Apache Spark is an open-source, distributed processing platform to handle workloads of big data. In this article, we will discuss Resilient Distributed Datasets and Dataframes in Spark development. RDD is the read-only collection of different types of objects, while Dataframe is the distributed collection of a dataset. We will discuss the difference in features of Apache Spark RDD vs Dataframe. The article will provide the complete introduction, specifications, and use cases of both. Moreover, we will clarify the differences through the comparison, technical differences, and problems in both solutions. After reading this article, you will get to know about the features of RDD and Dataframes, this way you can easily choose the one for grouping the data.
Apache Spark RDD
RDD is a fundamental structure of data and a fixed collection of data that calculates on a cluster’s different nodes. It enables a developer to process computations on large clusters inside the memory in a resilient manner. This way, it increases the efficiency of the task.
When to Use RDDs?
Spark RDDs are useful in the following scenarios:
- If the data is unstructured like text and media streams, RDD will be beneficial in terms of performance.
- If the transformation is of a low level, RDD will be beneficial to fasten and straightforward the data manipulation when closer to the source of data.
- If the schema is not important, RDD will not impose it, but it will use schema to access specific data based on the column.
Apache Spark Dataframes
As compared to RDD, data in the Dataframe is arranged into named columns. It is a fixed distributed data collection that enables Spark developers to implement a structure on distributed data. This way, it allows abstraction at a higher level.
When to Use Dataframes?
Spark Dataframes are useful in the following scenarios:
- If the data is structured or semi-structured and you want high-level abstractions, Dataframe provides a schema for such data.
- If you want to store one-dimensional or multidimensional data matrices in tabular form.
- If high-level processing is required in datasets, Dataframe provides high-level functions and ease to use.
Difference Between RDD and Dataframes
In Spark development, RDD refers to the distributed data elements collection across various devices in the cluster. It is a set of Scala or Java objects to represent data.
Spark Dataframe refers to the distributed collection of organized data in named columns. It is like a relational database table.
Format of Data
Spark RDD can easily process structured and unstructured data, but it does not provide the schema of added data and users need to identify it.
A dataframe can process structured and semi-structured data only because it is like a relational database, and it can manage the schema.
Integration with Data Sources API
When RDD is integrated with the data sources API, it allows RDD to come from any source of data like a text file or a database. This way, without any predefined structure, data handling becomes easier.
When Dataframe is integrated with the data sources API, it allows the processing of data in various formats like JSON, HIVE tables, AVRO, MySQL, and CSV. This way, it can easily write and read from these data sources.
Compile-Time Type Safety
RDD supports OOP through compile-time type safety.
Dataframe does not support OOP through compile-time type safety because if a column is not presented and Spark developers try to access it, an attribute error will occur during runtime.
Immutability
RDD’s nature is immutable. It means that nothing is changeable in RDD but it can be created through various transformations. This way, the nature of all the calculations is consistent. This way, the nature of all the calculations is consistent.
For Dataframe, a domain object cannot be regenerated after transformation. This way, if one test Dataframe is generated, the original RDD cannot be recovered again from the test class.
Use Cases of RDD
RDD has the following use cases:
- It can easily handle data that has no predefined structure as RDD can come from any source of data.
- RDD is useful if you want the calculations right away.
- If your project is based on Java, Scala, R, and Python.
- If you want to specify a schema.
- For high-level abstraction and low-level transformation.
Use Cases of Dataframe
Spark Dataframe has the following use cases:
- It can handle data that come from specific sources like JSON, MySQL, CSV, etc.
- A dataframe is useful if you want the calculations right after the action performs.
- If your project is based on Java, Scala, R, and Python.
- If you do not want to specify a schema.
- For unstructured data and high-level abstractions.
| Features | RDD | Dataframe |
| Version | Spark 1.0 | Spark 1.3 |
| Representation of data | Distributed data elements | Data elements are organized in columns |
| Formats of data | Structured and unstructured | Structured and semi-structured |
| Sources of data | Various | Various |
| Compile-time type safety | Available | Unavailable |
| Optimization | No built-in engine for optimization | Catalyst optimizer for optimization |
| Serialization | Use Java serialization | Serialization occurs in memory |
| Lazy Evaluation | Yes | Yes |
RDD vs Dataframe: Which One is Better?
As discussed above, Apache Spark RDD offers low-level transformation and control. While Dataframe offers high-level operations that are domain-specific, run at high speed, and save the available space.
If you have Spark developers who also know Java, Scala, R, or Python, then based on your project’s specifications, you can select either RDD or Dataframe. For example, if you want to process distributed data elements, you can use RDD and if you want to process structured data, you can use Dataframe.
How can Algoscale Help you to Overcome any Problem?
If you want to develop a highly efficient system and do not want to hire a spark developer, we can assist you. Algoscale is proving itself in Spark development with an efficient team of Spark developers. Based on your project’s specifications and requirements, we can provide you with a solution either with Spark RDD or Dataframe.
If you want to perform basic operations like a grouping of data, then we prefer Spark Dataframe because RDD is somehow slower in this scenario and Dataframe performs faster aggregation.
Algoscale Utilizing Spark RDD and Dataframe
We hope that this article has clarified the difference between RDD and Dataframe. Each technology has its advantages and limitations based on the scope and specifications of the project being developed.
Although both are useful for different requirements, they can also be integrated with various third-party APIs. If you want to work in Apache Spark, you need to hire a Spark developer.
Based on the complexity of the project, you can consider the right choice into account to deliver the best software application. Algoscale can assist you in choosing either RDD or Dataframe. So, based on the requirements and scope of the project, we can help you to choose the best framework as per your needs.
