Dotnet core features top 10

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. 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

Dotnet core features

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. 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

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