sparkcontext example

Sparkcontext This is a developed country spark admissions application, it is responsible for interacting with the entire cluster and it involves creating an rdd. accumulators and broadcast variables. Understanding the spark architecture, we need to start with the portal. is the official website of the chart.Driverprogram is a user-submitted program, where an instance of Sparkcontext is defined.Sparkcontext

important components (variables) of spark execution, including Mapoutputtracker, Shufflefetcher, Blockmanager, etc. This is accomplished through the Create method within the Sparkenv class's companion object Sparkenv object.Private[spark] Val env = sparkenv.create ( conf, "3. Create TaskScheduler and DagschedulerThe following code is important, which initializes two key variables in Sparkcontex, TaskScheduler and Dagscheduler.Private[spark] var TaskScheduler = Sparkcontext.createtasksched

Initialization of the SparkcontextSparkcontext is the spark context object that was created when the app was launched, is the primary interface for spark application development, and is a broker for the spark upper application and the underlying implementation (Sparkcontext is responsible for sending a task to executors).Sparkcontext in the initialization process, mainly related to the content:

Sparkcontext.scala implements a Sparkcontext class and Object,sparkcontext spark-like portals that connect spark clusters, create RDD, accumulate amounts, and broadcast volumes.In the spark framework, the class is loaded only once in a JVM. In the stage of loading classes, the properties, code blocks, and functions defined in the Sparkcontext class are loaded.(1)

Many examples on the web, including the official website example, are using Textfile to load a file to create an RDD, similar to Sc.textfile ("Hdfs://n1:8020/user/hdfs/input")The Textfile parameter is a path, which can be:1. A file path where only the specified file is loaded2. A directory path in which only all files under the specified directory ( excluding files under subdirectories ) are loaded3. Load multiple files in the form of wildcards or loa

Original link:textfile use of local (or HDFs) files and Sparkcontext instances loaded in SparkThe default is to read the file from HDFs, or you can specify Sc.textfile ("path"). Precede the path with hdfs://to read the local file read Sc.textfile ("path") from the HDFs file system. Precede the path with file:// Reads from the local file system, such as File:///home/user/spark/README.mdMany examples on the web, including the official website

DemandSparkcontext Custom extension textfiles, support for entering text files from multiple directoriesExtendedclassSparkcontext (Pyspark. Sparkcontext):def __init__(Self, Master=none, Appname=none, Sparkhome=none, Pyfiles=none, Environment=none, batchsize=0, serializer= Pickleserializer (), Conf=none, Gateway=none, jsc=None): Pyspark. Sparkcontext.__init__(Self, master=master, Appname=appname, Sparkhome=s

parsing:Sparkcontex is located in the project's source path \spark-master\core\src\main\scala\org\apache\spark\ Sparkcontext.scala, the source file contains the Sparkcontextclasss declaration and its associated object SparkcontextobjectClass Sparkcontext extends the logging. Logging is a trait, which is a container for storing tool methods, trait encapsulation methods and fields. By mixing trait into classes, you can reuse them. A class that can inhe

This is a note that reads the code of the Sparkcontext class. When reading this class, the main task is to figure out how Sparkcontext is constructed, and the initialization of Java and C # class is put in one method, and Scala's main constructor code is almost scattered in the Sparkcontext class, This requires us to organize it, in order to have a structured rea

Java.lang.IllegalArgumentException:System memory 100663296 must is at least 4.718592E8. Please use a larger heap size.When you develop a spark project in Eclipse and try to run the program directly in Spark, you encounter the following error:Obviously, this is a JVM application that does not have enough memory to start Sparkcontext. But how do you set it up?But I checked the startup script.#!/bin/bash/usr/local/spark-1.6.0/bin/spark-submit --class cn

　　SparkcontextUsually as an entry function, you can create and return an RDD.such as the spark cluster as the service side that spark driver is the client, Sparkcontext is the core of the client;As the note says, Sparkcontext is used to connect spark clusters, create RDD, accumulators (accumlator), broadcast variables (broadcast variables)Map Operation :Each input is specified, and then an object is returne

