We have discussed a high level view of YARN Architecture in my post on Understanding Hadoop 2.x Architecture but YARN it self is a wider subject to understand. Keeping that in mind, we’ll about discuss YARN Architecture, it’s components and advantages in this post.
Prior to learn the concepts of Hadoop 2.x Architecture, I strongly recommend you to refer the my post on Hadoop Core Components, internals of Hadoop 1.x Architecture and its limitations. It will give you the idea about Hadoop2 Architecture requirement. And we have already learnt about the basic Hadoop components like Name Node, Secondary Name Node, Data Node, Job Tracker and Task Tracker.
Before we learn to install Apache Hive on CentOS let me give you the introduction of it. Hive is basically a data warehouse tool to store and process the structured data residing on HDFS. Hive was developed by Facebook and than after it is shifted to Apache Software Foundation and became an open source Apache Hive.
Hadoop 1.x Architecture is a history now because in most of the Hadoop applications are using Hadoop 2.x Architecture. But still understanding of Hadoop 1.x Architecture will provide us the insights of how hadoop has evolved over the time.
In order to access files from HDFS one can use various Hadoop commands from UNIX shell. Additionally, Hadoop also provides powerful Java APIs using which a programmer can write a code for accessing files over HDFS. Before we go into the more details let’s understand the terminology to access files from HDFS.
Before this post we have discussed about what is Hadoop and what kind of issues are solved by Hadoop. Now Let’s deep dive in to various components of Hadoop. Hadoop as a whole distribution provides only two core components and HDFS (which is Hadoop Distributed File System) and MapReduce (which is a distributed batch processing framework). And a complete bunch of machines which are running HDFS and MapReduce are known as Hadoop Cluster.
In my earlier post about Brief Introduction of Hadoop, we have understood “What is Hadoop and What kind of problems it solves”. The next step is to understand Hadoop Core Concepts which talks more about,
- Distributed system design
- How data is distributed across multiple systems
- What are the different components involved and how they communicate with each others
Apache Hadoop solves very different kind of problems in Big Data world. So before we get an introduction of Hadoop it becomes necessary to understand the core problems in large scale computation. Than after we’ll try to understand how Hadoop solves these problems.
Before we move ahead lets learn a bit on Setup Apache Spark,
So, What is Apache Spark?
Apache Spark is a fast, real time and extremely expressive computing system which executes job in distributed (clustered) environment.
It is quite compatible with Apache Hadoop and more almost 10x faster than Hadoop MapReduce on Disk Computing and 100x faster using in memory computations. It provides rich APIs in Java, Scala and Python along with Functional Programming capabilities.
Today is the era of parallel computation and whenever we talk about processing very large chunk of datasets the first word that comes in everyone’s mind is HADOOP. Apache Hadoop sits at the peak of Apache Project lists. In this post I’ll explain you all steps of setting up a Bazic Multi Node Hadoop Cluster (we’ll setup two node cluster).