Difference between DataFrame (in Spark 2.0 i.e Dat

Question

I'm just wondering what is the difference between an RDD and DataFrame (Spark 2.0.0 DataFrame is a mere type alias for Dataset[Row]) in Apache Spark?

Can you convert one to the other?

Answer 1

All(RDD, DataFrame and DataSet) in one picture

image credits

RDD

RDD is a fault-tolerant collection of elements that can be operated on in parallel.

DataFrame

DataFrame is a Dataset organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood.

Dataset

Dataset is a distributed collection of data. Dataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine.

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Note:

Dataset of Rows (Dataset[Row]) in Scala/Java will often refer as DataFrames.

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Nice comparison of all of them with a code snippet

source

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Q: Can you convert one to the other like RDD to DataFrame or vice-versa?

Yes, both are possible

1. RDD to DataFrame with .toDF()

val rowsRdd: RDD[Row] = sc.parallelize(
  Seq(
    Row("first", 2.0, 7.0),
    Row("second", 3.5, 2.5),
    Row("third", 7.0, 5.9)
  )
)

val df = spark.createDataFrame(rowsRdd).toDF("id", "val1", "val2")

df.show()
+------+----+----+
|    id|val1|val2|
+------+----+----+
| first| 2.0| 7.0|
|second| 3.5| 2.5|
| third| 7.0| 5.9|
+------+----+----+

more ways: Convert an RDD object to Dataframe in Spark

2. DataFrame/DataSet to RDD with .rdd() method

val rowsRdd: RDD[Row] = df.rdd() // DataFrame to RDD

Answer 2

RDD

The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel.

RDD Features:-

• Distributed collection:
  RDD uses MapReduce operations which is widely adopted for processing and generating large datasets with a parallel, distributed algorithm on a cluster. It allows users to write parallel computations, using a set of high-level operators, without having to worry about work distribution and fault tolerance.

• Immutable: RDDs composed of a collection of records which are partitioned. A partition is a basic unit of parallelism in an RDD, and each partition is one logical division of data which is immutable and created through some transformations on existing partitions.Immutability helps to achieve consistency in computations.

• Fault tolerant: In a case of we lose some partition of RDD , we can replay the transformation on that partition in lineage to achieve the same computation, rather than doing data replication across multiple nodes.This characteristic is the biggest benefit of RDD because it saves a lot of efforts in data management and replication and thus achieves faster computations.

• Lazy evaluations: All transformations in Spark are lazy, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset . The transformations are only computed when an action requires a result to be returned to the driver program.

• Functional transformations: RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset.

• Data processing formats:

It can easily and efficiently process data which is structured as well as unstructured data.

• Programming Languages supported:
  RDD API is available in Java, Scala, Python and R.

RDD Limitations:-

• No inbuilt optimization engine: When working with structured data, RDDs cannot take advantages of Spark’s advanced optimizers including catalyst optimizer and Tungsten execution engine. Developers need to optimize each RDD based on its attributes.

• Handling structured data: Unlike Dataframe and datasets, RDDs don’t infer the schema of the ingested data and requires the user to specify it.

Dataframes

Spark introduced Dataframes in Spark 1.3 release. Dataframe overcomes the key challenges that RDDs had.

A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a R/Python Dataframe. Along with Dataframe, Spark also introduced catalyst optimizer, which leverages advanced programming features to build an extensible query optimizer.

Dataframe Features:-

• Distributed collection of Row Object: A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database, but with richer optimizations under the hood.

• Data Processing: Processing structured and unstructured data formats (Avro, CSV, elastic search, and Cassandra) and storage systems (HDFS, HIVE tables, MySQL, etc). It can read and write from all these various datasources.

• Optimization using catalyst optimizer: It powers both SQL queries and the DataFrame API. Dataframe use catalyst tree transformation framework in four phases,

  1.Analyzing a logical plan to resolve references
  2.Logical plan optimization
  3.Physical planning
  4.Code generation to compile parts of the query to Java bytecode.
  

• Hive Compatibility: Using Spark SQL, you can run unmodified Hive queries on your existing Hive warehouses. It reuses Hive frontend and MetaStore and gives you full compatibility with existing Hive data, queries, and UDFs.

• Tungsten: Tungsten provides a physical execution backend whichexplicitly manages memory and dynamically generates bytecode for expression evaluation.

• Programming Languages supported:
  Dataframe API is available in Java, Scala, Python, and R.

Dataframe Limitations:-

• Compile-time type safety: As discussed, Dataframe API does not support compile time safety which limits you from manipulating data when the structure is not know. The following example works during compile time. However, you will get a Runtime exception when executing this code.

Example:

case class Person(name : String , age : Int) 
val dataframe = sqlContect.read.json("people.json") 
dataframe.filter("salary > 10000").show 
=> throws Exception : cannot resolve 'salary' given input age , name

This is challenging specially when you are working with several transformation and aggregation steps.

• Cannot operate on domain Object (lost domain object): Once you have transformed a domain object into dataframe, you cannot regenerate it from it. In the following example, once we have create personDF from personRDD, we won’t be recover the original RDD of Person class (RDD[Person]).

