Dataframe是Row对象的RDD，每个对象代表一条记录。一个 Dataframe还知道它的行的模式(即数据字段)。虽然Dataframes 看起来像常规的rdd，它们内部以更有效的方式存储数据，利用它们的模式。此外，它们还提供了rdd上不可用的新操作，例如运行SQL查询的能力。数据帧可以从外部数据源、查询结果或常规rdd中创建。

参考文献:Zaharia M.， et al。学习火花(O'Reilly, 2015)

Apache Spark - RDD, DataFrame和DataSet

Spark RDD –

RDD代表弹性分布式数据集。只读 记录的分区集合。RDD是最基本的数据结构 的火花。它允许程序员在内存中执行计算 采用容错方式的大型集群。因此，加快任务的速度。

星火数据帧 –

与RDD不同，数据被组织成命名列。比如一张表 在关系数据库中。的不可变分布式集合 数据。Spark中的DataFrame允许开发人员在上面强加一个结构 数据的分布式集合，允许更高层次的抽象。

Spark数据集-

Apache Spark中的数据集是DataFrame API的扩展 提供类型安全的面向对象编程接口。数据集 通过暴露表达式来利用Spark的Catalyst优化器 和数据字段到查询计划器。

Apache Spark提供了三种类型的api

抽样 DataFrame 数据集

这里是RDD, Dataframe和Dataset之间的api比较。

RDD

Spark提供的主要抽象是一个弹性分布式数据集(RDD)，它是跨集群节点划分的元素集合，可以并行操作。

抽样特性:

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.

抽样的局限性:

没有内置优化引擎: 在处理结构化数据时，rdd无法利用Spark的高级优化器，包括catalyst优化器和Tungsten执行引擎。开发人员需要根据每个RDD的属性来优化它。 处理结构化数据: 与Dataframe和数据集不同，rdd不推断所摄取数据的模式，并要求用户指定它。

Dataframes

Spark在Spark 1.3版本中引入了Dataframes。Dataframe克服了rdd所面临的主要挑战。

DataFrame是一个分布式的数据集合，它被组织成命名的列。它在概念上等同于关系数据库或R/Python Dataframe中的表。除了Dataframe, Spark还引入了catalyst优化器，它利用高级编程特性来构建可扩展的查询优化器。

Dataframe特点:-

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限制:

编译时类型安全: 如前所述，Dataframe API不支持编译时安全，这限制了你在不知道结构时操作数据。下面的示例在编译时工作。但是，在执行这段代码时，您将得到一个运行时异常。

例子:

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

这很有挑战性，特别是当您正在处理多个转换和聚合步骤时。

无法操作域对象(丢失域对象): 一旦将域对象转换为数据框架，就不能从中重新生成数据框架。在下面的例子中，一旦我们从personRDD创建了personDF，我们将不会恢复Person类的原始RDD (RDD[Person])。

例子:

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

Datasets火

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.

数据集的特性:

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.

例子:

case class Person(name : String , age : Int)
val personRDD = sc.makeRDD(Seq(Person("A",10),Person("B",20)))
val personDF = sqlContext.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]

互操作:数据集允许您轻松地将现有的rdd和dataframe转换为数据集，而无需样板代码。

数据集API限制:-

需要类型转换为字符串: 目前从数据集中查询数据需要我们将类中的字段指定为字符串。查询完数据后，必须将列强制转换为所需的数据类型。另一方面，如果我们在数据集上使用map操作，它将不会使用Catalyst优化器。

例子:

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

不支持Python和R:从1.6版开始，数据集只支持Scala和Java。Python支持将在Spark 2.0中引入。

Datasets API与现有的RDD和Dataframe API相比，具有更好的类型安全性和函数式编程优势。面对API中类型强制转换需求的挑战，您仍然无法获得所需的类型安全性，并将使您的代码变得脆弱。

一个。 RDD （Spark1.0） —> Dataframe（Spark1.3） —> Dataset（Spark1.6）

b. RDD让我们决定如何做，这限制了Spark在底层处理上的优化。dataframe/dataset让我们决定我们想做什么，并把一切都留给Spark来决定如何进行计算。

作为内存中的jvm对象，RDD涉及到垃圾收集和Java(或稍微好一点的Kryo)序列化的开销，当数据增长时，这些开销是昂贵的。这会降低性能。

数据帧比rdd提供了巨大的性能提升，因为它有2个强大的特性:

自定义内存管理(又名Project Tungsten) 优化的执行计划(又名Catalyst Optimizer) RDD ->数据帧->数据集

d.数据集(Project Tungsten和Catalyst Optimizer)如何在数据帧上得分是它拥有的另一个功能:编码器

DataFrame相当于RDBMS中的表，也可以以类似于rdd中的“原生”分布式集合的方式进行操作。与rdd不同，dataframe跟踪模式并支持各种关系操作，从而实现更优化的执行。 每个DataFrame对象表示一个逻辑计划，但由于它们的“惰性”性质，直到用户调用特定的“输出操作”才会执行。

因为DataFrame是弱类型的，开发人员没有得到类型系统的好处。例如，假设你想从SQL中读取一些东西，并对其运行一些聚合:

val people = sqlContext.read.parquet("...")
val department = sqlContext.read.parquet("...")

people.filter("age > 30")
  .join(department, people("deptId") === department("id"))
  .groupBy(department("name"), "gender")
  .agg(avg(people("salary")), max(people("age")))

当你说people("deptId")时，你得到的不是Int或Long对象，你得到的是你需要操作的Column对象。在具有丰富类型系统的语言(如Scala)中，您最终失去了所有类型安全，这增加了在编译时可以发现的运行时错误的数量。

相反，输入数据集[T]。当你这样做时:

val people: People = val people = sqlContext.read.parquet("...").as[People]

您实际上得到了一个People对象，其中deptId是一个实际的整型而不是列型，从而利用了类型系统。

从Spark 2.0开始，DataFrame和DataSet api将是统一的，其中DataFrame将是DataSet[Row]的类型别名。