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





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 : [email protected] •Twitter : @markrittman

http://www.rittmanmead.com/blog

mailto:[email protected]





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



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, automaticallycreates 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



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 anylanguage (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



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)



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



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



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, automaticallycreates MapReduce jobs against data previously loaded into the Hive HDFS tables

•Approach used by ODI and OBIEEto gain access to Hadoop data

•Allows Hadoop data to be accessed just like any other data source (sort of...)



How Hive Provides SQL Access over Hadoop

•Hive uses a RBDMS metastore to holdtable 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 HiveQLCREATE TABLE

command

HDFS files loaded into HDFSusing external process, then

mapped into Hive usingCREATE EXTERNAL TABLE

command

Metastore



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

MapTask

MapTask

MapTask

ReduceTask

ReduceTask

Result





Leveraging Hadoop with OBIEE 11g Demonstration of Hive



•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 throughOBIEE connectivity & metadata layer

DW 2013: The Mixed Architecture with Federated Queries



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 betweenOBIEE and Endeca



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



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



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 ProtocolArraySize=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





Leveraging Hadoop with OBIEE 11g Demonstration of OBIEE 11.1.1.7 accessing Hadoopthrough Hive Connectivity



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?



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 throughExalytics package



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

Exal

ytic

s

Aggregates

Data WarehouseDetail-level

Data



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



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



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



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



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 makeHadoop “faster”…



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…)



How Impala Works

•A replacement for Hive, but uses Hive concepts anddata dictionary (metastore)

•MPP (Massively Parallel Processing) query enginethat 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-timeSQL-like access to Hadoop

Impala

Hadoop

HDFS etc

BI Server

Presentation Svr

Cloudera ImpalaODBC Driver

Impala

Hadoop

HDFS etc

Impala

Hadoop

HDFS etc

Impala

Hadoop

HDFS etc

Impala

Hadoop

HDFS etc

Multi-NodeHadoop Cluster



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



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



Importing RPD using Impala Metadata

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



Impala / OBIEE Issue with ORDER BY Clause

•Although checking rows in the BI Administration tool worked, any query that aggregatesdata 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





Leveraging Hadoop with OBIEE 11g Demonstration of OBIEE 11.1.1.7 accessing Hadoopthrough Impala Connectivity - Flight Delays Dataset



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 thereal-time query replacement for Hive, in time ‣Oracle announced planned support for Impala at OOW2013 - watch this space





Thank You for Attending!

•Thank you for attending this presentation, and more information can be found at http://www.rittmanmead.com

•Contact us at [email protected] or [email protected] •Look out for our book, “Oracle Business Intelligence Developers Guide” out now! •Follow-us on Twitter (@rittmanmead) or Facebook (facebook.com/rittmanmead)

http://www.rittmanmead.com





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

obiee, hadoop and big data analysis (odtug kscope'14)

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