Example:

case class Person(name : String , age : Int)
val personRDD = sc.makeRDD(Seq(Person("A",10),Person("B",20)))
val personDF = sqlContect.createDataframe(personRDD)
personDF.rdd // returns RDD[Row] , does not returns RDD[Person]

Datasets API

Dataset API is an extension to DataFrames that provides a type-safe, object-oriented programming interface. It is a strongly-typed, immutable collection of objects that are mapped to a relational schema.

At the core of the Dataset, API is a new concept called an encoder, which is responsible for converting between JVM objects and tabular representation. The tabular representation is stored using Spark internal Tungsten binary format, allowing for operations on serialized data and improved memory utilization. Spark 1.6 comes with support for automatically generating encoders for a wide variety of types, including primitive types (e.g. String, Integer, Long), Scala case classes, and Java Beans.

Dataset Features:-

• Provides best of both RDD and Dataframe: RDD(functional programming, type safe), DataFrame (relational model, Query optimazation , Tungsten execution, sorting and shuffling)

• Encoders: With the use of Encoders, it is easy to convert any JVM object into a Dataset, allowing users to work with both structured and unstructured data unlike Dataframe.

• Programming Languages supported: Datasets API is currently only available in Scala and Java. Python and R are currently not supported in version 1.6. Python support is slated for version 2.0.

• Type Safety: Datasets API provides compile time safety which was not available in Dataframes. In the example below, we can see how Dataset can operate on domain objects with compile lambda functions.

Example:

case class Person(name : String , age : Int)
val personRDD = sc.makeRDD(Seq(Person("A",10),Person("B",20)))
val personDF = sqlContect.createDataframe(personRDD)
val ds:Dataset[Person] = personDF.as[Person]
ds.filter(p => p.age > 25)
ds.filter(p => p.salary > 25)
 // error : value salary is not a member of person
ds.rdd // returns RDD[Person]

• Interoperable: Datasets allows you to easily convert your existing RDDs and Dataframes into datasets without boilerplate code.

Datasets API Limitation:-

• Requires type casting to String: Querying the data from datasets currently requires us to specify the fields in the class as a string. Once we have queried the data, we are forced to cast column to the required data type. On the other hand, if we use map operation on Datasets, it will not use Catalyst optimizer.

Example:

ds.select(col("name").as[String], $"age".as[Int]).collect()

No support for Python and R: As of release 1.6, Datasets only support Scala and Java. Python support will be introduced in Spark 2.0.

The Datasets API brings in several advantages over the existing RDD and Dataframe API with better type safety and functional programming.With the challenge of type casting requirements in the API, you would still not the required type safety and will make your code brittle.

Answer 3

Few insights from usage perspective, RDD vs DataFrame:

1. RDDs are amazing! as they give us all the flexibility to deal with almost any kind of data; unstructured, semi structured and structured data. As, lot of times data is not ready to be fit into a DataFrame, (even JSON), RDDs can be used to do preprocessing on the data so that it can fit in a dataframe. RDDs are core data abstraction in Spark.
2. Not all transformations that are possible on RDD are possible on DataFrames, example subtract() is for RDD vs except() is for DataFrame.
3. Since DataFrames are like a relational table, they follow strict rules when using set/relational theory transformations, for example if you wanted to union two dataframes the requirement is that both dfs have same number of columns and associated column datatypes. Column names can be different. These rules don't apply to RDDs. Here is a good tutorial explaining these facts.
4. There are performance gains when using DataFrames as others have already explained in depth.
5. Using DataFrames you don't need to pass the arbitrary function as you do when programming with RDDs.
6. You need the SQLContext/HiveContext to program dataframes as they lie in SparkSQL area of spark eco-system, but for RDD you only need SparkContext/JavaSparkContext which lie in Spark Core libraries.
7. You can create a df from a RDD if you can define a schema for it.
8. You can also convert a df to rdd and rdd to df.

I hope it helps!

Answer 4

A DataFrame is equivalent to a table in RDBMS and can also be manipulated in similar ways to the "native" distributed collections in RDDs. Unlike RDDs, Dataframes keep track of the schema and support various relational operations that lead to more optimized execution. Each DataFrame object represents a logical plan but because of their "lazy" nature no execution occurs until the user calls a specific "output operation".

Answer 5

Most of answers are correct only want to add one point here

In Spark 2.0 the two APIs (DataFrame +DataSet) will be unified together into a single API.

"Unifying DataFrame and Dataset: In Scala and Java, DataFrame and Dataset have been unified, i.e. DataFrame is just a type alias for Dataset of Row. In Python and R, given the lack of type safety, DataFrame is the main programming interface."

Datasets are similar to RDDs, however, instead of using Java serialization or Kryo they use a specialized Encoder to serialize the objects for processing or transmitting over the network.

Spark SQL supports two different methods for converting existing RDDs into Datasets. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. This reflection based approach leads to more concise code and works well when you already know the schema while writing your Spark application.

The second method for creating Datasets is through a programmatic interface that allows you to construct a schema and then apply it to an existing RDD. While this method is more verbose, it allows you to construct Datasets when the columns and their types are not known until runtime.

Here you can find RDD tof Data frame conversation answer

How to convert rdd object to dataframe in spark

Answer 6

You can use RDD's with Structured and unstructured where as Dataframe/Dataset can only process Structured and Semi Structured Data (It is having proper schema)