从使用的角度来看，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.

我希望这能有所帮助!

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

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。

首先，DataFrame是从SchemaRDD演变而来的。

是的. .Dataframe和RDD之间的转换是绝对可能的。

下面是一些示例代码片段。

df。rdd就是rdd [Row]

下面是一些创建数据框架的选项。

1) yourrddOffrow。toDF转换为DataFrame。 2)使用sql context的createDataFrame Val df = spark。createDataFrame (rddOfRow模式)

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...

事实上，现在有3个Apache Spark api ..

火灾等级:

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().

抽样的例子:

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

示例:RDD按属性过滤

rdd.filter(_.age > 21)

DataFrame火

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

示例SQL样式:

df。Filter ("age > 21");

限制: 因为代码是按名称引用数据属性的，所以编译器不可能捕捉到任何错误。如果属性名不正确，则只有在运行时创建查询计划时才会检测到错误。

DataFrame API的另一个缺点是它非常以scala为中心，虽然它确实支持Java，但支持是有限的。

例如，当从现有的Java对象RDD创建DataFrame时，Spark的Catalyst优化器无法推断模式，并假设DataFrame中的任何对象都实现了scala。产品界面。Scala case类解决了这个问题，因为它们实现了这个接口。

数据集火

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.

示例数据集API SQL样式:

dataset.filter(_.age < 21);

DataFrame和DataSet之间的评估不同:

阴极级流..(解密spark峰会上的数据框架和数据集演示)

进一步阅读…databricks文章-三个Apache Spark api的故事:rdd vs dataframe和数据集

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

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。