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Why does map partition reduce data?

Before reduce phase, partitioning of the map output take place on the basis of the key. Hadoop Partitioning specifies that all the values for each key are grouped together. It also makes sure that all the values of a single key go to the same reducer. This allows even distribution of the map output over the reducer.

What is combiner and partitioning in MapReduce?

The combiner is an optimization to the reducer. The default partitioning function is the hash partitioning function where the hashing is done on the key. However it might be useful to partition the data according to some other function of the key or the value.

How can you perform combiner and partition in a MapReduce?

Record having same key value goes into the same partition (within each mapper), and then each partition is sent to a Reducer. Partition phase takes place in between mapper and reducer. In a MapReduce job first mapper executes then Combiner followed by Partitioner.

What is the function of MapReduce partitioner?

The Partitioner in MapReduce controls the partitioning of the key of the intermediate mapper output. By hash function, key (or a subset of the key) is used to derive the partition. A total number of partitions depends on the number of reduce task.

Why are partitions shuffled in MapReduce?

In Hadoop MapReduce, the process of shuffling is used to transfer data from the mappers to the necessary reducers. It is the process in which the system sorts the unstructured data and transfers the output of the map as an input to the reducer.

What does reducer do in MapReduce?

Reducer in Hadoop MapReduce reduces a set of intermediate values which share a key to a smaller set of values. In MapReduce job execution flow, Reducer takes a set of an intermediate key-value pair produced by the mapper as the input.

What is partitioning in HDFS?

Data partitioning is a technique for physically dividing the data during the loading of the Master Data. We can’t forget we are working with huge amounts of data and we are going to store the information in a cluster, using a distributed filesystem. One of the most popular is Hadoop HDFS.

Why do we reduce map?

The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers. Decomposing a data processing application into mappers and reducers is sometimes nontrivial.

How does MapReduce Work?

A MapReduce job usually splits the input datasets and then process each of them independently by the Map tasks in a completely parallel manner. The output is then sorted and input to reduce tasks. Both job input and output are stored in file systems. Tasks are scheduled and monitored by the framework.

How does a partitioner in MapReduce work?

The key or a subset of the key is used to derive the partition by a hash function. The total number of partitions is almost same as the number of reduce tasks for the job. Partitioner runs on the same machine where the mapper had completed its execution by consuming the mapper output.

How does a partitioner work in a reducer?

Therefore, the data passed from a single partitioner is processed by a single Reducer. A partitioner partitions the key-value pairs of intermediate Map-outputs. It partitions the data using a user-defined condition, which works like a hash function. The total number of partitions is same as the number of Reducer tasks for the job.

How is total number of partitions equal to number of Reduce tasks?

The total number of partitions is equal to the number of reduce tasks. On the basis of key value, framework partitions, each mapper output. Records as having the same key value go into the same partition (within each mapper). Then each partition is sent to a reducer. Partition class decides which partition a given (key, value) pair will go.

When does partitioning take place in Hadoop reducer?

Before reduce phase, partitioning of the map output take place on the basis of the key. Hadoop Partitioning specifies that all the values for each key are grouped together. It also makes sure that all the values of a single key go to the same reducer. This allows even distribution of the map output over the reducer.