Reduce Function
Reduce has been a part of functional programming languages for a long time, just as the map function. The reduce function takes the result of a map function and "reduces" it in any way the programmer wants. The reduction function needs the first step of assigning a value to an accumulator, which stores an initial value.
The reduce function examines each member of the list and performs the action you require throughout the list after saving a beginning value in the accumulator. The reduction function returns a value at the end of the list based on the operation you wish to execute on the output list. Now go back to the map function example to see what the reduce function can achieve.
Optimizing MapReduce Tasks
Several optimization strategies can be applied to improve the application code to increase the reliability and speed of MapReduce tasks. Hardware/network topology, synchronization, and file system are the three categories in which they are classified.
-
Hardware/network topology: The best hardware and networks, regardless of application, will almost certainly result in the fastest run speeds for the program. MapReduce has the advantage of being able to execute on low-cost commodity hardware clusters using conventional networks. In the data centre, commodity hardware is frequently placed in racks. When compared to transporting data and code from rack to rack, the closeness of the hardware within the rack provides a performance benefit. You may set up your MapReduce engine to be aware of and make use of this closeness during implementation.
-
Synchronization: Because it's wasteful to keep all of your mapping results in one node, the synchronisation mechanisms copy the mapping results to the reducing nodes as soon as they're finished, allowing processing to start right away. The same reducer receives all values from the same key, guaranteeing superior performance and efficiency. Because the reduction outputs are sent directly to the file system, they must be carefully planned and calibrated.
- File System: A distributed file system is used to support the MapReduce implementation. The capacity difference is the most significant distinction between local and distributed file systems. To accommodate the massive volumes of data generated by big data, file systems must be distributed among several workstations or network nodes. MapReduce implementations use a master-slave distribution model, with the master node storing all metadata, access privileges, file and block mapping and location, and so on. The slaves are the nodes that store the real data. All requests are sent to the master, who subsequently forwards them to the relevant slave node.
FAQs
When to use MapReduce with Big Data?
MapReduce is a key component of the Apache Hadoop open-source ecosystem, and it's widely used in the Hadoop Distributed File System for searching and choosing data (HDFS).
What is one of the significant advantage of using MapReduce?
MapReduce's main benefit is that it's simple to expand data processing over several computer nodes. The data processing primitives of the MapReduce paradigm are known as mappers and reducers. It's not always easy to break down a data processing application into mappers and reducers. Scaling an application to operate over hundreds, thousands, or even tens of thousands of servers in a cluster is only a configuration modification once we build it in MapReduce style.
Conclusion
In this article, we have extensively discussed File Systems in Map Reduce and its optimization.
We hope this blog has helped you enhance your knowledge regarding the Map-Reduce function. Some official documentation on big data that can help you improve your understanding is Big Data and Database Vs Data Warehouse.
If you would like to learn more, check out our articles on Columnar Database, cloud platform comparison, and 10 AWS best books.
Practice makes a man perfect. To practice and improve yourself in the interview, you can check out Top 100 SQL problems, Interview experience, Coding interview questions, and the Ultimate guide path for interviews.
Do upvote our blog to help other ninjas grow. Happy Coding!