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https://issues.apache.org/jira/browse/FLINK-6747?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16093113#comment-16093113
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ASF GitHub Bot commented on FLINK-6747:
---------------------------------------
Github user sunjincheng121 commented on a diff in the pull request:
https://github.com/apache/flink/pull/4365#discussion_r128250087
--- Diff: docs/dev/table/streaming.md ---
@@ -22,21 +22,166 @@ specific language governing permissions and limitations
under the License.
-->
-**TO BE DONE:** Intro
+Flink's [Table API](tableApi.html) and [SQL support](sql.html) are unified
APIs for batch and stream processing. This means that Table API and SQL queries
have the same semantics regardless whether their input is bounded batch input
or unbounded stream input. Because the relational algebra and SQL were
originally designed for batch processing, relational queries on unbounded
streaming input are not as well understood as relational queries on bounded
batch input.
+
+On this page, we explain concepts, practical limitations, and
stream-specific configuration parameters of Flink's relational APIs on
streaming data.
* This will be replaced by the TOC
{:toc}
-Dynamic Table
--------------
+Relational Queries on Data Streams
+----------------------------------
+
+SQL and the relational algebra have not been designed with streaming data
in mind. As a consequence, there are few conceptual gaps between relational
algebra (and SQL) and stream processing.
+
+<table class="table table-bordered">
+ <tr>
+ <th>Relational Algebra / SQL</th>
+ <th>Stream Processing</th>
+ </tr>
+ <tr>
+ <td>Relations (or tables) are bounded (multi-)sets of
tuples.</td>
+ <td>A stream is an infinite sequences of tuples.</td>
+ </tr>
+ <tr>
+ <td>A query that is executed on batch data (e.g., a table in a
relational database) has access to the complete input data.</td>
+ <td>A streaming query cannot access all data when is started
and has to "wait" for data to be streamed in.</td>
+ </tr>
+ <tr>
+ <td>A batch query terminates after it produced a fixed sized
result.</td>
+ <td>A streaming query continuously updates its result based on
the received records and never completes.</td>
+ </tr>
+</table>
+
+Despite these differences, processing streams with relational queries and
SQL is not impossible. Advanced relational database systems offer a feature
called *Materialized Views*. A materialized view is defined as a SQL query,
just like a regular virtual view. In contrast to a virtual view, a materialized
view caches the result of the query such that the query does not need to be
evaluated when the view is accessed. A common challenge for caching is to
prevent a cache from serving outdated results. A materialized view becomes
outdated when the base tables of its definition query are modified. *Eager View
Maintenance* is a technique to update materialized views and updates a
materialized view as soon as its base tables are updated.
+
+The connection between eager view maintenance and SQL queries on streams
becomes obvious if we consider the following:
+
+- A database table is the result of a *stream* of `INSERT`, `UPDATE`, and
`DELETE` DML statements, often called *changelog stream*.
+- A materialized view is defined as a SQL query. In order to update the
view, the query is continuously processes the changelog streams of the view's
base relations.
+- The materialized view is the result of the streaming SQL query.
+
+With these points in mind, we introduce Flink's concept of *Dynamic
Tables* in the next section.
+
+Dynamic Tables & Continuous Queries
+---------------------------------------
+
+*Dynamic tables* are the core concept of Flink's Table API and SQL support
for streaming data. In contrast to the static tables that represent batch data,
dynamic table are changing over time. They can be queried like static batch
tables. Querying a dynamic table yields a *Continuous Query*. A continuous
query never terminates and produces a dynamic table as result. The query
continuously updates its (dynamic) result table to reflect the changes on its
input (dynamic) table. Essentially, a continuous query on a dynamic table is
very similar to the definition query of a materialized view.
+
+It is important to note that the result of a continuous query is always
semantically equivalent to the result of the same query being executed in batch
mode on a snapshot of the input tables.
