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https://issues.apache.org/jira/browse/FLINK-10625?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16688368#comment-16688368
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ASF GitHub Bot commented on FLINK-10625:
----------------------------------------
alpinegizmo commented on a change in pull request #7070: [FLINK-10625]
Documentation for MATCH_RECOGNIZE clause
URL: https://github.com/apache/flink/pull/7070#discussion_r233914439
##########
File path: docs/dev/table/streaming/match_recognize.md
##########
@@ -0,0 +1,843 @@
+---
+title: 'Detecting Patterns in Tables'
+nav-parent_id: streaming_tableapi
+nav-title: 'Detecting Patterns'
+nav-pos: 5
+is_beta: true
+---
+<!--
+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 a set of event patterns, especially in
case of data streams. Flink
+comes with a [complex event processing (CEP) library]({{ site.baseurl
}}/dev/libs/cep.html) which allows for pattern detection in event streams.
Furthermore, Flink's
+SQL API provides a relational way of expressing queries with a large set of
built-in functions and rule-based optimizations that can be used out of the box.
+
+In December 2016, the International Organization for Standardization (ISO)
released a new version of the SQL standard which includes _Row Pattern
Recognition in SQL_ ([ISO/IEC TR
19075-5:2016](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)).
It allows Flink to consolidate CEP and SQL API using the `MATCH_RECOGNIZE`
clause for complex event processing in SQL.
+
+A `MATCH_RECOGNIZE` clause enables the following tasks:
+* Logically partition and order the data that is used with `PARTITION BY` and
`ORDER BY` clauses.
+* Define patterns of rows to seek using the `PATTERN` clause. These patterns
use a syntax similar to that of regular expressions.
+* Specify 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.
+
+The following example illustrates the syntax for basic pattern recognition:
+
+{% highlight sql %}
+SELECT T.aid, T.bid, T.cid
+FROM MyTable
+MATCH_RECOGNIZE (
+ PARTITION BY userid
+ ORDER BY proctime
+ MEASURES
+ A.id AS aid,
+ B.id AS bid,
+ C.id AS cid
+ PATTERN (A B C)
+ DEFINE
+ A AS name = 'a',
+ B AS name = 'b',
+ C AS name = 'c'
+) AS T
+{% endhighlight %}
+
+This page will explain each keyword in more detail and will illustrate more
complex examples.
+
+<span class="label label-danger">Attention</span> The `MATCH_RECOGNIZE` clause
implementation in Flink is just a subset of the bigger pattern recognition
standard. Only the features that are documented in the following sections are
supported so far. Since the development is still in an early phase, please also
take a look at the [known limitations](#known-limitations).
+
+* This will be replaced by the TOC
+{:toc}
+
+Introduction and Examples
+-------------------------
+
+### Installation Guide
+
+The pattern recognition feature uses the Apache Flink's CEP library
internally. In order to be able to use the `MATCH_RECOGNIZE` clause,
+the library needs to be added as a dependency to your Maven project.
+
+{% highlight xml %}
+<dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-cep{{ site.scala_version_suffix }}</artifactId>
+ <version>{{ site.version }}</version>
+</dependency>
+{% endhighlight %}
+
+Alternatively, you can also add the dependency to the cluster classpath (see
the [dependency section]({{ site.baseurl}}/projectsetup/dependencies.html) for
more information).
+
+If you want to use the `MATCH_RECOGNIZE` clause in the [SQL Client]({{
site.baseurl}}/dev/table/sqlClient.html),
+you don't have to do anything as all the dependencies are included by default.
+
+### SQL Semantics
+
+Every `MATCH_RECOGNIZE` query consists of the following clauses:
+
+* [PARTITION BY](#partitioning) - defines the logical partitioning of the
table; similar to a `GROUP BY` operation.
+* [ORDER BY](#order-of-events) - specifies how the incoming rows should be
ordered; this is essential as patterns depend on an order.
+* [MEASURES](#define--measures) - defines output of the clause; similar to a
`SELECT` clause.
+* [ONE ROW PER MATCH](#output-mode) - output mode which defines how many rows
per match should be produced.
+* [AFTER MATCH SKIP](#after-match-strategy) - allows to specify where the next
match should start; this is also a way to control how many distinct matches a
single event can belong to.
+* [PATTERN](#defining-pattern) - allows constructing patterns that will be
searched for using a _regular expression_-like syntax.
