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https://issues.apache.org/jira/browse/FLINK-9877?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16556030#comment-16556030
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ASF GitHub Bot commented on FLINK-9877:
---------------------------------------
xccui commented on a change in pull request #6407: [FLINK-9877][docs] Add
documentation page for different datastream joins
URL: https://github.com/apache/flink/pull/6407#discussion_r205194408
##########
File path: docs/dev/stream/operators/joining.md
##########
@@ -0,0 +1,286 @@
+---
+title: "Joining"
+nav-id: streaming_joins
+nav-show_overview: true
+nav-parent_id: streaming
+nav-pos: 10
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+* toc
+{:toc}
+
+# Window Join
+A window join will join the elements of two streams that share a common key
and lie in the same window. These windows can be defined by using a [window
assigner]({{ site.baseurl}}/dev/stream/operators/windows.html#window-assigners)
and are evaluated on a union of both streams. This is especially important for
session window joins, which will be demonstrated below.
+
+The joined elements are then passed to a user-defined `JoinFunction` or
`FlatJoinFunction` where the user can perform transformations on the joined
elements.
+
+The general usage always looks like the followning:
+
+```java
+stream.join(otherStream)
+ .where(<KeySelector>)
+ .equalTo(<KeySelector>)
+ .window(<WindowAssigner>)
+ .apply(<JoinFunction>)
+```
+
+Some notes on semantics:
+- The creation of pairwise combinations of elements of the two streams behaves
like an inner-join, meaning elements from one stream will not be emitted if
they don't have a corresponding element from the other stream to be joined with.
+- Those elements that do get joined will have as their timestamp the largest
timestamp that still lies in the respective window. For example a window with
`[5, 10)` as its boundaries would result in the joined elements having nine as
their timestamp.
+
+In the following section we are going to give an overview over how different
kinds of windows can be used for a window join and what the results of those
joins would look like using examplary scenarios.
+
+## Tumbling Window
+When performing a tumbling window join, all elements with a common key and a
common tumbling window are joined as pairwise combinations and passed on to the
user-defined function. Because this behaves like an inner join, elements of one
stream that do not have elements from another stream in their tumbling window
are not emitted!
+
+### Example
+<img src="{{ site.baseurl }}/fig/tumbling-window-join.svg" class="center"
style="width: 80%;" />
+
+In our example we are defining a tumbling window with the size of 2
milliseconds, which results in windows of the form `[0,1], [2,3], ...`. The
image shows the pairwise combinations of all elements in each window which will
be passed on to the user-defined function. You can also see how in the tumbling
window `[6,7]` nothing is emitted because no elements from the green stream
exist to be joined with the orange elements ⑥ and ⑦.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+
+```java
+import org.apache.flink.api.java.functions.KeySelector;
+import
org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+...
+
+DataStream<Integer> orangeStream = ...
+DataStream<Integer> greenStream = ...
+
+orangeStream.join(greenStream)
+ .where(<KeySelector>)
+ .equalTo(<KeySelector>)
+ .window(TumblingEventTimeWindows.of(Time.seconds(2)))
+ .apply (new JoinFunction<Integer, Integer, String> () {
+ @Override
+ public String join(Integer first, Integer second) {
+ return first + "," + second;
+ }
+ });
+```
+</div>
+<div data-lang="scala" markdown="1">
+
+```scala
+import
org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+...
+
+val orangeStream: DataStream[Integer] = ...
+val greenStream: DataStream[Integer] = ...
+
+orangeStream.join(greenStream)
+ .where(elem => /* select key */)
+ .equalTo(elem => /* select key */)
+ .window(TumblingEventTimeWindows.of(Time.milliseconds(2)))
+ .apply { (e1, e2) => e1 + "," + e2 }
+```
+
+</div>
+</div>
+
+## Sliding Window Join
+When performing a sliding window join, all elements with a common key and
common sliding window are joined are pairwise combinations and passed on to the
user-defined function. Elements of one stream that do not have elements from
the other stream in the current sliding window are not emitted! Note that some
elements might be joined in one sliding window but not in another!
+
+<img src="{{ site.baseurl }}/fig/sliding-window-join.svg" class="center"
style="width: 80%;" />
+
+In this example we are using sliding windows with a duration of two
milliseconds and slide them by one millisecond, resulting in the sliding
windows `[-1, 0],[0,1],[1,2],[2,3], …`.<!-- TODO: Can -1 actually exist?--> The
joined elements below the x-axis are the ones that are passed to the
user-defined function for each sliding window. Here you can also see how for
example the orange ② is joined with the green ③ in the window `[2,3]`, but is
not joined with anything in the window `[1,2]`.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+
+```java
+import org.apache.flink.api.java.functions.KeySelector;
+import
org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+...