+
+The following figure visualizes the relationship of streams, dynamic
tables, and continuous queries:
+
+<center>
+<img alt="Dynamic tables" src="{{ site.baseurl
}}/fig/table-streaming/stream-query-stream.png" width="80%">
+</center>
+
+1. A stream is converted into a dynamic table.
+1. A continuous query is evaluated on the dynamic table yielding a new
dynamic table.
+1. The resulting dynamic table is converted back into a stream.
+
+**Note:** Dynamic tables are foremost a logical concept. Dynamic tables
are not necessarily (fully) materialized during query execution.
+
+In the following, we will explain the concepts of dynamic tables and
continuous queries with a stream of click events that have the following schema:
+
+```
+[
+ user: VARCHAR, // the name of the user
+ cTime: TIMESTAMP, // the time when the URL was accessed
+ url: VARCHAR // the URL that was accessed by the user
+]
+```
+
+### Defining a Table on a Stream
+
+In order to process a stream with a relational query, it has to be
converted into a `Table`. Conceptually, each record of the stream is
interpreted as an `INSERT` modification on the resulting table. Essentially, we
are building a table from an `INSERT`-only changelog stream.
+
+The following figure visualizes how the stream of click event (left-hand
side) is converted into a table (right-hand side). The resulting table is
continuously growing as more records of the click stream are inserted.
+
+<center>
+<img alt="Append mode" src="{{ site.baseurl
}}/fig/table-streaming/append-mode.png" width="60%">
+</center>
+
+**Note:** A table which is defined on a stream is internally not
materialized.
+
+### Continuous Queries
+
+A continuous query is evaluated on a dynamic table and produces a new
dynamic table as result. In contrast to a batch query, a continuous query never
terminates and updates its result table according to the updates on its input
tables. At any point in time, the result of a continuous query is semantically
equivalent to the result of the same query being executed in batch mode on a
snapshot of the input tables.
+
+In the following we show two example queries on a `clicks` table that is
defined on the stream of click events.
+
+The first query is a simple `GROUP-BY COUNT` aggregation query. It groups
the `clicks` table on the `user` field and counts the number of visited URLs.
The following figure shows how the query is evaluated over time as the `clicks`
table is updated with additional rows.
+
+<center>
+<img alt="Continuous Non-Windowed Query" src="{{ site.baseurl
}}/fig/table-streaming/query-groupBy-cnt.png" width="90%">
+</center>
+
+The input table `clicks` is shown on the left-hand side. Initially, the
table consists of six rows. Evaluating the query (shown in the middle) on these
six records yields a result table which is shown on the right-hand side at the
top. When the `clicks` table is updated by appending an additional row
(originating from the stream of click events), the query updates the current
result table and increases the appropriate count. The updated result table is
show on the right-hand side in the middle (the updated row is highlighted).
Finally, another row is added and the result is shown on the right bottom of
the figure.
+
+The second query is similar to the first one but groups the `clicks` table
in addition to the `user` attribute also on an [hourly tumbling
window](./sql.html#group-windows) before it counts the number of URLs. Again,
the figure shows the input and output at different points in time to visualize
the changing nature of dynamic tables.
+
+<center>
+<img alt="Continuous Group-Window Query" src="{{ site.baseurl
}}/fig/table-streaming/query-groupBy-window-cnt.png" width="100%">
+</center>
+
+The input table `clicks` is shown on the left. The query continuously
computes results every hour and updates the result table. The clicks table
contains four rows with timestamps (`cTime`) between `12:00:00` and `12:59:59`.
The query computes two results rows from this input (one for each `user`) and
appends them to the result table. For the next window between `13:00:00` and
`13:59:59`, the `clicks` table contains three rows, which results in another
two rows being appended to the result table. As more records arrive over time,
the result table is appropriately updated.
+
+**Note:** Time-based computations such as windows are based on special
[Time Attributes](#time-attributes), which are discussed below.
+
+#### Update and Append Queries
+
+Although the two example queries appear to be quite similar (both compute
a grouped count aggregate), they differ in one important aspect. The first
query must update previously emitted results, i.e., the changelog stream that
defines the result table contains `INSERT` and `UPDATE` changes. In contrast,
the second query only appends to the result table, i.e., the changelog stream
of the result table consists only of `INSERT` changes.
