IlOUG Tech Days 2016 - Big Data for Oracle Developers - Towards Spark, Real-Time and Predictive Analytics

info@rittmanmead.com www.rittmanmead.com @rittmanmead
Big Data for Oracle Developers & DBAs -  
Towards Spark, Real-Time and Predictive Analytics
Mark Rittman, CTO, Rittman Mead
IlOUG Tech Day 2016 Day 2 Keynote, 31st May 2016 @ 9.15am

info@rittmanmead.com www.rittmanmead.com @rittmanmead 2
•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
About the Speaker

•Everyone’s talking about Hadoop and “Big Data”
Hadoop is the Big Hot Topic In IT / Analytics

•Gives us an ability to store more data, at more detail, for longer

•Provides a cost-effective way to analyse vast amounts of data

•Hadoop & NoSQL technologies can give us “schema-on-read” capabilities

•There’s vast amounts of innovation in this area we can harness

•And it’s very complementary to Oracle BI & DW
Why is Hadoop of Interest to Us?

Flexible Cheap Storage for Logs, Feeds + Social Data
$50k
Hadoop
Node
Voice + Chat
Transcripts
Call Center LogsChat Logs iBeacon Logs Website LogsCRM Data Transactions Social FeedsDemographics
Raw Data
Customer 360 Apps
Predictive  
Models
SQL-on-Hadoop
Business analytics
Real-time Feeds, 
batch and API

Incorporate Hadoop Data Reservoirs into DW Design
Virtualization& 
QueryFederation
Enterprise
Performance
Management
Pre-built &  
Ad-hoc  
BI Assets
Information 
Services
Data Ingestion
Information Interpretation
Access & Performance Layer
Foundation Data Layer
Raw Data Reservoir
Data  
Science
Data Engines &  
Poly-structured  
sources
Content
Docs Web & Social Media
SMS
Structured
Data 
Sources
•Operational Data
•COTS Data
•Master & Ref. Data
•Streaming & BAM
Immutable raw data reservoir
Raw data at rest is not interpreted
Immutable modelled data. Business
Process Neutral form. Abstracted from
business process changes
Past, current and future interpretation of
enterprise data. Structured to support agile
access & navigation
Discovery Lab Sandboxes Rapid Development Sandboxes
Project based data stores to
support specific discovery
objectives
Project based data stored to
facilitate rapid content /
presentation delivery
Data Sources

•Oracle Engineered system for big data processing and analysis

•Start with Oracle Big Data Appliance Starter Rack - expand up to 18 nodes per rack

•Cluster racks together for horizontal scale-out using enterprise-quality infrastructure
Oracle Big Data Appliance
Starter Rack + Expansion
• Cloudera CDH + Oracle software
• 18 High-spec Hadoop Nodes with
InfiniBand switches for internal
Hadoop traffic, optimised for network
throughput
• 1 Cisco Management Switch
• Single place for support for H/W + S/
W 
Deployed on Oracle Big Data Appliance
Oracle Big Data Appliance
Starter Rack + Expansion
• Cloudera CDH + Oracle software
• 18 High-spec Hadoop Nodes with
InfiniBand switches for internal
Hadoop traffic, optimised for network
throughput
• 1 Cisco Management Switch
• Single place for support for H/W + S/
W 
Enriched  
Customer Profile
Modeling
Scoring
Infiniband

•Hadoop, through MapReduce, breaks processing down into simple stages

‣Map : select the columns and values you’re interested in, pass through as key/value pairs

‣Reduce : aggregate the results

•Most ETL jobs can be broken down into filtering,  
projecting and aggregating

•Hadoop then automatically runs job on cluster

‣Share-nothing small chunks of work

‣Run the job on the node where the data is

‣Handle faults etc

‣Gather the results back in
Hadoop Tenets : Simplified Distributed Processing
Mapper
Filter, Project
Mapper
Filter, Project
Mapper
Filter, Project
Reducer
Aggregate
Reducer
Aggregate
Output 
One HDFS file per reducer, 
in a directory

