spark and cassandra

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

Build a spark cluster entirely from 0Note: This step, only suitable for the use of root to build, formal environment should have permission classes of things behind another experiment to write tutorials1, install each software, set environment variables (each software needs to download separately)Export java_home=/usr/java/jdk1.8.0_71Export Java_bin=/usr/java/jdk1.8.0_71/binExport path= $JAVA _home/bin: $PATHExport classpath=.: $JAVA _home/lib/dt.jar:

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

This article, it is necessary to read, write well. But after looking, don't forget to check out the Apache Spark website. Because this article understanding or with the source code, official documents inconsistent. A little mistake! "The Cnblogs Code Editor does not support Scala, so the language keyword is not highlighted"In data analysis, processing Key,value pair data is a very common scenario, for example, we can group, aggregate, or combine two o

[Email protected] ~]$ HDFs dfs-cat People.json{"Name": "Alice", "Pcode": "94304"}{"Name": "Brayden", "age": +, "Pcode": "94304"}{"Name": "Carla", "age": +, "Pcoe": "10036"}{"Name": "Diana", "Age": 46}{"Name": "Etienne", "Pcode": "94104"}[Email protected] ~]$HDFs Dfs-cat Pcodes.json{"Pcode": "10036", "City": "New York", "state": "NY"}{"Pcode:" 87501 "," City ":" Santa Fe "," state ":" NM "}{"Pcode": "94304", "City": "Palo Alto", "state": "CA"}{"Pcode": "94104", "City": "San Francisco", "state": "

Jobs that users submit through different threads can run concurrently, but are subject to resource constraints. Job to the dispatch pool (pool) To request resources, the dispatch pool will be based on the project configuration, decide which scheduling mode to use. FIFO mode by default, the Spark Scheduler Dispatches job execution in FIFO (first-in first Out) mode. Each job is cut into multiple stage. The first job takes all available resources, and

First half Source: http://blog.csdn.net/lsshlsw/article/details/51213610 The latter part is my optimization plan for everyone's reference. +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Sparksql Shuffle the error caused by the operation Org.apache.spark.shuffle.MetadataFetchFailedException: Missing An output location for shuffle 0 Org.apache.spark.shuffle.FetchFailedException: Failed to connect to hostname/192.168.xx.xxx:50268 Error from Rdd's shuf

Reprinted from http://www.csdn.net/article/2015-06-08/2824889http://www.zhihu.com/question/26568496Now, Spark has been widely recognized and supported at home: In 2014, spark Summit China in Beijing, the scene is hot, the same year, Spark Meetup in Beijing, Shanghai, Shenzhen and Hangzhou four cities, of which only Beijing has successfully held 5 times, The conte

1 installing spark-dependent Scala 1.2 Configure environment variables for Scala 1.3 validation Scala 2 Download and decompression spark 3 Spark-related configuration 3.1 Configuring environment variables 3.2 Configure the files in the Conf directory 3.2.1 New Spark-env.h file 3.2.2 New Slaves file 4 test st

Summary: The advent of Apache Spark has made it possible for ordinary people to have big data and real-time data analysis capabilities. In view of this, this article through hands-on Operation demonstration to lead everyone to learn spark quickly. This article is the first part of a four-part tutorial on the Apache Spark Primer series.The advent of Apache

Tags: first trap city ace files register disabled who DDEInstalling spark requires installing the JDK first and installing Scala.1. Create a Directory> Mkdir/opt/spark> Cd/opt/spark2. Unzip, create a soft connection> Tar zxvf spark-2.3.0-bin-hadoop2.7.tgz> Link-s spark-2.3.0-bin-hadoop2.7 Spark4. Edit/etc/profile> Vi/e

Apache Spark Memory Management detailedAs a memory-based distributed computing engine, Spark's memory management module plays a very important role in the whole system. Understanding the fundamentals of spark memory management helps to better develop spark applications and perform performance tuning. The purpose of this paper is to comb out the thread of

1. Official website Download source code, address: http://spark.apache.org/downloads.html2. Use MAVEN to compile:Note Before you translate, you need to set the Java heap size and the permanent generation size to avoid MVN memory overflow.Under Windows Settings:%maven_home%\bin\mvn.cmd, place one of theAdd a row below this line of commentsSet maven_opts=-xmx2048m-xx:permsize=512m-xx:maxpermsize=1024mTo compile laterPackageWhen the compilation is complete, import the project into IntelliJFile->imp

Below is a look at the use of Union:Use the collect operation to see the results of the execution:Then look at the use of Groupbykey:Execution Result:The join operation is the process of a Cartesian product operation, as shown in the following example:To perform a join operation on RDD3 and RDD4:Use collect to view execution results:It can be seen that the join operation is exactly a Cartesian product operation;The reduce itself, which is an action-type operation in an RDD operation, causes the