Comparison between RDD vs DataSets- Apache Spark

There is always a question tickling in mind that why they should be using datasets rather than Spark RDD. In this tutorial, we will give you answer this question by comparing Spark RDD vs datasets.

First, we will discuss the brief introduction of datasets as well as RDD. Afterwards, we will compare datasets vs RDD on the basis of different features. Furthermore, we will also focus that what are the usage areas of RDD and dataset.

Introduction of Apache Spark RDD vs DataSets

1. Spark RDD

RDD refers to Resilient Distributed Datasets. It is the basic data structure of Spark RDD, is a read-only partition collection of records. RDDs can perform in-memory computations over large clusters in a fault-tolerant manner.

As a result, it speeds up the task also known as Spark’s core abstraction.

2. Spark DataSets

We can say in Apache Spark, datasets are an extension of dataframe, offers type-safe and object-oriented programming interface. In addition, we can use catalyst optimizer by exposing expression to a query planner.

Comparison between RDD vs DataSets

1. Spark Release

RDD–  Since the 1.0 release, the RDD APIs have been on Spark.

DataSets- Recently, in Spark 1.6 release dataset has been introduced in Spark.

2. Data Formats

RDD- We can easily process data which is structured as well as unstructured.

DataSets- Datasets also easily processes structured and unstructured data. In datasets, we can represent data in the form of JVM objects of row or a collection of row object. Through encoders, that is represented in tabular forms.

3. Data Representation

RDD-  All the data elements are distributed over many machines across the cluster. It is a set of Scala or Java objects representing data.

DataSets-  Datasets provides the functionality type-safe, object-oriented programming interface of the RDD API. Also, performance benefits of the catalyst query optimizer and of a dataframe API.

4. Optimization

RDD- In RDD, there is no inbuilt optimization engine is available.

DataSets- We can use dataframe catalyst optimizer for optimizing query plan.

5. Serialization

RDD-  It uses Java serialization, while needs to distribute the data over the cluster or write the data to disk.

DataSets-  While we talk about serializing data, in spark dataset API, there is a concept of an encoder. That handles conversion of JVM objects to tabular representation.

6. Efficiency/Memory use

RDD-  When serialization takes place, one by one on java & scala object, efficiency reduces.

DataSets- When we perform operations on serialized data in datasets, memory usage improves.

7. Compile-time type safety

RDD- It offers compile-time type safety with object-oriented programming style.

DataSets- Datasets offers compile-time type safety.

8. Data Sources API

RDD- RDD can handle data with no predefined structure. It could come from any data source such as text file, a database via JDBC etc.

DataSets-  Spark dataset API also support data from different sources.

9. Immutability and Interoperability

RDD- The major feature of RDD is immutability, helps to achieve consistency in computations. Moreover, by using todf() method, we can move RDD to dataframe if RDD is in a tabular format. Also, can do the reverse by the .rdd method.

DataSets- Dataframe has a limitation that it cannot regenerate RDD from dataframe, so datasets overcome that limitation. It allows us to convert our existing RDD and dataframes into datasets.

10. Lazy Evolution

RDD- In Spark RDDs evaluates lazily.

DataSets- Similarly, it also evaluates lazily as RDD.

11. Programming Language Support

RDD-  Available in Java, Scala, Python, and R languages

DataSets- Available in Scala and Java.

12. Schema Projection

RDD- User needs to define the schema manually.

DataSets- No need to specify the schema of the files because of spark SQL engine, it automatically infers the schema.

13. Aggregation

RDD- Simple grouping and aggregation operations are slower in RDD.

DataSets- To perform aggregation operation on a lot of data sets is faster.

14. Spark RDD and Datasets Usage area


1. On unstructured data, like streams.

2. While data manipulation involves constructs of functional programming.

3. When the data access and processing is free of schema impositions.

4. While needed low-level transformations and actions.


1. With high-degree safety at runtime.

2. To use typed JVM objects.

3. If we want to take advantage of the catalyst optimizer.

4. Also, helps to save space.

5. For faster execution.


As a result, we have seen that RDD in spark offers low-level functionality, while dataset allows custom view and structure. Since, datasets provide high-level domain-specific operations, saves space, and executes at high speed.

After analyzing comparison of both API spark RDD vs datasets we concluded that we can use dataset over RDD, but still, we can use any of them up to our requirements.