Turn from: http://www.infoq.com/cn/articles/spark-core-rdd/thanks to Zhang Yicheng teacher for his selfless sharingRDD, called resilient distributed Datasets, is a fault-tolerant, parallel data structure that allows users to explicitly store data in disk and memory, and to control the partitioning of data. The RDD also provides a rich set of operations to manipulate the data. In these operations, conversion
Summary:RDD: Elastic distributed DataSet, is a special set of ' support multiple sources ' have fault tolerant mechanism ' can be cached ' support parallel operation, an RDD represents a dataset in a partitionThere are two operators of Rdd:Transformation (conversion):transformation is a deferred calculation, when an RDD is converted to another RDD without immed
Tags: spark catalyst SQL Spark SQL sharkAfter an article on spark SQL Catalyst Source Code Analysis Physical plan, this article will introduce the specifics of the implementation of the physical plan Tordd:We all know a SQL, the real execution is when you call its collect () method to execute the spark Job, and finally calculate the RDD. Lazy val Tordd:rdd[row] = Executedplan.execute ()The Spark plan basically contains 4 types of operations, the Basi
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protected] scala> val path = "Hdfs://namenode-host:9000/input/dean/obd_hdfs-writer-4-9-1447126914492.log" path:string = Hdfs://namenode-host:9000/input/dean/obd_hdfs-writer-4-9-1447126914492.log scala> val c = SqlContext.read.json (path) c:org.apache.spark.sql.dataframe = [data:structConvert into tablesNow write to the temp table OBD and iterate over the contents of the table:C.registertemptable ("OBD")
val set = Sqlcontext.sql ("SELECT * from OBD")
Set.collect (). foreach (println)wil
The RDD that holds the key/value pair is called the pair rdd.1. Create the pair RDD:1.1 How to create a pair RDD:Many data formats generate a pair RDD directly when the RDD is imported. We can also use the map () to convert the common Rd
---------------------The content of this section:· Spark Conversion RDD Operation Example· Example of the Spark action RDD operation· Resources---------------------Everyone has their own way of learning how to program. For me personally, the best way is to do more hands-on demo, to write more code, to understand the more profound, this section in the form of examples to explain the use of various spark
Summary:RDD: Elastic distributed DataSet, is a special set of ' support multiple sources ' have fault tolerant mechanism ' can be cached ' support parallel operation, an Rdd represents a dataset in a partitionThere are two operators of Rdd: Transformation (conversion):transformation is a deferred calculation, when an RDD is converted to another
Before introducing the RDD, let's start by saying something before:
Because I'm using the Java API, the first thing to do is create a Javasparkcontext object that tells Spark how to access the cluster
sparkconf conf = new sparkconf (). Setappname (AppName). Setmaster (master);
Javasparkcontext sc = new Javasparkcontext (conf);
This appName parameter is a name that shows the application on the cluster UI. Master is the URL address of a Spark,mesos or
Before learning spark any technology, please understand spark correctly, and you can refer to: Understanding spark correctlyThe following is a Python API description of the three ways to create the RDD, the single-type RDD basic Transformation API, the sampling API, and the pipe operation.Three ways to create an RDD
Create an
Elastic distributed Data Set (RDD)Spark operates at the center of the RDD concept. The RDD is a fault-tolerant collection of elements that can be manipulated in parallel. There are two ways to create an rdd: to parallelize A collection that already exists in your driver, and to reference a dataset from an external stor
16th Lesson: Rdd CombatDue to the non-modifiable nature of RDD, the operation of Rdd is different from normal object-oriented operation, and the operation of RDD is basically divided into 3 categories: Transformation,action,contoller1. TransformationTransformation is the creation of a new
Rdd SourceElastic distributed Data Set (RDD), which is a simple extension and extension of the MapReduce model, the RDD needs to ensure that the RDD has the capability to efficiently share data between parallel computing phases in order to achieve iterative, interactive, and streaming queries. The
(i), RDD definition
immutable distributed Objects Collection
For example, the following figure is RDD1 data, its Redcord is a number, distributed on three nodes, and its content is not variable
There are two ways of creating an Rdd:
1) distribution in driver (Parallelize method)
Create a collection (copy past) in the driver (Driver) as a distributed dataset (the number of partitions is the default and th
Transformations (conversion)
Transformation
Description
Map (func)
Each element in the original Rdd object is processed according to the incoming function, and after each new element is processed, an object is returned, which is assembled to get a new rdd, and the new Rdd and the old RDD
Spark Pair Rdd Operation
1. Create a pair RDD
Val pairs = Lines.map (x = = (X.split ("") (0), X)
2. The conversion method of the Pair Rdd
Table 1 Conversion method of pair Rdd (set {(3,4), (3, 6)} as key-value pairs)
function name
example
result
reducebykey ()
Org.apache.spark.rddRDDAbstract class Rdd[t] extends Serializable with LoggingA Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable, partitioned collection of elements, can is operated on in parallel. This class contains the basic operations available on all RDDs, such as map, filter, and persist. In addition, Org.apache.spark.rdd.PairRDDFunctions contains operations
1. Create an RDDVal lines = sc.parallelize (List ("Pandas", "I like Pandas"))2. Load the local file to the RDDVal Linesrdd = Sc.textfile ("Yangsy.txt")3. Filtering the filter requires that the filter does not filter on the original RDD, but instead re-creates an RDD based on the contents of the filterVal spark = linesrdd.filter (line = Line.contains ("Damowang"))4.count () is also a aciton operation because
The following is an elaboration of the Java API for three ways to create the RDD, the single-type RDD basic Transformation API, the sampling API, and the pipe operation.Three ways to create an RDD
Create an RDD from a stable file storage system, such as local filesystem or HDFS, as follows:
Create javardd
An important reason Apache Spark attracts a large community of developers is that Apache Spark provides extremely simple, easy-to-use APIs that support the manipulation of big data across multiple languages such as Scala, Java, Python, and R.This article focuses on the Apache Spark 2.0 rdd,dataframe and dataset three APIs, their respective usage scenarios, their performance and optimizations, and the scenarios that use Dataframe and datasets instead o
Spark Fast Big Data analytics8.4 Key Performance ConsiderationsDegree of parallelismthe logical representation of an RDD is actually a collection of objects . During physical execution, theRdd is divided into a series of partitions,Each partition is a subset of the entire data. When Spark dispatches and runs a task, spark makes the data in each partitionCreates a task that, by default, requires a compute node in the cluster to execute.Spark also autom
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