Full Form of RDD
RDD stands for Resilient Distributed Dataset. It is a fundamental data structure of Apache Spark, an open-source distributed computing system.
Key Features of RDD:
- Resilient:
RDDs are fault-tolerant; they can recover from node failures.
Distributed:
Data is distributed across multiple nodes in a cluster, allowing for parallel processing.
Dataset:
- RDDs represent a collection of objects that can be processed in parallel.
Advantages of RDD:
- In-Memory Computation:
RDDs enable faster data processing by storing intermediate results in memory.
Lazy Evaluation:
Transformations on RDDs are not computed until an action is called, optimizing performance.
Immutable:
- Once created, the data in an RDD cannot be changed, which helps in maintaining consistency.
Common Operations on RDDs:
- Transformations:
- map(): Applies a function to each element in the RDD.
filter(): Returns a new RDD containing elements that satisfy a predicate.
Actions:
- collect(): Returns all the elements of the RDD to the driver program.
- count(): Returns the number of elements in the RDD.
Usage Scenarios:
- Big Data Processing:
Ideal for handling large datasets and performing operations like batch processing.
Machine Learning:
- RDDs can be used for scalable machine learning algorithms.
In summary, RDD (Resilient Distributed Dataset) is a pivotal concept in Apache Spark, providing an efficient and fault-tolerant way to handle large-scale data processing tasks.