+* [DEFINE](#define--measures) - this section defines conditions on rows that
should be met in order to be qualified to the corresponding pattern variable.
+
+<span class="label label-danger">Attention</span> Currently, the
`MATCH_RECOGNIZE` clause can only be applied to an [append
table](dynamic_tables.html#update-and-append-queries). Furthermore, it always
produces
+an append table as well.
+
+### Examples
+
+For our examples, we assume that a table `Ticker` has been registered. The
table contains prices of stocks at a particular point in time. Each row
represents an updated characteristic of the ticker.
+
+The table has a following schema:
+
+{% highlight text %}
+Ticker
+ |-- symbol: Long # symbol of the stock
+ |-- price: Long # price of the stock
+ |-- tax: Long # tax liability of the stock
+ |-- rowtime: TimeIndicatorTypeInfo(rowtime) # point in time when the
change to those values happened
+{% endhighlight %}
+
+For simplification, we only consider the incoming data for a single stock
`ACME`. A ticker could look similar to the following table where rows are
continuously appended.
+
+{% highlight text %}
+symbol rowtime price tax
+====== ==================== ======= =======
+'ACME' '01-Apr-11 10:00:00' 12 1
+'ACME' '01-Apr-11 10:00:01' 17 2
+'ACME' '01-Apr-11 10:00:02' 19 1
+'ACME' '01-Apr-11 10:00:03' 21 3
+'ACME' '01-Apr-11 10:00:04' 25 2
+'ACME' '01-Apr-11 10:00:05' 18 1
+'ACME' '01-Apr-11 10:00:06' 15 1
+'ACME' '01-Apr-11 10:00:07' 14 2
+'ACME' '01-Apr-11 10:00:08' 24 2
+'ACME' '01-Apr-11 10:00:09' 25 2
+'ACME' '01-Apr-11 10:00:10' 19 1
+{% endhighlight %}
+
+The task is now to find periods of a constantly decreasing price of a single
ticker. For this, one could write a query like:
+
+{% 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 PRICE_UP
+ PATTERN (STRT_ROW PRICE_DOWN+ PRICE_UP)
+ DEFINE
+ PRICE_DOWN AS
+ (LAST(PRICE_DOWN.price, 1) IS NULL AND PRICE_DOWN.price <
STRT_ROW.price)) OR
+ PRICE_DOWN.price < LAST(PRICE_DOWN.price, 1)
+ PRICE_UP AS
+ PRICE_UP.price > LAST(PRICE_DOWN.price, 1)
+ ) MR;
+{% endhighlight %}
+
+The query partitions the `Ticker` table by the `symbol` column and orders it
by the `rowtime` time attribute.
+
+The `PATTERN` clause specifies that we are interested in a pattern with a
starting event `STRT_ROW` that is followed by one or more `PRICE_DOWN` events
and concluded with a `PRICE_UP` event. If such a pattern can be found, the next
pattern match will be seeked at the last `PRICE_UP` event as indicated by the
`AFTER MATCH SKIP TO LAST` clause.
+
+The `DEFINE` clause specifies the conditions that need to be met for a
`PRICE_DOWN` and `PRICE_UP` event. Although the `STRT_ROW` pattern variable is
not present it has an implicit condition that is evaluated always as `TRUE`.
+
+A pattern variable `PRICE_DOWN` is defined as a row with a price that is
smaller than the price of the last row that met the `PRICE_DOWN` condition. For
the initial case or when there is no last row that met the `PRICE_DOWN`
condition, the price of the row should be smaller than the price of the
preceding row in the pattern (referenced by `START_ROW`).
+
+A pattern variable `PRICE_UP` is defined as a row with a price that is larger
than the price of the last row that met the `PRICE_DOWN` condition.
+
+The query produces a summary row for each found period in which the price was
constantly decreasing.
+
+The exact representation of output rows is defined in `MEASURES` part of the
query. The number of output rows is defined by the `ONE ROW PER MATCH` output
mode.
+
+{% highlight text %}
+ symbol start_tstamp bottom_tstamp end_tstamp
+========= ================== ================== ==================
+ACME 01-APR-11 10:00:04 01-APR-11 10:00:07 01-APR-11 10:00:08
+{% endhighlight %}
+
+The resulting row contains the found period of a decreasing price for a ticker
that started at `01-APR-11 10:00:04` and
+achieved the lowest price at `01-APR-11 10:00:07` that increased again at
`01-APR-11 10:00:08`.