Sparkcontext is the portal to spark, which connects clusters, creates RDD, broadcasts variables, and more.classSparkcontext (config:sparkconf)extendsLogging with executorallocationclient {PrivateVal Creationsite:callsite =Utils.getcallsite ()//If you live with 2 Sparkcontext, you will use warn to replace exception. Prevent exit PrivateVal Allowmultiplecontexts:boolean =Config.getboolean ("Spark.driver.allow

　　No hurry, step-up, first lay a good foundationThe Spark Shell principle　　First, we clearly locate the few.1, Spark-shell2, Spark-submit3, Spark-class4, Sparksubmit.scala5, Sparkiloop.scalaSource Code of InitializesparkDef Initializespark () { intp.bequietduring { Command ("" " @transient val sc = { Val _sc = Org.apache.spark.repl.Main.interp.createSparkContext () println ("Spark context available as SC.") _sc } """) Command ("" " @transient

16/03/04 00:21:09 WARN sparkcontext:using spark_mem to set amount of memory to use per executor process is deprecated, pl Ease use spark.executor.memory instead.16/03/04 00:21:09 ERROR sparkcontext:error initializing Sparkcontext.Org.apache.spark.SparkException:Could not the parse Master URL: ' at 'At org.apache.spark.sparkcontext$.org$apache$spark$sparkcontext$ $createTaskScheduler (sparkcontext.scala:2554)At Org.apache.spark.sparkcontext.At Com.bigd

Spark example: Sorting by array and spark example Array sorting is a common operation. The lower performance limit of a comparison-based sorting algorithm is O (nlog (n), but in a distributed environment, we can improve the performance. Here we show the implementation of array sorting in Spark, analyze the performance, and try to find the cause of performance improvement.Official

allowlocal * flag Specifies whether the scheduler can run the computation on the driver rather than * shipping it Out to the cluster, for short actions like first (). */def Runjob[t, U:classtag] (Rdd:rdd[t], func: (Taskcontext, iterator[t]) = = U, Partitions:seq[int] , Allowlocal:boolean, Resulthandler: (Int, U) = = Unit) {if (Stopped.get ()) {throw new illegalstate Exception ("Sparkcontext have been Shutdown")} val callSite = getcallsite val clean

Python decorator use example and actual application example, python example Test 1 Deco is running, but myfunc is not running Copy codeThe Code is as follows:Def deco (func ):Print 'before func'Return func Def myfunc ():Print 'myfunc () called'Myfunc = deco (myfunc) Test 2 Call myfunc in the required deco to executeCopy codeThe Code is as follows:Def deco (func

/*====================================================================== A Globalmem Driver As an example of char device drivers There are two same globalmems in this driver This example was to introduce the function of File->private_data The initial developer of the original code is Baohua Song ======================================================================*/ #include #include #include #include #in

partitionStoragelevel.memory_and_disk_ser). Map (x = X._2.split ("\\|~\\|",-1))The log is delimited with |~| Kafkastream.foreachrdd (RDD:rdd[array[String]], time:Time) = {Val SqlContext =Sqlcontextsingleton.getinstance (Rdd.sparkcontext)Import Sqlcontext.implicits._Construct case Class:daplog to extract the corresponding fields in the logVal logdataframe = rdd.map (w =Daplog (W (0). substring (0,), W (2), W (6)). TODF ()Registered as TempTable logdataframe.registertemptable ("Daplog")Query the

(ScalaOperatorDemo.getClass.getSimpleName). Setmaster (" Local ") val sc = new Sparkcontext (conf) val datas:array[string] = Array ( " {' id ': 1, ' name ': ' XL1 ', ' pwd ': ' Xl123 ', ' Sex ': 2} ", " {' id ': 2, ' name ': ' Xl2 ', ' pwd ': ' xl123 ', ' Sex ': 1} ', ' {' id ': 3, ' name ': ' Xl3 ', ' pwd ': ' xl123 ', ' Sex ': 2} ') sc.parallelize (datas) . Map (v = = { new Gson (). Fromjson