Distributed Event Based Systems – Complex Event … Linnert, [email protected], Netzprogrammierung WS 2015 /16 Distributed Event Based Systems – Complex Event Processing

Barry Linnert, [email protected], Netzprogrammierung WS 2015/16

Distributed Event Based Systems – Complex Event Processing Netzprogrammierung (Algorithmen und Programmierung V)

2

Descriptive models for distributed system design

Architectural model

Our topics last week

Physical model

Architectural elements

Communicating entities

Communication paradigm Roles and res- ponsibilities

Placement

Inter-process communication

Remote invocation

Indirect communication

Processes

Objects

Components

Web Services

TCP sockets

UDP sockets

Multi- cast

Architectural styles

Client-server

Peer-to-peer

Multiple server

Proxy/Cache

Mobile code

Architectural patterns

Vertical distribution Horizontal distribution

Interaction model

Multi-tier Thin/Fat Client

Interaction model

Failure model

Security model


3

Our topics today

• Distributed Event Based Systems • Complex Event Processing • CEP Languages • CEP Reference Architecture and CEP Functions


4

Event Processing vs. Databases

?

Database

Database Queries

Query Processor

Incoming Events

Time

?

?

?

?

Processing moment of events

Future past

Event

Subscriptions

Ex-Post Data Queries Real Time Data Processing


5

Knowledge Value of Events

Proactive actions

Value of Events

At event Before the event Some time after event e.g. 1 hour

Real-Time

Late reaction or Long term report

Historical Event

Post-Processing

Time


6

Complex Events – What are they?

Complex Events are aggregates, derivations, etc. of Simple Events

Complex Events Simple Events

Simple Events Simple Events

Simple Events

Event Patterns

Complex Event Processing (CEP) will enable, e.g. – Detection of state changes based on observations

– Prediction of future states based on past behaviours

Realt Time Data

Processing

Data


7

Complex Event Processing – What is it?

Event Cloud (unordered events)

new auto pay account login

account login deposit

withdrawal

logout

account balance

transfer

deposit new auto pay

enquiry enquiry

logout



activity history

withdrawal

logout transfer

deposit new auto pay

enquiry

enquiry

book

request incident

A

B

C

INSERT INTO OrdersMatch_s SELECT B.ID, S.ID, B.Price FROM BuyOrders_s B, Trades_s T, SellOrders_s S, MATCHING [30 SECONDS: B, !T, S] ON B.Price = S.Price = T.Price;

Continuous Event Queries

CEP is about complex event detection and reaction to complex events - Efficient (near real-time) processing of large numbers of events - Detection, prediction and exploitation of relevant complex events - Supports situation awareness, track & trace, sense & respond

Com

plex

Eve

nts

Event Streams (ordered events)

Patterns, Rules


8

Complex Event Processing – What is it?

Complex Event Processing (CEP) is a discipline that deals with event-driven behavior Selection, aggregation, and event abstraction for generating higher level complex events of interest

Abstract View

Technical View Com

plex

Eve

nt P

roce

ssin

g M

edia


9

CEP – Example Application Domains Monitoring, BAM,

ITSM, Monitor and detect exceptional IT service and business behavior from occurred events

RTE Quick decisions, and reactions to threats and opportunities according to events in business transactions

Information Dissemination

Valuable Information at the Right Time to the Right Recipient

CEP Media Detect Decide

Respond Enterprise Decision Management Expert Systems

Expert Decision Management


10

3. Event Selection

6. Event Consumption 2. Event

Definition

4. Event Aggregation

5. Event Handling

1. Event Production

CEP Media

Core CEP Life Cycle


11

Basic Definition (1) Atomic Event (also raw event or primitive event)

- An atomic event (also raw event or primitive event) is defined as an instantaneous (at a specific point in time), significant (relates to a context), indivisible (cannot be further decomposed and happens completely or not at all) occurrence of a happening.

