Post on 27-Nov-2014
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Oracle Big Data Appliance for Customers and Partners
Jean-Pierre DijcksOracleBig Data Product Management
Paul KentSASVP Big Data
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 2
Oracle Big Data Appliance for Customers and Partners
Big Data Appliance Recap
Why You Should Consider Big Data Appliance
Driving Business Value with SAS on Big Data Appliance
Q&A
1
2
3
4
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Big Data Management System
SOU
RCES
Oracle Database
Oracle IndustryModels
Oracle Advanced Analytics
Oracle Spatial & Graph
Big Data Appliance
Cloudera Hadoop
Oracle NoSQL Database
Oracle R Advanced Analytics for Hadoop
Oracle R Distribution
Oracle Database
Oracle Advanced Security
Oracle Advanced Analytics
Oracle Spatial & Graph
Oracle Exadata
Oracle Big DataConnectors
Oracle DataIntegrator
Oracle Big Data SQL
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 4
Recap: Big Data Appliance OverviewBig Data Appliance X4-2
Sun Oracle X4-2L Servers with per server:• 2 * 8 Core Intel Xeon E5 Processors• 64 GB Memory• 48TB Disk space
Integrated Software:• Oracle Linux, Oracle Java VM• Oracle Big Data SQL*• Cloudera Distribution of Apache Hadoop – EDH Edition• Cloudera Manager• Oracle R Distribution• Oracle NoSQL Database
* Oracle Big Data SQL is separately licensed
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 5
Recap: Standard and Modular
Starter Rack is a fully cabled and configured for growth with 6 servers
In-Rack Expansion delivers 6 server modular expansion block
Full Rack delivers optimal blend of capacity and expansion options
Grow by adding rack – up to 18 racks without additional switches
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 6
Recap: Harness Rapid Evolution
bb b
BDA 4.0
BDA 4.0 – Sept 2014• Big Data SQL• Node Migration
BDA 3.x – April 2014• CDH 5.0 (MR2 & YARN)• AAA Security• Encryption
BDA 2.x – April 2013• Starter Rack• In-Rack Expansion• EM Integration
BDA 1.0 – Jan 2012• Initial BDA• Mammoth Install
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Operational Simplicity Simplify Access to ALL Data
Core Design Principles for Big Data Appliance
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 8
Operational Simplicity Simplify Access to ALL Data
• Oracle Big Data SQL – Oracle SQL on ALL your data– All Native Oracle SQL Operators– Smart Scan for Optimized Performance
• Oracle Security – Govern all Data through a Single Set of
Security Policies
Core Design Principles for Big Data Appliance
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted 9
Oracle Big Data SQL – A New Architecture
• Powerful, high-performance SQL on Hadoop– Full Oracle SQL capabilities on Hadoop– SQL query processing local to Hadoop nodes
• Simple data integration of Hadoop and Oracle Database– Single SQL point-of-entry to access all data– Scalable joins between Hadoop and RDBMS data
• Optimized hardware– Balanced Configurations– No bottlenecks
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 10
Big Data SQL
SELECT w.sess_id, c.nameFROM web_logs w, customers cWHERE w.source_country = ‘Brazil’AND w.cust_id = c.customer_id;
Relevant SQL runs on BDA nodes
10’s of Gigabytes of Data
Only columns and rows needed to answer query are returned
Hadoop Cluster
Big Data SQL
Oracle Database
CUSTOMERSWEB_LOGS
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 11
Big Data SQL
SELECT w.sess_id, c.nameFROM web_logs w, customers cWHERE w.source_country = ‘Brazil’AND w.cust_id = c.customer_id;
Relevant SQL runs on BDA nodes
10’s of Gigabytes of Data
Only columns and rows needed to answer query are returned
Hadoop Cluster
Big Data SQL
Oracle Database
CUSTOMERSWEB_LOGS
SQL Push Down in Big Data SQL
• Hadoop Scans on Unstructured Data• WHERE Clause Evaluation• Column Projection• Bloom Filters for Better Join Performance• JSON Parsing, Data Mining Model Evaluation
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Feedback Loop
Data Management
Big Data Platform
(Hadoop/NoSQL)
Relational Data Warehouse
(OCDM)
Analytic Apps
Customer Experience
Operations
Monetization
Adapters
ETL/ELT Adapters
Real-Time Adapters
ThirdParty
DataSources
Oracle Comms Apps (BSS/OSS)
Oracle Comms Ntwk Products (Tekelec
& Acme)
Other Oracle Apps (CRM, ERP, etc.)
