Introduction
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Hadoop : A framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models
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Based on Java
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Save data in Google File System (2003) and process this big data with Map Reduce (2004)
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Modules :
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Hadoop Distributed File System : A distributed file system that provides high-throughput access to application data
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Hadoop YARN : A framework for job scheduling and cluster resource management
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Hadoop MapReduce : A YARN-based system for parallel processing of large data sets.
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1. Hadoop Distributed File System (HDFS)
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Components :
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Master (Name-node)
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(1) Managing Meta data
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(2) Monitoring data-node : data-node sends heartbeat signal for status and block-information every 3secs.
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(3) process client request
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Slave (Data-node)
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Characteristics:
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Block File System :
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Save data into splited block (128MB/block - in Hadoop 2.0)
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Metadata (file name, trees, … ) of these blocks are saved in Name node
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$ hdfs dfs -ls
$ hdfs dfs -put [LOCAL_FILE_NAME] [DEST]
$ hdfs dfs -get [FILE_NAME or FOLDER_PATH] [DEST_LOCAL]
$ hdfs dfs -rm [-f] [-r|-R] [-skipTrash] URI [URI ...]