Merge pull request #276326 from santhoshravindran7/patch-3

Update apache-spark-performance.md

Author: prmerger-automator[bot]

articles/synapse-analytics/spark/apache-spark-performance.md

@@ -57,7 +57,13 @@ Spark provides its own native caching mechanisms, which can be used through diff
 Spark operates by placing data in memory, so managing memory resources is a key aspect of optimizing the execution of Spark jobs.  There are several techniques you can apply to use your cluster's memory efficiently.
 
 * Prefer smaller data partitions and account for data size, types, and distribution in your partitioning strategy.
-* Consider the newer, more efficient [Kryo data serialization](https://github.com/EsotericSoftware/kryo), rather than the default Java serialization.
+* In Synapse Spark (Runtime 3.1 or higher), Kryo data serialization is enabled by default Kryo data serialization.
+* You can customize the kryoserializer buffer size using Spark configuration based on your workload requirements:
+
+  ```scala
+  // Set the desired property
+  spark.conf.set("spark.kryoserializer.buffer.max", "256m")
+
 * Monitor and tune Spark configuration settings.
 
 For your reference, the Spark memory structure and some key executor memory parameters are shown in the next image.
@@ -172,6 +178,7 @@ MAX(AMOUNT) -> MAX(cast(AMOUNT as DOUBLE))
 
 ## Next steps
 
+- [Learn about Azure Synapse runtimes for Apache Spark](./apache-spark-version-support.md)
 - [Tuning Apache Spark](https://spark.apache.org/docs/2.4.5/tuning.html)
 - [How to Actually Tune Your Apache Spark Jobs So They Work](https://www.slideshare.net/ilganeli/how-to-actually-tune-your-spark-jobs-so-they-work)
 - [Kryo Serialization](https://github.com/EsotericSoftware/kryo)