[SPARK-46729][DOCS] Withdraw the recommendation of using Concurrent Mark Sweep (CMS) Garbage Collector

docs/streaming-programming-guide.md

@@ -2338,10 +2338,6 @@ There are a few parameters that can help you tune the memory usage and GC overhe
 * **Clearing old data**: By default, all input data and persisted RDDs generated by DStream transformations are automatically cleared. Spark Streaming decides when to clear the data based on the transformations that are used. For example, if you are using a window operation of 10 minutes, then Spark Streaming will keep around the last 10 minutes of data, and actively throw away older data.
 Data can be retained for a longer duration (e.g. interactively querying older data) by setting `streamingContext.remember`.
 
-* **CMS Garbage Collector**: Use of the concurrent mark-and-sweep GC is strongly recommended for keeping GC-related pauses consistently low. Even though concurrent GC is known to reduce the
-overall processing throughput of the system, its use is still recommended to achieve more
-consistent batch processing times. Make sure you set the CMS GC on both the driver (using `--driver-java-options` in `spark-submit`) and the executors (using [Spark configuration](configuration.html#runtime-environment) `spark.executor.extraJavaOptions`).
-
 * **Other tips**: To further reduce GC overheads, here are some more tips to try.
     - Persist RDDs using the `OFF_HEAP` storage level. See more detail in the [Spark Programming Guide](rdd-programming-guide.html#rdd-persistence).
     - Use more executors with smaller heap sizes. This will reduce the GC pressure within each JVM heap.