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Apache Spark: RDD, DataFrame or Dataset?


There are now 3 Apache Spark APIs. Here’s how to choose the right one.

By Andy Grove

spark_logoApache Spark is evolving at a rapid pace, including changes and additions to core APIs. One of the most disruptive areas of change is around the representation of data sets. Spark 1.0 used the RDD API but in the past twelve months, two new alternative and incompatible APIs have been introduced. Spark 1.3 introduced the radically different DataFrame API and the recently released Spark 1.6 release introduces a preview of the new Dataset API.

Many existing Spark developers will be wondering whether to jump from RDDs directly to the Dataset API, or whether to first move to the DataFrame API. Newcomers to Spark will have to choose which API to start learning with.

This article provides an overview of each of these APIs, and outlines the strengths and weaknesses of each one. A companion github repository provides working examples that are a good starting point for experimentation with the approaches outlined in this article.

The RDD (Resilient Distributed Dataset) API has been in Spark since the 1.0 release. This interface and its Java equivalent, JavaRDD, will be familiar to any developers who have worked through the standard Spark tutorials. From a developer’s perspective, an RDD is simply a set of Java or Scala objects representing data.

The RDD API provides many transformation methods, such as map()filter(), and reduce() for performing computations on the data. Each of these methods results in a new RDD representing the transformed data. However, these methods are just defining the operations to be performed and the transformations are not performed until an action method is called. Examples of action methods arecollect() and saveAsObjectFile().

Example of RDD transformations and actions


rdd.filter(_.age > 21) // transformation
   .map(._last)            // transformation
   .saveAsObjectFile("under21.bin"); // action


rdd.filter(p -> p.getAge() < 21) // transformation .map(p -&gt; p.getLast())
   .saveAsObjectFile("under21.bin"); // action

The main advantage of RDDs is that they are simple and well understood because they deal with concrete classes, providing a familiar object-oriented programming style with compile-time type-safety. For example, given an RDD containing instances of Person we can filter by age by referencing the age attribute of each Person object:

Example: Filter by attribute with RDD


rdd.filter(_.age > 21)


rdd.filter(person -> person.getAge() > 21)

The main disadvantage to RDDs is that they don’t perform particularly well. Whenever Spark needs to distribute the data within the cluster, or write the data to disk, it does so using Java serialization by default (although it is possible to use Kryo as a faster alternative in most cases). The overhead of serializing individual Java and Scala objects is expensive and requires sending both data and structure between nodes (each serialized object contains the class structure as well as the values). There is also the overhead of garbage collection that results from creating and destroying individual objects.

DataFrame API

Spark 1.3 introduced a new DataFrame API as part of the Project Tungsten initiative which seeks to improve the performance and scalability of Spark. The DataFrame API introduces the concept of a schema to describe the data, allowing Spark to manage the schema and only pass data between nodes, in a much more efficient way than using Java serialization. There are also advantages when performing computations in a single process as Spark can serialize the data into off-heap storage in a binary format and then perform many transformations directly on this off-heap memory, avoiding the garbage-collection costs associated with constructing individual objects for each row in the data set. Because Spark understands the schema, there is no need to use Java serialization to encode the data.

The DataFrame API is radically different from the RDD API because it is an API for building a relational query plan that Spark’s Catalyst optimizer can then execute. The API is natural for developers who are familiar with building query plans, but not natural for the majority of developers. The query plan can be built from SQL expressions in strings or from a more functional approach using a fluent-style API.

Example: Filter by attribute with DataFrame

Note that these examples have the same syntax in both Java and Scala

SQL Style

df.filter("age > 21");

Expression builder style:


Because the code is referring to data attributes by name, it is not possible for the compiler to catch any errors. If attribute names are incorrect then the error will only detected at runtime, when the query plan is created.

Another downside with the DataFrame API is that it is very scala-centric and while it does support Java, the support is limited. For example, when creating a DataFrame from an existing RDD of Java objects, Spark’s Catalyst optimizer cannot infer the schema and assumes that any objects in the DataFrame implement thescala.Product interface. Scala case classes work out the box because they implement this interface.

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