•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 as the Hadoop SQL Access Layer

•Data integration tools such as Oracle Data Integrator can load and process Hadoop data

•BI tools such as Oracle Business Intelligence 12c can report on Hadoop data

•Generally use MapReduce and Hive to access data

‣ODBC and JDBC access to Hive tabular data

‣Allows Hadoop unstructured/semi-structured 
data on HDFS to be accessed like RDBMS
Hive Provides a SQL Interface for BI + ETL Tools
Access direct Hive or extract using ODI12c
for structured OBIEE dashboard analysis
What pages are people visiting?
Who is referring to us on Twitter?
What content has the most reach?

•Most Oracle DBAs and developers know about Hadoop, but assume…
Common Developer Understanding of Hadoop Today
‣Hadoop is just for batch (because of the MapReduce JVN spin-up issue)
‣Hadoop is just for large datasets, not ad-hoc work or micro batches
‣Hadoop will always be slow because it stages everything to disk
‣All Hadoop can do is Map (select, filter) and Reduce (aggregate)
‣Hadoop == MapReduce

too slow 
for ad-hoc querying

Hadoop 2.0 Processing Frameworks + Tools

•MapReduce’s great innovation was to break processing down into distributed jobs

•Jobs that have no functional dependency on each other, only upstream tasks

•Provides a framework that is infinitely scalable and very fault tolerant

•Hadoop handled job scheduling and resource management

‣All MapReduce code had to do was provide the “map” and “reduce” functions

‣Automatic distributed processing

‣Slow but extremely powerful
Hadoop 1.0 and MapReduce

•A typical Hive or Pig script compiles down into multiple MapReduce jobs

•Each job stages its intermediate results to disk

•Safe, but slow - write to disk, spin-up separate JVMs for each job
MapReduce - Scales By Writing Intermediate Results to Disk
SELECT
LOWER(hashtags.text),
COUNT(*) AS total_count
FROM (
SELECT * FROM tweets WHERE regexp_extract(created_at,"(2015)*",1) = "2015"
) tweets
LATERAL VIEW EXPLODE(entities.hashtags) t1 AS hashtags
GROUP BY LOWER(hashtags.text)
ORDER BY total_count DESC
LIMIT 15
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 5.34 sec HDFS Read: 10952994 HDFS Write: 5239 SUCCESS
Stage-Stage-2: Map: 1 Reduce: 1 Cumulative CPU: 2.1 sec HDFS Read: 9983 HDFS Write: 164 SUCCESS
Total MapReduce CPU Time Spent: 7 seconds 440 msec
OK
1
2

•MapReduce 2 (MR2) splits the functionality of the JobTracker 
by separating resource management and job scheduling/monitoring

•Introduces YARN (Yet Another Resource Manager)

•Permits other processing frameworks to MR

‣For example, Apache Spark

•Maintains backwards compatibility with MR1

•Introduced with CDH5+
MapReduce 2 and YARN
Node 
Manager
Node 
Manager
Node 
Manager
Resource 
Manager
Client
Client

•Runs on top of YARN, provides a faster execution engine than MapReduce for Hive, Pig etc

•Models processing as an entire data flow graph (DAG), rather than separate job steps

‣DAG (Directed Acyclic Graph) is a new programming style for distributed systems

‣Dataflow steps pass data between them as streams, rather than writing/reading from disk

•Supports in-memory computation, enables Hive on Tez (Stinger) and Pig on Tez

•Favoured In-memory / Hive v2  
route by Hortonworks
Apache Tez
InputData
TEZ DAG
Map()
Map()
Map()
Reduce()
OutputData
Reduce()
Reduce()
Reduce()
InputData
Map()
Map()
Reduce()
Reduce()

Tez Advantage - Drop-In Replacement for MR with Hive, Pig
set hive.execution.engine=mr
set hive.execution.engine=tez
4m 17s
2m 25s

