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The above is the corresponding RDD operation, compared to maoreduce only map, reduce two operations, spark for RDD operation is more***********************************************Map (func)Returns a new distributed dataset consisting of each original element after the Func function is converted***********************************************Filter (func)Returns a new dataset consisting of the original elemen
Last night I listened to Liaoliang's spark IMF saga 18th lesson: Rdd Persistence, broadcast, accumulator, homework is unpersist test, read the accumulator source code see internal working mechanism:scala> val rdd = sc.parallelize (1 to 1000) Rdd:org.apache.spark.rdd.rdd[int]= Parallelcollectionrdd[0] at parallelize at Scala>Rdd.persistres0:rdd.type= Parallelcollectionrdd[0] at parallelize at Scala>Rdd.count
The fold,foldbykey,treeaggregate of the basic RDD operator for Spark programming, Treereduce1) Fold def fold (zerovalue:t) (OP: (T, T) + t): T This API operator receives an initial value, the fold operator passes in a function, merges two values of the same type, and returns a value of the same type This operator merges the values in each partition. Each partition is merged with a zerovalue as the initial value at each time each partition is merged.
Transferred from: http://www.ithao123.cn/content-6053935.html You can see the difference between the cache and the persist by observing the Rdd.scala source code: def persist(newlevel:storagelevel): This.type = {if (storagelevel! = Storagelevel.none Newlevel! = storagelevel) {throw new Unsupportedoperationexception ("Cannot change storage level of an RDD after it is already assigned a level")}Sc.persistrdd (This)Sc.cleaner.foreach (_.regi
Before you learn any spark technology, be sure to understand spark correctly, as a guide: understanding spark correctlyHere is the use of the Spark RDD Java API to read data from a relational database using a derby local database, which can be a relational database such as MySQL or Oracle:packagecom.twq.javaapi.java7;importorg.apache.spark.api.java.javardd;import Org.apache.spark.api.java.javasparkcontext;importorg.apache.spark.api.java.function.func
First, local CSV file read: The easiest way: Import pandas as PD lines = pd.read_csv (file) lines_df = Sqlcontest.createdataframe (lines) Or use spark to read directly as Rdd and then in the conversion lines = sc.textfile (' file ')If your CSV file has a title, you need to remove the first line Header = Lines.first () #第一行 lines = lines.filter (lambda row:row!= header) #删除第一行 At this time lines for RDD
Aggregatebykey This rdd is a bit cumbersome, and tidy up the use examples for referenceDirectly on the codeImportOrg.apache.spark.rdd.RDDImportOrg.apache.spark. {sparkcontext, sparkconf}/*** Created by Edward on 2016/10/27. */Object Aggregatebykey {def main (args:array[string]) {val sparkconf:sparkconf=NewSparkconf (). Setappname ("Aggregatebykey"). Setmaster ("Local") Val Sc:sparkcontext=NewSparkcontext (sparkconf) val Data= List ((1, 3), (1, 2), (1,
Tags: http io ar sp on art bs html adAttempting to run http://spark.apache.org/docs/latest/quick-start.html#a-standalone-app-in-scala from source.This lineVal wordCounts = textFile. flatMap (line => line. split (""). map (word => (word, 1). Performancebykey (_ + _)Reports compileValue performancebykey is not a member of org. apache. spark. rdd. RDD [(String, Int)]Resolution:Import the implicit con
Example of the RDD FlatMap operation:FlatMap, performs a function operation on each element (line) of the original Rdd, and then "beats" each line[Email protected] ~]$ HDFs dfs-put cats.txt[Email protected] ~]$ HDFs dfa-cat cats.txtError:could not find or load main class DFA[Email protected] ~]$ HDFs dfs-cat cats.txtThe Cat on the matThe aardvark sat on the sofaMydata=sc.textfile ("Cats.txt")Mydata.count ()
return value is unit and no result is returned.RDD Type source code Analysis:Class Rdd It's an abstract class,Private[spark] def conf = sc.confPrivate[class_name] Specifies the class that can access the field, the level of access is stricter, and at compile time, the get and set methods are automatically generated, and the class_name must be the outer class of the currently defined class or class.The class Rdd
1. HDFs can only be read, or created by other means2, Transfrmation is lazy.3, traditional fault-tolerant mode, data checkpoint or record data updateFault tolerance is the most difficult part of distribution.Data checkpoint: Replicate large datasets across the network of the data center, between the machines where they are connected. Consumes network and disk.Record Data update: Many updates, the record cost is very high.4. RDD Fault Tolerant ModeAll
