RDDs vs. Dataframes vs. Datasets – What is the Difference and Why Should Data Engineers Care?
- Understand the difference between 3 spark APIs – RDDs, Dataframes, and Datasets
- We will see how to create RDDs, Dataframes, and Datasets
It has been 11 years now since Apache Spark came into existence and it impressively continuously to be the first choice of big data developers. Developers have always loved it for providing simple and powerful APIs that can do any kind of analysis on big data.
Initially, in 2011 in they came up with the concept of RDDs, then in 2013 with Dataframes and later in 2015 with the concept of Datasets. None of them has been depreciated, we can still use all of them. In this article, we will understand and see the difference between all three of them.
Table of contents
What are RDDs?
RDDs or Resilient Distributed Datasets is the fundamental data structure of the Spark. It is the collection of objects which is capable of storing the data partitioned across the multiple nodes of the cluster and also allows them to do processing in parallel.
It is fault-tolerant if you perform multiple transformations on the RDD and then due to any reason any node fails. The RDD, in that case, is capable of recovering automatically.
There are 3 ways of creating an RDD:
- Parallelizing an existing collection of data
- Referencing to the external data file stored
- Creating RDD from an already existing RDD
When to use RDDs?
We can use RDDs in the following situations-
- When we want to do low-level transformations on the dataset. Read more about RDD Transformations: PySpark to perform Transformations
- It does not automatically infer the schema of the ingested data, we need to specify the schema of each and every dataset when we create an RDD. Learn how to infer the schema to the RDD here: Building Machine Learning Pipelines using PySpark
What are Dataframes?
It was introduced first in Spark version 1.3 to overcome the limitations of the Spark RDD. Spark Dataframes are the distributed collection of the data points, but here, the data is organized into the named columns. They allow developers to debug the code during the runtime which was not allowed with the RDDs.
Dataframes can read and write the data into various formats like CSV, JSON, AVRO, HDFS, and HIVE tables. It is already optimized to process large datasets for most of the pre-processing tasks so that we do not need to write complex functions on our own.
It uses a catalyst optimizer for optimization purposes. If you want to read more about the catalyst optimizer I would highly recommend you to go through this article: Hands-On Tutorial to Analyze Data using Spark SQL
Let’s see how to create a data frame using PySpark.
What are Datasets?
Spark Datasets is an extension of Dataframes API with the benefits of both RDDs and the Datasets. It is fast as well as provides a type-safe interface. Type safety means that the compiler will validate the data types of all the columns in the dataset while compilation only and will throw an error if there is any mismatch in the data types.
Users of RDD will find it somewhat similar to code but it is faster than RDDs. It can efficiently process both structured and unstructured data.
We cannot create Spark Datasets in Python yet. The dataset API is available only in Scala and Java only
RDDs vs Dataframes vs Datasets
|Data Representation||RDD is a distributed collection of data elements without any schema.||It is also the distributed collection organized into the named columns||It is an extension of Dataframes with more features like type-safety and object-oriented interface.|
|Optimization||No in-built optimization engine for RDDs. Developers need to write the optimized code themselves.||It uses a catalyst optimizer for optimization.||It also uses a catalyst optimizer for optimization purposes.|
|Projection of Schema||Here, we need to define the schema manually.||It will automatically find out the schema of the dataset.||It will also automatically find out the schema of the dataset by using the SQL Engine.|
|Aggregation Operation||RDD is slower than both Dataframes and Datasets to perform simple operations like grouping the data.||It provides an easy API to perform aggregation operations. It performs aggregation faster than both RDDs and Datasets.||Dataset is faster than RDDs but a bit slower than Dataframes.|
Frequently Asked Questions
A. RDD (Resilient Distributed Dataset) is the fundamental data structure in Apache Spark, representing an immutable distributed collection of objects. It offers low-level operations and lacks optimization benefits provided by higher-level abstractions.
DataFrames, on the other hand, are higher-level abstractions built on top of RDDs. They provide structured and optimized distributed data processing with a schema, supporting SQL-like queries, and various optimizations for better performance.
A. RDDs are useful in scenarios where you require low-level control over data and need to perform complex custom transformations or need to access RDD-specific operations not available in DataFrames. Additionally, RDDs are suitable when working with unstructured data or when integrating with non-Spark libraries that expect RDDs as input.
In this article, we have seen the difference between the three major APIs of Apache Spark. So to conclude, if you want rich semantics, high-level abstractions, type-safety then go for Dataframes or Datasets. If you need more control over the pre-processing part, you can always use the RDDs.
I recommend you go through these additional resources on Apache Spark to enhance your knowledge-
- Getting Started with Apache Hive – A Must Know Tool For all Big Data and Data Engineering Professionals
- Data Engineering 101 – Data Sources in Apache Spark Every Data Engineer Must Know!
- What are the differences between Data Lake and Data Warehouse?
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