The document compares the performance of IBM Streams to other streaming analytics offerings using the Linear Road benchmark, finding that IBM Streams achieved an L-Rating of 200 using 4 Azure nodes, significantly outperforming Apache Apex and Apache Storm. It also describes how Walmart uses streaming analytics for real-time inventory control and logistics monitoring, and how IBM Streams was able to implement the Linear Road benchmark in under 15 days of development time.

1. © 2016 IBM Corporation1
IBM Streams
22 April 2016
Matt Grover, Walmart ISD Enterprise Architecture
Roger Rea, IBM Streams Offering Manager
Mike Spicer, IBM Lead Architect IBM Streams
Linear Road Benchmark
Performance Comparison of Streaming Analytic Offerings

2. © 2016 IBM Corporation2
Walmart and IBM
Today, nearly 260 million
customers visit our more than
11,500 stores under 72 banners
in 28 countries and e-commerce
sites in 11 countries each week.
We employ 2.2 million
associates around the world,
1.4 million in the U.S. alone.
International Business Machines
Corporation ('IBM') is a globally
integrated enterprise operating in
over 170 countries, has 380.000
employees. It brings innovative
solutions to a diverse client base to
help solve some of their toughest
business challenges.

3. © 2016 IBM Corporation3
Why rewrite Linear Road
• No comprehensive streaming benchmark
available
• The storage design did not represent the current
state of streaming data systems
Requirement for Streaming Analytics at Walmart
• Worldwide monitoring of logistics
• Real time inventory control
• Real time analytics
Linear Road Benchmark (2004)
• Original White paper (link)
• Open Source benchmark
• Enables comparison between offerings
• Sophisticated application with state management
Why Linear Road?

4. 4 © 2016 IBM Corporation© 2016 IBM Corporation
Linear Road Benchmark
 Linear city is a fictional metropolis 100x100 miles
 10 Expressways every 10 miles
 Every mile each has an exit and onramp
 Each expressway has 4 lanes in each direction
 3 travel lanes and one lane for entrance and exit
 Every vehicle emits position report every 30 seconds
 One accident occurs randomly on each expressway
every 20 minutes, taking 10 to 20 minutes to clear
Linear Road on github

5. 5 © 2016 IBM Corporation© 2016 IBM Corporation
Linear Road Benchmark
Four types of events
 Type 0: 99% of events are real-time position reports
 Type 2: Historical requests for account balances
 Type 3: Daily expenditure for a specific day in the
past 10 weeks
 Type 4: travel time predictions
GOAL: Maximum L-Rating
(max # expressways)
Linear Road on github

6. 6 © 2016 IBM Corporation© 2016 IBM Corporation
High level Linear Road architecture
Linear Road
data generator
Courtesy of:
Wal-Mart Stores Inc.
Linear Road
(Solution
implementation
using vendor
specific Streaming
analytics middleware)
Results
Validator
(Rewritten in
Python by
Wal-Mart Stores Inc.)
Determine
L-Rating

7. 7 © 2016 IBM Corporation© 2016 IBM Corporation
Why did IBM select Redis ?
 Great maturity level
 Top performance
 API is tremendously easy and very flexible
 Clustered in memory Key Value Store with fault tolerance
 Option for in memory or in memory backed by persistence
 Easy installation and monitoring

8. 8 © 2016 IBM Corporation© 2016 IBM Corporation
High level Linear Road architecture with Redis and IBM Streams
Linear Road Data
Feeder streaming
the events via
TCP or Kafka
Type 3 results
Event
router
TCP
receiver
Data Feeder IBM Streams Linear Road logic
Kafka
consumer
Daily
expenditure
analytics
Account
Balance
analytics
Type 2 results
Position
report
analytics
(for each
xway and
direction)
1 .. N
Type 1 accident alerts
Type 0 toll notifications
Historical reference
data loader (A
separate Streams
application)
Distributed
state keeper

9. 9 © 2016 IBM Corporation© 2016 IBM Corporation
Cloud Service (all nodes on a vnet named Subnet-1)
Streaming analytics test bed
Linux or
Windows
Jump box
IBM
Network
Subnet-1
CPU: Intel Xeon E5-2670 @ 2.60 GHZ
(16 cores on all the machines)
Memory: 110GB on Nodes 1 to 6)
Redis: Total of 10 instances running on
5 machines
Streams
Management Server
[Node 1]
Streams Application
Server [Node 2]
Streams Application
Server [Node 3]
Streams Application
Server [Node 4]
Streams Application
Server (Ingest)
[Node 5]
Standby and scratch
work Server [Node 6]

