This document provides an overview of upcoming features in WSO2 Complex Event Processor 3.0.0. It discusses the Siddhi CEP engine, event processing queries including filters, windows, joins, patterns and event tables. It also covers high availability, persistence, scaling, integration with BAM, and performance comparisons with Esper. The document concludes with a demo of monitoring stock prices and tweets to detect significant stock price changes when a company is highly discussed on Twitter.

1. WSO2 Complex Event Processor
3.0.0
An overview of upcoming features
By
S. Suhothayan
Associate Technical Lead,
Team Lead CEP.

2. About WSO2
• Providing the only complete open source componentized cloud platform
– Dedicated to removing all the stumbling blocks to enterprise agility
– Enabling you to focus on business logic and business value
• Recognized by leading analyst firms as visionaries and leaders
– Gartner cites WSO2 as visionaries in all 3 categories of application
infrastructure
– Forrester places WSO2 in top 2 for API Management
• Global corporation with offices in USA, UK & Sri Lanka
– 200+ employees and growing
• Business model of selling comprehensive support & maintenance for our products

3. 150+ globally positioned support customers

4. Outline
! Scenarios of Event Processing
! WSO2 CEP Server & SOA integrates
! The Siddhi Runtime CEP Engine.
! High availability, Persistence and Scalability of
WSO2 CEP
! How CEP can be combined with Business
Activity Monitoring (BAM).
! Demo

5. CEP Is & Is NOT!
! Is NOT!
o Simple filters
• Simple Event Processing
• E.g. Is this a gold or platinum customer?
o Joining multiple event streams
• Event Stream Processing
! Is !
o Processing multiple event streams
o Identify meaningful patterns among streams
o Using temporal windows
• E.g. Notify if there is a 10% increase in overall trading
activity AND the average price of commodities has
fallen 2% in the last 4 hours

6. WSO2 CEP Server
! Enterprise grade server for CEP runtimes
! Supports several transports (network access)
! Supports several data formats
! Support for multiple CEP runtimes
! Governance
! Monitoring
! Tools (WSO2 Dev Studio)

7. WSO2 CEP Architecture

8. Siddhi CEP Runtime
! Apache License, a java library, Tuple based event model
! Supports distributed processing
! Supports multiple query models
• Based on a SQL-like language
• Supports
! Partitions
! Filters
! Windows
! Joins
! Ordering
! Output Rate Limiting
! and others

9. CEP Event Adaptors
! Is an adaptor for receiving and publishing events
! Has the configurations to connect to external endpoints
! Its many-to-many with CEP engine

10. CEP Event Adaptors
Support for several transports (network access) and data formats
● SOAP/WS-Eventing
XML messages
● REST
JSON messages
● JMS
Map messages
XML messages
Text messages
JSON messages
● SMTP (Email)
Text messages
JSON messages
XML messages
● Thrift - WSO2 data format High Performant Event Capturing &
Delivery Framework supports Java/C/C++/C# via Thrift language
bindings
WSO2 Event

11. CEP Event Adaptors
● Cassandra (from CEP 3.0.0)
Map messages
● Fix (from CEP 3.0.0+)
Map messages
● MYSQL (from CEP 3.0.0)
Map messages
● HBase (from CEP 3.0.0+)
Map messages
& Event adaptors are pluggable !

12. CEP Event Builders
• Event builder converts different input event types to a type compatible with the
execution plan. • Subscribes to an Input Event Adaptor to listen for events and sends a converted
WSO2 Event or Basic Event to the Execution Plan • Receives events in different formats and exposes those input streams as stream
definitions • Has a one to many relationship with execution plans in execution plan.

13. CEP Execution Plan
● Is an isolated logical execution unit
● Each execution plan has a set of
Queries
Input & Output stream mappings.
● Its one-to-one with a CEP Backend Runtime Engine
● It deals with Siddhi processing engine.

14. CEP Event Formatter
Event formatter does the inverse – listens to events coming from event
processor and sends converted events to Event adaptors.
There are 5 types of output mapping types are available
● Map
● Text
● WSO2Event
● XML
● JSON

15. Monitoring (Event Tracer & Event Statistics)
! Provides real-time statistical visual illustrations of
request & response counts per time based on CEP
server, execution plan, transport adaptor, event
builder and formatter.

