Leveraging Hadoop with OBIEE 11g and ODI 11g - UKOUG Tech'13

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Leveraging Hadoop with OBIEE 11g and ODI 11g Mark Rittman, CTO, Rittman Mead UKOUG Tech’13 Conference, Manchester, December 2013





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





Leveraging Hadoop with OBIEE 11g and ODI 11g Part 1 : Hadoop, Big Data and DW Architectures



Traditional Data Warehouse / BI Architectures

Staging Foundation / ODS

Performance /Dimensional

ETL ETL

BI Tool (OBIEE)with metadatalayer

OLAP / In-Memory Tool with data load into own database

DirectRead

DataLoad

Traditional structureddata sources

DataLoad

DataLoad

DataLoad

Traditional Relational Data Warehouse

•Three-layer architecture - staging, foundation and access/performance •All three layers stored in a relational database (Oracle) •ETL used to move data from layer-to-layer



Recent Innovations and Developments in DW Architecture

•The rise of “big data” and “hadoop” ‣New ways to process, store and analyse data ‣New paradigm for TCO - low-cost servers, open-source software, cheap clustering

•Explosion in potential data-source types ‣Unstructured data ‣Social media feeds ‣Schema-less and schema-on-read databases

•New ways of hosting data warehouses ‣In the cloud ‣Do we even need an Oracle database or DW?

•Lots of opportunities for DW/BI developers - make our systems cheaper, wider range of data



Introduction of New Data Sources : Unstructured, Big Data



ETL ETL



DirectRead

DataLoad


DataLoad

DataLoad

DataLoad


Schema-less / NoSQLdata sources

Unstructured/Social / Doc

data sources

Hadoop / Big Data

data sources

DataLoad

DataLoad



Unstructured, Semi-Structured and Schema-Less Data

•Gaining access to the vast amounts of non-financial / application data out there ‣Data in documents, spreadsheets etc

-Warranty claims, supporting documents, notes etc ‣Data coming from the cloud / social media ‣Data for which we don’t yet have a structure ‣Data who’s structure we’ll decide when wechoose to access it (“schema-on-read”)

•All of the above could be useful informationto have in our DW and BI systems ‣But how do we load it in? ‣And what if we want to access it directly?



data sources

Hadoop / Big Data

data sources



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



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



Hadoop & HDFS as a Low-Cost Pre-Staging Layer



ETL ETL



DirectRead

DataLoad


DataLoad

DataLoad

DataLoad




data sources

Hadoop / Big Data

data sources DataLoad

Pre-ETL Filtering &

Aggregation(MapReduce)

Low-costfile store (HDFS)

DataLoad

Hadoop



Big Data and the Hadoop “Data Warehouse”


DirectRead

DataLoad

DataLoad

DataLoad



data sources

Hadoop / Big Data

data sources DataLoad

Pre-ETL Filtering &

Aggregation(MapReduce)

Low-costfile store (HDFS)

Hadoop

Hadoop DW Layer (Hive)

Cloud-Baseddata sources

•Rather than load Hadoop datainto the DW, access it directly

•Hadoop has a “DW layer” calledHive, which provides SQL access

•Could even be used instead of a traditional DW or data mart •Limited functionality now •But products maturing •and unbeatable TCO



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



An example Hive Query Session: Connect and Display Table List

[oracle@bigdatalite ~]$ hive Hive history file=/tmp/oracle/hive_job_log_oracle_201304170403_1991392312.txt

hive> show tables; OK dwh_customer dwh_customer_tmp i_dwh_customer ratings src_customer src_sales_person weblog weblog_preprocessed weblog_sessionized Time taken: 2.925 seconds

Hive Server lists out all “tables” that have been defined within the Hive environment



An example Hive Query Session: Display Table Row Count

hive> select count(*) from src_customer;!

