1717
1818package org .apache .spark .sql
1919
20- import java .lang .IllegalArgumentException
21-
2220import org .apache .spark .annotation .{Experimental , InterfaceStability }
2321import org .apache .spark .sql .catalyst .plans .logical .LogicalKeyedState
2422
@@ -36,7 +34,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalKeyedState
3634 * `Dataset.groupByKey()`) while maintaining user-defined per-group state between invocations.
3735 * For a static batch Dataset, the function will be invoked once per group. For a streaming
3836 * Dataset, the function will be invoked for each group repeatedly in every trigger.
39- * That is, in every batch of the [[ streaming.StreamingQuery StreamingQuery ]] ,
37+ * That is, in every batch of the ` streaming.StreamingQuery` ,
4038 * the function will be invoked once for each group that has data in the batch.
4139 *
4240 * The function is invoked with following parameters.
@@ -65,7 +63,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalKeyedState
6563 *
6664 * Scala example of using KeyedState in `mapGroupsWithState`:
6765 * {{{
68- * /* A mapping function that maintains an integer state for string keys and returns a string. */
66+ * // A mapping function that maintains an integer state for string keys and returns a string.
6967 * def mappingFunction(key: String, value: Iterator[Int], state: KeyedState[Int]): String = {
7068 * // Check if state exists
7169 * if (state.exists) {
@@ -88,7 +86,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalKeyedState
8886 *
8987 * Java example of using `KeyedState`:
9088 * {{{
91- * /* A mapping function that maintains an integer state for string keys and returns a string. */
89+ * // A mapping function that maintains an integer state for string keys and returns a string.
9290 * MapGroupsWithStateFunction<String, Integer, Integer, String> mappingFunction =
9391 * new MapGroupsWithStateFunction<String, Integer, Integer, String>() {
9492 *
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