Difference between DataFrame Dataset and RDD in Spark

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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

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Spark RDD  resilient distributed dataset      RDD is the core data abstraction API and is available since very first release of Spark  Spark 1 0   It is a lower-level API for manipulating distributed collection of data  The RDD APIs exposes some extremely useful methods which can be used to get very tight control over underlying physical data structure  It is an immutable  read only  collection of partitioned data distributed on different machines  RDD enables in-memory computation on large clusters to speed up big data processing in a fault tolerant manner  To enable fault tolerance  RDD uses DAG  Directed Acyclic Graph  which consists of a set of vertices and edges  The vertices and edges in DAG represent the RDD and the operation to be applied on that RDD respectively  The transformations defined on RDD are lazy and executes only when an action is called  Spark DataFrame    Spark 1 3 introduced two new data abstraction APIs     DataFrame and DataSet  The DataFrame APIs organizes the data into named columns like a table in relational database  It enables programmers to define schema on a distributed collection of data  Each row in a DataFrame is of object type row  Like an SQL table  each column must have same number of rows in a DataFrame  In short  DataFrame is lazily evaluated plan which specifies the operations needs to be performed on the distributed collection of the data  DataFrame is also an immutable collection   Spark DataSet    As an extension to the DataFrame APIs  Spark 1 3 also introduced DataSet APIs which provides strictly typed and object-oriented programming interface in Spark  It is immutable  type-safe collection of distributed data  Like DataFrame  DataSet APIs also uses Catalyst engine in order to enable execution optimization  DataSet is an extension to the DataFrame APIs   Other Differences -

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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 organised into named columns  It is conceptually equivalent to a table in a relational database or a data frame in R Python  but with richer optimisations 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    Note  Dataset of Rows  Dataset Row   in Scala Java will often refer as DataFrames     Nice comparison of all of them with a code snippet   source   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  quot first quot   2 0  7 0       Row  quot second quot   3 5  2 5       Row  quot third quot   7 0  5 9         val df   spark createDataFrame rowsRdd  toDF  quot id quot    quot val1 quot    quot val2 quot    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

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Apache Spark provide three type of APIs  RDD DataFrame Dataset   Here is the APIs comparison between RDD  Dataframe and Dataset  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   sqlContext read json  quot people json quot    dataframe filter  quot salary  gt  10000 quot   show    gt  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  quot A quot  10  Person  quot B quot  20    val personDF   sqlContext 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  quot A quot  10  Person  quot B quot  20    val personDF   sqlContext createDataframe personRDD  val ds Dataset Person    personDF as Person  ds filter p   gt  p age  gt  25  ds filter p   gt  p salary  gt  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  quot name quot   as String     quot age quot  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

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Simply RDD is core component  but DataFrame is an API introduced in spark 1 30   RDD  Collection of data partitions called RDD  These RDD must follow few properties such is     Immutable  Fault Tolerant  Distributed   More    Here RDD is either structured or unstructured   DataFrame  DataFrame is an API available in Scala  Java  Python and R  It allows to process any type of Structured and semi structured data  To define DataFrame  a collection of distributed data organized into named columns called DataFrame  You can easily optimize the RDDs in the DataFrame  You can process JSON data  parquet data  HiveQL data at a time by using DataFrame   val sampleRDD   sqlContext jsonFile  hdfs   localhost 9000 jsondata json    val sample DF   sampleRDD toDF     Here Sample DF consider as DataFrame  sampleRDD is  raw data  called RDD

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Few insights from usage perspective  RDD vs DataFrame   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  Not all transformations that are possible on RDD are possible on DataFrames  example subtract   is for RDD vs except   is for DataFrame  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  There are performance gains when using DataFrames as others have already explained in depth  Using DataFrames you don t need to pass the arbitrary function as you do when programming with RDDs  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  You can create a df from a RDD if you can define a schema for it  You can also convert a df to rdd and rdd to df   I hope it helps

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A DataFrame is defined well with a google search for  DataFrame definition       A data frame is a table  or two-dimensional array-like structure  in   which each column contains measurements on one variable  and each row   contains one case    So  a DataFrame has additional metadata due to its tabular format  which allows Spark to run certain optimizations on the finalized query    An RDD  on the other hand  is merely a Resilient Distributed Dataset that is more of a blackbox of data that cannot be optimized as the operations that can be performed against it  are not as constrained   However  you can go from a DataFrame to an RDD via its rdd method  and you can go from an RDD to a DataFrame  if the RDD is in a tabular format  via the toDF method  In general it is recommended to use a DataFrame where possible due to the built in query optimization