Total MapReduce jobs = 1 Launching Job 1 out of 1 Number of reduce tasks determined at compile time: 1 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer= In order to limit the maximum number of reducers: set hive.exec.reducers.max= In order to set a constant number of reducers: set mapred.reduce.tasks= Starting Job = job_201303171815_0003, Tracking URL = http://localhost.localdomain:50030/jobdetails.jsp?jobid=job_201303171815_0003Kill Command = /usr/lib/hadoop-0.20/bin/ hadoop job -Dmapred.job.tracker=localhost.localdomain:8021 -kill job_201303171815_0003 2013-04-17 04:06:59,867 Stage-1 map = 0%, reduce = 0% 2013-04-17 04:07:03,926 Stage-1 map = 100%, reduce = 0% 2013-04-17 04:07:14,040 Stage-1 map = 100%, reduce = 33% 2013-04-17 04:07:15,049 Stage-1 map = 100%, reduce = 100% Ended Job = job_201303171815_0003 OK !25 Time taken: 22.21 seconds

Request count(*) from table

Hive server generates MapReduce job to “map” table key/value pairs, and then reduce the results to table count

MapReduce job automatically run by Hive Server

Results returned to user

http://localhost.localdomain:50030/jobdetails.jsp?jobid=job_201303171815_0003





Leveraging Hadoop with OBIEE 11g and ODI 11g Demonstration of Hive and HiveQL



•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’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)



Oracle Loader for Hadoop

•Oracle technology for accessing Hadoop data, and loading it into an Oracle database •Pushes data transformation, “heavy lifting” to the Hadoop cluster, using MapReduce •Direct-path loads into Oracle Database, partitioned and non-partitioned •Online and offline loads •Key technology for fast load of Hadoop results into Oracle DB



Oracle Direct Connector for HDFS

•Enables HDFS as a data-source for Oracle Database external tables •Effectively provides Oracle SQL access over HDFS •Supports data query, or import into Oracle DB •Treat HDFS-stored files in the same way as regular files ‣But with HDFS’s low-cost ‣… and fault-tolerance



Oracle Data Integration Adapter for Hadoop

•ODI 11g Application Adapter (pay-extra option) for Hadoop connectivity •Works for both Windows and Linux installs of ODI Studio ‣Need to source HiveJDBC drivers and JARs from separate Hadoop install

•Provides six new knowledge modules ‣IKM File to Hive (Load Data) ‣IKM Hive Control Append ‣IKM Hive Transform ‣IKM File-Hive to Oracle (OLH) ‣CKM Hive ‣RKM Hive



ODI as Part of Oracle’s Big Data Strategy

•ODI is the data integration tool for extracting data from Hadoop/MapReduce, and loading into Oracle Big Data Appliance, Oracle Exadata and Oracle Exalytics

•Oracle Application Adaptor for Hadoop provides required data adapters ‣Load data into Hadoop from local filesystem,or HDFS (Hadoop clustered FS) ‣Read data from Hadoop/MapReduce usingApache Hive (JDBC) and HiveQL, loadinto Oracle RDBMS usingOracle Loader for Hadoop

•Supported by Oracle’s Engineered Systems ‣Exadata ‣Exalytics ‣Big Data Appliance



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



Opportunities for OBIEE and ODI with Big Data Sources and Tools

•Load data from a Hadoop/HDFS/NoSQL environment into a structured DW for analysis •Provide OBIEE as an alternative to Java coding or HiveQL for analysts

•Leverage Hadoop & HDFS formassively-parallel staging-layernumber crunching

•Make use of low-cost, fault-toleranthardware for parts of your BI platform

•Provide the reporting and analysisfor customers who have boughtOracle Big Data Appliance



OBIEE and ODI Access to Hive: MapReduce with no Java Coding

•Requests in HiveQL arrive via HiveODBC, HiveJDBCor through the Hive command shell

•JDBC and ODBC access requires Thift server ‣Provides RPC call interface over Hive for external procs

•All queries then get parsed, optimized and compiled, thensent to Hadoop NameNode and Job Tracker

•Then Hadoop processes the query, generating MapReducejobs and distributing it to run in parallel across all data nodes

•Hadoop access can still be performed procedurally if needed,typically coded by hand in Java, or through Pig, etc ‣The equivalent of PL/SQL compared to SQL ‣But Hive works well with the OBIEE/ODI paradigm



Complementary Technologies: HDFS, Cloudera Manager, Hue etc

•You can download your own Hive binaries, libraries etc from Apache Hadoop website •Or use pre-built VMs and distributions from the likes of Cloudera ‣Cloudera CDH3/4 is used on Oracle Big Data Appliance ‣Open-source + proprietary tools (Cloudera Manager)

