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https://issues.apache.org/jira/browse/FLINK-6198?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16034938#comment-16034938
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ASF GitHub Bot commented on FLINK-6198:
---------------------------------------
Github user alpinegizmo commented on a diff in the pull request:
https://github.com/apache/flink/pull/4041#discussion_r119853002
--- Diff: docs/dev/libs/cep.md ---
@@ -98,48 +128,106 @@ val result: DataStream[Alert] =
patternStream.select(createAlert(_))
</div>
</div>
-Note that we use Java 8 lambdas in our Java code examples to make them
more succinct.
-
## The Pattern API
-The pattern API allows you to quickly define complex event patterns.
-
-Each pattern consists of multiple stages or what we call states.
-In order to go from one state to the next, the user can specify conditions.
-These conditions can be the contiguity of events or a filter condition on
an event.
-
-Each pattern has to start with an initial state:
-
-<div class="codetabs" markdown="1">
-<div data-lang="java" markdown="1">
-{% highlight java %}
-Pattern<Event, ?> start = Pattern.<Event>begin("start");
-{% endhighlight %}
-</div>
-
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-val start : Pattern[Event, _] = Pattern.begin("start")
-{% endhighlight %}
-</div>
-</div>
-
-Each state must have a unique name to identify the matched events later on.
-Additionally, we can specify a filter condition for the event to be
accepted as the start event via the `where` method.
-These filtering conditions can be either an `IterativeCondition` or a
`SimpleCondition`.
-
-**Iterative Conditions:** This type of conditions can iterate over the
previously accepted elements in the pattern and
-decide to accept a new element or not, based on some statistic over those
elements.
-
-Below is the code for an iterative condition that accepts elements whose
name start with "foo" and for which, the sum
-of the prices of the previously accepted elements for a state named
"middle", plus the price of the current event, do
-not exceed the value of 5.0. Iterative condition can be very powerful,
especially in combination with quantifiers, e.g.
-`oneToMany` or `zeroToMany`.
+The pattern API allows you to quickly define complex pattern sequences
that you want to extract
+from your input stream.
+
+Each such complex pattern sequence consists of multiple simple patterns,
i.e. patterns looking for
+individual events with the same properties. These simple patterns are
called **states**. A complex pattern
+can be seen as a graph of such states, where transition from one state to
the next happens based on user-specified
+*conditions*, e.g. `event.getName().equals("start")`. A *match* is a
sequence of input events which visit all
+states of the complex pattern graph, through a sequence of valid state
transitions.
+
+<span class="label label-danger">Attention</span> Each state must have a
unique name to identify the matched
+events later on.
+
+<span class="label label-danger">Attention</span> State names **CANNOT**
contain the character `:`.
+
+In the remainder, we start by describing how to define [States](#states),
before describing how you can
+combine individual states into [Complex Patterns](#combining-states).
+
+### Individual States
+
+A **State** can be either a *singleton* state, or a *looping* one.
Singleton states accept a single
+event, while looping ones can accept more than one. In pattern matching
symbols, in the pattern `a b+ c? d` (or `a`,
+followed by *one or more* `b`'s, optionally followed by a `c`, followed by
a `d`), `a`, `c?`, and `d` are
+singleton patterns, while `b+` is a looping one. By default, a state is a
singleton state and you can transform
+it to a looping one using [Quantifiers](#quantifiers). In addition, each
state can have one or more
+[Conditions](#conditions) based on which it accepts events.
+
+#### Quantifiers
+
+In FlinkCEP, looping patterns can be specified using the methods:
`pattern.oneOrMore()`, for states that expect one or
+more occurrences of a given event (e.g. the `b+` mentioned previously),
and `pattern.times(#ofTimes)` for states that
+expect a specific number of occurrences of a given type of event, e.g. 4
`a`'s. All states, looping or not, can be made
+optional using the `pattern.optional()` method. For a state named `start`,
the following are valid quantifiers:
+
+ <div class="codetabs" markdown="1">
+ <div data-lang="java" markdown="1">
+ {% highlight java %}
+ // expecting 4 occurrences
+ start.times(4);
+
+ // expecting 0 or 4 occurrences
+ start.times(4).optional();
+
+ // expecting 1 or more occurrences
+ start.oneOrMore();
+
+ // expecting 0 or more occurrences
+ start.oneOrMore().optional();
+ {% endhighlight %}
+ </div>
+
+ <div data-lang="scala" markdown="1">
+ {% highlight scala %}
+ // expecting 4 occurrences
+ start.times(4)
+
+ // expecting 0 or 4 occurrences
+ start.times(4).optional()
+
+ // expecting 1 or more occurrences
+ start.oneOrMore()
+
+ // expecting 0 or more occurrences
+ start.oneOrMore().optional()
+ {% endhighlight %}
+ </div>
+ </div>
+
+#### Conditions
+
+At every state, and in order to go from one state to the next, you can
specify additional **conditions**.
+These conditions can be related to:
+
+ 1. a [property of the incoming event](#conditions-on-properties), e.g.
its value should be larger than 5,
+ or larger than the average value of the previously accepted events.
+
+ 2. the [contiguity of the matching events](#conditions-on-contiguity),
e.g. detect pattern `a,b,c` without
+ non-matching events between any matching ones.
+
+The latter refers to "looping" states, i.e. states that can accept more
than one event, e.g. the `b+` in `a b+ c`,
+which searches for one or more `b`'s.
+
+##### Conditions on Properties
+
+Conditions on the event properties can be specified via the
`pattern.where()` method. These can be either
+`IterativeCondition`s or `SimpleCondition`s.
+
+**Iterative Conditions:** This is the most general type of conditions.
This allows to specify a condition that accepts
+any subsequent event based on some statistic over a subset of the
previously accepted events.
+
+Below is the code for an iterative condition that accepts the next event
for a state named "middle" if its name starts
+with "foo" and the sum of the prices of the previously accepted events for
that state plus the price of the current
+event, do not exceed the value of 5.0. Iterative conditions can be very
powerful, especially in combination with looping
--- End diff --
The comma doesn't work where it is. I suggest
Below is the code for an iterative condition that accepts the next event
for a state named "middle" if its name starts with "foo", and if the sum of the
prices of the previously accepted events for that state plus the price of the
current event do not exceed the value of 5.0.
> Update the documentation of the CEP library to include all the new features.
> ----------------------------------------------------------------------------
>
> Key: FLINK-6198
> URL: https://issues.apache.org/jira/browse/FLINK-6198
> Project: Flink
> Issue Type: Sub-task
> Components: CEP
> Affects Versions: 1.3.0
> Reporter: Kostas Kloudas
> Assignee: Kostas Kloudas
> Priority: Critical
> Fix For: 1.3.0
>
>
> New features to include:
> * Iterative Functions
> * Quantifiers
> * Time handling
> * Migration from FilterFunction to IterativeCondition
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