•Cloudera’s answer to Hive query response time issues

•MPP SQL query engine running on Hadoop, bypasses MapReduce for
direct data access

•Mostly in-memory, but spills to disk if required

•Uses Hive metastore to access Hive table metadata

•Similar SQL dialect to Hive - not as rich though and no support for Hive
SerDes, storage handlers etc
Cloudera Impala - Fast, MPP-style Access to Hadoop Data

•Beginners usually store data in HDFS using text file formats (CSV) but these have limitations

•Apache AVRO often used for general-purpose processing

‣Splitability, schema evolution, in-built metadata, support for block compression

•Parquet now commonly used with Impala due to column-orientated storage

‣Mirrors work in RDBMS world around column-store

‣Only return (project) the columns you require across a wide table
Apache Parquet - Column-Orientated Storage for Analytics

•But Parquet (and HDFS) have significant limitation for real-time analytics applications

‣Append-only orientation, focus on column-store  
makes streaming ingestion harder

•Cloudera Kudu aims to combine best of HDFS + HBase

‣Real-time analytics-optimised

‣Supports updates to data

‣Fast ingestion of data

‣Accessed using SQL-style tables 
and get/put/update/delete API
Cloudera Kudu - Combining Best of HBase and Column-Store

•Part of Oracle Big Data 4.0 (BDA-only)

‣Also requires Oracle Database 12c, Oracle Exadata Database Machine

•Extends Oracle Data Dictionary to cover Hive

•Extends Oracle SQL and SmartScan to Hadoop

•Extends Oracle Security Model over Hadoop

‣Fine-grained access control

‣Data redaction, data masking

‣Uses fast c-based readers where possible 
(vs. Hive MapReduce generation)

‣Map Hadoop parallelism to Oracle PQ

‣Big Data SQL engine works on top of YARN

‣Like Spark, Tez, MR2
Oracle Big Data SQL
Exadata 
Storage Servers
Hadoop 
Cluster
Exadata Database 
Server
Oracle Big 
Data SQL
SQL Queries
SmartScan SmartScan

“where we’re going 
we don’t need roads”

•Apache Drill is another SQL-on-Hadoop project that focus on schema-free data discovery

•Inspired by Google Dremel, innovation is querying raw data with schema optional

•Automatically infers and detects schema from semi-structured datasets and NoSQL DBs

•Join across different silos of data e.g. JSON records, Hive tables and HBase database

•Aimed at different use-cases than Hive -  
low-latency queries, discovery  
(think Endeca vs OBIEE)
Introducing Apache Drill - “We Don’t Need No Roads”

•Most modern datasource formats embed their schema in the data (“schema-on-read”)

•Apache Drill makes these as easy to join to traditional datasets as “point me at the data”

•Cuts out unnecessary work in defining Hive schemas for data that’s self-describing

•Supports joining across files, 
databases, NoSQL etc
Self-Describing Data - Parquet, AVRO, JSON etc

•Files can exist either on the local filesystem, or on HDFS

•Connection to directory or file defined in storage configuration

•Can work with CSV, TXT, TSV etc

•First row of file can provide schema (column names)
Apache Drill and Text Files
SELECT * FROM dfs.`/tmp/csv_with_header.csv2`;
+-------+------+------+------+
| name | num1 | num2 | num3 |
+-------+------+------+------+
| hello | 1 | 2 | 3 |
| hello | 1 | 2 | 3 |
| hello | 1 | 2 | 3 |
| hello | 1 | 2 | 3 |
| hello | 1 | 2 | 3 |
| hello | 1 | 2 | 3 |
| hello | 1 | 2 | 3 |
+-------+------+------+------+
7 rows selected (0.12 seconds)
SELECT * FROM dfs.`/tmp/csv_no_header.csv`;
+------------------------+
| columns |
+------------------------+
| ["hello","1","2","3"] |
| ["hello","1","2","3"] |
| ["hello","1","2","3"] |
| ["hello","1","2","3"] |
| ["hello","1","2","3"] |
| ["hello","1","2","3"] |
| ["hello","1","2","3"] |
+------------------------+