+
+Partitioning
+------------
+
+It is possible to look for patterns in partitioned data, e.g., trends for a
single ticker or a particular user. This can be expressed using the `PARTITION
BY` clause. The clause is similar to using `GROUP BY` for aggregations.
+
+<span class="label label-danger">Attention</span> It is highly advised to
partition the incoming data because otherwise the `MATCH_RECOGNIZE` clause will
be translated
+into a non-parallel operator to ensure global ordering.
+
+Order of Events
+---------------
+
+Apache Flink allows for searching for patterns based on time; either
[processing time or event time](time_attributes.html).
+
+In case of event time, this assumption is very important because it enables to
sort the events before they are passed to the internal pattern state machine.
As a consequence, the
+produced output will be correct regardless of the order in which rows are
appended to the table. Instead, the pattern is evaluated in the order specified
by the time contained in each row.
+
+The `MATCH_RECOGNIZE` clause assumes a [time attribute](time_attributes.html)
with ascending ordering as the first argument to `ORDER BY` clause.
+
+For the example `Ticker` table, a definition like `ORDER BY rowtime ASC, price
DESC` is valid but `ORDER BY price, rowtime` or `ORDER BY rowtime DESC, price
ASC` is not.
+
+Define & Measures
+-----------------
+
+The `DEFINE` and `MEASURES` keywords have similar meaning as `WHERE` and
`SELECT` clauses in a simple SQL query.
+
+The `MEASURES` clause defines what will be included in the output of a
matching pattern. It can project columns and defines expressions for evaluation.
+The number of produced rows depends on the [output mode](#output-mode) setting.
+
+The `DEFINE` clause allows to specify conditions that rows have to fulfill in
order to be classified to a corresponding [pattern variable](#defining-pattern).
+If a condition is not defined for a pattern variable, a default condition will
be used which evaluates to `true` for every row.
+
+For a more detailed explanation about expressions that can be used in those
clauses, please have a look at the [event stream
navigation](#pattern-navigation) section.
+
+Defining a Pattern
+------------------
+
+The `MATCH_RECOGNIZE` clause allows users to search for patterns in event
streams using a powerful and expressive syntax
+that is somewhat similar to the widespread regular expression syntax.
+
+Every pattern is constructed from basic building blocks, called _pattern
variables_, to which operators (quantifiers and other modifiers) can be
applied. The whole pattern must be enclosed in
+brackets.
+
+An example pattern could look like:
+
+{% highlight sql %}
+PATTERN (A B+ C*? D)
+{% endhighlight %}
+
+One may use the following operators:
+
+* _Concatenation_ - a pattern like `(A B)` means that the contiguity is strict
between `A` and `B`. Therefore, there can be no rows that were not mapped to
`A` or `B` in between.
+* _Quantifiers_ - modify the number of rows that can be mapped to the pattern
variable.
+ * `*` — _0_ or more rows
+ * `+` — _1_ or more rows
+ * `?` — _0_ or _1_ rows
+ * `{ n }` — exactly _n_ rows (_n > 0_)
+ * `{ n, }` — _n_ or more rows (_n ≥ 0_)
+ * `{ n, m }` — between _n_ and _m_ (inclusive) rows (_0 ≤ n ≤ m, 0 < m_)
+ * `{ , m }` — between _0_ and _m_ (inclusive) rows (_m > 0_)
+
+<span class="label label-danger">Attention</span> Patterns that can
potentially produce an empty match are not supported.
+Examples of such patterns are `PATTERN (A*)`, `PATTERN (A? B*)`, `PATTERN
(A{0,} B{0,} C*)`, etc.
+
+### Greedy & Reluctant Quantifiers
+
+Each quantifier can be either _greedy_ (default behavior) or _reluctant_.
Greedy quantifiers try to match
+as many rows as possible while reluctant quantifiers try to match as few as
possible.
