What Is the Fundamental Data Structure of Spark?


Heather Bennett

Apache Spark is an open-source, distributed computing system that is designed for big data processing. It provides a flexible and efficient way to analyze large datasets across multiple machines in a scalable manner. At the core of Spark’s processing engine lies its fundamental data structure – Resilient Distributed Datasets (RDDs).

What are RDDs?

RDDs represent a fault-tolerant collection of elements that can be processed in parallel across a cluster of machines. They are immutable, meaning once created, their contents cannot be modified. RDDs can be created from various data sources such as Hadoop Distributed File System (HDFS), local file systems, or even from existing RDDs.

RDDs provide two types of operations: transformations and actions. Transformations are operations that create a new RDD from an existing one by applying some computation. Actions, on the other hand, return a value or write data to an external storage system.

Key Characteristics of RDDs

RDDs have several important characteristics that make them suitable for big data processing:

  • Distributed: RDDs are distributed across multiple machines in a cluster, enabling parallel processing.
  • Resilient: RDDs are fault-tolerant and can recover from failures by recomputing lost partitions.
  • Immutable: Once created, RDDs cannot be modified. However, you can apply transformations to create new RDDs.
  • Lazily Evaluated: Transformations on RDDs are lazily evaluated, meaning they are not executed immediately but rather when an action is called.
  • Cached: RDDs can be cached in memory, allowing for faster access during iterative algorithms or interactive data analysis.

Example Usage of RDDs

Let’s consider a simple example to illustrate the usage of RDDs. Suppose we have a text file containing information about students’ grades. We can create an RDD by reading the file:

val gradesRDD = sc.textFile("grades.txt")

We can then perform various transformations on this RDD, such as filtering out failed grades:

val passedGradesRDD = gradesRDD.filter(grade => grade >= 60)

Finally, we can trigger an action to count the number of students who passed:

val passedCount = passedGradesRDD.count()
println("Number of students who passed: " + passedCount)


RDDs are the fundamental data structure in Apache Spark, providing a powerful and efficient way to process large-scale data. With their distributed and fault-tolerant nature, combined with the ability to perform transformations and actions, RDDs enable developers to build complex data processing workflows easily. By leveraging HTML styling elements like bold text, underlined text, lists, and subheaders, we can create visually engaging tutorials that make learning about Spark’s fundamental data structure more enjoyable.

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