Large cluster of machines in a fault-tolerant

MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. The solution is to provide a distributed memory abstraction that lets programmer perform in-memory computation on a large cluster of machines in a fault-tolerant manner.

Thus, the solution is to provide a distributed memory abstraction that lets programmer perform in-memory computation on a large cluster of machines in a fault-tolerant manner. Abstract MapReduce-based systems have emerged as a prominent framework forlarge-scale data analysis, having fault tolerance as one of its key features. MapRe-duce has introduced simple yet efficient mechanisms to handle different kinds offailures including crashes, omissions and arbitrary failures. Spark provides an abstraction for distributed-processing : It provides a distributed memory-abstraction. You have a big flat array, and this array is distributed across multiple machines, and you will have an API to seamlessly work on this array. And RDD provides that abstraction

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And your Big DATA system are not suited for individual rows, it works in batch-mode, that is , it is suited to work on billions of rows as a batch. So, when you are planning to store intermediate results into distributed shared memory through a database suited for shared memory, you are actually using a system which is well suited for fine-grained update, but the logic which you are going to implement for your iterative algorithms is of coarse-grained update. The modern Hadoop ecosystem not only provides a reliable distributed aggregation system that seamlessly delivers data parallelism, but also allows for analytics that can provide great data insights. And, further problems would be encountered when we would need a fault-tolerant version of this data. Updating such vast amount of data in a database is in itself a concern, and having a fault-tolerant version of this data in a database is going to be another big concern. So, Spark came with a concept called RDD(Resilient Distributed Data) to solve the problems of distributed share memory.

A stop-gap arrangement that can be thought of was that of a shared-memory. For example, put all the intermediate data-results into a table, and let this table be made available to all the computing-nodes of the cluster. But, this would have it’s own implementation issues It also surveys the state-of-the-art optimization mechanisms toimprove the fault tolerance in MapReduce, and in particular its open-source imple-mentation Hadoop.

Step By Step process on new technologies

  • Hadoop has a good programming abstraction for accessing a cluster as a computation resource, but has a poor abstraction for leveraging distributed memory. In cloud environment, node and task failure are no longer accidental but a common feature of large-scale systems.
  • Most current iterative algorithms need to access the results of previous computations. They have to reuse the intermediate results across multiple iterative computations.
  • Coarse-grained processing This means that all of billions of rows are going to be processing with a same specific logic.

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