+
+DataStream<Integer> orangeStream = ...
+DataStream<Integer> greenStream = ...
+
+orangeStream.join(greenStream)
+ .where(<KeySelector>)
+ .equalTo(<KeySelector>)
+ .window(SlidingEventTimeWindows.of(Time.milliseconds(2) /* size */,
Time.milliseconds(1) /* slide */))
+ .apply (new JoinFunction<Integer, Integer, String> () {
+ @Override
+ public String join(Integer first, Integer second) {
+ return first + "," + second;
+ }
+ });
+```
+</div>
+<div data-lang="scala" markdown="1">
+
+```scala
+import
org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+...
+
+val orangeStream: DataStream[Integer] = ...
+val greenStream: DataStream[Integer] = ...
+
+orangeStream.join(greenStream)
+ .where(elem => /* select key */)
+ .equalTo(elem => /* select key */)
+ .window(SlidingEventTimeWindows.of(Time.milliseconds(2) /* size */,
Time.milliseconds(1) /* slide */))
+ .apply { (e1, e2) => e1 + "," + e2 }
+```
+</div>
+</div>
+
+## Session Window Join
+When performing a session window join, all elements with the same key that
when _"combined"_ fulfill the session criteria are joined in pairwise
combinations and passed on to the user-defined function. Again this performs an
inner join, so if there is a session window that only contains elements from
one stream, no output will be emitted!
+
+<img src="{{ site.baseurl }}/fig/session-window-join.svg" class="center"
style="width: 80%;" />
+
+Here we define a session window join where each session is divided by a gap of
at least 1ms. There are three sessions, and in the first two sessions the
joined elements from both streams are passed to the user-defined function. In
the third session there are no elements in the green stream, so ⑧ and ⑨ are not
joined!
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+
+```java
+import org.apache.flink.api.java.functions.KeySelector;
+import
org.apache.flink.streaming.api.windowing.assigners.EventTimeSessionWindows;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+...
+
+DataStream<Integer> orangeStream = ...
+DataStream<Integer> greenStream = ...
+
+orangeStream.join(greenStream)
+ .where(<KeySelector>)
+ .equalTo(<KeySelector>)
+ .window(EventTimeSessionWindows.withGap(Time.milliseconds(1)))
+ .apply (new JoinFunction<Integer, Integer, String> () {
+ @Override
+ public String join(Integer first, Integer second) {
+ return first + "," + second;
+ }
+ });
+```
+</div>
+<div data-lang="scala" markdown="1">
+
+```scala
+import
org.apache.flink.streaming.api.windowing.assigners.EventTimeSessionWindows;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+...
+
+val orangeStream: DataStream[Integer] = ...
+val greenStream: DataStream[Integer] = ...
+
+orangeStream.join(greenStream)
+ .where(elem => /* select key */)
+ .equalTo(elem => /* select key */)
+ .window(EventTimeSessionWindows.withGap(Time.milliseconds(1)))
+ .apply { (e1, e2) => e1 + "," + e2 }
+```
+
+</div>
+</div>
+
+# Interval Join
+The interval join joins elements of two streams (we'll call them A & B for
now) with a common key and where elements of stream B have timestamps that lie
in a relative time interval to timestamps of elements in stream A.
+
+This can also expressed a little more formally as
+`b.timestamp ∈ [a.timestamp + lowerBound; a.timestamp + upperBound]` or
+`a.timestamp + lowerBound <= b.timestamp <= a.timestamp + upperBound`
+
+where a and b are elements of A and B that share a common key.
+
+The interval join currently only performs inner joins, meaning that if an
element from one stream has no element from the other stream to be joined with,
it will not be passed to the user-defined function.
+
+When joined elements are passed to the user-defined function they have the
maximum timestamp of either one of the two elements. The timestamps of either
one of those elements can also be accessed via the `ProcessJoinFunction.Context`
Review comment:
When a pair of elements are passed to the `JoinFunction`, they will be
assigned with the larger timestamp (which could be accessed via the
ProcessJoinFunction.Context) of the two elements.
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> Add separate docs page for different join types in DataStream API
> -----------------------------------------------------------------
>
> Key: FLINK-9877
> URL: https://issues.apache.org/jira/browse/FLINK-9877
> Project: Flink
> Issue Type: Sub-task
> Components: Documentation
> Reporter: Florian Schmidt
> Assignee: Florian Schmidt
> Priority: Major
> Labels: pull-request-available
> Fix For: 1.6.0
>
>
> https://github.com/apache/flink/pull/6407
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