MapReduce job
•Runs and collates the answer
•Great for large, distributed queries ...
•... but not so good for “speed-of-thought” dashboards
•So what if we could use Exalytics to speed-up Hadoop queries?

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Oracle Exalytics In-Memory Machine
•Engineered system, complements Oracle Exadata Database Machine (but can work standalone)
•Combination of high-end hardware (Sun x86_64 architecture, 3RU rack-mountable, 1-2TB RAM) 
and optimized versions of Oracle’s BI, In-Memory Database and OLAP software
•Delivers “in-memory analytics” focusing on analysis, aggregation and UI
‣Rich, interactive dashboards with split-second response times
‣1-2TB (and now 4TB) of RAM, to run your analysis in-memory
‣Infiniband connection to Exadata and Oracle BDA
‣40 CPU cores (and now 128) to support high user numbers
‣Lower TCO through known configuration,  
combined patch sets
‣Contains software features only licensable through 
Exalytics package

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Exalytics as the Query Performance Enhancer
•In conjunction with a well-tuned data warehouse, Exalytics adds an in-memory analysis layer
•Based around Oracle TimesTen for Exalytics, Oracle’s In-Memory Database
•Aggregates are recommended based on query patterns, automatically created in TimesTen
•Summary Advisor makes recommendations, which adapt as queries change
•Meant to be “plug-and-play” - no need for  
expensive data warehouse tuning
•So can we use this for speeding-up Hadoop/Hive queries?
TimesTen BI Server
Exalytics
Aggregates
Data Warehouse
Detail-level 
Data

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Summary Advisor for Aggregate Recommendation & Creation
•Utility within Oracle BI Administrator tool that recommends aggregates
•Bases recommendations on usage tracking and summary statistics data
•Captured based on past activity
•Runs an iterative algorithm that searches, 
each iteration, for the best aggregate

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Running Some Sample Hadoop / Hive Queries
•A simple Hadoop / Hive BMM was created, based off of a single Hive table
•Queries run against that BMM that requested aggregates
•Query details, and requested aggregates, go in usage tracking & summary statistics tables
•Avg. query response time = 30 secs+
select avg(T44678.age) as c1,
T44678.sales_pers as c2,
sum(T44678.age) as c3,
count(T44678.age) as c4
from
dwh_customer T44678
group by T44678.sales_pers

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Generate Aggregate Recommendations using Summary Advisor
•Ensure BMM has one or more logical dimensions + 2 or more logical levels
•Ensure S_NQ_SUMMARY_ADVISOR table has aggregate recordings + level details
•Generate summary recommendations using Summary Advisor, output as nqcmd script

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Implement Recommendations, Review Updated RPD
•Run generated logical SQL (Aggregate Persistence) script to create & populate TT tables
•Automatically updates RPD to “plug-in” new TimesTen aggregate tables

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Re-run Reports, now with TimesTen for Exalytics Acceleration
•Reports can now be re-run to test improvements from  
in-memory aggregation
•Response time is now instantaneous
•Aggregates will need to be refreshed once new data is  
loaded into Hadoop
•Can also be used to improve speed of federated  
RDBMS - Hadoop - OLAP queries too
‣But - relies on query caching - doesn’t make 
Hadoop “faster”…

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Dealing with Hadoop / Hive Latency Option 2 : Use Impala
•Hive is slow - because it’s meant to be used for batch-mode queries
•Many companies / projects are trying to improve Hive - one of which is Cloudera
•Cloudera Impala is an open-source but  
commercially-sponsored in-memory MPP platform
•Replaces Hive and MapReduce in the Hadoop stack
•Can we use this, instead of Hive, to access Hadoop?
‣It will need to work with OBIEE
‣Warning - it won’t be a supported data source (yet…)

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How Impala Works
•A replacement for Hive, but uses Hive concepts and 
data dictionary (metastore)
•MPP (Massively Parallel Processing) query engine 
that runs within Hadoop
‣Uses same file formats, security, 
resource management as Hadoop
•Processes queries in-memory
•Accesses standard HDFS file data
•Option to use Apache AVRO, RCFile, 
LZO or Parquet (column-store)
•Designed for interactive, real-time 
SQL-like access to Hadoop
Impala
Hadoop
HDFS etc
BI Server
Presentation Svr
Cloudera Impala 
ODBC Driver
Impala
Hadoop
HDFS etc
Impala
Hadoop
HDFS etc
Impala
Hadoop
HDFS etc
Impala
Hadoop
HDFS etc
Multi-Node 
Hadoop Cluster

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Connecting OBIEE 11.1.1.7 to Cloudera Impala
•Warning - unsupported source - limited testing and no support from MOS
•Requires Cloudera Impala ODBC drivers - Windows or Linux (RHEL etc/SLES) - 32/64 bit
•ODBC Driver / DSN connection steps similar to Hive

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Importing Impala Metadata
•Import Impala tables (via the Hive metastore) into RPD
•Set database type to “Apache Hadoop”
‣Warning - don’t set ODBC type to Hadoop- leave at ODBC 2.0
‣Create physical layer keys, joins etc as normal

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Importing RPD using Impala Metadata
•Create BMM layer, Presentation layer as normal
•Use “View Rows” feature to check connectivity back to Impala / Hadoop

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Impala / OBIEE Issue with ORDER BY Clause
•Although checking rows in the BI Administration tool worked, any query that aggregates 
data in the dashboard will fail
•Issue is that Impala requires LIMIT with all ORDER BY clauses
‣OBIEE could use LIMIT, but doesn’t for Impala  
at the moment (because not supported)
•Workaround - disable ORDER BY in  
Database Features, have the BI Server do sorting
‣Not ideal - but it works, until Impala supported

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Demonstration of OBIEE 11.1.1.7 accessing Hadoop 
through Impala Connectivity - Flight Delays Dataset

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So Does Impala Work, as a Hive Substitute?
•With ORDER BY disabled in DB features, it appears to
•But not extensively tested by me, or Oracle
•But it’s certainly interesting
•Reduces 30s, 180s queries down to 1s, 10s etc
•Impala, or one of the competitor projects 
(Drill, Dremel etc) assumed to be the 
real-time query replacement for Hive, in time
‣Oracle announced planned support for  
Impala at OOW2013 - watch this space

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Thank You for Attending!
•Thank you for attending this presentation, and more information can be found at http://
www.rittmanmead.com
•Contact us at info@rittmanmead.com or mark.rittman@rittmanmead.com
•Look out for our book, “Oracle Business Intelligence Developers Guide” out now!
•Follow-us on Twitter (@rittmanmead) or Facebook (facebook.com/rittmanmead)

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