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A DataFrame is an RDD that has a schema  You can think of it as a relational database table  in that each column has a name and a known type  The power of DataFrames comes from the fact that  when you create a DataFrame from a structured dataset  Json  Parquet     Spark is able to infer a schema by making a pass over the entire  Json  Parquet    dataset that s being loaded  Then  when calculating the execution plan  Spark  can use the schema and do substantially better computation optimizations   Note that DataFrame was called SchemaRDD before Spark v1 3 0

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First thing is DataFrame was evolved from SchemaRDD      Yes   conversion between Dataframe and RDD is absolutely possible    Below are some sample code snippets    df rdd is RDD Row    Below are some of options to create dataframe    1  yourrddOffrow toDF converts to DataFrame  2  Using createDataFrame of sql context  val df   spark createDataFrame rddOfRow  schema       where schema can be from some of below options as described by nice SO post     From scala case class and scala reflection api  import org apache spark sql catalyst ScalaReflection val schema   ScalaReflection schemaFor YourScalacaseClass  dataType asInstanceOf StructType        OR using Encoders  import org apache spark sql Encoders val mySchema   Encoders product MyCaseClass  schema       as described by Schema can also be created using StructType and   StructField  val schema   new StructType      add StructField  id   StringType  true      add StructField  col1   DoubleType  true      add StructField  col2   DoubleType  true   etc         In fact there Are Now 3 Apache Spark APIs       RDD API        The RDD  Resilient Distributed Dataset  API has been in Spark since   the 1 0 release       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      RDD Example    rdd filter   age  gt  21     transformation     map   last    transformation  saveAsObjectFile  under21 bin      action   Example  Filter by attribute with RDD  rdd filter   age  gt  21     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        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   Example SQL style     df filter  age  gt  21     Limitations    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 class works 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    Example Dataset API SQL style     dataset filter   age  lt  21     Evaluations diff  between DataFrame  amp  DataSet     Catalist level flow   Demystifying DataFrame and Dataset presentation from spark summit    Further reading    databricks article - A Tale of Three Apache Spark APIs  RDDs vs DataFrames and Datasets

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Apache Spark     RDD  DataFrame  and DataSet  Spark RDD          An RDD stands for Resilient Distributed Datasets  It is Read-only   partition collection of records  RDD is the fundamental data structure   of Spark  It allows a programmer to perform in-memory computations on   large clusters in a fault-tolerant manner  Thus  speed up the task    Spark Dataframe          Unlike an RDD  data organized into named columns  For example a table   in a relational database  It is an immutable distributed collection of   data  DataFrame in Spark allows developers to impose a structure onto   a distributed collection of data  allowing higher-level abstraction    Spark Dataset          Datasets in Apache Spark are an extension of DataFrame API which   provides type-safe  object-oriented programming interface  Dataset   takes advantage of Spark   s Catalyst optimizer by exposing expressions   and data fields to a query planner

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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

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Because DataFrame is weakly typed and developers aren t getting the benefits of the type system  For example  lets say you want to read something from SQL and run some aggregation on it   val people   sqlContext read parquet        val department   sqlContext read parquet         people filter  age  gt  30      join department  people  deptId       department  id       groupBy department  name     gender      agg avg people  salary     max people  age       When you say people  deptId    you re not getting back an Int  or a Long  you re getting back a Column object which you need to operate on  In languages with a rich type systems such as Scala  you end up losing all the type safety which increases the number of run-time errors for things that could be discovered at compile time   On the contrary  DataSet T  is typed  when you do   val people  People   val people   sqlContext read parquet        as People    You re actually getting back a People object  where deptId is an actual integral type and not a column type  thus taking advantage of the type system   As of Spark 2 0  the DataFrame and DataSet APIs will be unified  where DataFrame will be a type alias for DataSet Row

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A Dataframe is an RDD of Row objects  each representing a record  A Dataframe also knows the schema  i e   data fields  of its rows  While Dataframes look like regular RDDs  internally they store data in a more efficient manner  taking advantage of their schema  In addition  they provide new operations not available on RDDs  such as the ability to run SQL queries  Dataframes can be created from external data sources  from the results of queries  or from regular RDDs   Reference  Zaharia M   et al  Learning Spark  O Reilly  2015

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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

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