APACHE SPARK: RDD, DATAFRAME OR DATASET?

[from] http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/

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There Are Now 3 Apache Spark APIs. Here’s How to Choose the Right One

See Apache Spark 2.0 API Improvements: RDD, DataFrame, DataSet and SQL here.

Apache 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.

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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 are collect() and saveAsObjectFile().

Example of RDD transformations and actions

Scala:

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

Java:

     
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        rdd 
        . 
        filter 
        ( 
        p 
        -> 
        p 
        . 
        getAge 
        ( 
        ) 
        < 
        21 
        ) 
              
        // transformation  
       
        . 
        map 
        ( 
        p 
        -> 
        p 
        . 
        getLast 
        ( 
        ) 
        ) 
                     
        // transformation 
       
        . 
        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

Scala:

Java:

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

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 the scala.Product interface. Scala case classes work out the box because they implement this interface.

Dataset API

The Dataset API, released as an API preview in Spark 1.6, aims to provide the best of both worlds; the familiar object-oriented programming style and compile-time type-safety of the RDD API but with the performance benefits of the Catalyst query optimizer. Datasets also use the same efficient off-heap storage mechanism as the DataFrame API.

When it comes to serializing data, the Dataset API has the concept of encoders which translate between JVM representations (objects) and Spark’s internal binary format. Spark has built-in encoders which are very advanced in that they generate byte code to interact with off-heap data and provide on-demand access to individual attributes without having to de-serialize an entire object. Spark does not yet provide an API for implementing custom encoders, but that is planned for a future release.

Additionally, the Dataset API is designed to work equally well with both Java and Scala. When working with Java objects, it is important that they are fully bean-compliant. In writing the examples to accompany this article, we ran into errors when trying to create a Dataset in Java from a list of Java objects that were not fully bean-compliant.

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Example: Creating Dataset from a list of objects

Scala

     
 
 
   
    
     
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        val 
        sc 
        = 
        new 
        SparkContext 
        ( 
        conf 
        ) 
       
 
        val 
        sqlContext 
        = 
        new 
        SQLContext 
        ( 
        sc 
        ) 
       
 
        import 
        sqlContext 
        . 
        implicits 
        . 
        _ 
       
 
        val 
        sampleData 
        : 
        Seq 
        [ 
        ScalaPerson 
        ] 
        = 
        ScalaData 
        . 
        sampleData 
        ( 
        ) 
       
 
        val 
        dataset 
        = 
        sqlContext 
        . 
        createDataset 
        ( 
        sampleData 
        ) 
       
 
    
 
   
 

Java

     
 
 
   
    
     
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         3 
       

         4 
       
 
      
        JavaSparkContext  
        sc 
        = 
        new 
        JavaSparkContext 
        ( 
        sparkConf 
        ) 
        ; 
       
 
        SQLContext  
        sqlContext 
        = 
        new 
        SQLContext 
        ( 
        sc 
        ) 
        ; 
       
 
        List  
        data 
        = 
        JavaData 
        . 
        sampleData 
        ( 
        ) 
        ; 
       
 
        Dataset  
        dataset 
        = 
        sqlContext 
        . 
        createDataset 
        ( 
        data 
        , 
        Encoders 
        . 
        bean 
        ( 
        JavaPerson 
        . 
        class 
        ) 
        ) 
        ; 
       
 
    
 
   
 

Transformations with the Dataset API look very much like the RDD API and deal with the Person class rather than an abstraction of a row.

Example: Filter by attribute with Dataset

Scala

Java

Despite the similarity with RDD code, this code is building a query plan, rather than dealing with individual objects, and if age is the only attribute accessed, then the rest of the the object’s data will not be read from off-heap storage.

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Conclusions

If you are developing primarily in Java then it is worth considering a move to Scala before adopting the DataFrame or Dataset APIs. Although there is an effort to support Java, Spark is written in Scala and the code often makes assumptions that make it hard (but not impossible) to deal with Java objects.

If you are developing in Scala and need your code to go into production with Spark 1.6.0 then the DataFrame API is clearly the most stable option available and currently offers the best performance.

However, the Dataset API preview looks very promising and provides a more natural way to code. Given the rapid evolution of Spark it is likely that this API will mature very quickly through 2016 and become the de-facto API for developing new applications.

See Apache Spark 2.0 API Improvements: RDD, DataFrame, DataSet and SQL here.