MapReduce workflow - API - Combiners

The MapReduce workflow is a data processing framework that allows developers to process large volumes of data in a distributed and parallel manner. The workflow consists of the following steps:

1. Input Data: The first step in the workflow is to load the input data from the Hadoop Distributed File System (HDFS) or any other storage system that supports the Hadoop InputFormat API. The input data is split into small chunks called InputSplits that are processed by individual map tasks.
2. Map: The Map step takes each InputSplit and applies a user-defined Map function to it. The Map function transforms the input data into a set of key-value pairs that are intermediate results. The Map function runs in parallel on each node in the Hadoop cluster that has a copy of the InputSplit.
3. Shuffle and Sort: The Shuffle and Sort step collects the output from the Map function and sorts it by key. The intermediate results are partitioned based on their keys and sent to the nodes that will run the Reduce function. This step involves a network shuffle of data between the Map and Reduce nodes.
4. Reduce: The Reduce step applies a user-defined Reduce function to the intermediate results. The Reduce function combines the intermediate results that have the same key into a set of final output key-value pairs. The Reduce function runs in parallel on each node in the Hadoop cluster that has a copy of the intermediate results.
5. Output Data: The final step in the workflow is to write the output data to the Hadoop Distributed File System or any other storage system that supports the Hadoop OutputFormat API.

Hadoop is an evolving technology, and its APIs have undergone several changes over the years. Here are some notable changes in Hadoop APIs:

1. Hadoop 2.x introduced the YARN API, which replaced the older MapReduce API as the system responsible for managing resources in a Hadoop cluster.
2. Hadoop 3.x introduced several changes to the HDFS API, including support for erasure coding and improvements to the Namenode architecture.
3. Hadoop 3.x also introduced several changes to the MapReduce API, including support for container reuse and improvements to the Job History Server.
4. The HBase API has undergone several changes over the years, including the introduction of the HBase Thrift API and the HBase REST API.
5. The Hive API has also undergone several changes over the years, including improvements to query optimization and support for ACID transactions.
6. The Pig API has undergone several changes over the years, including improvements to the Pig Latin language and the introduction of the Pig Streaming API.
7. Overall, Hadoop APIs have evolved to provide better performance, scalability, and functionality for managing and processing large-scale data on a Hadoop cluster. Developers should stay up-to-date with the latest changes in Hadoop APIs to take advantage of the latest features and capabilities.

Combiners are a useful feature in Hadoop MapReduce to improve the performance of data processing jobs. They are optional functions that can be applied on the output of the Map phase before it is sent to the Reduce phase. The purpose of combiners is to reduce the amount of data that needs to be transferred across the network to the reducer and thereby improving the performance of the job.

Here are some ways in which combiners can be used to improve performance:

1. Reduce network I/O: Combiners can help reduce the amount of data that needs to be transferred across the network to the reducer. By applying a combiner function on the output of the Map phase, we can perform a local aggregation of the intermediate key-value pairs before sending them to the reducer. This can reduce network I/O and improve the performance of the job.
2. Reduce computational workload: Combiners can also help reduce the computational workload on the reducer. By performing local aggregation of the intermediate key-value pairs, the combiner can reduce the number of key-value pairs that the reducer needs to process. This can improve the performance of the reducer by reducing its computational workload.
3. Improve data locality: Combiners can help improve data locality by processing the intermediate key-value pairs on the same node where the Map task was executed. This can help reduce network I/O and improve the performance of the job.
4. Compatibility with Reducer: Combiners can be written using the same code as the reducer function, making it easy to implement and test. In some cases, the combiner can even be the same as the reducer function.
5. It's important to note that combiners are not always effective in improving the performance of a MapReduce job. Combiners are only useful when the key-value pairs produced by the Map function are suitable for aggregation, and when the combiner function is both associative and commutative. In some cases, using combiners can actually harm the performance of a MapReduce job if they increase the computational workload on the Map nodes. Therefore, it's important to test and evaluate the performance impact of using combiners before implementing them in a production environment.