Mike Spicer is the lead architect for the IBM Streams team. In his presentation, Mike provides an overview of the many key new features available in IBM Streams V4.1. Simpler development, simpler management, and Spark integration are a few of the capabilities included in IBM Streams V4.1.

1. © 2015 IBM Corporation
What’s New With Streams
Mike Spicer
Lead Architect IBM Streams

2. • IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal
without notice at IBM’s sole discretion.
• Information regarding potential future products is intended to outline our general product direction
and it should not be relied on in making a purchasing decision.
• The information mentioned regarding potential future products is not a commitment, promise, or
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• The development, release, and timing of any future features or functionality described for our
products remains at our sole discretion.
Performance is based on measurements and projections using standard IBM benchmarks in a
controlled environment. The actual throughput or performance that any user will experience will vary
depending upon many factors, including considerations such as the amount of multiprogramming in the
user’s job stream, the I/O configuration, the storage configuration, and the workload processed.
Therefore, no assurance can be given that an individual user will achieve results similar to those stated
here.
Please Note:
2

3. Streams Releases in 2015
2
Streams V4.0
• Excel user access
• Automated High Availability
• Next Generation Architecture
• Multi instance management
• Guaranteed processing
• Toolkits enhancements
March

4. Streams Releases in 2015
3
Streams V4.0
• Excel user access
• Automated High Availability
• Next Generation Architecture
• Multi instance management
• Guaranteed processing
• Toolkits enhancements
March
July
Streams V4.0.1
• Resource Manager support
including YARN
• Toolkit enhancements
• Guaranteed Processing
• Geospatial toolkit

5. Streams Releases in 2015
4
Streams V4.0
• Excel user access
• Automated High Availability
• Next Generation Architecture
• Multi instance management
• Guaranteed processing
• Toolkits enhancements
March
July
Streams V4.0.1
• Resource Manager support
including YARN
• Toolkit enhancements
• Guaranteed Processing
• Geospatial toolkit
October
Streaming Analytics Service
• Fully managed BlueMix Service
• Create an instance in seconds
• Easily deploy and manage applications
• Analytics & Connectors
• Messaging, stores, geospatial,
timeseries, SPSS and more

6. Streams Releases in 2015
5
Streams V4.0
• Excel user access
• Automated High Availability
• Next Generation Architecture
• Multi instance management
• Guaranteed processing
• Toolkits enhancements
March
July
Streams V4.1
• Data Lineage and Governance
• Java application API
• Customizable & application dashboards
• Support for Spark MLLib
• New DPS (Redis) & CyberSecurity Toolkits
• Stronger and Simpler security
• Platform Symphony Support
• New Platforms including Docker
Streams V4.0.1
• Resource Manager support
including YARN
• Toolkit enhancements
• Guaranteed Processing
• Geospatial toolkit
November
October
Streaming Analytics Service
• Fully managed BlueMix Service
• Create an instance in seconds
• Easily deploy and manage applications
• Analytics & connectors
• Messaging, stores, geospatial,
timeseries, SPSS and more

7. What’s New in V4.1

8. IBM Streams V4.1 Overview
• Simpler Development
 Generating connectors and schemas based on Governance
catalog
 Java Application API to develop Streams apps entirely in Java
• Simpler Management
 Customizable & application centric dashboards
 Lineage and Governance of Streams applications
• Spark integration and new analytics & toolkits
 Support for using Spark MLLib analytics in Streams applications
 New DPS (Redis) & CyberSecurity Toolkits
• Stronger and Simpler security
• New Resource Managers & Platforms
• Improved Guaranteed processing
7

9. Java Application API
• Allow Java developers to create IBM Streams applications in Java
 Natural for Java developer, No SPL knowledge required
• Note: This is not a replacement for SPL, its an additional way to develop
streaming applications in the Streams ecosystem
 Open Source https://github.com/IBMStreams/streamsx.topology
• Functional Style with operations on Streams and Windows
 First class objects are TStream and TWindow
• SPL has operator invocation as its first class object
• Utilize existing features & analytics
 Parallel Regions, Checkpointing, PE and Host Placement (with host tags)
 Support for SPL Streams, Composites and Toolkits
• Executes in embedded and distributed modes
 Deployable to Bluemix streaming analytic service
• Dynamic connections with pub/sub mechanism
• Supports Scala through the Java API with implicit conversions
 https://github.com/IBMStreams/streamsx.topology/wiki/Scala-Support
8

