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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-6747) Table API / SQL Docs: Streaming Page
Date Thu, 20 Jul 2017 13:22:00 GMT

    [ https://issues.apache.org/jira/browse/FLINK-6747?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16094669#comment-16094669

ASF GitHub Bot commented on FLINK-6747:

Github user fhueske commented on a diff in the pull request:

    --- 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
    -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
    +<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>
    +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
    +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
    +Dynamic Tables &amp; 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
    +<img alt="Dynamic tables" src="{{ site.baseurl }}/fig/table-streaming/stream-query-stream.png"
    +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.
    +<img alt="Append mode" src="{{ site.baseurl }}/fig/table-streaming/append-mode.png"
    +**Note:** A table which is defined on a stream is internally not materialized. 
    --- End diff --
    Not sure if that's necessary. At this point I am only talking about the table that is
logically constructed from the stream.

> 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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