Hadoop Datanode, namenode, secondary-namenode, job-tracker and task-tracker

Question

I am new in hadoop so I have some doubts. If the master-node fails what happened the hadoop cluster? Can we recover that node without any loss? Is it possible to keep a secondar

Answer 1

Although, It is too late to answer your question but just It may help others..firstly we will discuss role of Hadoop 1.X daemons and then your issues..

1. What is role of secondary name Node it is not exactly a backup node. it reads a edit logs and create updated fsimage file for name node periodically. it get metadata from name node periodically and keep it and uses when name node fails. 2. what is role of name node it is manager of all daemons. its master jvm proceess which run at master node. it interact with data nodes.

3. what is role of job tracker it accepts job and distributes to task trackers for processing at data nodes. its called as map process

4. what is role of task trackers it will execute program provided for processing on existing data at data node. that process is called as map.

limitations of hadoop 1.X

1. single point of failure which is name node so we can maintain high quality hardware for the name node. if name node fails everything will be inaccessible

Solutions solution to single point of failure is hadoop 2.X which provides high availability.

high availability with hadoop 2.X

now your topics ....

How can we restore the entire cluster data if anything happens? if cluster fails we can restart it..

If a node is failed before completion of a job, so there is job pending in job tracker, is that job continue or restart from the first in the free node? we have default 3 replicas of data(i mean blocks) to get high availability it depends upon admin that how much replicas he has set...so job trackers will continue with other copy of data on other data node

can we use C program in Mapreduce (For example, Bubble sort in mapreduce)? basically mapreduce is execution engine which will solve or process big data problem in(storage plus processing) distributed manners. we are doing file handling and all other basic operations using mapreduce programming so we can use any language of where we can handle files as per the requirements.

hadoop 1.X architecture hadoop 1.x has 4 basic daemons

I Just gave a try. Hope it will help you as well as others.

Suggestions/Improvements are welcome.