The Development And Support Of Apache Hadoop

Hadoop \cite{white2012hadoop} is an open-source framework for distributed storage and data-intensive processing, first developed by Yahoo!. It has two core projects: Hadoop Distributed File System (HDFS) and MapReduce programming model \cite{dean2008mapreduce}. HDFS is a distributed file system that splits and stores data on nodes throughout a cluster, with a number of replicas. It provides an extremely reliable, fault-tolerant, consistent, efficient and cost-effective way to store a large amount of data. The MapReduce model consists of two key functions: Mapper and Reducer. The Mapper processes input data splits in parallel through different map tasks and sends sorted, shuffled outputs to the Reducers that in turn groups and processes them using a reduce task for each group.

MapReduce Parallel programming model if we ever get a chance. In Hadoop, there are two nodes in the cluster when using the algorithm, Master node and Slave node. Master node runs Namenode, Datanode, Jobtracker and Task tracker processes. Slave node runs the Datanode and Task tracker processes. Namenode manages partitioning of input dataset into blocks and on which node it has to store. Lastly, there are two core components of Hadoop: HDFS layer and MapReduce layer. The MapReduce layer read from and write into HDFS storage and processes data in parallel.

The Hadoop employs MapReduce paradigm of computing which targets batch-job processing. It does not directly support the real time query execution i.e OLTP. Hadoop can be integrated with Apache Hive that supports HiveQL query language which supports query firing, but still not provide OLTP tasks (such as updates and deletion at row level) and has late response time (in minutes) due to absence of pipeline

An important characteristic of Hadoop is the partitioning of data and computation across many (thousands) of hosts, and the execution of application computations in parallel close to their data. A Hadoop cluster scales computation capacity, storage capacity and I/O bandwidth by simply adding commodity servers. Hadoop clusters at Yahoo! span 40,000 servers, and store 40 petabytes of application data, with the largest cluster

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a Parallel and distributed computing environment. It makes Use of the commodity hardware Hadoop is Highly Scalable and Fault Tolerant. Hadoop runs in cluster and eliminates the use of a Super computer. Hadoop is the widely used big data processing engine with a simple master slave setup. Big Data in most companies are processed by Hadoop by submitting the jobs to Master. The Master distributes the job to its cluster and process map and reduce tasks sequencially.But nowdays the growing data need and the and competition between Service Providers leads to the increased submission of jobs to the Master. This Concurrent job submission on Hadoop forces us to do Scheduling on Hadoop Cluster so that the response time will be acceptable for each job.

Listed below are the concepts that should be learned in order to properly understand HADOOP technology.

Through the attractiveness of Hadoop, a number of companies became bundle Hadoop and some related technologies into their own Hadoop distributions. Cloudera was the first vendor to offer Hadoop as a package and continues to be a leader in the industry. Its Cloudera CDH distribution, which contains all the open source components, is the most popular Hadoop distribution.

The main purpose of this report is to provide a critical review of the processes and own experiences of Hadoop within the context of the assignment which was given to us. The review concentrates on the discussion and evaluation of the overall steps followed during the progress of the project and the reasons for which we have chosen these particular steps. It also draws attention at the main points that were accomplished, both with respect to individual, and with respect to the group 's perspectives. Finally, it concentrates on the project 's progress in terms of changes for a future implementation.

Inspired in part by MapReduce, Hadoop provides a Java based software framework for distributed processing of data intensive transformation and analytics. The top three commercial database suppliers Oracle, IBM, and Microsoft have all adopted Hadoop, some within a cloud infrastructure.

Many database vendors offer commercial Hadoop distribution with their appliance or hardware. This offer can easily be integrated with the current data warehouse infrastructure. However, I work with customers who adopted commodity server to control costs and work within their budgets. Commodity servers are simply a bunch of disks with single power supplies to store data. Adopting commodity servers will require resources to be trained, to manage and gain knowledge of how Hadoop works on commodity. Adopting commodity can be an alternative but may not be suited for your

The rise of Big Data and its attendant complexities has spawned a whole ecosystem to support the ever growing requirements of a 24x7 world. One of the key technologies coming out of the initial stages of Big Data has been Hadoop. Conceived in response to the rapidly growing needs of Yahoo!’s search engine, Hadoop provides a mechanism to store and collect vast amounts of data across a highly distributed environment using commodity hardware.

Other than disturbed file system, many higher-level programming frame work has developed. Most used commonly framework is MapReduce frame work for data intensive apps which is developed by google. MapReduce uses the ideas of the functional programming here the developer assigns map and reduce tasks to the process sets of distributed data. Implementation of MapReduce this ensures all the calculations of high scale data to be executed on the computer hubs which can withstand the hardware failures during computation. The main advantages of the map reduce programming framework are high degree of parallelism proportional with programming framework and its large variety of app domains. The map functions input pairs key1, value1 returning some other pairs that is key2, value2. The reduce output new function that is key3, value3

Abstract: these days, data-intensive issues are thus prevailing that varied organizations in numerous IT industries face them in their business operation. It 's usually crucial for enterprises to own the potential of analyzing massive volumes of knowledge in a good and timely manner. Mapreduce and its ASCII text file implementation of Hadoop dramatically simplified the event of parallel knowledge computing applications for normal users for knowledge intensive parallerly, and therefore the combination of Hadoop and cloud computing created large-scale parallel knowledge computing rather more accessible and reliable to all or any potential users than ever before. Though Hadoop has become the foremost in style knowledge management framework for parallel data-intensive computing within the clouds, the Hadoop computer hardware isn 't an ideal match for the cloud environments. In this paper, we discuss the issues of Hadoop task assignment scheme and present an improved scheme for heterogeneous computing environments using Apache Ambari with Parallel Fast Fourier Transform. We conducted in depth simulation to judge the performance of the projected theme compared with the Hadoop theme in two forms of heterogeneous computing environments that are typical on the general public cloud platforms. The simulation

Abstract - Hadoop Distributed File System, a Java based file system provides reliable and scalable storage for data. It is the key component to understand how a Hadoop cluster can be scaled over hundreds or thousands of nodes. The large amounts of data in Hadoop cluster is broken down to smaller blocks and distributed across small inexpensive servers using HDFS. Now, MapReduce functions are executed on these smaller blocks of data thus providing the scalability needed for big data processing. In this paper I will discuss in detail on Hadoop, the architecture of HDFS, how it functions and the advantages.

Applications in HDFS will require streaming access to data sets. Batch processing is done rather than interactive use by the users.