Third Party Sources
Oracle Communications Data ModelReference Architecture
To Other Apps
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 13
Operational Simplicity Simplify Access to ALL Data
Core Design Principles for Big Data Appliance
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 14
• No Bottlenecks• Full Stack Install and Upgrades• Simplified Management
– Cluster Growth– Critical Node Migration
• Always Highly Available• Always Secure• Very Competitive Price Point
Operational Simplicity Simplify Access to ALL Data
Core Design Principles for Big Data Appliance
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 15
Successful Big Data Systems GrowFrom Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Day 1• 12 node BDA for Production• Hadoop HA and Security Set-up • Ready to Load Data
RCK_1
Full install with a single command:
./mammoth –i rck_1
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Successful Big Data Systems GrowFrom Cluster Install with HA to Large Clusters to Dealing with Operational Issues
NN Example Service: Hadoop Name Nodes
Day 1
RCK_1
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Successful Big Data Systems GrowFrom Cluster Install with HA to Large Clusters to Dealing with Operational Issues
N
RCK_1 RCK_2
Day 90Add 12 New Nodes across two Racks
N
Cluster expansion with a single command:
mammoth –e newhost1,…,newhostn
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 18
Successful Big Data Systems GrowFrom Cluster Install with HA to Large Clusters to Dealing with Operational Issues
N
RCK_1 RCK_2This expansion automatically optimizes HA setup across multiple racks
N
Cluster Expansion with a single command:
mammoth –e newhost1,…,newhostn
Because of uniform nodes and IB networking,no data is moved
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 19
Successful Big Data Systems GrowFrom Cluster Install with HA to Large Clusters to Dealing with Operational Issues
N
RCK_1 RCK_2
N
Day nCritical Node Failure => Primary Name Node
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 20
Successful Big Data Systems GrowFrom Cluster Install with HA to Large Clusters to Dealing with Operational Issues
N
RCK_1 RCK_2
N
• Automatic Failover to other NameNode
• Automatic Service Request to Oracle for HW Failure
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 21
Successful Big Data Systems GrowFrom Cluster Install with HA to Large Clusters to Dealing with Operational Issues
N
RCK_1 RCK_2
N
• Restore HA with a Single commandbdacli admin_cluster migrate N1
• Reinstate the Repaired Node with a Single Command:
bdacli admin_cluster reprovision N1
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 22
Operational Simplicity
Core Design Principles for Big Data Appliance
30%
21%
Quicker to Deploy
Cheaper to Buy
“Oracle Big Data Appliance is an excellent choice for customers looking to work with the full suite of Cloudera’s leading Hadoop-based technology. It’s more cost-effective and quicker to deploy than a DIY cluster.”
⁻Mike Olson, Cloudera founder, Chief Strategy Officer, and Chairman of the Board
Source: ESG White Paper
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Real-time access to better data means better insights, which means better decisions and better business results
Integrate data associated with customer telemetry, configurations, service history, diagnostics, knowledge & support information
Big Data Initiative @ Oracle Global Support Services
Anticipate Detect Predict Automate Delight
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 24
Operational Simplicity
Simplify Access to ALL Data
Core Design Principles Enable Success
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 25
There is one more thing…
Business Value = Applications
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Big Data Appliance powers instant Business Value
Customer Experience Management
Cyber SecuritySolutions
CommunicationsData Model
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 27
Introducing
Paul Kent - SAS
Copyright © 2014, SAS Institute Inc. All rights reserved.
Big Data and Big Analytics – So Much more Gunpowder!Paul KentVP BigData, SAS Research and Development
Copyright © 2014, SAS Institute Inc. All rights reserved.
1. Change 2. Safari Pics
Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.
