wdick closed pull request #23362: [MINOR][DOCS]Document subquery support for
the `table` parameter
URL: https://github.com/apache/spark/pull/23362
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diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 9751528654ffb..cd5798d389863 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -235,6 +235,19 @@ class DataFrameReader private[sql](sparkSession:
SparkSession) extends Logging {
* Construct a `DataFrame` representing the database table accessible via
JDBC URL
* url named table and connection properties.
*
+ * The `table` parameter can be based on a derived table, which is generated
within the scope of
+ * a query FROM clause. For example, you could use this subquery as `table:
+ * {{{
+ * "(SELECT * FROM customers WHERE ...) AS tmp"
+ * }}}
+ *
+ * @param url JDBC database url of the form `jdbc:subprotocol:subname`.
+ * @param table Name of the table in the external database.
+ * @param properties JDBC database connection arguments, a list of arbitrary
string
+ * tag/value. Normally at least a "user" and
"password" property
+ * should be included. "fetchsize" can be used
to control the
+ * number of rows per fetch and "queryTimeout"
can be used to wait
+ * for a Statement object to execute to the
given number of seconds.
* @since 1.4.0
*/
def jdbc(url: String, table: String, properties: Properties): DataFrame = {
@@ -251,6 +264,13 @@ class DataFrameReader private[sql](sparkSession:
SparkSession) extends Logging {
* url named table. Partitions of the table will be retrieved in parallel
based on the parameters
* passed to this function.
*
+ * The `table` parameter can be based on a derived table, which is generated
within the scope of
+ * a query FROM clause. For example, you could use this subquery as `table:
+ * {{{
+ * "(SELECT * FROM customers WHERE ...) AS tmp"
+ * }}}
+ *
+ *
* Don't create too many partitions in parallel on a large cluster;
otherwise Spark might crash
* your external database systems.
*
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