10. Building & Deploying
Java Streaming Applications
1. Declare Topology

11. Building & Deploying
Java Streaming Applications
1. Declare Topology
2. Submit Topology

12. Lineage and Governance - Asset Discovery
• IBM Governance Catalog asset discovery in Streams studio
• Drag and drop assets from the catalog directly onto the graph
• Code generation based on assets available properties
• Connection info, Schemas etc.
11

13. Lineage and Governance – Asset Registration
• Registration of new assets in the Catalog from Streams studio
• Automatic asset and flow registration of Streams applications
• Streams instance properties to mark instance as governed
• Applications automatically registered, source, sink operators
connected to the underlying catalog assets they use
12
Source and sink operators registered as
Input and Output Streams in application
Flows registered to the files and directories used
Details about the application execution

14. Lineage and Governance – Data Lineage
• Streams application data lineage available in Catalog
13
By Application to see data source and
targets for a Streams application
By data source/target to see who
uses or modifies specific data
By Stream Import & export to see which
streams applications connect to each other.

15. Support for SparkMLLib
• Toolkit provides the ability to use SparkMLLib models on
Streams
 On Spark use SparkMLib to create and save a model
 On Streams SparkMLLib operators load the model and perform
scoring on the stream
 Open Source
https://github.com/IBMStreams/streamsx.sparkMLLib
• Operators/Models supported
 Classification : Linear SVM and Naïve Bayes
 Clustering : Clustering KMeans
 Collaborative filtering : Collaborative Filtering ALS
 Regression : Isotonic, Linear and Logistic Regression
 Tree : Decision tree, Ensemble Gradient Boosted Trees and Ensemble Random Forest
14

16. • Spark
 Toward a better Hadoop ecosystem
 Better data access & programming language
support
• Spark Streaming
 Time-based batch processing
 Simple integration with Spark
• Streams
 Complements both capabilities
 Interoperate via Kafka & HDFS
 Can Score Spark MLlib models faster (as
well as SPSS, R, PMML and Matlab models)
 Enterprise integration
 Low latency
 Event processing
 Extended analytics
Spark and Streams: Better Together
Batch
Processing
Stream
Processing
Micro
Batch
IBM InfoSphere
Streams
Apache Kafka
Streams
Apache Spark
Data Producers
and Consumers

17. New Toolkits & Accelerators
• Cybersecurity Accelerator
 Provides composites for accelerating development of cybersecurity
applications
• Sources to read packet data
• Models for analyzing DNS traffic :
– DomainProfiling: Detects if any domains are behaving suspiciously
– HostProfiling: Detects if individual hosts are behaving suspiciously
– PredictiveBlacklisting: Predicts if a domain should be added to an internal blacklist
• Distributed Process Store Toolkit
 Provides the ability to manage application state using Redis key value store
• State can be shared across PEs, applications and hosts
– Based on open Source https://github.com/IBMStreams/streamsx.dps
– Store API: Managing stores and CRUD operations on KV data
– Time to Live API: A global store where KV data expires after a specified time
– Distributed Locking API: Managing and using distributed locks
16

18. Other Features
• Customizable & Application Centric Dashboard
 Create custom dashboards for the data you need, in the format you want
 Application dashboard focuses on application management
• Health summary, streams graph, flow rate chart, resource load, PE scatter chart
• Improved Guaranteed processing
 Messaging toolkit support
 Incremental checkpointing
 SPL operator exception handling
• IBM Platform Symphony Resource Manager Support
 Supports allocation and release of resources managed by Platform Symphony
• Security Enhancements
 Authentication via PKI certificates and pluggable authentication modules
• X.509 certificates and Java Authentication and Authorization Service (JAAS) modules
 LDAP Authorization without Anonymous Bind
 REST API support for Cross-Origin Resource Sharing (CORS)
• Support for Docker, Power8 Little Endian & RHEL 7
17

19. Streaming Analytics Service on Bluemix
• The capabilities of IBM Streams on the cloud
• Fully managed BlueMix Service
 Multi-tenant, Highly available and elastic service
 Create an instance in seconds
 Easily deploy and manage applications
 Grow and Shrink resources available
• Integration with other blue mix services
 Internet of Things, Cloudant, Message Hub,BigInsights HDFS
 More coming soon
• Analytics & connectors
 HTTP, Web Sockets, TCP, FTP, Apache Kafka, MQTT, JMS,
and XMS (MQ)
 Geospatial, TimeSeries, SPSS, R, CEP, Text and Telco
18

20. 19
Notices and Disclaimers
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21. 20
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