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https://issues.apache.org/jira/browse/FLINK-10625?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16683627#comment-16683627
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ASF GitHub Bot commented on FLINK-10625:
----------------------------------------
twalthr commented on a change in pull request #7070: [FLINK-10625]
Documentation for MATCH_RECOGNIZE clause
URL: https://github.com/apache/flink/pull/7070#discussion_r232604261
##########
File path: docs/dev/table/streaming/match_recognize.md
##########
@@ -0,0 +1,654 @@
+---
+title: 'Detecting event patterns <span class="label label-danger"
style="font-size:50%">Experimental</span>'
+nav-parent_id: streaming_tableapi
+nav-title: 'Detecting event patterns'
+nav-pos: 5
+---
+<!--
+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.
+-->
+
+It is a common use-case to search for set event patterns, especially in case
of data streams. Apache Flink
+comes with [CEP library]({{ site.baseurl }}/dev/libs/cep.html) which allows
for pattern detection in event streams. On the other hand Flink's
+Table API & SQL provides a relational way to express queries that comes with
multiple functions and
+optimizations that can be used out of the box. In December 2016, ISO released
a new version of the
+international SQL standard (ISO/IEC 9075:2016) including the Row Pattern
Recognition for complex event processing,
+which allowed to consolidate those two APIs using MATCH_RECOGNIZE clause.
+
+* This will be replaced by the TOC
+{:toc}
+
+Example query
+-------------
+
+Row Pattern Recognition in SQL is performed using the MATCH_RECOGNIZE clause.
MATCH_RECOGNIZE enables you to do the following tasks:
+* Logically partition and order the data that is used in the MATCH_RECOGNIZE
clause with its PARTITION BY and ORDER BY clauses.
+* Define patterns of rows to seek using the PATTERN clause of the
MATCH_RECOGNIZE clause.
+ These patterns use regular expression syntax, a powerful and expressive
feature, applied to the pattern variables you define.
+* Specify the logical conditions required to map a row to a row pattern
variable in the DEFINE clause.
+* Define measures, which are expressions usable in other parts of the SQL
query, in the MEASURES clause.
+
+For example to find periods of constantly decreasing price of a Ticker one
could write a query like this:
+
+{% highlight sql %}
+SELECT *
+FROM Ticker
+MATCH_RECOGNIZE (
+ PARTITION BY symbol
+ ORDER BY rowtime
+ MEASURES
+ STRT_ROW.rowtime AS start_tstamp,
+ LAST(PRICE_DOWN.rowtime) AS bottom_tstamp,
+ LAST(PRICE_UP.rowtime) AS end_tstamp
+ ONE ROW PER MATCH
+ AFTER MATCH SKIP TO LAST UP
+ PATTERN (STRT_ROW PRICE_DOWN+ PRICE_UP+)
+ DEFINE
+ PRICE_DOWN AS PRICE_DOWN.price < LAST(PRICE_DOWN.price, 1) OR
+ (LAST(PRICE_DOWN.price, 1) IS NULL AND PRICE_DOWN.price <
STRT_ROW.price))
+ PRICE_UP AS PRICE_UP.price > LAST(PRICE_UP.price, 1) OR
LAST(PRICE_UP.price, 1) IS NULL
+ ) MR;
+{% endhighlight %}
+
+This query given following input data:
+
+{% highlight text %}
+SYMBOL ROWTIME PRICE
+====== ==================== =======
+'ACME' '01-Apr-11 10:00:00' 12
+'ACME' '01-Apr-11 10:00:01' 17
+'ACME' '01-Apr-11 10:00:02' 19
+'ACME' '01-Apr-11 10:00:03' 21
+'ACME' '01-Apr-11 10:00:04' 25
+'ACME' '01-Apr-11 10:00:05' 12
+'ACME' '01-Apr-11 10:00:06' 15
+'ACME' '01-Apr-11 10:00:07' 20
+'ACME' '01-Apr-11 10:00:08' 24
+'ACME' '01-Apr-11 10:00:09' 25
+'ACME' '01-Apr-11 10:00:10' 19
+{% endhighlight %}
+
+will produce a summary row for each found period in which the price was
constantly decreasing.
+
+{% highlight text %}
+SYMBOL START_TST BOTTOM_TS END_TSTAM
+========= ================== ================== ==================
+ACME 01-APR-11 10:00:04 01-APR-11 10:00:05 01-APR-11 10:00:09
+{% endhighlight %}
+
+The aforementioned query consists of following clauses:
+
+* [PARTITION BY](#partitioning) - defines logical partitioning of the stream,
similar to `GROUP BY` operations.
+* [ORDER BY](#order-of-events) - specifies how should the incoming events be
order, this is essential as patterns define order.
+* [MEASURES](#define--measures) - defines output of the clause, similar to
`SELECT` clause
+* [ONE ROW PER MATCH](#output-mode) - output mode which defines how many rows
per match will be produced
+* [AFTER MATCH SKIP](#after-match-skip) - allows to specify where next match
should start, this is also a way to control to how many distinct matches a
single event can belong
+* [PATTERN](#defining-pattern) - clause that allows constructing patterns that
will be searched for, pro
Review comment:
`pro`?
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> Add MATCH_RECOGNIZE documentation
> ---------------------------------
>
> Key: FLINK-10625
> URL: https://issues.apache.org/jira/browse/FLINK-10625
> Project: Flink
> Issue Type: Sub-task
> Components: Documentation, Table API & SQL
> Affects Versions: 1.7.0
> Reporter: Till Rohrmann
> Assignee: Dawid Wysakowicz
> Priority: Major
> Labels: pull-request-available
> Fix For: 1.7.0
>
>
> The newly added {{MATCH_RECOGNIZE}} functionality needs to be documented.
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