Complex Event - composed (composite event) or derived (derived event) from occurred atomic

or other complex event instances, e.g. according to the operators of an event algebra or as a result of applying an algorithmic function or process to one or more other events

- included events are called components while the resulting complex event is the parent event

- first event instance contributing to the detection of a complex event is called initiator, where the last is the terminator; all others are called interiors

Event Pattern - An event pattern (also event class, event definition, event schema, or event

type) describes the structure and properties of an (atomic or complex) event - It describes on an abstract level the essential factors that uniquely identify the

occurrence of an event of that type, i.e. its detection condition(s) and its properties with respect to instantiation, selection and consumption.


12

Basic Definition (2) Event Instance

- A concrete instantiation of an event pattern is a specific event instance (also event object).

Situation - A situation is initiated or terminated by one or more (complex)

events, i.e. the effect of a complex event (complex event + conditional context)

Situation Individuals - Situation individuals are discrete individuals of situations with a

fixed context allocation of time, date, location, participant, etc. They are not repeatable and are temporally connected.

Complex Action - Compound action from occurred atomic or other complex action

instances


13

Example Snoop Event Algebra Operators: •Sequence Operator (;): (E1;E2) •Disjunction Operator (∨): (E1 ∨ E2 ) , at least one •Conjunction Operator (∧): (E1 ∧ E2) •Simultaneous Operator (=): (E1 = E2) •Negation Operator (¬): (E1 ∧ ¬ Ε2 ) •Quantification (Any): Any(n) E1, when n events of type E1 occurs •Aperiodic Operator (Ap): Ap(E2, E1, E3) , E2 Within E1 & E3 •Periodic Operator (Per): Per(t, E1, E2) , every t time-steps in between E1 and E2

Complex Event Patterns – How?


14

–Example: D = A;(B;C)

T1 T2 T3 T4

A B C

[B,C] [A,A]

[[A,A],[B,C]]

Example – Interval-based Sequence

D = [A,C]

Composition

Consolidation


15

Event Detection Operators & Windowing

t-8 t-7 t-6 t-4 t-3 t-2 t-1 t t+1 t+2 t+3

A1 B1 C1 A2 B2 D1 A3 B3 C3

Time

Events

Event Detection Pattern [A ; B] Matches Time t → 2 or 3 (based on event consumption policy) Time t-1 → 1 Time t-2 → 1 Time t-3 → 2 or 3

t+4 t+5 t+6 t+7

A1 B1 C1 A2 B2 D1 A3 B3 C3

A1 B1 C1 A2 B2 D1 A3 B3 C3

A1 B1 C1 A2 B2 D1 A3 B3 C3

Window [t-5 <–> t]

t-5

Future Processing time

t-1

t-2

t-3


16 IBM Haifa Research Lab – Event Processing © 2008 IBM Corporation

EPN Example

Fever F

Fever C

c2

c3

c8

Enrich: Is diabetic?

Translate: C2F fever

Pattern detection:Alert physician

Pattern Detection: Alert Nurse

c6

Fever F

Blood pressure reading

p2

p4

p3

p6

p5

p10

p9

p8

p1

p11

p12

p12

admittance

Aggregator: Max.

p7

Pattern Detection: Continuous Fever

c7

c9

Filter: Diabetes c1 c5

p13p14

p15

p16

p17

p18

p19

p20

Event Processing Agent and Event Processing Network

Event Processing Network is a collection of Event Processing Agents. The EPN describes the “programming in the large”, while each individual agent describes the “programming in the small”.


17

What is the idea ?

The EPN (event processing network) represents the event processing application as a directed graph:

- The nodes represent event processing agents (and states in some models)

- The edges represent either individual events or event streams (depends on the processing type). A generalized EPN may support both.

The coverage of EPN varies - It may cover some language operator (e.g. SQL like

language, rule language, …) - It may take a broader approach and cover also routing

decisions, pub/sub etc… - EPN can have a specific programming model, or an hybrid

programming model, where one or more languages represent the agents.


