In this chapter, we learned about map-side and reduce-side tasks' enhancement and introduced some techniques that may help you to improve the performance of your MapReduce job. We learned how important the impact of the block size is and how to identify slow map-side performance due to small and unsplittable files. Also, we learned about spilling files and how to eliminate them by allocating an accurate amount of memory buffer.

Then, we moved ahead and learned how to identify a low performance job within the Shuffle and Merge steps during the Reduce phase. In the last section, we covered different techniques to calculate mappers' and reducers' numbers to tune your MapReduce configuration and enhance its performance.

In the next chapter, we will learn more about the optimization of MapReduce task and take a look at how combiners and intermediate data compression will enhance the MapReduce job performance. Keep reading!