PackageCom.latrobe.sparkImportOrg.apache.spark. {sparkconf, Sparkcontext}/*** Created by Spark on 15-1-18.* Countapproxdistinct:rdda method that is useful forRDDThe collection content is de-re-counted. * The statistic is an approximate statistic, the parametersrelativesdcontrol the accuracy of statistics. * RELATIVESDthe smaller the result, the more accurate */Objectcountapproxdistinct {defMain(args:array[String]) {Valconf =NewSparkconf (). Setappname ("Spark-demo"). Setmaster ("Local")Valsc =
PackageRDDImportOrg.apache.spark. {sparkconf, Sparkcontext}/*** Created by Legotime on 2016/5/5. */ObjectPairrdd {defmyfunc1(Index:Int,Iter:Iterator[(String)]) :Iterator[String] = {Iter.toList.map (x = ="[PartID:"+ Index +", Val:"+ x +"]"). Iterator}defMYFUNC2(Index:Int,Iter:Iterator[(Int,String)]):Iterator[String]={Iter.toList.map (x ="[PartID:"+ Index +", Val:"+ x +"]"). Iterator}defMain(args:array[String]) {Valconf =NewSparkconf (). Setappname ("Pair RDD
This lesson demonstrates the most important of the two operators in the RDD, join and Cogroup through code combatJoin operator Code Combat:Demonstrating join operators through codeVal conf = new sparkconf (). Setappname ("Rdddemo"). Setmaster ("local")Val sc = new Sparkcontext (conf)Val arr1 = Array (Tuple2 (1, "Spark"), Tuple2 (2, "Hadoop"), Tuple2 (3, "Tachyon"))Val arr2 = Array (Tuple2 (1, 3), Tuple2 (2, 90), Tuple2Val rdd1 = sc.parallelize (arr1)V
I heard Liaoliang's seventh lesson tonight. Spark operating principle and rdd decryption, after-school assignment is: Spark Fundamentals, my summary is as follows:1 Spark is a distributed memory-based computing framework that is particularly suitable for iterative computing2 MapReduce is two-stage map and reduce, and spark is constantly iterative, more flexible, more powerful, and easier to construct complex algorithms.3 Spark does not replace hive,hi
Before learning Spark any point of knowledge, make a correct understanding of spark, and you can refer to: Understanding Spark correctlyThis article provides an explanation of the join-related APIsSparkconfconf=newsparkconf (). Setappname ("AppName"). Setmaster ("local"); Javasparkcontextsc=newjavasparkcontext (conf); javapairrddFrom the above can be seen, the most basic operation is cogroup this operation, the following is the schematic diagram of Cougroup:650) this.width=650; "Src=" https://s5
packagecom.twq.javaapi.java7;importorg.apache.spark.sparkconf;import org.apache.spark.api.java.javardd;importorg.apache.spark.api.java.javasparkcontext;import org.apache.spark.api.java.function.function2;importorg.apache.spark.api.java.function.voidfunction; Importscala. Tuple2;importjava.io.serializable;importjava.util.arrays;importjava.util.comparator;import java.util.Iterator;importjava.util.concurrent.TimeUnit;/***Createdby tangweiqunon2017/9/16.*/publicclassbaseactionapitest{ publicstaticvo
Package com.xh.movies import Org.apache.spark.rdd.RDD import Org.apache.spark. {sparkconf, sparkcontext} import scala.collection.mutable import org.apache.log4j. {Level,logger}/** * Created by ssss on 3/11/2017. * Need understand what ' s relationshop between DataSet RDD * Occupations Small data set need to be broadcast * Production env should use parquet, but not easy for user to read the contents * Here we use 4 files below * 1, "rat Ings.dat
Label:Scala Connection Database BULK INSERT: Scala> Import Java.sql.DriverManager scala> var url = "Jdbc:mysql://localhost:3306/mydb?useunicode=truecharacterencoding=utf8" scala> var username = "Cui" scala> var password = "Dbtest" Scala> Val conn= drivermanager.getconnection (Url,username,password) scala> val pstat = conn.preparestatement ("INSERT into ' TEST ' (' ID ', ' age ') VALUES (?,?)") Scala> Pstat.clearbatch Scala> Pstat.setint (1,501) Scala> Pstat.setint (2,501) Scala> Pstat.addbatch S
Tonight listen to Liaoliang's spark IMF legendary action 16th course Rdd, class notes are as follows:Rdd operation type: Transformation, action, ContollerReduce must conform to the Exchange law and the binding lawVal textlines = Linecount.reducebykey (_+_,1) TextLines.collect.foreach (pair=> println (pair._1 + "=" +pair._2)) def Collect (): array[t] = withscope { val results = Sc.runjob (this, (iter:iterator[t]) = Iter.toarray) Array.con Cat (Re
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