+
+Whether a query produces an append-only table or an updated table has some
implications:
+- Queries that produce update changes usually have to maintain more state
(see the following section).
+- The conversion of an append-only table into a stream is different from
the conversion of an updated table (see the [Table to Stream
Conversion](#table-to-stream-conversion) section).
+
+#### Query Restrictions
+
+Many, but not all, semantically valid queries can be evaluated as
continuous queries on streams. Some queries are too expensive to compute,
either due to the size of state that they need to maintain or because computing
updates is too expensive.
+
+- **State Size:** Continuous queries are evaluated on unbounded streams
and are often supposed to run for weeks or months. Hence, the total amount of
data that a continuous query processes can be very large. Queries that have to
update previously emitted results need to maintain all emitted rows in order to
be able to update them. For instance, the first example query needs to store
the URL count for each user to be able to increase the count and sent out a new
result when the input table receives a new row. If only registered users are
tracked, the number of counts to maintain might not be too high. However, if
non-registered users get a unique user name assigned, the number of counts to
maintain would grow over time and might eventually cause the query to fail.
+
+{% highlight sql %}
+SELECT user, COUNT(url)
+FROM clicks
+GROUP BY user;
+{% endhighlight %}
+
+- **Computing Updates:** Some queries require to recompute and update a
large fraction of the emitted result rows even if only a single input record is
added or updated. Clearly, such queries are not well suited to be executed as
continuous queries. An example is the following query which computes for each
user a `RANK` based on the time of the last click. As soon as the `clicks`
table receives a new row, the `lastAction` of the user is updated and a new
rank must be computed. However since two rows cannot have the same rank, all
lower ranked rows need to be updated as well.
+
+{% highlight sql %}
+SELECT user, RANK() OVER (ORDER BY lastLogin)
+FROM (
+ SELECT user, MAX(cTime) AS lastAction FROM clicks GROUP BY user
+);
+{% endhighlight %}
+
+The [QueryConfig](#query-configuration) section discusses parameters to
control the execution of continuous queries. Some parameters can be used to
trade the size of maintained state for result accuracy.
+
+### Table to Stream Conversion
+
+A dynamic table can be continuously modified by `INSERT`, `UPDATE`, and
`DELETE` changes just like a regular database table. It might be a table with a
single row, which is constantly updated, an insert-only table without `UPDATE`
and `DELETE` modifications, or anything in between.
+
+When converting a dynamic table into a stream or writing it to an external
system, these changes need to be encoded. Flink's Table API and SQL support
three ways to encode the changes of a dynamic table:
+
+* **Append-only stream:** A dynamic table that is only modified by
`INSERT` changes can be converted into a stream by emitting the inserted rows.
+
+* **Retract stream:** A retract stream is a stream with two types of
messages, *add messages* and *retract messages*. A dynamic table is converted
into an retract stream by encoding an `INSERT` change as add message, a
`DELETE` change as retract message, and an `UPDATE` change as a retract message
for the updated row and an add message for the updating row. The following
figure visualizes the conversion of a dynamic table into a retract stream.
--- End diff --
`and an `UPDATE` change as a retract message for the updated row and an add
message for the updating row.`
To:
`and an `UPDATE` change as both a retract and an add message for the
updating row.
`
What do you think?
> Table API / SQL Docs: Streaming Page
> ------------------------------------
>
> Key: FLINK-6747
> URL: https://issues.apache.org/jira/browse/FLINK-6747
> Project: Flink
> Issue Type: Task
> Components: Documentation, Table API & SQL
> Affects Versions: 1.3.0
> Reporter: Fabian Hueske
> Assignee: Fabian Hueske
>
> Extend {{./docs/dev/table/streaming.md}} page.
> Missing are sections about
> - Dynamic Tables
> - QueryConfiguration (state retention time)
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