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18

Fever F

Fever C

c2

c3

c8

Enrich: Is diabetic?

Translate: C2F fever

Pattern detection: Alert physician

Pattern Detection: Alert Nurse

c6

Fever F

Blood pressure reading

p2

p4

p3

p6

p5

p10

p9

p8

p1

p11

p12

p12

admittance

Aggregator: Max.

p7

Pattern Detection: Continuous Fever

c7

c9

Filter: Diabetes c1 c5

p13 p14

p15

p16

p17

p18

p19

p20

EPN example


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http://images.google.com/imgres?imgurl=http://www.humec.k-state.edu/images/careers/physician.jpg&imgrefurl=http://www.humec.k-state.edu/future-students/careers/physician.php&h=338&w=225&sz=41&hl=en&start=8&um=1&tbnid=CNLSU2FVXdzlCM:&tbnh=119&tbnw=79&prev=/images?q%3Dphysician%26svnum%3D10%26um%3D1%26hl%3Den%26rls%3DGGLD,GGLD:2005-07,GGLD:en%26sa%3DN

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19

Example Event Processing Languages

Inference Rules

State oriented

Agent Oriented

Imperative/ Script Based

RuleCore

Oracle

Aleri Streambase

Esper

EventZero

TIBCO WBE

Apama

Spade

AMiT

Netcool Impact

ECA / Reaction Rules

SQL extension

Coral8

Agent Logic Starview

XChangeEQ

Reaction RuleML

Prova


20

Some EPN, event flow and data flow oriented languages

Agent Logic Oracle/BEA

IBM InfoSphere Streams

© 2008, 2009 IBM Corporation194 Tutorial on the SPADE language

A SPADE program builds a data-flow network out of operators

sinkoperator

sourceoperator

processingoperator

sourceoperator

processingoperator

processingoperator

stream

stream

stream

stream

stre

am

stream

sinkoperator

Spade (IBM System S)

Event Zero

Streambase


21

Example Research Prototypes

•Finite State Automata, e.g.: –ADAM, ACOOD, ODE, SAMOS, COMPOSE, SNOOP (active database research in 80s & 90s) –SASE, Cayuga, Esper

•Rule-Based CEP Engines, e.g.: –Prolog, Prova, Drools, ETALIS

•Data Stream Engines, e.g.: –Stream CQL, PIPE, TelegraphCQ, Gigascope, Aurora Borealis, SPADE

•Stream Reasoning, e.g.: –C-SPARQL, EP-SPARQL, SCEPter, SPARQLStream, CQELS


22

Commercial CEP Market

Source: Tibco Software Inc. 2010 Barry Linnert, [email protected], Netzprogrammierung WS 2015/16

23

Implementations - Example Systems: TIBCO BusinessEvents

TIBCO BusinessEvents “event server” / event processing engines Multiple languages / engines in various packaging options

- UML-based event and concept (object) class hierarchical models - Java-based language - Eclipse-based IDE

Distributed execution model:

- Inference agents, Query agents, Datagrid agents - In-memory, memory+grid, explicit grid operational modes - “Grid Options” of TIBCO ActiveSpaces / Oracle Coherence - eXtreme Event Processing use cases: Fedex, US Govt, …

Transactional model option:

- TIBCO AS Transactions - eXtreme Event Processing use cases: Telco

Extensible: eg OWL import, RDF import, etc


24

Implementations - Example Systems: TIBCO BusinessEvents (ctd)

Event Processing

Events

Event Storage

Information Event and Data Structures

States and Transitions

Inference Rules

Sets and Queries Stored

Events and Data

via JMS, RV, MQ, TCP/IP, etc…

Continuously process events using procedural and declarative event processing elements

