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To combine multiple mapper’s output used reducer. Reducer has 3 PRIMARY phases sort, shuffle and REDUCE. It’s possible to process DATA without reducer, but used when the shuffle and sort is required. 

To combine multiple mapper’s output used reducer. Reducer has 3 primary phases sort, shuffle and reduce. It’s possible to process data without reducer, but used when the shuffle and sort is required. 

Scheduling is a systematic procedure of ALLOCATING RESOURCES in the best possible way among multiple tasks. Hadoop task tracker performing MANY procedures, sometime a particular procedure should finish quickly and provide more prioriety, to do it few job schedulers come into the PICTURE. Default Schedule is FIFO. Fair scheduling, FIFO and CapacityScheduler are most POPULAR hadoop scheduling in hadoop. 

Scheduling is a systematic procedure of allocating resources in the best possible way among multiple tasks. Hadoop task tracker performing many procedures, sometime a particular procedure should finish quickly and provide more prioriety, to do it few job schedulers come into the picture. Default Schedule is FIFO. Fair scheduling, FIFO and CapacityScheduler are most popular hadoop scheduling in hadoop. 

MAPREDUCE Framework consists of a single JOB Tracker PER Cluster, one Task Tracker per NODE. Usually A cluster has multiple nodes, so each cluster has single Job Tracker and multiple TaskTrackers.JobTracker can schedule the job and monitor the Task Trackers. If Task Tracker FAILED to execute tasks, try to re-execute the failed tasks.

TaskTracker follow the JobTracker’s instructions and execute the tasks. As a slave node, it report the job status to Master JobTracker in the form of Heartbeat. 

MapReduce Framework consists of a single Job Tracker per Cluster, one Task Tracker per node. Usually A cluster has multiple nodes, so each cluster has single Job Tracker and multiple TaskTrackers.JobTracker can schedule the job and monitor the Task Trackers. If Task Tracker failed to execute tasks, try to re-execute the failed tasks.

TaskTracker follow the JobTracker’s instructions and execute the tasks. As a slave node, it report the job status to Master JobTracker in the form of Heartbeat. 

Scheduling the JOB’s TASKS on the slaves. Slaves execute the tasks as DIRECTED by the JobTracker. Monitoring the tasks, if FAILED, re­execute the failed tasks. 

Scheduling the job’s tasks on the slaves. Slaves execute the tasks as directed by the JobTracker. Monitoring the tasks, if failed, re­execute the failed tasks. 

Namenode is a logical daemon name for a particular node. It’s HEART of the ENTIRE Hadoop system. Which store the METADATA in FsImage and GET all block information in the form of Heartbeat.

Namenode is a logical daemon name for a particular node. It’s heart of the entire Hadoop system. Which store the metadata in FsImage and get all block information in the form of Heartbeat.

Mapreduce is not recommended for Iterative kind of processing. It means repeat the OUTPUT in a LOOP manner.To process Series of Mapreduce JOBS, MapReduce not suitable. each job PERSISTS data in local disk, then again load to another job. It’s COSTLY operation and not recommended.

Mapreduce is not recommended for Iterative kind of processing. It means repeat the output in a loop manner.To process Series of Mapreduce jobs, MapReduce not suitable. each job persists data in local disk, then again load to another job. It’s costly operation and not recommended.

It’s used to set/get of parameter NAME & value pairs in XML file.It’s used to INITIALIZE values, read from EXTERNAL file and set as a value parameter.Parameter values in the PROGRAM always OVERWRITE with new values which are coming from external configure files.Parameter values received from Hadoop’s default values.

It’s used to set/get of parameter name & value pairs in XML file.It’s used to initialize values, read from external file and set as a value parameter.Parameter values in the program always overwrite with new values which are coming from external configure files.Parameter values received from Hadoop’s default values.

Compute nodes for PROCESSING the data, STORAGE nodes for STORING the data. By default Hadoop FRAMEWORK tries to minimize the network wastage, to achieve that GOAL Framework follows the Data locality concept. The Compute code execute where the data is stored, so the data node and compute node are the same. 

Compute nodes for processing the data, Storage nodes for storing the data. By default Hadoop framework tries to minimize the network wastage, to achieve that goal Framework follows the Data locality concept. The Compute code execute where the data is stored, so the data node and compute node are the same. 

Based on the CONFIGURATION, the MapReduce Job first splits the input data into independent chunks CALLED Blocks. These blocks processed by MAP() and Reduce() functions. First Map function PROCESS the data, then processed by reduce function. The Framework takes care of sorts the Map outputs, SCHEDULING the tasks. 

Based on the configuration, the MapReduce Job first splits the input data into independent chunks called Blocks. These blocks processed by Map() and Reduce() functions. First Map function process the data, then processed by reduce function. The Framework takes care of sorts the Map outputs, scheduling the tasks. 

MAPREDUCE is a set of PROGRAMS USED to process or analyze VAST of data over a Hadoop cluster. It process the vast amount of the datasets parallelly across the clusters in a fault­tolerant manner across the Hadoop FRAMEWORK.

MapReduce is a set of programs used to process or analyze vast of data over a Hadoop cluster. It process the vast amount of the datasets parallelly across the clusters in a fault­tolerant manner across the Hadoop framework.