wuchong commented on a change in pull request #9802: [FLINK-13361][documention]
Add documentation for JDBC connector for Table API & SQL
URL: https://github.com/apache/flink/pull/9802#discussion_r332878876
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File path: docs/dev/table/connect.md
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@@ -1075,6 +1075,88 @@ CREATE TABLE MyUserTable (
{% top %}
+### JDBC Connector
+
+<span class="label label-primary">Source: Batch</span>
+<span class="label label-primary">Sink: Batch</span>
+<span class="label label-primary">Sink: Streaming Append Mode</span>
+<span class="label label-primary">Sink: Streaming Upsert Mode</span>
+<span class="label label-primary">Temporal Join: Sync Mode</span>
+
+The JDBC connector allows for reading from an JDBC client.
+The JDBC connector allows for writing into an JDBC client.
+
+The connector can operate in [upsert mode](#update-modes) for exchanging
UPSERT/DELETE messages with the external system using a [key defined by the
query](./streaming/dynamic_tables.html#table-to-stream-conversion).
+
+For append-only queries, the connector can also operate in [append
mode](#update-modes) for exchanging only INSERT messages with the external
system.
+
+To use this connector, add the following dependency to your project:
+
+{% highlight xml %}
+<dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-connector-jdbc{{ site.scala_version_suffix }}</artifactId>
+ <version>{{site.version }}</version>
+</dependency>
+{% endhighlight %}
+
+And must also specify JDBC library, for example, if want to use Mysql library,
the following dependency to your project:
+
+{% highlight xml %}
+<dependency>
+ <groupId>mysql</groupId>
+ <artifactId>mysql-connector-java</artifactId>
+ <version>8.0.17</version>
+</dependency>
+{% endhighlight %}
+
+**Library support:** Now, we only support mysql, derby, postgres.
+
+The connector can be defined as follows:
+
+<div data-lang="DDL" markdown="1">
+{% highlight sql %}
+CREATE TABLE MyUserTable (
+ ...
+) WITH (
+ 'connector.type' = 'jdbc', -- required: specify this table type is jdbc
+
+ 'connector.url' = 'jdbc:derby:memory:upsert', -- required: JDBC DB url
+
+ 'connector.table' = 'jdbc_table_name', -- required: jdbc table name
+
+ 'connector.driver' = 'driver', -- optional: jdbc driver, if not set, it will
automatically guess based on the URL.
+
+ 'connector.username' = 'name', -- optional: jdbc user name and password
+ 'connector.password' = 'password',
+
+ -- scan options, optional, used when reading from table
+ 'connector.read.partition.column' = 'column_name', -- optional, name of the
column used for partitioning the input.
Review comment:
I think we can take Spark's documentation as a reference for this. I think
it is still confusing.
> These options must all be specified if any of them is specified. In
addition, numPartitions must be specified. They describe how to partition the
table when reading in parallel from multiple workers. partitionColumn must be a
numeric, date, or timestamp column from the table in question. Notice that
lowerBound and upperBound are just used to decide the partition stride, not for
filtering the rows in table. So all rows in the table will be partitioned and
returned. This option applies only to reading.
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