Temporal Patterns

Decision Tables


25

Implementations - Example Systems: Oracle Event Processing (formerly Oracle CEP) Development platform for event processing applications Application model based on EPN (event processing network) abstraction running on top of OSGi-based Java container. Language is an extension of SQL with stream and pattern matching extensions


26

Implementations - Example Systems: Oracle Event Processing (formerly Oracle CEP)

CQL: Continuous Query Language Leverages SQL, extended with Stream, Pattern-matching, and Java. Continuous: driven by time, and events Highly-sophisticated push-down technology to RDBMS, Cache, Hadoop


27

Implementations - Example Systems: Prova (http://prova.ws) • Java JVM based, open source rule

language for reactive agents and event processing

• Leverages declarative ISO Prolog standard extended with (event,message) reaction logic, type systems (Java, Ontologies), query built-ins, dynamic Java integration.

• Combines declarative, imperative (object-oriented) and functional programming styles

• Designed to work in distributed Enterprise Service Bus and OSGi environments

• Supports strong, loose and decoupled interaction

• Compatible with rule interchange standards such as Reaction RuleML

Event Cloud

(unordered events)



withdrawal logout

account balance

transfer deposit new auto pay

enquiry enquiry

logout



activity history

withdrawal logout transfer

deposit new auto pay enquiry

enquiry

book

request incident

Event Streams (ordered events)

Agent / Service Interface

Rule Service Consumer

Message Interchange

0.0000

Request

0.0000

Response

Prova Agent

Prova Agent

…

Computer

Prova

direct API implementation

Prova Agent

Prova Agent

Prova Agent Prova

Agent

Strong Coupling

Loose Coupling

Decoupled Prova Event Processing

Network


28

Implementations - Example Systems: IBM WODM Decision Server Events

Decision Server Events component of IBM WebSphere Operational Decision Management (WODM) Manages business events flowing across systems and people to provide timely insight and responses Detect, evaluate, and respond to events Discover event patterns and initiate actions


29

Implementations - Example Systems: IBM CICS Transaction Server for z/OS CICS TS


30

Implementations - Example Systems: IBM InfoSphere Streams – Stream Processing Language IBM InfoSphere Streams: platform for analyzing big data in motion i.e. high-volume, continuous data streamsSPL IBM Streams Processing Language (SPL)

- Programming language for InfoSphere Streams - Exposes a simple graph-of-operators view - Provides a powerful code-generation interface to C++ and Java

Streams Processing Language and IDE

Runtime Environment

Tools and Technology Integration

Highly Scalable stream processing runtime

Streams Console & Monitoring, Built-in Stream Relational Analytics,

Adapters, Toolkits

Streams Studio Eclipse IDE for SPL

Front Office 3.0


http://www.fixprotocol.org/

31

Event Processing, Complex Event Processing, Event

Stream Processing

Event Abstraction, Event Pattern Detection, Event Composition

etc. etc.

Functional View source: definitions of EP

Even

t M

anag

emen

t

Event Consumer

(Event sink, event handler, event

listener)

Run time Administration

Even

t T

ype

Def

initi

ons,

Even

t Pr

oces

sing

Rul

es,

etc.

Design time

Events Derived

Events

Event Producer

(Event Source, Event Emitter)


32

EPTS Reference Architecture - Functional View Architect and Developer perspective

- includes the 3 main functions (development, run-time and administration),

- targets primarily the automated event processing operations

Run-time functions in 2 main groups: - the event infrastructure (sources and consumers) external to

the event processor under consideration, - the event processor.