•Other tools for managing Hive, HFDS etc including ‣Hue (HDFS file browser + management) ‣Beeswax (Hive administration + querying)

•Other complementary/required Hadoop tools ‣Sqoop ‣HDFS ‣Thrift





Leveraging Hadoop with OBIEE 11g and ODI 11g Part 2 : ODI 11g and Hadoop / Big Data Sources



How ODI Accesses Hadoop Data

•ODI accesses data in Hadoop clusters through Apache Hive ‣Metadata and query layer over MapReduce ‣Provides SQL-like language (HiveQL) and a data dictionary ‣Provides a means to define “tables”, into which file data is loaded, and then queried via MapReduce ‣Accessed via Hive JDBC driver(separate Hadoop install requiredon ODI server, for client libs)

•Additional access throughOracle Direct Connector for HDFSand Oracle Loader for Hadoop

Hadoop Cluster

Hive Server

ODI 11gOracle RDBMS

HiveQL

MapReduce

Direct-path loads using Oracle Loader for Hadoop, transformation logic in MapReduce



Relationship Between ODI and OBIEE with Big Data Sources

•OBIEE now has the ability to reportagainst Hadoop data, via Hive ‣Assumes that data is already loadedinto the Hive warehouse tables

•ODI therefore can be used to loadthe Hive tables, through either: ‣Loading Hive from files ‣Joining and loading from Hive-Hive ‣Loading and transforming via shell scripts (python, perl etc)

•ODI could also extract the Hive dataand load into Oracle, if more appropriate



Configuring ODI 11.1.1.6+ for Hadoop Connectivity

•Obtain an installation of Hadoop/Hive from somewhere (Cloudera CDH3/4 for example) •Copy the following files into a temp directory, archive and transfer to ODI environmentfor example... !!!

•Copy JAR files into userlib directory and (standalone) agent lib directory !!

•Restart ODI Studio

$HIVE_HOME/lib/*.jar $HADOOP_HOME/hadoop-*-core*.jar, $HADOOP_HOME/Hadoop-*-tools*.jar

/usr/lib/hive/lib/*.jar /usr/lib/hadoop-0.20/hadoop-*-core*.jar, /usr/lib/hadoop-0.20/Hadoop-*-tools*.jar

c:\Users\Administrator\AppData\Roaming\odi\oracledi\userlib



Registering HDFS and Hive Sources and Targets in ODI

•For Hive sources and targets, use Hive technology ‣JDBC Driver : Apache Hive JDBC Driver ‣JDBC URL : jdbc:hive://[server_name]:10000/default ‣(Flexfield Name) Hive Metastore URIs : thrift://[server_name]:10000 !

•For HFDS sources, use File technology ‣JDBC URL : hdfs://[server_name]:port ‣Special HDFS “trick” to use File tech(no specific HDFS technology)



Reverse Engineering Hive, HDFS and Local File Datastores + Models

•Hive tables reverse-engineer just like regular tables •Define model in Designer navigator, uses Hive RKM to retrieve table metadata • Information on Hive-specific metadata stored in flexfields ‣Hive Buckets ‣Hive Partition Column ‣Hive Cluster Column ‣Hive Sort Column





Leveraging Hadoop with OBIEE 11g and ODI 11g Demonstration of ODI 11.1.1.6 Configured for Hadoop Access, with Hive/HFDS source and targets registered



Oracle Data Integration Adapter for Hadoop

•ODI 11g Application Adapter (pay-extra option) for Hadoop connectivity •Works for both Windows and Linux installs of ODI Studio ‣Need to source HiveJDBC drivers and JARs from separate Hadoop install

•Provides six new knowledge modules ‣IKM File to Hive (Load Data) ‣IKM Hive Control Append ‣IKM Hive Transform ‣IKM File-Hive to Oracle (OLH) ‣CKM Hive ‣RKM Hive



Oracle Loader for Hadoop

•Oracle technology for accessing Hadoop data, and loading it into an Oracle database •Pushes data transformation, “heavy lifting” to the Hadoop cluster, using MapReduce •Direct-path loads into Oracle Database, partitioned and non-partitioned •Online and offline loads •Key technology for fast load of Hadoop results into Oracle DB



