What are RDD, Dataframe and Dataset in Apache Spark
In Apache Spark, RDD (Resilient Distributed Dataset) is the fundamental data structure for distributed data processing. An RDD is an immutable distributed collection of data that can be processed in parallel. RDDs are fault-tolerant and can be created from data stored in external storage systems, such as HDFS (Hadoop Distributed File System), or by transforming existing RDDs using operations called transformations.
Apache Spark is an open-source big data processing framework that provides a unified platform for large scale data processing. In Spark, data is processed in a distributed fashion, with the data split into partitions that can be processed in parallel across multiple nodes in a cluster. Spark provides several abstractions for working with data, including Resilient Distributed Datasets (RDDs), DataFrames, and Datasets.
Resilient Distributed Datasets (RDDs)
RDDs are the fundamental data structure in Spark. An RDD is a distributed collection of objects that can be processed in parallel across multiple nodes in a cluster. RDDs are immutable, meaning that once created, they cannot be changed. Instead, transformations are performed on RDDs to create new RDDs. RDDs are created either by parallelizing an existing collection of objects or by transforming an existing RDD.
RDDs provide a low-level API for data processing, and as such, require manual transformations and actions. The API provides several operations for transforming and aggregating data, including map, filter, reduce, and groupBy. While RDDs are well-suited for unstructured or semi-structured data, they are the slowest of the three abstractions due to a lack of optimizations.
DataFrames
DataFrames are a distributed collection of data organized into named columns. DataFrames are similar to RDDs in that they can be processed in parallel across multiple nodes in a cluster. However, unlike RDDs, DataFrames are optimized for structured data and provide a higher-level API for data processing.
DataFrames are created by reading structured data from a file or by converting an RDD to a DataFrame. The API provides several operations for transforming and aggregating data, including select, groupBy, and aggregate. DataFrames also provide built-in optimizations for structured data, making them faster than RDDs for this type of data.
Datasets
Datasets are a type-safe, object-oriented programming interface for DataFrames. A Dataset is a strongly-typed collection of objects that can be processed in parallel across multiple nodes in a cluster. Datasets provide the best of both worlds, with optimizations for structured data processing and strong typing.
Datasets are created by converting a DataFrame to a Dataset. The API provides several operations for transforming and aggregating data, including map, filter, and reduce. Datasets also provide compile-time type checking, making them ideal for structured data processing.
In conclusion, RDDs, DataFrames, and Datasets are different abstractions for working with data in Apache Spark. RDDs provide a low-level API for data processing and are best suited for unstructured or semi-structured data. DataFrames provide a higher-level API with optimizations for structured data. Datasets offer the best of both worlds, with strong type safety and optimizations for structured data processing. The choice between RDD, DataFrame, and Dataset will depend on the specific requirements of the application and the type of data being processed.
Comparison
| Feature | RDD | DataFrame | Dataset |
|---|---|---|---|
| Definition | Resilient Distributed Dataset – a fundamental data structure in Spark that is an immutable distributed collection of objects. | A distributed collection of data organized into named columns. | A type-safe, object-oriented programming interface for DataFrames. |
| Performance | Slowest due to its lack of optimizations. | Faster than RDD due to optimizations for structured data. | Fastest due to optimizations for both structured data and strong typing. |
| Type Safety | No type information. | Has type information for columns, but no compile-time type checking. | Strongly typed with compile-time type checking. |
| API | Low-level API that requires manual transformations and actions. | Higher-level API with built-in optimizations, but still requires manual transformations. | High-level API with optimizations and compile-time type checking. |
| Use Cases | Best suited for unstructured or semi-structured data. | Good for structured data processing, with built-in optimizations. | Ideal for structured data processing, with strong type safety and optimizations. |
Overview
A DataFrame is a distributed collection of data that is organized into named columns. DataFrames are similar to RDDs in that they can be processed in parallel, but they also have a schema that defines the data types of each column. DataFrames can be created from structured data sources, such as databases or CSV files, or from RDDs.
A Dataset is a distributed collection of data that is strongly typed and is built on top of the DataFrame API. Datasets allow you to manipulate data using a functional programming style, similar to RDDs, but with the added benefit of type safety.
The RDD, DataFrame, and Dataset APIs in Spark provide a rich set of functions for manipulating and processing data. Some common functions in these APIs include:
map: This function applies a function to each element of an RDD or Dataset and returns a new RDD or Dataset with the transformed elements.filter: This function returns a new RDD or Dataset that contains only the elements that meet a certain condition.reduce: This function combines the elements of an RDD or Dataset using a function and returns a single result.join: This function joins two RDDs, DataFrames, or Datasets based on a common key.groupBy: This function groups the elements of an RDD or Dataset based on a function and returns a new RDD or Dataset with the groups.
These are just a few examples of the functions available in the RDD, DataFrame, and Dataset APIs. There are many other functions available for manipulating and processing data in Spark.