Access Control Layer On Top Of Pig Using Xacml

1366 words - 5 pages

Apache Hadoop is an open-source software framework for storage and large scale
processing of data-sets on clusters of commodity hardware. Hadoop, an Apache top-level project
is built and used by a global community of contributors and users. Rather than relying on
hardware to deliver high-availability, the library is designed to detect and handle failures at the
application layer itself. It delivers a highly-available service on top of a cluster of computers,
each of which may be prone to failures.
A small Hadoop cluster has a single master and multiple worker nodes. The master
node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker
node acts as both a DataNode and TaskTracker, though it is possible to have data-only
worker nodes and compute-only worker nodes. These are normally used only in nonstandard
applications. Hadoop requires Java Runtime Environment (JRE) 1.6 or higher. The standard
start-up and shutdown scripts require ssh to be set up between nodes in the cluster.
The Apache Hadoop framework is composed of the modules Hadoop Common which
contains libraries and utilities for other Hadoop modules, Hadoop MapReduce is a
programming model for large scale data processing, Hadoop Distributed File System (HDFS)
is a distributed file-system which stores data that provides very high aggregate bandwidth
across the cluster and Hadoop YARN, a resource-management platform that manages
computer resources in clusters and uses them for scheduling of user applications.
The Hadoop distributed file system is a distributed, scalable, and portable file-system
written in Java for the Hadoop framework. Each node in a Hadoop instance has a single
namenode; a cluster of datanodes form the HDFS cluster as shown in Figure 4.2. Each node
does not require a datanode to be present. Each datanode serves up blocks of data over the
network using a block protocol which is specific to HDFS. The file system
TCP/IP layer for communication. Clients use RPC for communicating between each other.
HDFS stores large files (in the range of gigabytes to terabytes) on multiple machines. It does
not require RAID storage because it achieves reliability by replicating the data on multiple
hosts. Data is stored on three nodes: two on the same rack, and one on a different rack with
the default replicating value 3. Data nodes can talk to each other to rebalance data, to move
copies around, and to keep the replication of data high.
Hadoop MapReduce is software framework for writing applications which processes
large datasets in parallel on large clusters. It splits input data into independent chunks and
then these chunks are processed by the map task parallely. Outputs of map are sorted and
becomes input for the reduce tasks. Both input and output are stored in a file-system.
written in Java for the Hadoop framework. Each node in a Hadoop instance has a single
namenode; a cluster of datanodes form the HDFS cluster as shown in Figure 4.2. Each node

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