•JSON (Javascript Object Notation) documents are
often used for data interchange

•Exports from Twitter and other consumer services

•Web service responses and other B2B interfaces

•A more lightweight form of XML that is “self-
describing”

•Handles evolving schemas, and optional attributes

•Drill treats each document as a row, and has features
to

•Flatten nested data (extract elements from arrays)

•Generate key/value pairs for loosely structured data
Apache Drill and JSON Documents
use dfs.iot;
show files;
select in_reply_to_user_id, text from `all_tweets.json`
limit 5;
+---------------------+------+
| in_reply_to_user_id | text |
+---------------------+------+
| null | BI Forum 2013 in Brighton has now sold-out |
| null | "Football has become a numbers game |
| null | Just bought Lyndsay Wise’s Book |
| null | An Oracle BI "Blast from the Past" |
| 14716125 | Dilbert on Agile Programming |
+---------------------+------+
select name, flatten(fillings) as f  
from dfs.users.`/donuts.json`  
where f.cal < 300;

•Drill can connect to Hive to make use of metastore (incl. multiple Hive metastores)

•NoSQL databases (HBase etc)

•Parquet files (native storage format - columnar + self describing)
Apache Drill and Hive, HBase, Parquet Sources etc
USE hbase;
SELECT * FROM students;
+-------------+-----------------------+-----------------------------------------------------+
| row_key | account | address |
+-------------+-----------------------+------------------------------------------------------+
| [B@e6d9eb7 | {"name":"QWxpY2U="} | {"state":"Q0E=","street":"MTIzIEJhbGxtZXIgQXY="} |
| [B@2823a2b4 | {"name":"Qm9i"} | {"state":"Q0E=","street":"MSBJbmZpbml0ZSBMb29w"} |
| [B@3b8eec02 | {"name":"RnJhbms="} | {"state":"Q0E=","street":"NDM1IFdhbGtlciBDdA=="} |
| [B@242895da | {"name":"TWFyeQ=="} | {"state":"Q0E=","street":"NTYgU291dGhlcm4gUGt3eQ=="} |
+-------------+-----------------------+----------------------------------------------------------------------+
SELECT firstname,lastname FROM  
hiveremote.`customers` limit 10;` 
+------------+------------+
| firstname | lastname |
+------------+------------+
| Essie | Vaill |
| Cruz | Roudabush |
| Billie | Tinnes |
| Zackary | Mockus |
| Rosemarie | Fifield |
| Bernard | Laboy |
| Marianne | Earman |
+------------+------------+
SELECT * FROM dfs.`iot_demo/geodata/region.parquet`;
+--------------+--------------+-----------------------+
| R_REGIONKEY | R_NAME | R_COMMENT |
+--------------+--------------+-----------------------+
| 0 | AFRICA | lar deposits. blithe |
| 1 | AMERICA | hs use ironic, even |
| 2 | ASIA | ges. thinly even pin |
| 3 | EUROPE | ly final courts cajo |
| 4 | MIDDLE EAST | uickly special accou |
+--------------+--------------+-----------------------+

•Drill developed for real-time, ad-hoc data exploration with schema discovery on-the-fly
•Individual analysts exploring new datasets, leveraging corporate metadata/data to help

•Hive is more about large-scale, centrally curated set-based big data access

•Drill models conceptually as JSON, vs. Hive’s tabular approach

•Drill introspects schema from whatever it connects to, vs. formal modeling in Hive
Apache Drill vs. Apache Hive
Interactive Queries 
(Data Discovery, Tableau/VA)
Reporting Queries 
(Canned Reports, OBIEE)
ETL 
(ODI, Scripting, Informatica)
Apache Drill Apache Hive
Interactive Queries
100ms - 3mins
Reporting Queries
3mins - 20mins
ETL & Batch Queries
20mins - hours

what’s all this about “Spark”?

