Introduction to MapReduce v1

MapReduce is a software framework used to write applications that simultaneously process vast amounts of data on large clusters of commodity hardware in a reliable, fault-tolerant manner. It is a batch-oriented model where a large amount of data is stored in Hadoop Distributed File System (HDFS), and the computation on data is performed as MapReduce phases. The basic principle for the MapReduce framework is to move computed data rather than move data over the network for computation. The MapReduce tasks are scheduled to run on the same physical nodes on which data resides. This significantly reduces the network traffic and keeps most of the I/O on the local disk or within the same rack.

The high-level architecture of the MapReduce framework has three main modules:

• MapReduce API: This is the end-user API used for programming the MapReduce jobs to be executed on the HDFS data.
• MapReduce framework: This is the runtime implementation of various phases in a MapReduce job such as the map, sort/shuffle/merge aggregation, and reduce phases.
• MapReduce system: This is the backend infrastructure required to run the user's MapReduce application, manage cluster resources, schedule thousands of concurrent jobs, and so on.

The MapReduce system consists of two components—JobTracker and TaskTracker.

• JobTracker is the master daemon within Hadoop that is responsible for resource management, job scheduling, and management. The responsibilities are as follows:
  • Hadoop clients communicate with the JobTracker to submit or kill jobs and poll for jobs' progress
  • JobTracker validates the client request and if validated, then it allocates the TaskTracker nodes for map-reduce tasks execution
  • JobTracker monitors TaskTracker nodes and their resource utilization, that is, how many tasks are currently running, the count of map-reduce task slots available, decides whether the TaskTracker node needs to be marked as blacklisted node, and so on
  • JobTracker monitors the progress of jobs and if a job/task fails, it automatically reinitializes the job/task on a different TaskTracker node
  • JobTracker also keeps the history of the jobs executed on the cluster
• TaskTracker is a per node daemon responsible for the execution of map-reduce tasks. A TaskTracker node is configured to accept a number of map-reduce tasks from the JobTracker, that is, the total map-reduce tasks a TaskTracker can execute simultaneously. The responsibilities are as follows:
  • TaskTracker initializes a new JVM process to perform the MapReduce logic. Running a task on a separate JVM ensures that the task failure does not harm the health of the TaskTracker daemon.
  • TaskTracker monitors these JVM processes and updates the task progress to the JobTracker on regular intervals.
  • TaskTracker also sends a heartbeat signal and its current resource utilization metric (available task slots) to the JobTracker every few minutes.