org.apache.hadoop.mapred.lib.IdentityMapper: Implements the identity function, mapping inputs directly to outputs. If MapReduce programmer does not set the Mapper Class using JobConf.setMapperClass then IdentityMapper.class is used as a default value.
org.apache.hadoop.mapred.lib.IdentityReducer : Performs no reduction, writing all input values directly to the output. If MapReduce programmer does not set the Reducer Class using JobConf.setReducerClass then IdentityReducer.class is used as a default value.
Explain The Use Of Tasktracker In The Hadoop Cluster?
A Tasktracker is a slave node in the cluster which that accepts the tasks from JobTracker like Map, Reduce or shuffle operation. Tasktracker also runs in its own JVM Process.
Every TaskTracker is configured with a set of slots; these indicate the number of tasks that it can accept. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker.
The Tasktracker monitors these task instances, capturing the output and exit codes. When the Task instances finish, successfully or not, the task tracker notifies the JobTracker.
The TaskTrackers also send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These messages also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.
What Do You Mean By Taskinstance?
Task instances are the actual MapReduce jobs which run on each slave node. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the entire task tracker.Each Task Instance runs on its own JVM process. There can be multiple processes of task instance running on a slave node. This is based on the number of slots configured on task tracker. By default a new task instance JVM process is spawned for a task.
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How Many Daemon Processes Run On A Hadoop Cluster?
Hadoop is comprised of five separate daemons. Each of these daemons runs in its own JVM.
Following 3 Daemons run on Master nodes.
NameNode : This daemon stores and maintains the metadata for HDFS.
Secondary NameNode : Performs housekeeping functions for the NameNode.
JobTracker : Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker. Following 2 Daemons run on each Slave nodes
DataNode : Stores actual HDFS data blocks.
TaskTracker : It is Responsible for instantiating and monitoring individual Map and Reduce tasks.
How Many Maximum Jvm Can Run On A Slave Node?
One or Multiple instances of Task Instance can run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.
What Is Nas?
It is one kind of file system where data can reside on one centralized machine and all the cluster member will read write data from that shared database, which would not be as efficient as HDFS.
How Hdfa Differs With Nfs?
Following are differences between HDFS and NAS
In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware.
HDFS is designed to work with MapReduce System, since computation is moved to data. NAS is not suitable for MapReduce since data is stored separately from the computations.
HDFS runs on a cluster of machines and provides redundancy using replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.
How Does A Namenode Handle The Failure Of The Data Nodes?
HDFS has master/slave architecture. An HDFS cluster consists of a single
NameNode, a master server that manages the file system namespace and regulates access to files by clients.
In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. The NameNode and DataNode are pieces of software designed to run on commodity machines.
NameNode periodically receives a Heartbeat and a Block report from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. When NameNode notices that it has not received a heartbeat message from a data node after a certain amount of time, the data node is marked as dead. Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead DataNode. The NameNode Orchestrates the replication of data blocks from one DataNode to another. The replication data transfer happens directly between DataNode and the data never passes through the NameNode.
Speculative execution is a way of coping with individual Machine performance. In large clusters where hundreds or thousands of machines are involved there may be machines which are not performing as fast as others.
This may result in delays in a full job due to only one machine not performaing well. To avoid this, speculative execution in hadoop can run multiple copies of same map or reduce task on different slave nodes. The results from first node to finish are used.
When The Reducers Are Are Started In A Mapreduce Job?
In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.
If reducers do not start before all mappers finish then why does the progress on MapReduce job shows something like Map(50%) Reduce(10%)? Why reducers progress percentage is displayed when mapper is not finished yet?
Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer.
Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.
How The Hdfs Blocks Are Replicated?
HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time.
The NameNode makes all decisions regarding replication of blocks. HDFS uses rack-aware replica placement policy. In default configuration there are total 3 copies of a datablock on HDFS, 2 copies are stored on datanodes on same rack and 3rd copy on a different rack.
What Is Hadoop Framework?
Hadoop is a open source framework which is written in java by apche software foundation. This framework is used to wirite software application which requires to process vast amount of data (It could handle multi tera bytes of data). It works in-paralle on large clusters which could have 1000 of computers (Nodes) on the clusters. It also process data very reliably and fault-tolerant manner.
What Are Some Of The Characteristics Of Hadoop Framework?
Hadoop framework is written in Java. It is designed to solve problems that involve analyzing large data (e.g. petabytes). The programming model is based on Google’s MapReduce. The infrastructure is based on Google’s Big Data and Distributed File System. Hadoop handles large files/data throughput and supports data intensive distributed applications. Hadoop is scalable as more nodes can be easily added to it.
Give A Brief Overview Of Hadoop History?
In 2002, Doug Cutting created an open source, web crawler project.
In 2004, Google published MapReduce, GFS papers.
In 2006, Doug Cutting developed the open source, Mapreduce and HDFS project.
In 2008, Yahoo ran 4,000 node Hadoop cluster and Hadoop won terabyte sort benchmark.
In 2009, Facebook launched SQL support for Hadoop.
Give Examples Of Some Companies That Are Using Hadoop Structure?
A lot of companies are using the Hadoop structure such as Cloudera, EMC, MapR, Hortonworks, Amazon, Facebook, eBay, Twitter, Google and so on.
HDFS works with commodity hardware (systems with average configurations) that has high chances of getting crashed any time. Thus, to make the entire system highly fault-tolerant, HDFS replicates and stores data in different places. Any data on HDFS gets stored at at least 3 different locations. So, even if one of them is corrupted and the other is unavailable for some time for any reason, then data can be accessed from the third one. Hence, there is no chance of losing the data. This replication factor helps us to attain the feature of Hadoop called Fault Tolerant.
Since The Data Is Replicated Thrice In Hdfs, Does It Mean That Any Calculation Done On One Node Will Also Be Replicated On The Other Two?
Since there are 3 nodes, when we send the MapReduce programs, calculations will be done only on the original data. The master node will know which node exactly has that particular data. In case, if one of the nodes is not responding, it is assumed to be failed. Only then, the required calculation will be done on the second replica.
What Is A Job Tracker?
Job tracker is a daemon that runs on a namenode for submitting and tracking MapReduce jobs in Hadoop. It assigns the tasks to the different task tracker. In a Hadoop cluster, there will be only one job tracker but many task trackers. It is the single point of failure for Hadoop and MapReduce Service. If the job tracker goes down all the running jobs are halted. It receives heartbeat from task tracker based on which Job tracker decides whether the assigned task is completed or not.
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