•Another DAG execution engine running on YARN

•More mature than TEZ, with richer API and more vendor support

•Uses concept of an RDD (Resilient Distributed Dataset)

‣RDDs like tables or Pig relations, but can be cached in-memory

‣Great for in-memory transformations, or iterative/cyclic processes

•Spark jobs comprise of a DAG of tasks operating on RDDs

•Access through Scala, Python or Java APIs

•Related projects include

‣Spark SQL

‣Spark Streaming
Apache Spark

•Native support for multiple languages  
with identical APIs

‣Python - prototyping, data wrangling

‣Scala - functional programming features

‣Java - lower-level, application integration

•Use of closures, iterations, and other  
common language constructs to minimize code

•Integrated support for distributed + 
functional programming

•Unified API for batch and streaming
Rich Developer Support + Wide Developer Ecosystem
scala> val logfile = sc.textFile("logs/access_log")
14/05/12 21:18:59 INFO MemoryStore: ensureFreeSpace(77353)  
called with curMem=234759, maxMem=309225062
14/05/12 21:18:59 INFO MemoryStore: Block broadcast_2  
stored as values to memory (estimated size 75.5 KB, free 294.6 MB)
logfile: org.apache.spark.rdd.RDD[String] =  
MappedRDD[31] at textFile at <console>:15
scala> logfile.count()
14/05/12 21:19:06 INFO FileInputFormat: Total input paths to process : 1
14/05/12 21:19:06 INFO SparkContext: Starting job: count at <console>:1
...
14/05/12 21:19:06 INFO SparkContext: Job finished:  
count at <console>:18, took 0.192536694 s
res7: Long = 154563
scala> val logfile = sc.textFile("logs/access_log").cache
scala> val biapps11g = logfile.filter(line => line.contains("/biapps11g/"))
biapps11g: org.apache.spark.rdd.RDD[String] = FilteredRDD[34] at filter at <console>:17
scala> biapps11g.count()
...
14/05/12 21:28:28 INFO SparkContext: Job finished: count at <console>:20, took 0.387960876 s
res9: Long = 403

•Spark SQL, and Data Frames, allow RDDs in Spark to be processed using SQL queries

•Bring in and federate additional data from JDBC sources

•Load, read and save data in Hive, Parquet and other structured tabular formats
Spark SQL - Adding SQL Processing to Apache Spark
val accessLogsFilteredDF = accessLogs
.filter( r => ! r.agent.matches(".*(spider|robot|bot|slurp).*"))
.filter( r => ! r.endpoint.matches(".*(wp-content|wp-admin).*")).toDF()
.registerTempTable("accessLogsFiltered")
val topTenPostsLast24Hour = sqlContext.sql("SELECT p.POST_TITLE, p.POST_AUTHOR, COUNT(*)  
as total  
FROM accessLogsFiltered a  
JOIN posts p ON a.endpoint = p.POST_SLUG  
GROUP BY p.POST_TITLE, p.POST_AUTHOR  
ORDER BY total DESC LIMIT 10 ")
// Persist top ten table for this window to HDFS as parquet file
topTenPostsLast24Hour.save("/user/oracle/rm_logs_batch_output/topTenPostsLast24Hour.parquet" 
, "parquet", SaveMode.Overwrite)

Hadoop is insecure
and has fragmented security
…doesn’t it?
but …

Consistent Security and Audit Now Emerging on Platform

•Clusters by default are unsecured (vunerable to account spooﬁng) & need Kerberos enabled

•Data access controlled by POSIX-style permissions on HDFS ﬁles

•Hive and Impala can Apache Sentry RBAC

‣Result is data duplication and complexity

‣No consistent API or abstracted security model
Hadoop Security Initially Was a Mess
/user/mrittman/scratchpad
/user/ryeardley/scratchpad
/user/mpatel/scratchpad
/data/rm_website_analysis/logfiles/incoming
/data/rm_website_analysis/logfiles/archive
/data/rm_website_analysis/tweets/incoming
/data/rm_website_analysis/tweets/archive

