Perks of Apache Spark
Explore the core benefits of Apache Spark, including its graceful degradation during memory shortages, strategies for enhancing performance through reducing wide dependencies, resilience to request broker failures using Zookeeper, and mechanisms for ensuring data persistence with commit protocols. Learn practical examples like pre-aggregation to optimize data processing in distributed environments.
Keeps application running
Spark provides graceful degradation in cases where memory is not enough so that the application does not fail but keeps running with decreased performance.
For instance, Spark can recalculate any partitions on demand when they don’t fit in memory or spill them to disk.
Increasing performance
Wide dependencies cause more data to be exchanged between nodes compared to narrow dependencies, so performance is increased significantly by reducing wide dependencies, or the amount of data that needs to be shuffled. One way to do this is by pre-aggregating data, also known as map-side reduction.
Note: As explained previously, the map-side reduction is a capability provided in the MapReduce framework through ...