10. 10 © 2016 IBM Corporation
Streams results
 L-Rating 50 on one Azure node, 200 on
4 Azure nodes
 1 node, 16 cores, nearly 1B events
 4 nodes, 64 cores, nearly 4B events
 Linear scalability
 Handles bursty traffic
 99% of responses sub-second
# of x-ways # of cars Entries Memory CPU
1 278973 19.2 Million 2.2 GB 2%
2 558726 38.5 Million 4.5 GB 4%
5 1.3 Million 96.3 Million 10.9 GB 7%
10 2.7 Million 192.5 Million 22.0 GB 11%
15 4.1 Million 289.7 Million 33.0 GB 16%
20 5.6 Million 385.2 Million 43.5 GB 20%
25 6.9 Million 482.0 Million 54.5 GB 26%
50 14.0 Million 963.1 Million 109.0 GB 31%
100 27.6 Million 1.9 Billion 220 GB 22%
150 41.5 Million 2.8 Billion 330 GB 33%
200 55.0 Million 3.8 Billion 440 GB 45%
0
50
100
1 10 20 30 40 50
Avg.
Throughput(K
events/second)
Number of expressways
0
100
200
300
400
50 100 150 200Avg.
Throughput(K
events/second)
Number of expressways

11. 11 © 2016 IBM Corporation
Streams results
 Development effort: one person, 14.5 days
 1.5 days install Linux & Streams on 5 Azure nodes
 2 days design application
 8 days iterative development
 3 days unit testing & tuning
 Scale automated with User Defined Parallelization
One Way

12. 12 © 2016 IBM Corporation© 2016 IBM Corporation
Comparison to other technologies
Technology Hardware
on Azure
L-Rating
IBM Streams Option 1 200
Apache Apex Option 1 102
Apache Storm Option 2 10
Four nodes of Option 1 or 2 for application processing:
Option 1: Azure A11 (16 cores, 112 GB RAM, 382
GB Disk, 10 Gbit/s networking), or
•CPU model: 45, Intel(R) Xeon(R) CPU E5-
2670 0 @ 2.60GHz
Option 2: Azure D14 (16 cores, 112 GB RAM, 800
GB Disk (SSD), 1 Gbit/s)
•CPU model: 45, Intel(R) Xeon(R) CPU E5-
2660 0 @ 2.20GHz
Two nodes for ingesting data:
•If A11 selected, then A10 (8 cores, 56 GB RAM,
382 GB Disk, 10 Gbit/s networking
•If D14 selected, then D13 (8 cores, 56 GB RAM,
400 GB Disk, 1 Gbit/s)
Plus: an A10 or D13 Windows Server, or Linux, jump box
(Windows if a GUI is needed)
Six total nodes
IBM Streams:
2x better than Apex
20x better than Storm
* Twitter has replaced Storm

13. 13 © 2016 IBM Corporation© 2016 IBM Corporation
IBM recognized as a leader
The Forrester Wave™: Big Data
Streaming Analytics Platforms, Q1 ‘16
The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of
Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in
the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.
“IBM’s architecture can flex to handle any
streaming challenge.”
IBM had the highest possible scores in
Architecture, Operational Management,
Streaming Operators, Application Development
and Business Applications, Roadmap, Ability to
Execute, Implementation Support and Partners.
.
“The development environment provides one
of the richest set of operators in the market.”
“Streams can ingest and understand the always-
on stream of data to make the decisions that
underlie cognitive solutions.”
© 2016 IBM Corporation

14. 14 © 2016 IBM Corporation© 2016 IBM Corporation
Stream Computing
OpenSource
Extensibleplatform
ManagedService
Batch&Streaming
CommandLinei/face
Web&JMXmgmt
AtLeastOnce
Exactlyone
State
Windows
Backpressure
MachineLearning
Modelscoring
Video/Image
Geospatial
TextAnalytics
Visualdevelopment
AutomatedHA
Enterpriseadapters
Opensourceadapters
Esper
IBM Streams
Storm
Flink
Spark Streaming
Dataflow

15. 15 © 2016 IBM Corporation© 2016 IBM Corporation
Affordable Realtime Analytics
IBM Streams
100 Azure nodes
$110,261/Mo
5 Azure nodes
$5,513/Mo

16. 16 © 2016 IBM Corporation© 2016 IBM Corporation
Streams is the industry leading stream computing runtime for real time
analytic processing for large-scale, in-memory distributed data processing.
Why do customers choose Streams?
• Superior performance and low latency
• Superior reliability and management
• Widest range of adapters
• Rapid development/debug capabilities
• User Community – StreamsDev, github
• Advanced Analytics – Machine Learning, Audio/Video, Geospatial,
Natural Language Processing
• Enterprise integration & reliability
• IBM worldwide services and support
IBM Streams Success

17. 17 © 2016 IBM Corporation© 2016 IBM Corporation
Additional resources
Visit:
ibm.com/streams
github.com/Walmart
github.com/IBMStreams/benchmarks