The Power Of Event Chapter 5

Chapter 5Events, Timing, and Causality

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Topics covered in this Chapter

● What events are● How events are created● The form, significance, and relativity of an event● Timing, causality, and aggregation● Genetic parameters of events● Partial orderings of events● Timing requirements expressed as patterns of events● Examples of causal tracking of interprocess acitivity

5.1 What Events Are 1/3

● An event is an object that is a record of an activity in a system.

● The event siginfies the activity● An event may be reated to other events.● An events has three aspects

○ Form: The form of an event is an object.○ Significance: An event signifies an acitivity.○ Relativity: An activity is related to other activities by

time, causality, and aggregation.


● The form of an input event would be an object of a Java class called InputEvent:

● Every event has a unique identifer field, Event_Id● The Time filed is the timestamp.● The Causality attribute of the event provides a way of

tracing its causal history.

Class InputEvent {Name NewOrder;Event_Id E_Id;Customer Id;OrderNo O_Id;Order(CD X, Book …);Time T;Causality (Id1, Id2, …)

}


● There are two confusions between everyday usage and CEP that we ant to avoid.○ The first is confusing the everday usage of “event” as “something that

happens” with the CEP concept of an event as an object signifying an activity.In event processing, we are processing event object and not activities.

○ Another confusion happens often in computer science: It is quite common to confuse an event with its form, such as “An event is just a message.” This confusion ignores the other aspects of events. The forms of events may be message, but the events also have significance and relativity. Event processing is different from message processing because it must deal with the relationships between events.

5.2 How Events Are Created

● To apply CEP to a target enterprise system, we must be able to create events that signify the activities that are happening in the system.○ Observation Step○ Adaptation Step

● There are three principal sources of events in modern enterprise systems.○ IT Layer○ Instruementation○ CEP

5.3 Time, Causality, and Aggregation 1/4

● Time: Time is a relationship that orders events-for example, event A happened before event B.

● A time relationship between events depends upon a clock.

● Typically, when the event’s activity happens, the event is created and a reading from a clock is entered into the event as its timestamp.

● The order of their timestamps defines the time relationship between the events.


● Cause: If the activity signified by event A had to happen in order for the activity signified by event B to happen, then A caused B.

● Causality is a dependence relationship between activities in a system.


● Aggregation: If event A signfies an activity that consists of the activities of a set of events, B1, B2, B3, ..., then A is an aggregation of all the events Bi. Conversely, the events Bi are members of A.

● Aggregation is an abstraction relationship.● A is higer-level event.● We call A a complex event.


● All these relationships between events have some simple mathematical properties.○ transitive:○ [Ex: Time]If A is eariler than B, and B is eariler than C, then A is earlier

than C.○ asymmetric(antisymmetric):○ [Ex: Causality] if A caused B and A isn’t B, then B did not cause A.

● Each of these relationships is called a partial ordering.● That is, events A and B can exist such that neither A R

B nor B R A is true, where R is any of our three relationships.

5.3.1 The Cause-Time Axiom

● Systems that satisfy the cause-time axiom have clocks that all agree in the sense that they do not ‘see’ paris of causally related events in different orders.

If event A caused event B in system S, then no clock in S gives B an earlier timestamp than it gives A.

5.4 Genetic Parameters in Events

● Special data parameters are added to an event to encode its timing and its casual relationships to other events.

● They are often referred to as genetic parameter.○ Timestamps○ Casual vector: A parameter of an event that

contains the identifiers of the set of events that caused the event.

5.4.1 Timestamps

● Timestamps are a common way to represent the timing of an event.

● We say “timestamps”, plural, because there are many situations relating an event to time.○ The activity may happen over a time interval, in

which case the event contains timestamps for its

start time and end time according to the reading of a clock in the system.

○ An event can have timestamps indicating readings from different clocks.

5.4.2 Causal Vectors

● A causal vector is a parameter of an event that contains the identifiers of the set of events that caused the event.

● Placing causal vectors in the events gives us a practical way to trace causal relationships in complex systems.

Event A

Event B

Event C

Event D

A B C

5.5 Time

● A timing relationship between events created by a system tells how the system is performing.

● Timing is also an important filter in debugging.

5.5 Time Example 1 1/2

● A typical example is a publish/subscribe middleware system.

● Suppose the system has to be set up as the IT layer of some enterprise to meet a timing requirement.

