The cache is cleared based on LRU. Clearly, for a map outputting small records, a higher value than the default will likely decrease the number of spills to disk. Is it correct and safe to assume? How can you add the arbitrary key-value pairs in your mapper? It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept.
You are commenting using your Facebook account. Notify me of new comments via email. But I still wanted to write the program to give me the word count for all words in the input files So I wrote a driver program of hadoop with map class as a TokenCounterMapper Class.
Just the recipe that I ordered … Now I needed a reducer which could actually count.. Bingo and the program does what it is supposed to do. Published by Shantanu Deo. Hello, just wanted to mention, I liked thhis blog post.
Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead DataNode.
The replication data transfer happens directly between DataNode and the data never passes through the NameNode. Can Reducer talk with each other? No, Reducer runs in isolation. This is typically a temporary directory location which can be setup in config by the Hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes. What is the use of Combiners in the Hadoop framework? Combiners are used to increase the efficiency of a MapReduce program.
They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative.
The execution of combiner is not guaranteed; Hadoop may or may not execute a combiner. Also, if required it may execute it more than 1 times. Implements the identity function, mapping inputs directly to outputs. Performs no reduction, writing all input values directly to the output.
What is the meaning of speculative execution in Hadoop? Why is it important? 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. Why reducers progress percentage is displayed when mapper is not finished yet? 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 it is different from traditional file systems? This is a distributed file system designed to run on commodity hardware.
It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. Applications that are compatible with HDFS are those that deal with large data sets.
These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files. How is it different from traditional file system block size? In HDFS data is split into blocks and distributed across multiple nodes in the cluster. Each block is typically 64Mb or Mb in size. Each block is replicated multiple times.
Default is to replicate each block three times. Replicas are stored on different nodes. HDFS Block size can not be compared with the traditional file system block size. What is a NameNode? How many instances of NameNode run on a Hadoop Cluster? It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It does not store the data of these files itself. There is only One NameNode process run on any hadoop cluster.
NameNode runs on its own JVM process. In a typical production cluster its run on a separate machine. When the NameNode goes down, the file system goes offline.
The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives. What is a DataNode? How many instances of DataNode run on a Hadoop Cluster? There is only One DataNode process run on any hadoop slave node.
DataNode runs on its own JVM process. On startup, a DataNode connects to the NameNode. DataNode instances can talk to each other, this is mostly during replicating data. Client applications can talk directly to a DataNode, once the NameNode has provided the location of the data. 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 data block on HDFS, 2 copies are stored on datanodes on same rack and 3rd copy on a different rack. Posted by Michael Holder at Newer Post Older Post Home. After analyzing datanode's container logs, I have found the reason why these errors.
Then I upload some data for testing. My run command is: No job jar file set. User classes may not be found. See Job or Job setJar String. Total input paths to process: Submitting tokens for job: