spark webinars

First of all, of course, is to download a spark source code, in the http://archive.cloudera.com/cdh5/cdh/5/to find their own source code, compiled their own packaging, about how to compile packaging can refer to my original written article: http://blog.csdn.net/xiao_jun_0820/article/details/44178169 After execution you should be able to get a compressed package similar to SPARK-1.6.0-CDH5.7.1-BIN-CUSTOM-SP

= Info.index info.marksuccessful () removerunningtask (TID)//This are called by "Taskschedulerimpl.han Dlesuccessfultask "which holds"//"Taskschedulerimpl" lock until exiting. To avoid the SPARK-7655 issue, we should not//"deserialize" the value when holding a lock to avoid blocking other th Reads. So we called//"Result.value ()" in "Taskresultgetter.enqueuesuccessfultask" before reaching here. Note: "Result.value ()" is deserializes the value wh

Description In Spark, the map function and the Flatmap function are two more commonly used functions. whichMap: operates on each element in the collection.FLATMAP: operates on each element in the collection and then flattens it.Understanding flattening can give a simple example Val arr=sc.parallelize (Array ("A", 1), ("B", 2), ("C", 3)) Arr.flatmap (x=> (x._1+x._2)). foreach (println) The output result is A 1 B 2 C 3 If you use map Val arr=sc.paral

We typically develop spark applications using the IDE (for example, IntelliJ idea), while the program debug runtime prints out all the log information in the console. It describes all the behavior of the (pseudo) cluster operation and execution of the program. In many cases, this information is irrelevant to us, and we are more concerned with the end result, whether it is a normal output or an abnormal stop. Fortunately, we can actively control

Source: http://www.cnblogs.com/shishanyuan/p/4747735.html 1. Introduction to Spark streaming 1.1 Overview Spark Streaming is an extension of the Spark core API that enables the processing of high-throughput, fault-tolerant real-time streaming data. Support for obtaining data from a variety of data sources, including KAFK, Flume, Twitter, ZeroMQ, Kinesis, and

absrtact: This article mainly introduces TalkingData in the process of building big data platform, introducing spark gradually, and build mobile big data platform based on Hadoop yarn and spark.Now, Spark has been widely recognized and supported at home: In 2014, spark Summit China in Beijing, the scene is hot, the same year,

ObjectiveIn the field of big data computing, Spark has become one of the increasingly popular and increasingly popular computing platforms. Spark's capabilities include offline batch processing in big data, SQL class processing, streaming/real-time computing, machine learning, graph computing, and many different types of computing operations, with a wide range of applications and prospects. In the mass reviews, many students have tried to use

class (according to the CLK. TSV Data Format) Case class click (D: Java. util. Date, UUID: String, landing_page: INT) // Load the file Reg. TSV on HDFS and convert each row of data to a register object; Val Reg = SC. textfile ("HDFS: // chenx: 9000/week2/join/Reg. TSV "). map (_. split ("\ t ")). map (r => (r (1), register (format. parse (R (0), R (1), R (2), R (3 ). tofloat, R (4 ). tofloat ))) // Load the CLK. TSV file on HDFS and convert each row of data to a click object; Val CLK = SC.

3, hands on the abstract class in ScalaThe definition of an abstract class requires the use of the abstract keyword: The above code defines and implements the abstract method, it is important to note that we put the direct running code in the trait subclass of the app, about the inside of the app helps us implement the Main method and manages the code written by the engineer;Here's a look at the use of uninitialized variables in an abstract class: 4, hands-on trait in ScalaTrait

none, and below we look at the use of option: Next, take a look at filter processing: Here's a look at the zip operation for the collection: Here's a look at the partition of the collection: We can use flatten's multi-collection for flattening operations: Flatmap is a combination of map and flatten operations, first map operation and then flatten operation: "Spark Asia-Pacific Research ser

The collection mainly has list, set, Tuple, map, etc., we follow the hands-on practical way to learn. We create a list instance in the Eclipse IDE: Now let's look at the code implementation: In the source code, it is stated that the internal is the method of apply to complete the instantiation; In the same way we can instantiate set: You can also see the implementation of the set instantiation object at this point: Next we'll look at the set in the command-line terminal, first of all set:

5. Apply method and Singleton object in Scala to create a new class: As an additional point, the methods placed in object objects are static methods, as follows: Next look at the use of the Apply method: The above code always when we use "val a = Applytest ()" will cause the call of the Apply method and return the value of the method call, that is, the instantiated object of the applytest. C The lass can also be used by the Apply method, as shown in the following ways: Because the methods

Copy an object The content of the copied "input" folder is as follows: The content of the "conf" file under the hadoop installation directory is the same. Now, run the wordcount program in the pseudo-distributed mode we just built: After the operation is complete, let's check the output result: Some statistical results are as follows: At this time, we will go to the hadoop Web console and find that we have submitted and successfully run the task: After hadoop co