+
+In order to illustrate the difference, one can view the following example with
a query where a greedy quantifier is applied to the `B` variable:
+
+{% highlight sql %}
+SELECT *
+FROM Ticker
+ MATCH_RECOGNIZE(
+ PARTITION BY symbol
+ ORDER BY rowtime
+ MEASURES
+ C.price AS lastPrice
+ PATTERN (A B* C)
+ ONE ROW PER MATCH
+ AFTER MATCH SKIP PAST LAST ROW
+ DEFINE
+ A AS A.price > 10
+ B AS B.price < 15
+ C AS B.price > 12
+ )
+{% endhighlight %}
+
+Given we have the following input:
+
+{% highlight text %}
+ symbol tax price rowtime
+======= ===== ======== =====================
+ XYZ 1 10 2018-09-17 10:00:02
+ XYZ 2 11 2018-09-17 10:00:03
+ XYZ 1 12 2018-09-17 10:00:04
+ XYZ 2 13 2018-09-17 10:00:05
+ XYZ 1 14 2018-09-17 10:00:06
+ XYZ 2 16 2018-09-17 10:00:07
+{% endhighlight %}
+
+The pattern above will produce the following output:
+
+{% highlight text %}
+ symbol lastPrice
+======== ===========
+ XYZ 16
+{% endhighlight %}
+
+The same query where `B*` is modified to `B*?`, which means that `B*` should
be reluctant, will produce:
+
+{% highlight text %}
+ symbol lastPrice
+======== ===========
+ XYZ 13
+{% endhighlight %}
+
+The pattern variable `B` matches only to the row with price `12` instead of
swallowing the rows with prices `12`, `13`, and `14`.
+
+<span class="label label-danger">Attention</span> It is not possible to use a
greedy quantifier for the last
+variable of a pattern. Thus, a pattern like `(A B*)` is not allowed. This can
be easily worked around by introducing an artificial state
+(e.g. `C`) that has a negated condition of `B`. So you could use a query like:
+
+{% highlight sql %}
+PATTERN (A B* C)
+DEFINE
+ A AS condA()
+ B AS condB()
+ C AS NOT condB()
+{% endhighlight %}
+
+<span class="label label-danger">Attention</span> The optional reluctant
quantifier (`A??` or `A{0,1}?`) is not supported right now.
+
+Output Mode
+-----------
+
+The _output mode_ describes how many rows should be emitted for every found
match. The SQL standard describes two modes:
+- `ALL ROWS PER MATCH`
+- `ONE ROW PER MATCH`.
+
+Currently, the only supported output mode is `ONE ROW PER MATCH` that will
always produce one output summary row for each found match.
+
+The schema of the output row will be a concatenation of `[partitioning
columns] + [measures columns]` in that particular order.
+
+The following example shows the output of a query defined as:
+
+{% highlight sql %}
+SELECT *
+FROM Ticker
+ MATCH_RECOGNIZE(
+ PARTITION BY symbol
+ ORDER BY rowtime
+ MEASURES
+ FIRST(A.price) AS startPrice
+ LAST(A.price) AS topPrice
+ B.price AS lastPrice
+ PATTERN (A+ B)
+ ONE ROW PER MATCH
+ DEFINE
+ A AS LAST(A.price, 1) IS NULL OR A.price > LAST(A.price, 1),
+ B AS B.price < LAST(A.price)
+ )
+{% endhighlight %}
+
+For the following input rows:
+
+{% highlight text %}
+ symbol tax price rowtime
+======== ===== ======== =====================
+ XYZ 1 10 2018-09-17 10:00:02
+ XYZ 2 12 2018-09-17 10:00:03
+ XYZ 1 13 2018-09-17 10:00:04
+ XYZ 2 11 2018-09-17 10:00:05
+{% endhighlight %}
+
+The query will produce the following output:
+
+{% highlight text %}
+ symbol startPrice topPrice lastPrice
+======== ============ ========== ===========
+ XYZ 10 13 11
+{% endhighlight %}
+
+The pattern recognition is partitioned by the `symbol` column. Even though not
explicitly mentioned in the `MEASURES` clause, the partitioned column is added
at the beginning of the result.
+
+Pattern Navigation
+------------------
+
+The `DEFINE` and `MEASURE` clauses allow for navigating within a list of
(potentially) matching rows to a pattern.
+
+This section discusses this navigation for declaring conditions or producing
output results.
+
+### Pattern Variable Referencing
+
+A _pattern variable reference_ allows to reference a set of rows mapped to a
particular pattern variable in
Review comment:
A _pattern variable reference_ allows a set of rows mapped to a particular
pattern variable in `DEFINE` or `MEASURE` clauses to be referenced.
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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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