![]() ![]() In the next stage, when the original join Map Reduce task is running, it moves the data in the hash table file to the Hadoop distributed cache, which populates these files to each mapper’s local disk. ![]() ![]() After reading, it serializes the in-memory hash table into a hash table file. When we submit a map reduce task, a Map Reduce local task will be created before the original join Map Reduce task which will read data of the small table from HDFS and store it into an in-memory hash table. How will the map-side join optimize the task?Īssume that we have two tables of which one of them is a small table. The Map-side Join will be mostly suitable for small tables to optimize the task. Map-side Join is similar to a join but all the task will be performed by the mapper alone. The reducer’s job during reduce stage is to take this sorted result as input and complete the task of join. Further, in the shuffle stage, this intermediate file is then sorted and merged. A mapper’s job during Map Stage is to “read” the data from join tables and to “return” the ‘join key’ and ‘join value’ pair into an intermediate file. Whenever, we apply join operation, the job will be assigned to a Map Reduce task which consists of two stages- a ‘Map stage’ and a ‘ Reduce stage’. Now let us understand the functionality of normal join with an example. When we perform join operation on them, it will return the records which are the combination of all columns o f A and B. Join is a clause that combines the records of two tables (or Data-Sets).Īssume that we have two tables A and B. This is an important concept that you’ll need to learn to implement your Big Data Hadoop Certification projects. But before knowing about this, we should first understand the concept of ‘Join’ and what happens internally when we perform the join in Hive. In this blog, we shall discuss about Map side join and its advantages over the normal join operation in Hive. ![]()
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