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HDFS Architecture content:

HDFS Architecture or Hardtop distributed File System files which are divided into blocks and how these blocks are stored in multiple machines. It is designed to turn the industry servers into a massive storage system that can store unlimited data with multiple copies without any loss of data. This application allows parallel processing happen how it manages to store data in unlimited amount.

To make data reliable it is not dependant on any data protection mechanism instead file contents are replicated. One of the advantages is multiple data transfers.

Name Node files and directories are denoted by anodes. It records modification and access time, etc. It also helps in determining a mapping of blocks to Data Nodes. The application data of HDFS is stored in Data Node. HDFS files consist of many blocks. Each block is replicated and stored in Data Node. It provides read and writes requests for HDFS.

MapReduce Content:

MapReduce as a batch processing tool. Servers can run in parallel to process huge data stored in HDFS. It supports languages for developers, c++, and java. MapReduce is about two algorithms Map and Reduce.

Map converts set of data into another set wherein elements are individually broken into tuples. Reduce takes map data that are tuples and combines it into a smaller set. MapReduce has an advantage that it is its Simple Scalability.

It has three stages namely Mapstage, shuffle and reduce stage.

In Mapstage input data is processed which is stored in the form of a directory in HDFS. Data is processed in chunks. Blend of Mapstage and Reduce stage results in Shuffle. Data is processed further and stored in HDFS.

Contents of Study for HDFS Architecture are:

• Introduction of HDFS
• Architecture of HDFS
• Edits viewer
• Image viewers
• Blocks
• Name Node
• Data Node
• Data Nodes Functions and Heartbeat
• Data Node and Secondary Name Node
• Data Replication in HDFS
• Read and Write mechanism in HDFS
• loading data in HDFS
• Replications and Rack Awareness

Contents of study for MapReduce are :

• Yarn
• Algorithms
• Inputs and outputs
• Flow charts of data flow through Map Resource manager,
• Node manager
• Application Master
• Architect – MapReduce
• Partitioners and Combiners
• Installation of Hadoop 2.6.0 on Ubuntu Linux OS and
• Project in MapReduce

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