16. Writing CEP
Queries (using
Siddhi Runtime)

17. Siddhi Queries
! Filters and Projection
! Windows
o Events are processed within temporal windows.
(e.g. for aggregation and joins)
Time window vs. length window.
! Joins - Join two streams
! Event ordering - Identify event sequences and
patterns
! Event Partitions
! Event Tables

18. Filters
from <stream-name> [<conditions>]*
select <attributes>
insert into <stream-name>
! Filters the events by conditions, use to detect simple
condition
! Conditions
o >, <, = , <=, <=, !=
o contains, instanceof
o and, or, not
! Example
from cseEventStream[price >= 20 and symbol==’IBM’]
select symbol, volume
insert into StockQuote

19. Window
from <stream-name> [<conditions>]#window.<window-name>(<
parameters>)
select <attributes>
Insert into <stream-name>
Types of Windows
● (Time | Length) (Sliding| Batch) windows
● Type of aggregate functions
● sum, avg, max, min
Example
from cseEventStream[price >= 20]#window.lengthBatch(50)
select symbol, avg(price) as avgPrice
group by symbol
having avgPrice>50
insert into StockQuote

20. Join
from <stream>#<window> [unidirectional] join <stream>#<window>
on <condition> within <time>
insert into <stream>
! Use to join two streams based on a condition. There
must be at least one window defined
! Unidirectional – event arriving only to the
unidirectional stream triggers join
! Example
from TickEvent[symbol==’IBM’]#window.length(2000)
join NewsEvent#window.time(5 min)
on TickEvent.symbol==NewsEvent.company
select *
insert into JoinStream *

21. Pattern
from [every] <condition> → [every] <condition> … <condition> within
<time>
select <attributes>
insert into StockQuote
! Use to Check condition A happen before/after
condition B.
! Can do iterative checks via “every” keyword.
! Here with “within <time>”, SIddhi emits only events
that are within that time of each other
! Example
from every (a1 = purchase[price < 10] ) -> a2 = purchase [price >10000 and
a1.cardNo==a2.cardNo] within 1 day
select a1.cardNo as cardNo, a2.price as price, a2.place as place
insert into potentialFraud
y1 a1 x1 k5 a2 n7

22. Sequence
from <event-regular-expression> within <time>
select <attributes>
Insert into <stream>
! Regular Expressions supported
o * - Zero or more matches (reluctant).
o + - One or more matches (reluctant).
o ? - Zero or one match (reluctant).
o or – either event
! Here we have to refer events returned by * , + using square
brackets to access a specific occurrence of that event
from a1 = requestOrder[action == "buy"],
b1 = cseEventStream[price > a1.price and symbol==a1.symbol]+,
b2 = cseEventStream[price <b1.price]
select a1. symbol as symbol, b1[0].price as firstPrice, b2.price as orderPrice
insert into purchaseOrder
y1 a1 b1 b1 b2 n7

23. Event Paritions
define <partition-id> by <partition-type> (,<partition-type>)*
Partition types can be one of two types • Variable Partitions - Partitions are created by
the discrete values that are encountered for a
variable
define partition StockSymbol by StockStream.symbol
• Range partitions - Partitions are created
according to predefined ranges of variables
define partition stockVolume by range volume < 10 as 'SMALL',
range volume > 10 and volume < 100 as 'MEDIUM', range
volume > 100 as 'LARGE'

24. Event Tables
define table <table-name> (<attribute-name> <type> {,
<attribute-name> <type>}*) ( from <table-type>.<datasource-name>:<
database-name>.<table-name>)?
Event tables can be used in the same manner as an event stream,
with the difference being that events sent to an event table
being persisted to a data source. CEP supports event tables for • In Memory • Relational
o MySQL
o H2
define table cseEventTable(symbol string, price int, volume float)
from MYSQL.cepDataSource:cepdb.cepEventTable0

25. Working with Event Tables
from <stream> (select <attribute-name> (,<attribute-name>)*
)? insert into <table-name>
Inserts the selected attributes from the input stream into the
event table.
from cseEventCheckStream[symbol==cseEventTable.symbol in
cseEventTable] insert into outStream;
For update and delete
from <stream> update <table-name> (on <condition>)?
from <stream> delete <table-name> (on <condition>)?

26. Performance Results
! We compared Siddhi with Esper, the widely used
opensource CEP engine
! For evaluation, we did setup different queries using both
systems, push events in to the system, and measure the
time till all of them are processed.
! We used Intel(R) Xeon(R) X3440 @2.53GHz , 4 cores 8M
cache 8GB RAM running Debian 2.6.32-5-amd64 Kernel

27. Performance sending event within same JVM
Simple filter without window
from StockTick[prize >6] return symbol, price

28. Performance sending event within same JVM
State machine query for pattern matching
From f=FraudWarningEvent ->
p=PINChangeEvent(accountNumber=f.accountNumber)
return accountNumber;

29. Performance Sending Events over the network
! Here we publihsed data from two client publisher
nodes to the CEP Sever node and sent the triggered
notifications of CEP to a client subscriber node.
! To test the worsecase sinario, 100% of the data
published to CEP is recived at the subscriber node
after processing (No data is filtered)
! We used Intel® Core™ i7-2630QM CPU @ 2.00GHz, 8
cores, 8GB RAM running Ubnthu 12.04, 3.2.0-32-
generic Kernel, for running CEP and used Intel® Core™
i3-2350M CPU @ 2.30GHz, 4 cores, 4GB RAM running
Ubnthu 12.04, 3.2.0-32-generic Kernel, for the three
client nodes.

30. HA/ Persistence
! Ability to recover
runtime state in the
case of a failure.
! Enables queries to span
lifetimes much greater
than server uptime.
! Takes periodic
snapshots and stores
all state information to
a scalable persistence
store (Apache
Cassandra).
! Supports pluggable
persistent stores.

31. Scaling
! Vertically scaling
o Can be distributed as a pipeline
! Horizontally scaling
o Queries like windows, patterns, and Join have
shared states, hence hard to distribute!
o Use distributed cache (Hazelcast) to achieve this
• shared memory and batch processing

32. Event Recording
! Ability to record all/some of the events for
future processing
! Few options
o Publish them to Cassandra cluster using WSO2 data
bridge API or BAM (can process data in Cassandra
with Hadoop using WSO2 BAM).
o Write them to distributed cache
o Custom thrift based event recorder

33. Integration with WSO2 BAM
Data Receiving Data Analyzing Data
Presentation
Data
Publishing

34. CEP Role within WSO2 Platform

35. DEMO

36. Scenario
! Monitoring stock exchange for game changing
moments
! Two input event streams.
o Event stream of Stock Quotes from a stock
exchange
o Event stream of word count on various company
names from twitter pages
! Check whether the last traded price of the
stock has changed significantly(by 2%) within
last minute, and people are twitting about that
company (> 10) within last minute

38. Input events
! Input events are JMS Maps
o Stock Exchange Stream
Map<String, Object> map1 = new HashMap<String, Object>();
map1.put("symbol", "MSFT");
map1.put("price", 26.36);
publisher.publish("AllStockQuotes", map1);
o Twitter Stream
Map<String, Object> map1 = new HashMap<String, Object>();
map1.put("company", "MSFT");
map1.put("wordCount", 8);
publisher.publish("TwitterFeed", map1);

39. Queries

40. Queries
from allStockQuotes[win.time(60000)]
select symbol,price, avg(price) as averagePrice
group by symbol
having ((price > averagePrice*1.02) or (averagePrice*0.98 > price ))
insert into fastMovingStockQuotes
from twitterFeed[win.time(60000)]
select company as company, sum(wordCount) as words
group by company
having (words > 10)
insert into highFrequentTweets
from fastMovingStockQuotes[win.time(60000)] as fastMovingStockQuotes
join highFrequentTweets[win.time(60000)] as highFrequentTweets
on fastMovingStockQuotes.symbol==highFrequentTweets.company
select fastMovingStockQuotes.symbol as company,
fastMovingStockQuotes.averagePrice as amount,
highFrequentTweets.words as words
insert into predictedStockQuotes

41. Alert
! As a Email
Hi
Within last minute, people being twitting about {company}
{words} times, and the last traded price of {company} has
changed by 2% and now being trading at ${amount}.
From
CEP
! As a SMS

42. Useful links
! WSO2 CEP 3.0.0
http://ec2-54-224-94-128.compute-1.amazonaws.com/chunk-02/
N-25_09_2013/wso2cep-3.0.0.zip
! WSO2 CEP http://wso2.com/products/complex-event-processor/
! CEP Performance Info
http://srinathsview.blogspot.com/2013/08/cep-performance-processing-100k-to.
html
! Distributed Processing Sample With Siddhi CEP
and ActiveMQ JMS Broker.
http://suhothayan.blogspot.com/2012/08/distributed-processing-sample-for-wso2.
html
! Creating Custom Data Publishers to BAM/CEP
http://wso2.org/library/articles/2012/07/creating-custom-agents-publish-events-
bamcep
! WSO2 BAM 2.3.0
http://wso2.com/products/business-activity-monitor/

43. Engage with WSO2
• Helping you get the most out of your deployments
• From project evaluation and inception to development
and going into production, WSO2 is your partner in
ensuring 100% project success

44. Questions?

45. Thank you.