The document discusses streaming analytics and the WSO2 Stream Processor, highlighting its capabilities in orchestrating data flow, detecting patterns, and providing real-time analytics across various domains. It presents various use cases, such as sweet factory management, and outlines streaming data pre-processing, storage integration, and machine learning for predictive analytics. The WSO2 solution emphasizes ease of use, scalability, and efficient deployment, making it suitable for handling large event streams.

1. Streaming Analytics Patterns for
Your Digital Enterprise
Associate Director/Architect, WSO2
Sriskandarajah Suhothayan

2. • Introduction to streaming analytics
• About WSO2 Analytics Offering
– WSO2 Stream Processor
• Streaming analytics patterns
• Managing patterns
• Deployment of streaming analytics
Outline

3. What is
Streaming Analytics?

4. Software that provides analytical operators to
orchestrate data flow, calculate analytics, and
detect patterns on event data from multiple,
disparate live data sources to allow developers
to build applications that sense, think, and act
in real time.
- Forester
Streaming Analytics

5. WSO2 Analytics
Offering Stream Processor Core
Events
JMS, Thrift, SMTP, HTTP, MQTT, Kafka
Analytics Fabric
Complex Event
Processing
Incremental Time Series
Aggregation
Machine
Learning
Extension Store
FinancialandBanking
Analytics
RetailAnalytics
LocationAnalytics
OperationalAnalytics
SmartEnergyAnalytics
Custom
Analytics
Solutions
...
Solutions
StatusMonitoring
Rule
Mgmt.
Dashboard

6. WSO2 Stream Processor

7. • Lightweight, lean & cloud native
• Easy to learn Streaming SQL
• Native support for streaming Machine Learning
• Long term aggregations without batch analytics
• High performance analytics with just 2 nodes (HA)
• Highly scalable deployment with exactly-once
processing
• Tools for development, monitoring and business users
Overview of WSO2 Stream Processor

8. Siddhi App
Single configuration
for Analytics!
Stream Processing
from Sales#window.time(1 hour)
select region, brand, avg(quantity) as AvgQuantity
group by region, brand
insert into LastHourSales ;
Stream
Processor
Siddhi App
{ Siddhi }
Input Streams Output Streams
Filter Aggregate
JoinTransform
Pattern
Siddhi Extensions
Streaming SQL

9. Streaming Analytics Patterns

10. • To know what stream processing can do!
• To understand the difference between
– database applications & stream processing
• Where to use what?
• Best practices
Why Patterns ?

11. 1. Streaming data pre processing
2. Data store integration
3. Streaming data summarization
4. KPI analysis and alerts
5. Event correlation
6. Trend analysis
7. Real-time prediction
8. Streaming machine learning
Streaming Analytics Patterns

12. Use Case :
Sweet Factory Management

13. Use Case :
Sweet Factory Management

14. • Monitor Supply, Production & Sales
• Optimize resource utilization
• Detect and alert failures
• Predict demand
• Manage processing rules online
• Visualise realtime performance
Use Case : Sweet Factory Management

15. • Collect events from multiple sources
• Convert them to streams
• Filter events
• Add defaults to missing fields
• Change event stream structure
1. Streaming Data Pre Processing
Filtering, Add Defaults, and Projection
Filter
Transform
Process
Add
Defaults

16. 1. Streaming Data Pre Processing
@app:name(‘Sweet-Factory-Analytics’)
define stream SweetProductionStream(name string, amount double);
Define Stream

17. 1. Streaming Data Pre Processing
@app:name(‘Sweet-Factory-Analytics’)
@source(type = mqtt, …, @map(type = json, …))
define stream SweetProductionStream(name string, amount double);
Consume Events :
MQTT, HTTP, TCP, Kafka, JMS, RabitMQ, etc.
Map Events to Streams :
JSON, XML, Text, Binary, WSO2Event, KeyValue, etc.

18. 1. Streaming Data Pre Processing
@app:name(‘Sweet-Factory-Analytics’)
@source(type = mqtt, …, @map(type = json, …))
define stream SweetProductionStream(name string, amount double);
from SweetProductionStream
select *
insert into LawCandyProdcutionStream ;
Write Query

19. 1. Streaming Data Pre Processing
@app:name(‘Sweet-Factory-Analytics’)
@source(type = mqtt, …, @map(type = json, …))
define stream SweetProductionStream(name string, amount double);
from SweetProductionStream[amount < 100 and name == ‘candy’]
select *
insert into LawCandyProdcutionStream ;
Filter