Publish(Subject, TransactionId, Message, Time, ...)Publish(StockTrade, A05643094, IBM, 09.51EST, ...)Publish(StockTrade, A05643302, CSCO, 09.52EST, ...)Receive(StockTrade, A05643309, CSCO, 10.02EST, ...)Publish(StockTrade, A05650309, JWEB, 09.53EST, ...)Receive(StockTrade, A05643094, IBM, 09.55EST, ...)


● If middleware message contain timestamps, an in this example, we can use CEP to monitor that all Publish messages are routed to subscribers and received within a specified time deadline.

● By using CEP, we can easily deal with the issue that their order of observation might be different from their order of execution.

● The concept that makes this possible is event pattern matching.

● Event patterns are a powerful way to state our deadline requirement simply and concisely.


● This example uses a simple pattern language that is described in the netxt chapter.

Rule: Check Timely Delibery

Element Declarations

Variable Subject S, Message M, String Id, Time T, T1, T2

Event types Publish(Subject S, String Id, Message M, Time T),Receive(Subject S, String Id, Message M, Time T),Warning(String Id, Time T)

Relational operators and

Pattern Publish(StockTrade, Id, M, T1) and Receive(StockTrade, Id, M, T2)

Context test T2 - T1 >= 10 mins

Action create Warning(Id, T2 - T1)


● The variable row defines the variables in the pattern.● The event types row defines the types of events in the

pattern.● The relational operators row defines the relational

operator between events that is used in the pattern.

● CEP rules like this example are called constraints because their purpose is to check a system's performance for violation of requirements

Publish(StockTrade, A05643302, CSCO, 09.52EST, ...)Receive(StockTrade, A05643302, CSCO, 10.22EST, ...)

Warning(A05643302, 30min)

5.6 Causality and Posets 1/2

● A set of events together with their causal relationship is called poset, partially ordered set of events.

● Posets are often represented graphically by DAGs, directed acyclic graphs.

● The essential role of causality is to focus the search space for the causes of some activity.

● Each event in a poset has a causal history consisting of the events that caused it, their causes, and so on.


Transfer (5, A1, A2)

Deposit (5, A1) Deposit (10, A2)

Deposit (10, A1) Deposit (5, A2)

Withdraw (15, A1)

Overdraft (A1)

Deposit (10, A2)


● If we search for what caused the Overdraft event on account A1, we will look at its causal predecessors in the graph-that is, its causal history, which is shaded in the figure.

● This shows that the Overdraft on account A1 depends upon a Transfer from A1 to A2.

● This event will be chosen as the probable real cause.● The figure shows that a causal history can contain

irrelevent events, such as the first deposit on account A2.

5.7 Causal Event Executions- Real-time Posets

● A causal event execution is a poset consisting of events generated by a system and their relationships.

● We use "causal event execution" to emphasize that we are dealing with events and their relativities online real time.

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5.7 Causal Event Executions- Real-time Posets Ex1 1/2

1 3 4 6 time

SendM,0

WaitM,0

ReceiveM1,0

RecAckM,0

ReSendM,0

ReceiveM,0

SendM1,0

TimeOutM,0

SendM2,1

AckM1,0

WaitM2,1

ReceiveM,0

RecAckM1,0

AckM,0

AckM,0

● Events are transmitted between pairs of nodes in a network according to a protocol.

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5.7 Causal Event Executions- Real-time Posets Ex1 2/2

1 3 4 6 time

SendM,0

WaitM,0

ReceiveM1,0

RecAckM,0

ReSendM,0

ReceiveM,0

SendM1,0

TimeOutM,0

SendM2,1

AckM1,0

WaitM2,1

ReceiveM,0

RecAckM1,0

AckM,0

AckM,0

5.8 Orderly Obsercation

● If their time of arrival is consistent with their causal relationship in the target system, we say that the observation is orderly.

● Orderly observation is not necessary for CEP to apply to a system. But it is convenient and makes some applications of CEP simpler and easier to implement.

Orderly observation: for any pair of events A and B,if A --> B then ArrivalTime(A) <= ArrivalTime(B)

5.9 Obsercation and Uncertainty 1/2

● The events created by CEP can be thought of as event inferences drawn from observed events.

● CEP may also process these event inferences and use them to create more inferences.

● Event inferences can signify activities within the system that were not signified by events observed in the system itself.

5.9 Obsercation and Uncertainty 2/2

● The totality of what we can observe, together with what we can infer, is called observable system

● Observable System: The set of all events that can be observed from a target ssytem, or inferred from observations, is called the observable system.

● Uncetainty Principle○ An activity within a target system may not have any signifying

event in the observable system.○ We must always bear in mind that there may be activities going

on in the system that we cannot know about.

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