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From sunjincheng121 <...@git.apache.org>
Subject [GitHub] flink pull request #4365: [FLINK-6747] [docs] Add documentation for dynamic ...
Date Thu, 20 Jul 2017 01:23:14 GMT
Github user sunjincheng121 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. 
    +### 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.
    +<img alt="Continuous Non-Windowed Query" src="{{ site.baseurl }}/fig/table-streaming/query-groupBy-cnt.png"
    +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.
    +<img alt="Continuous Group-Window Query" src="{{ site.baseurl }}/fig/table-streaming/query-groupBy-window-cnt.png"
    +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.
    --- End diff --
    Sounds good.:)

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