diff --git a/python/pyspark/sql/streaming/readwriter.py b/python/pyspark/sql/streaming/readwriter.py
index 34af8cd9b070e..8c70457b426f7 100644
--- a/python/pyspark/sql/streaming/readwriter.py
+++ b/python/pyspark/sql/streaming/readwriter.py
@@ -1571,12 +1571,14 @@ def foreach(self, f: Union[Callable[[Row], None], "SupportsProcess"]) -> "DataSt
     def foreachBatch(self, func: Callable[["DataFrame", int], None]) -> "DataStreamWriter":
         """
         Sets the output of the streaming query to be processed using the provided
-        function. This is supported only the in the micro-batch execution modes (that is, when the
-        trigger is not continuous). In every micro-batch, the provided function will be called in
-        every micro-batch with (i) the output rows as a DataFrame and (ii) the batch identifier.
-        The batchId can be used deduplicate and transactionally write the output
-        (that is, the provided Dataset) to external systems. The output DataFrame is guaranteed
-        to exactly same for the same batchId (assuming all operations are deterministic in the
+        function. This is supported only in the micro-batch execution modes (that is, when the
+        trigger is not continuous). In every micro-batch, the provided function will be called
+        with (i) the output rows as a :class:`DataFrame <pyspark.sql.DataFrame>`,
+        and (ii) the batch identifier.
+        
+        The batchId can be used to deduplicate and transactionally write the resulting
+        DataFrame to external systems. The output DataFrame is guaranteed
+        to be exactly same for the same batchId (assuming all operations are deterministic in the
         query).
 
         .. versionadded:: 2.4.0
@@ -1596,14 +1598,17 @@ def foreachBatch(self, func: Callable[["DataFrame", int], None]) -> "DataStreamW
         >>> df = spark.readStream.format("rate").load()
         >>> my_value = -1
         >>> def func(batch_df, batch_id):
+        ...     from pyspark.sql.functions import lit
         ...     global my_value
-        ...     my_value = 100
-        ...     batch_df.collect()
+        ...     (batch_df
+        ...         .withColumn("my_value", lit(my_value)
+        ...         .collect())
         ...
         >>> q = df.writeStream.foreachBatch(func).start()
         >>> time.sleep(3)
         >>> q.stop()
-        >>> # if in Spark Connect, my_value = -1, else my_value = 100
+        >>> # if in Spark Connect, this will fail, because
+        >>> # the `func` doesn't have any global `my_value`
         """
         from py4j.java_gateway import java_import
         from pyspark.java_gateway import ensure_callback_server_started