flatmap

type.Transformation in the optionalOptionalWe need two steps to complete the transformation, which is not a big problem, but I still feel a bit awkward and redundant.Future (Possible)I recommend that class's forced transformation method return a Optional or Stream.Assume that the type of the object being passed is correct. Returns a optional or stream that includes the object.Otherwise, the returned optional or stream does not include whatever element.These methods are more trivial to implement

operation.Rxview.clicks (view). FlatMap (V-githubservice.user (user)). Subscribe ();SqlbriteIf your app uses Sqlite to save data, then Sqlbrite is a good library to work with RxJava.In addition to these main class libraries, there are libraries that encapsulate other Android services: The Rx Preferences accesses the sharedpreferences by RxJava Way. The rxpermissions is used to support Android M dynamic permission requests for libraries.

. The code example TOPNBSP;K algorithm sample code is as follows: Importorg.apache.spark.sparkcontextimportorg.apache.spark.sparkcontext._ Objecttopk{defmain (args:array[string]) {/* executive WordCount, statistics of the most high-frequency words */valspark= newsparkcontext ("local", "TopK", System.getenv ("Spark_home"), Sparkcontext.jarofclass (This.getclass)) valcount=spark.textfile ("Data"). FlatMap (line= >line.split (""). Map (word=> (word,1)).

the basic concept of stream is the difference between a stream and a collection: stream does not store its own elements. Elements are stored in the underlying collection or generated as needed. The stream operator does not change the source object. Instead, it returns a new stream that holds the result. 3. Stream operations can be deferred, which means they wait until the result is needed. The basic process of stream operation can be attributed to 3 parts: creating a stream. In one or more opera

value a into the context. (>>=)the function is to apply a function (which receives a normal value a but returns a value in context m b ) (a -> m b) to a value in one context m a and returns another value in the same context m b .Note : The pronunciation of the >>= function is bind , ReactiveCocoa students should pay attention to learning. In addition, the >>= function can be analogous to Swift the flatMap method.Maybe MonadSimilarly, we will substitu

Function interface 3. Collect (ToList ()); 4 assertequals (aslist ("A", "B", "HELLO"), collected);3.filterIterate through the data and examine the elements therein.1 list beginningwithnumbers2 = Stream.of ("A", "1abc", "ABC1")3 // must be predicate4 . Collect (ToList ());4.flatmapThe FlatMap method can replace a value with a stream and then concatenate multiple streams into a stream1 list))2// Must be function3. Collect (ToList ()); 4 assertequals

PageRank Program:File contents:Page1 Page3Page2 Page1Page4 Page1Page3 Page1Page4 Page2Page3 Page4def computecontribs (Neighbors,rank):For neighbor in Neighbors:yield (neighbor, Rank/len (neighbors))Links = sc.textfile ("Tst001.txt"). Map (Lambda Line:line.split ()). Map (lambda pages: (pages[0],pages[1])) \. Distinct (). Groupbykey (). Persist ()Ranks=links.map (Lambda (page,neighbors): (page,1.0))In [4]: for x in range (1):...: print "links Count:" +links.count ()...: print "ranks count:" Ranks

there is a special new package for these specific implementation java.util.ImmutableCollections, a feature that is really the same as Scala. 1 setkey1", "key2", "Key3");Optional to StreamTo option provides the stream function, about the use of optional, I told in my tutorial very detailed, if you have not mastered, hold on. 1 list 2 . FlatMap (Optional:: Stream) 3 15. Other General characteristics I introduce so much, you can also go

stream that does not exceed a given length, and the required length is passed as a parameter to the limit, and if the stream is ordered, it returns up to the first n elements. For example, the first 3 courses that filter calories over 300 calories:list4. Skipping elementsThe stream also supports the Skip (n) method, which returns a stream that discards the first n elements and returns an empty stream if the element u in the stream is less than N. For example: Skip the first two courses over 300

/OneRDD.javaOne: Create an actionThere are two ways of creating an Rdd:1 reading a data set (Sparkcontext.textfile ()):Javadstreamlines=jssc.textfilestream ("/users/huipeizhu/documents/sparkdata/input/");Javareceiverinputdstreamlines = Jssc.sockettextstream ("localhost", 9999);2 read a collection (Sparkcontext.parallelize ()):Listlist = Arrays.aslist (5, 4, 3, 2, 1);Javarddrdd = sc.parallelize (list), second: conversion operation1: Single RDD conversion operationMap (): operates on each element,

(intwritable.class); //which file (/out0502) The data is exported to the HDFS systemFileoutputformat.setoutputpath (Job,NewPath (args[1])); Job.setpartitionerclass (Hashpartitioner.class); Job.setnumreducetasks (2); //Submit Job BooleanIsOk = Job.waitforcompletion (true); System.exit (IsOk? 0:1); }}The Scala approach enables word frequency statistics: PackageCn.qmScala.day04Scala/*** Created by Administrator on 2018/6/2 0002. */Object Demo15wordcount {val acc=truedef main (Args:array[str

(weather); } });But sometimes we may not know Cityid at first, we only know CityName. So we need to access the server first, get the corresponding city name of the Cityid, and then through this Cityid to get weather data.Again, we need to define an interface to get Cityid:@GET("city")ObservablegetCityIdByName(@Query("cityName") String cityName);Then we can use the omnipotent Rxjava to fulfill the demand.ApiClient.weatherService.getCityIdByName ("Shanghai").