•Use standard Oracle Security over Hadoop & NoSQL

‣Grant & Revoke Privileges

‣Redact Data

‣Apply Virtual Private Database

‣Provides Fine-grain Access Control

•Great solution to extend existing Oracle 
security model over Hadoop datasets
Oracle Big Data SQL : Extend Oracle Security to Hadoop
Redacted
data
subset
SQL
JSON
Customer data
in Oracle DB
DBMS_REDACT.ADD_POLICY(
object_schema => 'txadp_hive_01',
object_name => 'customer_address_ext',
column_name => 'ca_street_name',
policy_name => 'customer_address_redaction',
function_type => DBMS_REDACT.RANDOM,
expression => 'SYS_CONTEXT(''SYS_SESSION_ROLES'',  
''REDACTION_TESTER'')=''TRUE'''
);

•Provides a higher level, logical abstraction for data (ie Tables or Views)

‣Can be used with Spark & Spark SQL, with Predicate pushdown, projection

•Returns schemed objects (instead of paths and bytes) in similar way to HCatalog

•Uniﬁed data access path allows platform-wide performance improvements

•Secure service that does not execute arbitrary user code

‣Central location for all authorization checks using Sentry metadata.
Cloudera RecordService

any predictive modelling
has to be done outside Hadoop, in R
…doesn’t it?
but …

•Part of Spark, extends Scala, Java & Python API

•Integrated workflow including ML pipelines

•Currently supports following algorithms:

‣Binary classification

‣Regression

‣Clustering

‣Collaborative filtering

‣Dimensionality Reduction
Spark MLLib : Adding Machine Learning Capabilities to Spark
// Compute raw scores on the test set.
val scoreAndLabels = test.map { point =>
val score = model.predict(point.features)
(score, point.label)
}
// Get evaluation metrics.
val metrics = new BinaryClassificationMetrics(scoreAndLabels)
val auROC = metrics.areaUnderROC()
println("Area under ROC = " + auROC)
// Save and load model
model.save(sc, "myModelPath")
val sameModel = SVMModel.load(sc, "myModelPath")

•Data enrichment tool aimed at domain experts, not programmers

•Uses machine-learning to automate  
data classification + profiling steps

•Automatically highlight sensitive data, 
and offer to redact or obfuscate

•Dramatically reduce the time required 
to onboard new data sources

•Hosted in Oracle Cloud for zero-install

‣File upload and download from browser

‣Automate for production data loads
Raw Data
Data stored in the
original format (usually
files) such as SS7, ASN.
1, JSON etc.
Mapped Data
Data sets produced by
mapping and
transforming raw data
Voice + Chat
Transcripts
Example Usage : Oracle Big Data Preparation Cloud Service

Identifying Schemas in Semi-/Unstructured Data

Use of Machine Learning to Identify Data Patterns
•Automatically profile, parse and classify incoming datasets using Spark MLLib Word2Vec
•Spot and obfuscate sensitive data automatically, automatically suggest column names

•Hadoop is evolving

‣Hadoop 2.0 breaks the dependency on MapReduce

‣Spark, Tez etc allow us to create execution plans that  
run in-memory, faster than before

‣New streaming models allow us to process data  
via sockets, micro batches or continuously

•And Oracle developers can make use of these new capabilities

‣Oracle Big Data SQL can access Hadoop data loaded in real-time

‣OBIEE, particularly in 11.1.1.9, can access Impala

‣ODI is likely to support Hive on Tez and Hive on Spark shortly,  
and will have support for Spark in the future
Summary

Big Data for Oracle Devs -  
Towards Spark, Real-Time and Predictive Analytics
Mark Rittman, CTO, Rittman Mead
Riga Dev Day 2016, Riga, March 2016

IlOUG Tech Days 2016 - Big Data for Oracle Developers - Towards Spark, Real-Time and Predictive Analytics

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IlOUG Tech Days 2016 - Big Data for Oracle Developers - Towards Spark, Real-Time and Predictive Analytics