flatmap

is returned as a new Observable data stream. So when the source Observable has data emitted, the data is immediately emitted into the Observable of the window return.You can see the difference between the two:window:If you do not already know the buffer, it is recommended that you go to the previous chapter and look at buffer. The function form of buffer and window is the same, the function is very similar, and easy to understand. Buffer can use window to implement its function:source.buffer(..

the above, map () converts an Integer type object in a parameter to a string object and returnsAt the same time, the parameter type of the event is also changed from the Integer type to the String type3.2 FlatMap ()Function: The sequence of events sent by the Observer is split individually converted, merged into a new sequence of events, and finally sentPrincipleCreates a Observable object for each event in the sequence of events;The new events that

Introduction to spark Basics, cluster build and Spark ShellThe main use of spark-based PPT, coupled with practical hands-on to enhance the concept of understanding and practice.Spark Installation DeploymentThe theory is almost there, and then the actual hands-on experiment:Exercise 1 using Spark Shell (native mode) to complete wordcountSpark-shell to Spark-shell native modeFirst step: Import data by file modescala> val rdd1 = Sc.textfile ("File:///tmp/wordcount.txt")Rdd1:org.apache.spark.rdd.rdd

observable does not emit a value within the specified time interval, it will trigger the onerror () function when it listens to the original observable.The Debounce () function filters out data that is too fast to be emitted by observable, and if it is not fired at a specified time interval, it will launch the last one.Just like the sample () and timeout () functions, debounce () uses the Timeunit object to specify the time interval.Transform-relatedRxjava provides several mapping functions: Ma

main portal, and pack them into a zip archives, Then upload this zip package via--py-files. Because the Python interpreter can handle the import scene of the ZIP archives by default, and because the uploaded zip is a package containing __init__.py, the Python interpreter on the cluster node machine automatically joins them in the search path to the module. , which solves the problem of import error.Q: How can I see debug information printed through print in a function?A: The debug information f

------...pagetoload$Cool, already has the input stream of 3 page numbers, now let's create a pageToLoad$ stream.private pageToLoad$ = // 将所有页码流合并成一个新的流Observable.merge(this.pageByManual$, this.pageByScroll$, this.pageByResize$) // 过滤掉重复的值.distinct() // 检查当前页码是否存在于缓存(就是组件里的一个数组属性)之中.filter(page => this.cache[page-1] === undefined); itemresults$The hardest part has been done. Now we have a stream with a page number, which is very useful. We no longer need to be concerned with individual scenario

/hadoop CP Slaves.template SlavesVim Slaves #添加节点xtyfb-csj06 or 127.0.1.1 4. start spark to see the cluster status Cd/usr/local/bigdata/spark/spark-1.6.0-bin-hadoop2.6/sbinStart:./start-all.sh JPS View process: One more master and worker process To view the details of a process using JPS-MLV You can see the access address of the master frontend http://172.16.80.226:8080/ Access address for worker Frontend http://172.16.80.226:8081/

frame design:1 Trait either[+e,+2case classextends either[e,nothing] 3 Case class extends Either[nothing,a]The above visible either need to handle two types of E and a:e representing the exception type, and a for the calculation type. As with option, either also has two states: the left represents the inability to complete the calculation, the return value E is a description of the exception condition, and right represents the normal completion of the calculation, and returns the result

Match {case Nil + nil case Cons (x, xs) = Cons (x +1, AddOne (XS))}8. Write a function that turns each value in a list[double] into a. StringConverts a double to a stringdef all2string (l:list[double]): list[string] = l Match {case Nil + nil case Cons (x, xs) = Cons (x.tostring, All2string (XS))}9. Implementing the Map functiondef Map[a, b] (L:list[a]) (f:a = b): list[b] = l Match {case Nil = nil case Cons (x, xs) = Cons (f (x), MA P (XS) (f))}Write a function that removes elements from a list

use for, you can greatly simplify this Code: 123 def calculateTotalWithFor: Option[Int] = { for (price This method has only one line, and its implementation behavior is exactly the same as the above Code. This is amazing. You don't have to judge whether the price and quantity exist, or decide whether to return None or Some based on the judgment result. How does it work? Let's take a look at the decompilation results: 1234567891011121314151617 public Option ca

there are two requests, a request is a keyword, and a request is the AB keyword. The surface is ' A ' request sent out first, ' AB ' request sent out. If the after-issued ' AB ' request returns first, the ' A ' request returns, then the result of the ' a ' request will overwrite the result of the ' AB ' request. resulting in incorrect search results. Solve the problemThis problem can be solved by using the powerful Rxjava debounce operator.subscription = Rxtextview. Textchanges(Etkey). Deb

();}} }This kind of code is boring and suffers from reading.This time optional can help you, if you decide to use it, you need to wrap all possible null properties in optional. For example, the Office property in the company class should look like this:private OptionalThe city property in the address class should look like this:　private OptionalBefore overriding the above code, look at some of the methods of the optional class: Method Describe Empty

can be sent) 2) Conversion of the sequence I want to convert a sequence: 1 to 1 conversions (such as a string into its length):map ... Type conversions:cast ... In order to get the ordinal of each element:Flux#index 1 to n conversions (such as strings into a string of characters): flatMap + Use a factory method 1 to N conversion customizable conversion method and/or status:handle Performs an asynch

1. Operator Classification From the general direction, the Spark operator can be broadly divided into the following two types of transformation: The operation is deferred calculation, that is, the conversion from one RDD to another rdd is not executed immediately, it is necessary to wait until there is an action action to actually trigger the operation. Action: Triggers the Spark submission job (job) and outputs the data to the spark system. From a small direction, the Spark operator can be bro

); } }); } }) Since every error has been flatmap, we cannot trigger a re-subscription by directly calling. OnNext (null) or. OnError (error) to avoid a re-subscription.ExperienceHere are some important points about. Repeatwhen () and. Retrywhen () that we should keep in mind. . Repeatwhen () is very similar to. retrywhen (), except that it no longer responds to onerror as a retry con