Odi Mclass 4 Big Data

T : +1 (888) 631-1410 E : [email protected] W: www.rittmanmead.com

BI Forum 2013 Master Class Introduction to Oracle Data Integrator (ODI)

Mark Rittman, Technical Director, Rittman Mead


T : +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com

Big Data, Hadoop and Unstructured Data Sources

• “Big data” is the hot topic in BI, DW and Analytics circles • The ability to harness vast datasets, at a highly-granular level, by harnessing massively-parallel computing • Crunching loosely-structured and modelled datasets using simple algorithms: Map (project) + Reduce (agg) • Largely based around open-source projects, non-relational technologies ‣ Apache Hadoop ‣ MapReduce ‣ Hadoop Distributed File System ‣ Apache Hive, Sqoop, HBase etc

• Emerging commercial vendors ‣ Cloudera ‣ Hortonworks etc

• Can be used standalone, or linked to an enterprise DW/BI architecture

+


Oracle’s Strategy for Business Analytics

• Connect to all of your data, from all your sources, • Subject it to the full range of possible inquiry • Package solutions for known problems and fixed sources, and • Deploy to PCs and mobile devices, on premise or in the cloud

On Premise, On Cloud, On Mobile

Any Data,

Any Source

Full Range of

Analytics

Integrated

Analytic Apps


Connect to All of Your Data, From All of Your Sources

• As well as traditional application and database files sources, unstructured source and “big data” sources are within scope for business decision-making ‣ Data of great volume, great velocity and great variety

Any Data,

Any Source

Your Data : Decisions based on your data Big Data : Decisions based on all data relevant to you

Transactions

Documents & Social Data

Machine-Generated Data


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)


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 using

Apache Hive (JDBC) and HiveQL, load into Oracle RDBMS using Oracle Loader for Hadoop

• Supported by Oracle’s Engineered Systems ‣ Exadata ‣ Exalytics ‣ Big Data Appliance (w/Cloudera Hadoop Distrib)


How ODI Accesses Hadoop and MapReduce

• ODI accesses data in Hadoop clusters through Apache Hive ‣ Metadata and query layer over MapReduce ‣ Provides SQL-like language (HiveQL) and a

metadata store (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 required on ODI server, for client libs)

• Additional access through Oracle Direct Connector for HDFS and Oracle Loader for Hadoop

Hadoop Cluster

Hive Server

ODI 11g Oracle RDBMS

HiveQL

MapReduce

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


Oracle Business Analytics and Big Data Sources

• OBIEE 11g, and other Oracle Business Analytics tools, can also make use of big data sources • Oracle Exalytics, through in-memory aggregates and InfiniBand connection to Exadata, can analyze vast (structured)

datasets held in relational and OLAP databases • Endeca Information Discovery can analyze unstructured and semi-structured sources •  InfiniBand connector to Big Data Applicance + Hadoop connector in OBIEE supports analysis via Map/Reduce • Oracle R distribution + Oracle Enterprise R supports SAS-style statistical analysis

of large data sets, as part of Oracle Advanced Analytics Option

• OBIEE can access Hadoop datasource through another Apache technology called Hive


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 for

massively-parallel staging-layer number crunching

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

• Provide the reporting and analysis for customers who have bought Oracle Big Data Appliance


What is Hadoop?

• 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 ‣ “Moves the compute to the data”

• MapReduce is the programming framework for filtering, sorting and aggregating data ‣ Map : filter and interpret input data, create key/value pairs ‣ Reduce : summarise and aggregate

• MapReduce jobs can be written in any language (Java etc), but it is complicated


What is HDFS?

• 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/oracleFound 5 itemsdrwx------ - oracle hadoop 0 2013-04-27 16:48 /user/oracle/.stagingdrwxrwxrwx - oracle hadoop 0 2012-09-18 17:02 /user/oracle/moviedemodrwxrwxrwx - oracle hadoop 0 2012-10-17 15:58 /user/oracle/movieworkdrwxr-xr-x - oracle hadoop 0 2013-05-03 17:49 /user/oracle/my_stuffdrwxr-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, 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...)


Hive Data and Metadata

• 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


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


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


OBIEE and ODI Access to Hive, Leveraging MapReduce with no Java Coding

• Requests in HiveQL arrive via HiveODBC, HiveJDBC or 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, then sent to Hadoop NameNode and Job Tracker

• Then Hadoop processes the query, generating MapReduce jobs 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, Beeswax 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


Demonstration Simple Data Selection and Querying using Hive on Cloudera CDH3


ODI + Big Data Examples : Providing the Bridge Between Hadoop + OBIEE

• OBIEE now has the ability to report against Hadoop data, via Hive ‣ Assumes that data is already loaded

into the Hive warehouse tables • ODI therefore can be used to load

the 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 data

and 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 environment

for 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 the ODI Topology

• 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


Demonstration ODI 11.1.1.6 Configured for Hadoop Access, with Hive/HFDS source and targets registered


ODI Application Adapter for Hadoop KMs

• 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


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

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

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


Load Data and Hadoop SerDe (Serializer-Deserializer) Transformations

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

• Hadoop interface that contains: ‣ Deserializer - converts incoming data

into Java objects for Hive manipulation ‣ Serializer - takes Hive Java objects &

converts to output for HDFS • Library of SerDe transformations readily

available for use with Hive • Use the OVERRIDE_ROW_FORMAT

option in IKM to override regular column mappings in Mapping tab


IKM Hive Control Append: Loading, Joining & Filtering Between Hive Tables

• Hive source and target, transformations according to HiveQL functionality (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 ability to transform data programmatically using Python, Perl etc scripts

• Options to map output of script to columns in Hive table

• Useful for more programmatic and complex data transformations


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

• Uses Oracle Loaded for Hadoop (OLH) to process any 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


Demonstration Data 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...!


BI Forum 2013 Master Class Introduction to Oracle Data Integrator (ODI)

Mark Rittman, Technical Director, Rittman Mead


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