Oracle Direct Connector for HDFS

•Enables HDFS as a data-source for Oracle Database external tables •Effectively provides Oracle SQL access over HDFS •Supports data query, or import into Oracle DB •Treat HDFS-stored files in the same way as regular files ‣But with HDFS’s low-cost ‣… and fault-tolerance



IKM File to Hive (Load Data): Loading Hive Tables from File or HDFS

•Uses the Hive Load Data command to load from local or HDFS files

•Calls Hadoop FS commands for simple copy/move into/around HDFS

•Commands generated by ODI through IKM File to Hive (Load Data)

hive> load data inpath '/user/oracle/movielens_src/u.data' > overwrite into table movie_ratings; Loading data to table default.movie_ratings Deleted hdfs://localhost.localdomain/user/hive/warehouse/ movie_ratings OK Time taken: 0.341 seconds



IKM File to Hive (Load Data): Loading Hive Tables from File or HDFS

• IKM File to Hive (Load Data) generates therequired HiveQL commands using a script template

•Executed over HiveJDBC interface •Success/Failure/Warning returned to ODI



Load Data and Hadoop SerDe (Serializer-Deserializer) Transforms

•Hadoop SerDe transformations can be accessed, for example to transform weblogs

•Hadoop interface that contains: ‣Deserializer - converts incoming datainto Java objects for Hive manipulation ‣Serializer - takes Hive Java objects &converts to output for HDFS

•Library of SerDe transformations readilyavailable for use with Hive

•Use the OVERRIDE_ROW_FORMAToption in IKM to override regular columnmappings in Mapping tab



IKM Hive Control Append: Load, Join & Filtering Between Hive Tables

•Hive source and target, transformations according to HiveQLfunctionality (aggregations, functions etc)

•Ability to join data sources •Other data sources can be used, but will involve staging tables and additional KMs (as per any multi-source join)



IKM Hive Transform: Use Custom Shell Scripts to Integrate into Hive Table

•Gives developer the abilityto transform data programmatically usingPython, Perl etc scripts

•Options to map outputof script to columns in Hive table

•Useful for more programmatic and complexdata transformations



IKM File-Hive to Oracle: Extract from Hive into Oracle Tables

•Uses Oracle Loaded for Hadoop (OLH) to processany filtering, aggregation, transformation in Hadoop,using MapReduce

•OLH part of Oracle Big Data Connectors (additional cost) •High-performance loader into Oracle DB •Optional sort by primary key, pre-partioning of data •Can utilise the two OLH loading modes: •JDBC or OCI direct load into Oracle •Unload to files, Oracle DP into Oracle DB





Leveraging Hadoop with OBIEE 11g and ODI 11g Demonstration of Integration Tasks using ODIAAH Hadoop KMs



NoSQL Data Sources and Targets with ODI 11g

•No specific technology or driver for NoSQL databases, but can use Hive external tables •Requires a specific “Hive Storage Handler” for key/value store sources ‣Hive feature for accessing data from other DB systems, for example MongoDB, Cassandra ‣For example, https://github.com/vilcek/HiveKVStorageHandler

•Additionally needs Hive collect_set aggregation method to aggregate results ‣Has to be defined in Languages panel in Topology



Pig, Sqoop and other Hadoop Technologies, and Hive

•Future versions of ODI might use other Hadoop technologies ‣Apache Sqoop for bulk transfer between Hadoop and RBDMSs

•Other technologies are not such an obvious fit ‣Apache Pig - the equivalent of PL/SQL for Hive’s SQL

•Commercial vendors may produce “better” versions of Hive, MapReduce etc ‣Cloudera Impala - more “real-time” version of Hive ‣MapR - solves many current issues with MapReduce, 100% Hadoop API compatibility

•Watch this space...!





Leveraging Hadoop with OBIEE 11g and ODI 11g Part 3 : OBIEE 11g and Hadoop / Big Data Sources



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



New in OBIEE 11.1.1.7 : Hadoop Connectivity through Hive

•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 OBIEE to gain access to Hadoop data •Allows Hadoop data to be accessed just like any other data source



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 and ODI 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



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





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Leveraging Hadoop with OBIEE 11g and ODI 11g - UKOUG Tech'13

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Transcript of Leveraging Hadoop with OBIEE 11g and ODI 11g - UKOUG Tech'13