[CON8279] Oracle Big Data Appliance: Deep Dive and Roadmap for Customers and PartnersOracle Big Data Appliance is the premier Hadoop appliance in the market. This session describes the roadmap for customers in the areas of high-performance SQL on Hadoop and securing big data, plus overall performance improvements for Hadoop.
A special focus in the session is the roadmap and benefits Oracle Big Data Appliance brings to Oracle partners.
To illustrate the benefits of running on a standardized and optimized Hadoop platform, SAS presents the findings of its tests of SAS In-Memory Analytics on Oracle Big Data Appliance.
Copyright © 2014, SAS Institute Inc. All rights reserved.
Agenda1. SAS & Oracle Partnership
2. Family Stories1. Hadoop
2. Oracle Engineered Systems Family
3. SAS Software Family
3. Deployment Patterns
Copyright © 2014, SAS Institute Inc. All rights reserved.
Reflection on a stronger partnership than ever
Both leaders in Big Data –
Jointly solving the most difficult and demanding Big Data Problems
Providing simplicity and agility to create flexible configurations
Extensive engineering collaboration
Can we answer:
How Does it Work?
How Does it Perform?
2014
Copy r ight © 2012, SAS Ins t i tu te Inc . A l l r ights reserved.
THE TAMOXIFEN DILEMMA
SOURCE: http://commons.wikimedia.org/wiki/File:Tamoxifen-3D-vdW.png
Copyright © 2014, SAS Institute Inc. All rights reserved.
Agenda1. SAS & Oracle Partnership
2. Family Stories1. Hadoop
2. Oracle Engineered Systems Family
3. SAS Software Family
3. Deployment Patterns
Copyright © 2014, SAS Institute Inc. All rights reserved.
Copyright © 2014, SAS Institute Inc. All rights reserved.
Elephant :: 3 Good Ideas !!1. Never forgets
2. Is a good (hard) worker
3. Is a Social Animal (teamwork)
Copyright © 2014, SAS Institute Inc. All rights reserved.
MPP (Massively Parallel) hardware running database-like software
“data” is stored in parts, across multiple worker nodes
“work” operates in parallel ,on the different parts of the table
Controller Worker Nodes
Hadoop – Simplified View
Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.
Head Node Data 1 Data 2 Data 3 Data 4…
MYFILE.TXT
..block1 -> block1
..block2 -> block2
..block3 -> block3
Idea #1 - HDFS. Never forgets!
Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.
Head Node Data 1 Data 2 Data 3 Data 4…
MYFILE.TXT
..block1 -> block1 block1 copy2
..block2 -> block2 block2 copy2
..block3 -> block3 copy2 block3
Idea #1 - HDFS. Never forgets!
Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.
Head Node Data 1 Data 2 Data 3 Data 4…
MYFILE.TXT
..block1 -> block1 block1copy2
..block2 -> block2 block2 copy2
..block3 -> block3 copy2 block3X
Idea #1 - HDFS. Never forgets!
X
Copyright © 2014, SAS Institute Inc. All rights reserved.
Redundancy Wins!
Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.
Idea #2 – MapReduce – Send the work to the Data
We Want the Youngest Person in the Room
Each Row in the audience is a data node
I’ll be the coordinator
• From outside to center, accumulate MIN• Sweep from back to front. • Youngest Advances
Copyright © 2014, SAS Institute Inc. All rights reserved.
Agenda1. SAS & Oracle Partnership
2. Family Stories1. Hadoop
2. Oracle Engineered Systems Family
3. SAS Software Family
3. Deployment Patterns
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Recap: Standard and Modular
44
Starter Rack is a fully cabled and configured for growth with 6 servers
In-Rack Expansion delivers 6 server modular expansion block
Full Rack delivers optimal blend of capacity and expansion options
Grow by adding rack – up to 18 racks without additional switches
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Big Data SQL – A New Architecture
• Powerful, high-performance SQL on Hadoop– Full Oracle SQL capabilities on Hadoop– SQL query processing local to Hadoop nodes
• Simple data integration of Hadoop and Oracle Database– Single SQL point-of-entry to access all data– Scalable joins between Hadoop and RDBMS data
• Optimized hardware– Balanced Configurations– No bottlenecks
Oracle Confidential – Internal/Restricted/Highly Restricted 45
Copyright © 2014, SAS Institute Inc. All rights reserved.
