Spark documentation and RDD features

Formally, an RDD is a read-only, partitioned collection of records. RDDs can be created through deterministic operations on either data on stable storage or other RDDs. RDD is a fault-tolerant collection of elements that can be operated on in parallel. The Spark documentation defines an RDD as a collection of elements partitioned across the nodes of the cluster that can be operated-on in parallel.

The Spark documentation defines an RDD as a collection of elements partitioned across the nodes of the cluster that can be operated-on in parallel. An RDD can be thought of as a collection, similar to a list or an array. The MapR Database OJAI Connector for Apache Spark supports loading data as an Apache Spark RDD. Starting in the MEP 4.0 release, the connector introduces support for Apache Spark DataFrames and Datasets. DataFrames and Datasets perform better than RDDs. Everything from the loading of the text file, to manipulating and saving the data, is done with an RDD. This interface makes it easy for the users to think in terms of collections. This distribution of work and data, also means that even if one point fails, the rest of the system continues processing, while the failure can be restarted immediately elsewhere. This design that makes fault-tolerance easy, is due to the fact that most functions in Spark are lazy.

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Apache Spark’s first abstraction was the RDD. It is an interface to a sequence of data objects that consist of one or more types that are located across a collection of machines (a cluster). RDDs can be created in a variety of ways and are the “lowest level” API available. So, instead of immediately executing the functions-instructions, those same instructions are stored for later use in a DAG, or Directed Acyclic Graph. This graph of instructions continues to grow through a series of calls to the transformations, such as map, filter, etc. It is this lineage awareness that makes it possible for Spark to handle failures so gracefully. Each RDD in the graph knows how it was built, which allows it to choose the best path for recovery. Operations such as collect, count, reduce, and other methods trigger the DAG execution and result in some final action against the data.

Subsequently, it was open sourced in 2010. Spark is mostly written in Scala language. It has some code written in Java, Python and R. Apache Spark provides several APIs for programmers which include Java, Scala, R and Python. Actions will trigger a execution of the graph. An RDD may be operated on like any other collection. But it’s really distributed across your cluster, to be executed in parallel. The Driver application is just like any other application, atleast until an Action is triggered, at which point the Driver and its Spark-Context distributes the tasks to each node, which transform their respective chunks as quickly as they can. Once all the nodes have completed their tasks, then the next stage of the DAG can be triggered.

Step By Step process on new technologies

  • RDD is a fault-tolerant collection of elements that can be operated in parallel Spark makes use of the concept of RDD to achieve faster and efficient MapReduce operations. Let us first discuss how MapReduce operations take place and why they are not so efficient.
  • Immutable because with RDDs, we only deal with transformations of RDDs.
  • It is distributed across multiple machines. And the operations that you do on these RDDs are always parallel in nature.

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