33

Reference Architecture: Functional View

Event Production Publication,

Retrieval

Event P

rocess Mon

itoring

, Con

trol

Event Preparation Identification, Selection, Filtering,

Monitoring, Enrichment

Complex Event Detection Consolidation, Composition,

Aggregation

Event Reaction Assessment, Routing, Prediction,

Discovery, Learning

Event Consumption Dashboard, Apps, External Reaction

Run time Administration

Even

t a

nd

Com

ple

x Ev

ent

(Pat

tern

, Con

trol

, Rul

e, Q

uery

, Re

gEx.

etc)

D

efin

itio

n,

Mod

elin

g, (

con

tin

uou

s) I

mp

rove

men

t

Design time

Event Analysis Analytics, Transforms, Tracking, Scoring, Rating, Classification

0..*

0..*

0..*

0..*

State M

anag

emen

t


34

Reference Architecture: Functional View / Runtime


Retrieval




Aggregation


Discovery, Learning


Run time


0..*

0..*

0..*

0..*

State M

anag

emen

t


35



Retrieval


Event Production: the source of events for event processing.

• Event Publication: As a part of event production, events may be published onto a communication mechanism (e.g., event bus) for use by event consumers (including participants in event processing). This is analogous to a "push" system for obtaining events.

• Event Retrieval: As a part of event production, events may be explicitly retrieved from some detection system. This is analogous to a "pull" system for obtaining events.


36



Retrieval


Event Consumption: the process of using events from event publication and processing. Event processing itself can be an event consumer, although for the purposes of the reference architecture, event consumers are meant to indicate downstream consumers of events generated in event processing.

• Dashboard: a type of event consumer that displays events as they occur to some user community.

• Applications: a type of event consumer if it consumes events for its own processes.

• External Reaction: caused through some event consumption, as the result of some hardware or software process.


37




Event Preparation: the process of preparing the event and associated payload and metadata for further stages of event processing. • Entity Identification: incoming events will need to be identified relative to prior events, such

as associating events with particular sources or sensors.

• Event Selection: particular events may be selected for further analysis. Different parts of event processing may require different selections of events. See also event filtering.

• Event Filtering: a stream or list of events may be filtered on some payload or metadata information such that some subset is selected for further processing.

• Event Monitoring: particular types of events may be monitored for selection for further processing. This may utilise specific mechanisms external to the event processing such as exploiting event production features.

• Event Enrichment: events may be "enriched" through knowledge gained through previous events or data.


38



Event Analysis: the process of analysing suitably prepared events and their payloads and metadata for useful information. • Event Analytics: the use of statistical methods to derive additional information about an

event or set of events.

• Event Transforms: processes carried out on event payloads or data, either related to event preparation, analysis or processing.

• Event Tracking: where events related to some entity are used to identify state changes in that entity.

• Event Scoring: the process by which events are ranked using a score, usually as a part of a statistical analysis of a set of events. See also Event Analytics

• Event Rating: where events are compared to others to associate some importance or other, possibly relative, measurement to the event.

• Event Classification: where events are associated with some classification scheme for use in downstream processing.


39



Aggregation

Complex Event Detection: the process by which event analysis results in the creation of new event information, or the update of existing complex events. • Event Consolidation: combining disparate events together into a "main" or "primary"

event. See also event aggregation.

• Event Composition: composing new, complex events from existing, possibly source, events.

• Event Aggregation: combining events to provide new or useful information, such as trend information and event statistics. Similar to event consolidation.


40



Discovery, Learning

Event Reaction: the process subsequent to event analysis and complex event detection to handle the results of analysis and detection. • Event Assessment: the process by which an event is assessed for inclusion in some

process, incorporation in some other event, etc.

• Event Routing: the process by which an event is redirected to some process, computation element, or other event sink.

• Event Prediction: where the reaction to some event processing is that some new event is predicted to occur.

• Event Discovery: where the reaction to some event processing is the disclosure of a new, typically complex, event type.

• Note that event prediction is predicting some future event, usually of a known type, whereas event discovery is the uncovering of a new event type. See also event-based learning.

• Event-based Learning: the reaction to some event processing that uses new event information to add to some, typically statistical-based, understanding of events.

• Note that event-based learning is a specialisation of general machine learning and predictive analytics.