Diversity. It’s a good thing!
Impala Nyala
Copyright © 2014, SAS Institute Inc. All rights reserved.
Agenda1. SAS & Oracle Partnership
2. Family Stories1. Hadoop
2. Oracle Engineered Systems Family
3. SAS Software Family
3. Deployment Patterns
Copyright © 2014, SAS Institute Inc. All rights reserved.
4 Important Things
#1 Join the Family
Copyright © 2014, SAS Institute Inc. All rights reserved.
HADOOP
Hive QLSAS
SERVER
SAS ACCESS to Hadoop
#2 Be Familiar
Copyright © 2014, SAS Institute Inc. All rights reserved.
SAS / High Performance Analytics
HADOOP
SAS HPA
Procedures
SAS
SERVER
#3 Use the Cluster!
Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.
Prepare Explore / Transform Model
• HPDS2
• HPDMDB
• HPSAMPLE
• HPSUMMARY
• HPCORR
• HPREDUCE
• HPIMPUTE
• HPBIN
• HPLOGISTIC
• HPREG
• HPNEURAL
• HPNLIN
• HPCOUNTREG
• HPMIXED
• HPSEVERITY
• HPFOREST
• HPSVM
• HPDECIDE
• HPQLIM
SAS / High Performance Analytics
•HPLSO
•HPSPLIT
•HPTMINE
•HPTMSCORE
Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.
Controller
Client
SAS / High Performance Analytics
Copyright © 2014, SAS Institute Inc. All rights reserved.
Copyright © 2014, SAS Institute Inc. All rights reserved.
#1 Join the Family
#2 Be Familiar
#3 Use the cluster
#4 Have a pretty face!
Copyright © 2014, SAS Institute Inc. All rights reserved.
Copyright © 2014, SAS Institute Inc. All rights reserved.
Copyright © 2014, SAS Institute Inc. All rights reserved.
4 Important Things (for cluster friendly software)
1.Join the Family
2.Be Familiar
3.Performance
4.Have a pretty face
Copyright © 2014, SAS Institute Inc. All rights reserved.
Agenda1. SAS & Oracle Partnership
2. Family Stories1. Hadoop
2. Oracle Engineered Systems Family
3. SAS Software Family
3. Deployment Patterns
62Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
SAS BIG DATA ON BIG DATA APPLIANCE
• Flexible Architectural options for SAS deployments• Can run on Starter, Half and Full configurations
• Optionally select nodes “N, N-1, N-2, …” for additional SAS
Services such as SAS Compute Tier, SAS MidTier
• Optionally select node subset “N, N-1, N-2, N-3, …) for more
dedicated resources for SAS Analytic Compute Environment by
shifting Big Data Appliance roles
• Option to selectively add more memory on a per node basis
depending on specific workload distribution
63Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
SAS Midtie
r
STARTER BDA
…
…
SAS Visual Analytics
Metadata ServerSAS Compute
SAS HPA Root Node
SAS VISUAL ANALYTICS, HIGH-PERFORMANCE ANALYTIC COMPUTE ENVIRONMENT CO-LOCATED WITH HADOOP
64Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
SAS Midtie
r
STARTER BDA
…
…
SAS Visual Analytics
Metadata ServerSAS Compute
SAS HPA Root Node
SAS VISUAL ANALYTICS, HIGH-PERFORMANCE ANALYTIC COMPUTE ENVIRONMENT CO-LOCATED WITH HADOOP
Consider:
Extra Memory for 5,6?
65Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
SAS Midtie
r
FULL RACK BDA
…
LASR Worker
17
HDFS Data 17
…
…
Metadata ServerSAS Compute
SAS HPA Root Node
LASR Worker
18
HDFS Data 18
SAS VISUAL ANALYTICS, HIGH-PERFORMANCE ANALYTIC COMPUTE ENVIRONMENT CO-LOCATED WITH HADOOP
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FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA
67Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA
68Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA
69Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA
Basic Smoke Tests Confirmed:Interoperate with Hadoop and Map Reduce
Read and Write text files to/from HDFS
Read and Write Tabular files to/from Hive (will confirm Oracle BIGSQL in OSC-SC)
Read and Write SAS binary format files to/from HDFS
High Degree Of Parallelism (DOP) reads via Map-Only jobs
SAS LASR server co-exists on/with datanodes
SAS HPA tasks scheduled on datanodes
70Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
Table 1: Summation of 5/20/100/200 columns; Baseline: DOP=1 (no parallelism)120M rows, 400 columns, reg_simtbl_400
SAS High-Performance Analytics PerformanceSAS Format Data (SASHDAT)
1107 var11.795 Mobs97GB5.7GB/node
1107 var73.744 Mobs608GB35.7GB/node 6x
Create 208.79 sec 2284.29 sec 11
Scan/Count 24.60 sec 259.38 sec 10.5
HPCORR 295.20 1410.40 4.7
HPCNTREG 336.79 1547.59 4.6
HPREDUCE (u) 236.55 2467.76 10.4
HPREDUCE (s) 219.50 2037.74 9.3
71Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
OSC-AU FullRack BDA
• 408 Threads
• 600 GB dataset
• 17 servers
Your Problem solved ASAP
72Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
73Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
74Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
75Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
EXADATA INTEGRATION
SAS EMBEDDED PROCESSING (EP) TO EXADATALEVERAGING BIG DATA SQL
…
SAS Midtie
r
LASR Worker
18
…HDFS Data 18
SAS Visual Analytics
Metadata ServerSAS Compute
SAS HPA Root Node
SAS EP
Big Data SQL
76Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
Table 1: Summation of 5/20/100/200 columns; Baseline: DOP=1 (no parallelism)120M rows, 400 columns, reg_simtbl_400
SAS High-Performance Analytics PerformanceSAS EP Parallel Data Feeders
DOP=1 DOP=24 DOP=24(flash cache)
Add(5) 1.25min 1.5min .5min
Add(20) 2.5min 1.5min .5min
Add(100) 13min 1.5min .6min
Add(200) 16min ~2min 1.25min (10x)
77Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
Table 2: Scan times for 2 tables (200 columns, 400 columns, 120M rows); Baseline: SAS/ACCESS vs. HPA EP feeder
SAS High-Performance Analytics PerformanceSAS EP Parallel Data Feeders
Access Access /DBSlice
SAS HPAUsing EP
Reg_sim_200 1:01:12 0:28:37 0:08:00
Reg_sim_400 1:49:11 0:55:33 0:16:05 (7x!)
78Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
Table 1: Summation of 5/20/100/200 columns; Baseline: DOP=1 (no parallelism)120M rows, 400 columns, reg_simtbl_400
SAS High-Performance Analytics PerformanceSAS Format Data (SASHDAT) and Oracle EXADATA
1107 var11.795 Mobs97GB5.7GB/nodeSASHDAT
907 var11.795 Mobs79.7GB4.7GB/nodeEXADATA
1107 var73.744 Mobs608GB35.7GB/nodeSASHDAT
Create 208.79 sec 931.22 sec 2284.29 sec
Scan/Count 24.60 sec 956.16 sec 259.38 sec
HPCORR 295.20 833.24 1410.40
HPCNTREG 336.79 756.97 1547.59
HPREDUCE (u) 236.55 1055.11 2467.76
HPREDUCE (s) 219.50 1051.93 2037.74
79Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
ORACLE ENGINEERED SYSTEMS FOR
SuperClusterExaData ExaLogic Virtual
Compute
Appliance
ZFS
Storage
Appliance
Big Data Appliance
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SAS AND ORACLE WORKING TOGETHER TO CREATE CUSTOMER VALUE
• Joint R & D development and Product Management teams in Cary and Redwood Shores
• Focus on driving SAS technology components to run natively in Oracle database
• Joint performance engineering optimizations
• Template physical architectures developed based on use-cases
• Physically tested and benchmarked together
• Reduction in physical effort• Overall reduction in lifecycle
costs
• Best Practice papers• SAS and Oracle Engineers
provide joint "Sizing and Architecture Analysis and Design"
Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.
SAS AND ORACLEBETTER TOGETHER
Paul.Kent @ sas.com
@hornpolish
paulmkent
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted 82