flatmap

Val SSC = new StreamingContext (conf, Seconds (5)) Val lines = SSC. Sockettextstream("Master",9999) Val words = lines. FlatMap(_. Split("")) Val wordcounts = words. Map(x= (x,1)). Reducebykey(_ + _) wordcounts. Foreachrdd{Rdd = Rdd. Foreachpartition{partitionofrecords = {//ConnectionPool is a static, lazily initialized pool of connections V Al connection = ConnectionPool. getconnection() partitionofrecords. foreach(record = {Val sql ="INSERT into Str

Sparkcontext creation: High level dagscheduler, bottom TaskScheduler, schedulerbackendApplication=driver+executorSpark's program is divided into two parts : Driver and ExecutorDriver Drive Executordriver Part of the source code: Sparkconf+sparkcontextExecutor specific implementationExecutor part of the specific source code: Textfile flatMap Map, etc...Cluster ManagerA service that obtains external resources in a cluster, a resource allocatorThe Spark

structure is unchanged, mainly map, FlatMap (map after the flat for a-dimensional rdd); The input and output are one to the other, but the result is that the partition structure of the RDD has changed, such as Union (two Rdd together), coalesce (partition reduction); The operator that selects a part of the element from the input, such as filter, distinct (remove redundant elements), subtract (this rdd has, the element it has no rdd), and samp

. nativecodeloader:unable to load Native-hadoop library for your platform ... using Builtin-java classes where applicable[Email protected]:/usr/local/hadoop-2.6.0/sbin# Hadoop dfs-mkdir/library/dataforsortedshuffleDeprecated:use of this script to the Execute HDFS command is DEPRECATED.Instead Use the HDFs command for it.16/02/13 13:19:27 WARN util. nativecodeloader:unable to load Native-hadoop library for your platform ... using Builtin-java classes where applicable[Email protected]:/usr/local/h

conf = new sparkconf (). Set AppName ("WC") val sc = new Sparkcontext (conf) sc.textfile (args (0)). FlatMap (_.split (")"). Map (((((_,1))). Reducebykey (_+_). SortBy (_._2). Saveastextfile (args (1)) sc.stop () }}The first thing to clarify is that our spark is created in maven form, so our pom file adds support for SparkWhen we were in the package, we would generate two jar packages in target, and we would choose a large size that might i

, returning a new list of dataExample1 Square Transformationval nums = List(1,2,3)val square = (x: Int) => x*x val squareNums1 = nums.map(num => num*num) //List(1,4,9)val squareNums2 = nums.map(math.pow(_,2)) //List(1,4,9)val squareNums3 = nums.map(square) //List(1,4,9)Example2 save a few columns in the text dataval text = List("Homeway,25,Male","XSDYM,23,Female")val usersList = text.map(_.split(",")(0)) val usersWithAgeList = text.map(line => { val fields = line.split("

, Mlruntime applies Spark's distributed computing to the Machine learning field. Mlbase provides a simple declarative way to specify machine learning tasks, and dynamically selects the optimal learning algorithm. 7, TachyonHigh-fault-tolerant Distributed file system. Claims that its performance is more than 3,000 times more than HDFs. There are Java-like interfaces, and HDFS interfaces are implemented, so spark and Mr Programs can run without any modification. Currently support HDFs, S3 an

crawl Kafka distributed message Framework data, the implementation of the specific class is kafkareceiver;4, receiver is an abstract class, the implementation of its fetching data subclass as shown:5, if the above implementation classes do not meet your requirements, you can define the receiver class, you only need to inherit the receiver abstract class to achieve their own sub-class business requirements.Four, StreamingContext, DStream, receiver combined flow analysis:(1) InputStream represent

. such as Map,filter, Flatmap,sample, Groupbykey, Reducebykey, Union,join, Cogroup,mapvalues, Sort,partionby and many other types of operation, They refer to these operations as transformations. It also provides count,collect, reduce, lookup, save, and many other actions. These various types of data set operations provide convenience to upper-level applications. The communication model between processing nodes is no longer the only data shuffle a patt

Today we learned the implementation of chained invocation styles in Scala, and in spark programming we often see the following code:Sc.textfile ("hdfs://..."). FlatMap (_.split ("")). Map (_,1). Reducebykey (_ + _) ...This style of programming is called chained invocation, and its implementation is described in the following code:Class Animal {def Breathe:this.type = this}Class Cat extends Animal {def eat:this.type = this}Object test51{def main (args:

= Lines.map (line = (line.length, 1)). Reducebykey (_ + _)The RDD API for Spark has a reduce () method that will reduce all key-value key values to a separate value.We now need to count the number of words beginning with capital letters, and for each line of text, a Mapper may need to count a number of key-value pairs, with the following code:public class Countuppercasemapper extends mapperIn Spark, the corresponding code is as follows:Lines.flatmap (_.split ("). Filter (Word = character.isuppe

logic that the spark streaming framework has generated for us, and the spark streaming framework's execution interval can be configured manually, such as a job call that occurs every second. So when developers write good spark code (such as: Flatmap, map, collect), will not cause the job to run, the job run is generated by the spark streaming framework, can be configured to produce a job call every second.Spark streaming The data that comes in is dst

extractor 127th speaking-scala programming Advanced: Comments on the actual details of the 128th-scala Programming Advanced: Commonly used annotations introduction 129th-scala Programming Advanced: XML BASIC operations 130th-scala Programming Advanced: XML embed Scala code 131th speaking-scala programming Advanced: XML modification elements of the actual combat details 132th-scala Programming Advanced: XML loading and writing external documents 133th-scala programming Advanced: Set element oper

) val rdd = sc.parallelize (1 to ten) //Create Rdd val map = Rdd.map (_*2) //R Each element in DD is multiplied by 2 map.foreach (x = print (x+ "")) sc.stop () }}Output:2 4 6 8 Ten A - - - -(Rdd dependency graph: The red block represents an rdd area, the black block represents the partition collection, the same as the same)2.flatMap (func):Similar to map, but each element entry can be mapped to 0 or more output items, re

1. Big data is now synonymous with spark, is a fast cluster computing system, one of its functions is streaming, support real-time data flow, the real-time data stream into discrete data stream discretized stream, wherein each discrete set of RDD resilient Distributed dataset2. Calculation functions include: FlatMap: One-to-many, map: one-to-one, Reducebykey: Merge value by key3. In Spark's program, a calculation formula is established, but not execut

data, the implementation of the specific class is kafkareceiver;4, receiver is an abstract class, the implementation of its fetching data subclass as shown:5, if the above implementation classes do not meet your requirements, you can define the receiver class, you only need to inherit the receiver abstract class to achieve their own sub-class business requirements.Four, StreamingContext, DStream, receiver combined flow analysis:(1) InputStream represents the data input stream (for example: Sock

first-order method 5. Stitching to implement reverse methodDEF reverse (Xs:list[int]): list[int] = xs Match {case List () + xs case y::ys = = Reverse (ys)::: List (y)} V Al List0 = List (4,5,3,6,1,7,0) println (reverse (list0))//list (0, 7, 1, 6, 3, 5, 4)6. Flat ListThe flatten method takes a list of lists and flattens it out to a single list7.drop Remove the top N, take the first n8.map corresponds to lapply in the R language9.flatmap if F returns

looks like this:It is important to note that the Kafka streams is a Java library, not a stream processing framework, which is significantly different from the Strom stream processing frameworkThis program differs from the 0.10.0.0 version in detail. For the Kafka 0.10.0.0 version of the Kafka Streams, the actual operational examples can be found under Kafka Streams project examples package. It is important to note that this example uses a lambda expression, which is a feature of JAVA8.In the st

with Hadoop in a shared pool of nodes. Spark is a common parallel computing framework for open source class Hadoop MapReduce for UC Berkeley AMP Labs, and Spark's distributed computing, based on the map reduce algorithm, has the benefits of Hadoop MapReduce But unlike MapReduce, where the job intermediate output and results can be stored in memory, which eliminates the need to read and write HDFs, Spark is better suited for map reduce algorithms such as data mining and machine learning that nee

): StringThe following types are produced:A: = = IntB: (Int) BooleanC: (Int) (String, String) stringPolymorphic Method TypesPolymorphic method types are internally represented as[Tps]t,[TPS]is the type parameter section[A1: L11,..., aN: LNN],n>=0,Tis a(Value or method)type. The type represents aS1,..., SNis a type parameter and produces a type ofTThe result of the named method, the parameter typeS1,..., SNand The NetherL1,..., LNand Upper boundsU1,..., UNconsistent(§3.2.4). Example 3.3.2 The fol