41

Reference Architecture: Functional View / Design time

Even

t a

nd

Com

ple

x Ev

ent

(Pat

tern

, Con

trol

, Rul

e, Q

uery

, Re

gEx.

etc)

D

efin

itio

n,

Mod

elin

g, (

con

tin

uou

s) I

mp

rove

men

t

Design time

Covers the definition, modeling, improvement / maintenance of the artefacts used in event processing:

• event definitions, including event metadata and payloads

• event and event object organisations and structures,

• event processing transformations / queries / rules / procedures / flows / states / decisions / expressions (although these can sometimes be considered as administrative updates in some situations)


42

Reference Architecture: Functional View / Administration

Event P

rocess Mon

itoring

, Con

trol

Administration

Administrative concepts of monitoring and control. This may involve

• starting and stopping the application and event processing elements, including application monitors

• providing and updating security levels to event inputs and outputs (also can design-time)

• management of high availability and reliability resources, such as hot standby processes

• resource utilisation monitoring of the event processing components

• process updates, such as how-swapping of event processing definitions to newer versions.


43

Summary - Event Processing Patterns Functions from Reference Architecture are a guide to possible event processing patterns




Aggregation


Discovery, Learning



44

So, what have we learned today? • Basic Definitions and Approach of Complex Event

Processing in Distributed Event Based Systems • event algebra operators for event pattern definitions • windowing techniques for real-time event pattern

matching

• Overview CEP Languages • Rule-based, agent-based, SQL extensions, state-

based, imperative, scripting

• CEP Reference Architecture with typical CEP functions • production, consumption, preparation, analysis,

detection, reaction functions


45

Questions • What is the purpose of complex event processing? • How does complex event processing in distributed event

based systems work? What is a complex event pattern? Name typical event algebra operators.

• Name examples of CEP language approaches. • Describe the main roles in the CEP reference architecture

and name the five functional groups in the runtime view. Name a function example for each group (production, consumption, preparation, analysis, detection, reaction functions).


46

Descriptive models for distributed system design

Architectural model

Our topics next week

Physical model

Architectural elements

Communicating entities

Communication paradigm Roles and res- ponsibilities

Placement

Inter-process communication

Remote invocation

Indirect communication

Processes

Objects

Components

Web Services

TCP sockets

UDP sockets

Multi- cast

Architectural styles

Client-server

Peer-to-peer

Multiple server

Proxy/Cache

Mobile code

Architectural patterns

Vertical distribution Horizontal distribution

Interaction model

Multi-tier Thin/Fat Client

Interaction model

Failure model

Security model


47

Distributed Objects and Components Next class


48

References David C. Luckham. 2001. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. Opher Etzion and Peter Niblett. 2010. Event Processing in Action (1st ed.). Manning Publications Co., Greenwich, CT, USA. Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Tutorial on advanced design patterns in event processing. DEBS 2012: 324-334; www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b Adrian Paschke, Paul Vincent, Florian Springer: Standards for Complex Event Processing and Reaction Rules. RuleML America 2011: 128-139 http://link.springer.com/chapter/10.1007%2F978-3-642-24908-2_17 http://www.slideshare.net/isvana/ruleml2011-cep-standards-reference-model Francois Bry, Michael Eckert, Opher Etzion, Adrian Paschke, Jon Ricke: Event Processing Language Tutorial, DEBS 2009, ACM, Nashville, 2009 http://www.slideshare.net/opher.etzion/debs2009-event-processing-languages-tutorial Prova Rule Engine http://www.prova.ws/


http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b

http://link.springer.com/chapter/10.1007/978-3-642-24908-2_17









http://www.slideshare.net/isvana/ruleml2011-cep-standards-reference-model









http://www.slideshare.net/opher.etzion/debs2009-event-processing-languages-tutorial









http://www.prova.ws/

Documents

Distributed Event Based Systems – Complex Event … Linnert, [email protected], Netzprogrammierung WS 2015 /16 Distributed Event Based Systems – Complex Event Processing