Addressed comments

Author: tdas

docs/structured-streaming-programming-guide.md

@@ -919,8 +919,8 @@ streamingDf = spark.readStream. ...
 streamingDf.dropDuplicates("guid")
 
 // With watermark using guid and eventTime columns
-streamingDf
-  .withWatermark("eventTime", "10 seconds")
+streamingDf \
+  .withWatermark("eventTime", "10 seconds") \
   .dropDuplicates("guid", "eventTime")
 {% endhighlight %}
 

examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredSessionization.java

@@ -253,6 +253,3 @@ public SessionUpdate(String id, long durationMs, int numEvents, boolean expired)
     public void setExpired(boolean expired) { this.expired = expired; }
   }
 }
-
-
-

sql/core/src/main/scala/org/apache/spark/sql/streaming/GroupState.scala

@@ -54,9 +54,9 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalGroupState
  * `flatMapGroupsWithState` is associated with an operation output mode, which can be either
  * `Append` or `Update`. Semantically, this defines whether the output records of one trigger
  * is effectively replacing the previously output records (from previous triggers) or is appending
- * to the list of previously output records. Essentially, this defines how the semantic Result Table
- * (refer to programming guide) is updated, and allows us to reason about the semantics of
- * later operations.
+ * to the list of previously output records. Essentially, this defines how the Result Table (refer
+ * to the semantics in the programming guide) is updated, and allows us to reason about the
+ * semantics of later operations.
  *
  * Important points to note about the function (both mapGroupsWithState and flatMapGroupsWithState).
  *  - In a trigger, the function will be called only the groups present in the batch. So do not