Apache Spark RDD Introduction, Welcome to the world of Apache spark RDD tutorial. In these Tutorials, one can explore an introduction to Spark RDD, What is Spark RDD, Why is Spark RDD is required and when to use Apache Spark RDD. Learn More advanced tutorials on Apache Spark RDD introduction for beginners from India’s Leading Apache Spark Training institute which Provides Advanced Apache Spark Course for those tech enthusiasts who wanted to explore the technology from scratch to advanced level like a Pro.
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Introduction to Spark RDD
Apache Spark RDD refers to Resilient Distributed Datasets in Spark. It is an API (application programming interface) of Spark. It collects all the elements of the data in the cluster which are well partitioned. We can perform different operations on RDD as well as on data storage to form other RDDs from it. For example, Transformation and Actions. Transformations mean to create new data sets from the existing ones. As we know RDDs are Immutable, we can transform the data from one to another. Likewise, actions are operations that return a value to the program. All the transformations done on a Resilient Distributed Datasets are later applied when an action is called.
We can categorize operations as coarse-grained operations and fine-grained operations. The coarse-grained operation means to apply operations on all the objects at once. Fine-grained operations mean to apply operations on a smaller set. We generally apply coarse-grained operation, as it works on the entire cluster simultaneously. We can also create RDDs by its cache and divide it manually. RDDs give power to users to control them and can save it in cache memory. Users may also persist an RDD to memory as RDDs can be reused across the parallel operation.
Through its name, RDD itself indicating its properties like:
Resilient – means that it is able to withstand all the losses itself.
Distributed – Indicates that the data at different locations or partitioned.
Datasets – Group of data on which we are performing different operations.
RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. For example, a map is a transformation that passes each dataset element through a function and returns a new RDD representing the results. On the other hand, reduce is an action that aggregates all the elements of the RDD using some function and returns the final result to the driver program (although there is also a parallel reduceByKey that returns a distributed dataset).
All transformations in Spark are lazy, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently. For example, we can realize that a dataset created through a map will be used in a reduce and return only the result of the reduce to the driver, rather than the larger mapped dataset.
Why is Spark RDD is required?
In terms of memory and speed, it is far better than the older MapReduce paradigm. To achieve faster and efficient operations higher speed is necessary.
In Hadoop MapReduce, we cannot reuse the data or data sharing is not possible. We need to store data in some intermediate data stores which results in a slower process. In Spark, if we need to perform multiple operations on the same data, we can store that data explicitly. It can be stored in the memory by calling cache or persist functions.
In parallel jobs, both iterative and interactive applications need faster data sharing. That was not possible in Hadoop. Iterative means Reuse intermediate results. Whereas interactive means allowing a two-way flow of information. As RDD supports parallel operations due to this faster data sharing is possible.
Where and when we can use RDD?
Some of the conditions where we can use RDDs:
We use RDDs when we opt for lower level transformations and actions. It helps to control the datasets on which we are working. Such as Low level; Map, Filter, etc.
When the data is unstructured, like media streams, streams of text we do use RDD to fetch the information from it.
When we can relinquish some optimization and benefits on the basis of performance. By the data frames and datasets even from structured as well as semi-structured data.
While we want to manipulate data with functional programming than domain-specific expressions. Functional programming refers to a program built up from functions instead of objects. Whereas domain-specific expressions mean with specific goals in design and implementation.