MAPREDUCE is a software framework and programming model used for processing huge amounts of data. MapReduce program work in two phases, namely, Map and Reduce. Map tasks deal with splitting and mapping of data while Reduce tasks shuffle and reduce the data.
Hadoop is capable of running MapReduce programs written in various languages: Java, Ruby, Python, and C++. MapReduce programs are parallel in nature, thus are very useful for performing large-scale data analysis using multiple machines in the cluster.
The input to each phase is key-value pairs. In addition, every programmer needs to specify two functions: map function and reduce function.
In this beginner training, you will learn-
- What is MapReduce in Hadoop?
- How MapReduce Works? Complete Process
- MapReduce Architecture explained in detail
- How MapReduce Organizes Work?
The whole process goes through four phases of execution namely, splitting, mapping, shuffling, and reducing.
Let's understand this with an example –
Consider you have following input data for your Map Reduce Program
Welcome to Hadoop Class Hadoop is good Hadoop is bad
The final output of the MapReduce task is
The data goes through the following phases
An input to a MapReduce job is divided into fixed-size pieces called input splits Input split is a chunk of the input that is consumed by a single map
This is the very first phase in the execution of map-reduce program. In this phase data in each split is passed to a mapping function to produce output values. In our example, a job of mapping phase is to count a number of occurrences of each word from input splits (more details about input-split is given below) and prepare a list in the form of <word, frequency>
This phase consumes the output of Mapping phase. Its task is to consolidate the relevant records from Mapping phase output. In our example, the same words are clubed together along with their respective frequency.
In this phase, output values from the Shuffling phase are aggregated. This phase combines values from Shuffling phase and returns a single output value. In short, this phase summarizes the complete dataset.
In our example, this phase aggregates the values from Shuffling phase i.e., calculates total occurrences of each word.
One map task is created for each split which then executes map function for each record in the split.
It is always beneficial to have multiple splits because the time taken to process a split is small as compared to the time taken for processing of the whole input. When the splits are smaller, the processing is better to load balanced since we are processing the splits in parallel.
However, it is also not desirable to have splits too small in size. When splits are too small, the overload of managing the splits and map task creation begins to dominate the total job execution time.
For most jobs, it is better to make a split size equal to the size of an HDFS block (which is 64 MB, by default).
Execution of map tasks results into writing output to a local disk on the respective node and not to HDFS.
Reason for choosing local disk over HDFS is, to avoid replication which takes place in case of HDFS store operation.
Map output is intermediate output which is processed by reduce tasks to produce the final output.
Once the job is complete, the map output can be thrown away. So, storing it in HDFS with replication becomes overkill.
In the event of node failure, before the map output is consumed by the reduce task, Hadoop reruns the map task on another node and re-creates the map output.
- Reduce task doesn't work on the concept of data locality. An output of every map task is fed to the reduce task. Map output is transferred to the machine where reduce task is running.
- On this machine, the output is merged and then passed to the user-defined reduce function.
- Unlike the map output, reduce output is stored in HDFS (the first replica is stored on the local node and other replicas are stored on off-rack nodes). So, writing the reduce output
Hadoop divides the job into tasks. There are two types of tasks:
Map tasks (Splits & Mapping)
Reduce tasks (Shuffling, Reducing)
as mentioned above.
The complete execution process (execution of Map and Reduce tasks, both) is controlled by two types of entities called a
Jobtracker: Acts like a master (responsible for complete execution of submitted job)
Multiple Task Trackers: Acts like slaves, each of them performing the job
For every job submitted for execution in the system, there is one Jobtracker that resides on Namenode and there are multiple tasktrackers which reside on Datanode.
A job is divided into multiple tasks which are then run onto multiple data nodes in a cluster.
It is the responsibility of job tracker to coordinate the activity by scheduling tasks to run on different data nodes.
Execution of individual task is then to look after by task tracker, which resides on every data node executing part of the job.
Task tracker's responsibility is to send the progress report to the job tracker.
In addition, task tracker periodically sends 'heartbeat' signal to the Jobtracker so as to notify him of the current state of the system.
Thus job tracker keeps track of the overall progress of each job. In the event of task failure, the job tracker can reschedule it on a different task tracker.