20. 1. Streaming Data Pre Processing
@app:name(‘Sweet-Factory-Analytics’)
@source(type = mqtt, …, @map(type = json, …))
define stream SweetProductionStream(name string, amount double);
from SweetProductionStream[amount < 100 and name == ‘candy’]
select name, (amount * 0.05) + 5 as cost, ‘GBP’ as currency
insert into LawCandyProdcutionCostStream ;
Transformation and Defaults

21. 1. Streaming Data Pre Processing
@app:name(‘Sweet-Factory-Analytics’)
@source(type = mqtt, …, @map(type = json, …))
define stream SweetProductionStream(name string, amount double);
from SweetProductionStream[amount < 100 and name == ‘candy’]
select name, calculateCost(amount,name) as cost, ‘GBP’ as currency
insert into LawCandyProdcutionCostStream ;
Functions :
Inbuilt, Custom UDF or Siddhi Extensions

22. • Incoming events to perform
operations on data stores
• Optimizations with
Primary and Indexing Keys
2. Data Store Integration
Store, Retrieve and Modify
• Update
• Contains
• Search
• Insert
• Delete

23. 2. Data Store Integration
define stream SweetProductionStream(name string, amount double);
define table ProductionTable(id string, name string, amount double);
Define Table (In-Memory)

24. 2. Data Store Integration
define stream SweetProductionStream(name string, amount double);
@primaryKey(‘id’)
@Index(amount)
define table ProductionTable(id string, name string, amount double);
Index and Primary Keys

25. 2. Data Store Integration
define stream SweetProductionStream(name string, amount double);
@store(type=‘rdbms’, … )
@primaryKey(‘id’)
@Index(amount)
define table ProductionTable(id string, name string, amount double);
Table Backed by:
RDBMS, MongoDB, HBase, Cassandra, Solr, Hazelcast, etc.

26. 2. Data Store Integration
define stream SweetProductionStream(name string, amount double);
@store(type=‘rdbms’, … )
@primaryKey(‘id’)
@Index(amount)
define table ProductionTable(id string, name string, amount double);
from SweetProductionStream
select UUID() as id, name, amount
insert into ProductionTable; Insert into Table

27. 2. Data Store Integration
define stream SweetProductionStream(name string, amount double);
@store(type=‘rdbms’, … )
@primaryKey(‘id’)
@Index(amount)
define table ProductionTable(id string, name string, amount double);
from SweetProductionStream
select UUID() as id, name, amount
update or insert into ProductionTable
set ProductionTable.amount = amount
on ProductionTable.name == name;
Update or Insert into Table

28. • Sum, Count, Min, Max, etc.
within the
– last 5 minutes
– last 20 events
3. Streaming Data Summarization
Aggregations Over Short Time Periods

29. 3. Streaming Data Summarization
define stream SweetProductionStream(name string, amount double);
from SweetProductionStream#window.time(‘1 min’)
select name, sum(amount) as totalSweets, currentTimeMillis() as timestamp
group by name
insert into LastMinProdStream;
Windows Sliding and Batch for Time, Length, etc.

30. • Incremental Aggregation for every
– Seconds, Minutes, Hours, Days, …, Year
• Support for out-of-order event arrival
• Fast data retrieval from memory and disk for
realtime update
3. Streaming Data Summarization
Current Min
Aggregations Over Long Time Periods
Current Hour
Sec
Min
Hour
0 - 1 - 5 ...
- 1
- 2 - 3 - 4 - 64 - 65 ...
- 2
- 124

31. 3. Streaming Data Summarization
Aggregations Over Long Time Periods
define stream SweetProductionStream(name string, amount double);
define aggregation ProductionAggirgation
from SweetProductionStream
select name, sum(amount) as totalSweets, count(*) as noOfPacks
group by name
aggregate every seconds ... years ;
Predefined Aggregation

32. 3. Streaming Data Summarization
Aggregations Over Long Time Periods
from ProductionAggirgation
select name, totalSweets, noOfPacks
on name == ‘candy’
within 2017/01/01 2017/02/01
per day ;
Data Retrieval for Dashboards

33. • Generate dashboard and widgets
• Fine grained permissions
– Dashboard level
– Widget level
– Data level
• Localization support
• Inter widget communication
• Shareable dashboards with widget
state persistence
Dashboard for
Business Users

34. • Identify KPIs using
– Filter, ifThenElse, having, etc.
• Send alerts using Sinks
4. KPI Analysis and Alerts
Generate Alerts Based on KPIs

35. 4. KPI Analysis and Alerts
define stream LastMinProdStream (name string, totalSweets long,
timestamp long);
@sink(type=‘email’, to=‘manager@sf.com’
@map(type=‘text’,
@payload(‘‘‘
Low Production of {{name}} at factory {{factoryId}}.
’’’))
define stream LowProdAlertStream (name string, factoryId int, ...);
from LastMinProdStream[totalSweets < 5000]
insert into LowProdAlertStream;
Publishing with Mapping via :
Email, HTTP, TCP, Kafka, RabbitMQ, MQTT, etc.

