flatmap

Myschemamodule ()) // . Migration (migration) . Build (); Realm.setdefaultconfiguration (realmconfiguration); } Realm with Rxjava Query method, Official document configuration Combining Realm, Retrofit and Rxjava (Using RETROLAMBDA syntax for brevity) //Load All persons and merge them with Their latest stats from GitHub (if they have any) Realm Realm = Realm.getdefaultinstance (); Githubservice API = Retrofit.create (githubservice.class); Realm.wh

) { System.out.println () is new person () . Country.flatmap (x-> X.provinec). Flatmap (province::getcity). Flatmap ( x-> x.name). OrElse (" Unkonwn ")); } Class Person { optional The first approach can be smoothed and integrated with existing JavaBean, entity, or poja without changing anything, and can be more easily integrated into Third-party interfaces (such as spring's beans). The proposa

example, using MapReduce to join both tables is a tricky process, as shown in the following illustration: The join in Mr is a very laborious operation, as long as the students who have written Mr Code can appreciate it. one of the advantages of 3.spark Apache Spark is an emerging engine of big data processing, and the main feature is the distributed memory abstraction of a cluster to support applications that require working sets. This abstraction is the RDD (resilient distributed Dataset), an

The spark operator can be broadly divided into three broad classes of operators: 1, the Value data type of the transformation operator, this transformation does not trigger the submission of the job, the data item processed is the value type of data. 2, Key-value data type of the transformation operator, this transformation does not trigger the submission of the job, the data item for processing is the Key-value type of data. 3, action operator, this kind of operator will trigger Sparkcontext su

, etc.)Next, let's start writing Java code!First step: Create a Sparkconf objectStep Two: Create SparkstreamingcontextWe create Sparkstreamingcontext objects in a configuration-based manner:The third step, Create spark streaming input data Source:We configure the data source as local port 9999 (note that port requirements are not being used):Fourth Step: We're like the Rdd . programming, based on Dstream for programming because of the Dstream It's an rdd . generated template, in spark streaming

=" margin:0px;padding:0px;border:0px; "/>The third step, Create spark streaming input data Source:We configure the data source as local port 9999 (note that port requirements are not being used), and if it is a program created under the Windows system, you can use the TCP/UDP to send the socket tool for testing if it is created under a Linux systemJava program, you can directly use the NC-LK 9999 command to enter content for testing650) this.width=650; "Src=" http://images2015.cnblogs.com/blog/8

the visual boxInstruction for use: I. Turn off the virtual machine: Open the Visual box interface, CD into MyvagrantVagrant up open virtual machine, vagrant Halt shut down virtual machineIi.ipython Notebook, enter http:\\localhost:8001Stop the running notebook, click Running, stopClick a. py file to run the note bookiii. Download the SSH software and log in to the virtual machine with the address 127.0.0.1, port 2222, username vagrant, password vagrantAfter entering, knock Pyspark, can enter Py

; B.collectRes11:array[int] = Array (2, 4, 6, 8, 10, 12, 14, 16, 18)In contrast, if you switch to Flatmap, the results are as follows:2)FlatMapSimilar to map, the difference is that elements in the original RDD can only generate one element after map processing, and the elements in the original RDD can be flatmap processed to generate multiple elements to construct a new rdd.Example: generating y elements f

The code jumps inside the for loop are map, withfilter, etc.For is more direct than MAP,FILTER,FLATMAP, can be implemented with a for replacementPackage Com.dt.scala.forexpressionObject For_advanced {def main (args:array[string]) {}def Map[a, b] (list:list[a], f:a = b): list[b] =for (element def Flatmap[a, B] (list:list[a], f:a = List[b]): list[b] =for (x def Filter[a] (list:list[a], f:a = Boolean): list[a]

Sc.textfile ("hdfs://..."). FlatMap (Line =>line.split ("")). Map (w = (w,1)). Reducebykey (_+_). foreach (println)Do not use ReducebykeySc.textfile ("hdfs://..."). FlatMap (L=>l.split ("")). Map (w=> (w,1)). Groupbykey (). Map (P: (string,iterable[ INT]) = = (p._1,p._2.sum)). Collect　　The call path to create from Spark-shell to Sparkcontext:Spark-shell, Spark-submit->spark-class->sparksubmit.main->sparkilo

lists the RDD transitions and actions in spark.Each operation is given an identifier when the brackets indicate the type parameter. As mentioned earlier, transitions are deferred operations. Used to define a new rdd, whereas an action initiates a calculation operation and returns a value to the user program or writes data to an external store. Table 1 The RDD conversions and actions supported in Spark Transformation Map (f:t) U): Rdd[t]) Rdd[u]Filter (F:t) Bool): Rdd

, we deal primarily with three objects: OBSERVABLE, Observer, Subscription: Take an example of an element's Click event to see how to use Observable: var clickstream = new rx.observable (Observer => var handle = evt => ' click ' return () = Element.removeeventlistener (' click ' = clickstream.subscribe (evt => {Console.log ( ' onNext: ' + Evt.id), err => {Console.error ( ' OnError ' => {Console.log ( ' OnComplete ' => {Subscription.unsubscribe ();}, +); 　　It would be too much trouble

Collect () is used to load more than 40 million data onto the dirver-=)Spark.take (1). foreach (println)6. Common conversion actions and action actions common conversion actions such as map () and filter ()For example, calculate the square of each value in the RDD:Val input = sc.parallelize (List (1,2,3,4= input.map (x = x*x) println (Result.collect (). Mkstring(" ,"))7.flatMap () is similar to map, but returns an iterator that returns a sequence of

The original code:javarddNewFlatmapfunction() { Private Static Final LongSerialversionuid = 10000L; ListNewArraylist(); PublicIterablethrowsException {string[] splits= Line.split ("\ t"); Articlereply Bean=Newarticlereply (); Bean.setareaid (split[0]); Bean.setagent (Integer.parseint (splits[1])); Bean.setserial (splits[2]); Newlist.add (Bean); returnNewList; } });Correct wording:javardd() { Private static final Long serialversionuid = 10000L;

/HdfsWordCount.scalaThird, how Spark streaming worksFour, the application of textfilestreaming1, prepare the databin/hdfs dfs -put wordcount.txt /spark/streaming2. Launch the Spark appbin/spark-shell--master local[2]3, writing codeimport org. Apache. Spark. _import org. Apache. Spark. Streaming. _import org. Apache. Spark. Streaming. StreamingContext. _val SSC = new StreamingContext (SC, Seconds ( -)) Val lines = SSC. Textfilestream("Hdfs://study.com.cn:8020/myspark") Val words = lines.

are directly filled, then a resource manager such as yarn or mesos is required.650) this.width=650; "src="/e/u261/themes/default/images/spacer.gif "style=" Background:url ("/e/u261/lang/zh-cn/ Images/localimage.png ") no-repeat center;border:1px solid #ddd;" alt= "Spacer.gif"/>The task runs above executor:650) this.width=650; "src="/e/u261/themes/default/images/spacer.gif "style=" Background:url ("/e/u261/lang/zh-cn/ Images/localimage.png ") no-repeat center;border:1px solid #ddd;" alt= "Spacer

 Practice 2.40This question wants us to write a procedure that passes an integer n and then returns a sequence pair (i, j) Unique-pairs, and then uses the definition of the prime-sum-pairs on the previous page. At this point we need to pay attention to which piece of code in prime-sum-pairs expresses this meaning. Yes, it's the flatmap function. So we write it into the unique-pairs .(Define (unique-pairs N) (Fla