36. • Identify complex patterns
– Followed by, non-occurrence, etc.
• Identify trends
– Peek, triple bottom, etc.
Event Correlation and Trend Analysis
Complex Event Processing

37. 5. Event Correlation
Patterns
define stream RawMaterialStream (name string, amount double);
define stream ProductionInputStream (name string, amount double);
from every (e1 = RawMaterialStream)
-> not ProductionInputStream[name == e1.name and
amount == e1.amount] for 15 min
select e1.name, e1.amount
insert into ProductionStartDelayed ;
Non-occurrence of an event detection

38. 6. Trend Analysis
Sequences
define stream LastMinProdStream (name string, totalSweets long,
timestamp long);
partition with (name of LastMinProdStream)
begin
from every e1=LastMinProdStream,
e2=LastMinProdStream[timestamp - e1.timestamp < 10
and e1.amount > amount]*,
e3=LastMinProdStream[timestamp - e1.timestamp > 10
and e2[last].amount > amount]
select e1.name, e1.amount as initialAmount, e3.amount as finalAmount
insert into ContinousProdReductionStream ;
end;
Identify decreasing
trend for 10 mins

39. • Import Machine Learning Models
for realtime predictions
– PMML, TensorFlow, etc
• Streaming Machine Learning
– Clustering, Classification, Regression
– Makove Models, Anomaly detection, etc...
Realtime Learning and Predictions
Machine Learning

40. define stream SugarSyrupDataStream (temperature double, density double);
from SugarSyrupDataStream#pmml:predict(“/home/user/ml.model”, “string”)
select *
Insert into PredictionStream ;
7. Real-Time Predictions
Run ML Models In Realtime

41. define stream SugarSyrupDataStream (temperature double, density double);
define stream SugarSyrupResultStream (temperature double, density double,
decision string);
from SugarSyrupResultStream
#streamingml:hoeffdingTreeTrain(‘Model’,temperature,density,decision)
...
from SugarSyrupDataStream
#streamingml:hoeffdingTreeClassifie(‘Model’,temperature,density)
...
8. Streaming Machine Learning
Continuous Learning and Feedback
Online Training !
Online Prediction !

42. Managing Patterns

43. Business
Rules for Non
Technical
Users
Define your own
Business Rules from
scratch
Modify templated
complex Business
Rules using rule
parameters

44. Editor
Debugger
Simulation
Testing
All in one
Development
Studio
for Siddhi Apps

45. Development
Studio
Editing
Simulation
Debugging
Testing
All in one

46. Streaming Analytics
Deployment

47. • High Performance
– Process around 100k events/sec
– Just 2 nodes
– While most others need 5+
• Zero Downtime
• Zero Event Loss
• Simple deployment with RDBMS
– No zookeeper, kafka, etc
• Multi Data Center Support
Stream Processor
Stream Processor
Minimum HA With 2 Nodes
Event Sources
Event
Store
Dashboard
Notification
Invocation
Data Source
Siddhi App
Siddhi App
Siddhi App
Siddhi App
Siddhi App
Siddhi App

48. • Exactly-once processing
• Fault tolerance
• Highly scalable
• No backpressure
• Distributed development configurations via
annotations
• Plugable distribution options (YARN, K8, etc)
Distributed Deployment with Kafka

49. Stream Processor
Stream Processor
Distributed Deployment with Kafka ...
Siddhi App
Siddhi App
KafkaEvent Sources Siddhi App
Event
Store
Dashboard
Notification
Invocation
Data Source
Siddhi App
Siddhi App
Stream Processor
Siddhi App
Worker
Siddhi App
Siddhi App
Siddhi App
Stream Processor
Job Manager
Worker
Worker

50. Monitor Apps, Nodes and Clusters

51. • Give your everything you need to build
Streaming Analytics
– Manage data streams
– Powerful Streaming SQL language
– Dashboards and more
• Can provide 100K+ events per second with two
node HA ( most alternatives need 5+ nodes) and
can scale more on top of Kafka
WSO2 Stream Processor
51

52. • We looked at what’s streaming analytics is ?
• Patterns of streaming analytics
• How WSO2 Stream Processor can help you implement
analytics patterns
• The business benefit of manageable rules and dashboards
• The ease of development, multi mode deployments and
monitoring with WSO2 Stream Processor
Summary
52

53. wso2.com