1, first download the image to local. https://hub.docker.com/r/gettyimages/spark/~$ Docker Pull Gettyimages/spark2, download from https://github.com/gettyimages/docker-spark/blob/master/docker-compose.yml to support the spark cluster DOCKER-COMPOSE.YML fileStart it$ docker-compose Up$ docker-compose UpCreating spark_master_1Creating spark_worker_1Attaching to Sp
Install spark
Spark must be installed on the master, slave1, and slave2 machines.
First, install spark on the master. The specific steps are as follows:
Step 1: Decompress spark on the master:
Decompress the package directly to the current directory:
In this case, create the spa
Step 1: Test spark through spark Shell
Step 1:Start the spark cluster. This is very detailed in the third part. After the spark cluster is started, webui is as follows:
Step 2: Start spark shell:
In this case, you can view the shell in the following Web console:
S
Step 1: Test spark through spark Shell
Step 1:Start the spark cluster. This is very detailed in the third part. After the spark cluster is started, webui is as follows:
Step 2:Start spark shell:
In this case, you can view the shell in the following Web console:
Step 3:Co
Install spark
Spark must be installed on the master, slave1, and slave2 machines.
First, install spark on the master. The specific steps are as follows:
Step 1: Decompress spark on the master:
Decompress the package directly to the current directory:
In this case, create the
command:Add the following content, including the bin directory to the pathMake it effective with source1.4 Verification
The input Scala version can be displayed as follows:Scala can also be programmed directly with Scala:2. Install Spark 2.1 Downloads Spark
Download Address:Http://spark.apache.org/downloads.htmlFor learning purposes, I downloaded the pre-compiled version 1.6.2.2 Decompression
The download
Spark Communication Module
1, Spark Cluster Manager can have local, standalone, mesos, yarn and other deployment methods, in order to
Centralized communication mode
1, RPC remote produce call
Spark Communication mechanism:
The advantages and characteristics of Akka are as follows:
1, parallel and distributed: Akka in design with asynchronous communication and dis
Step 1: software required by the spark cluster;
Build a spark cluster on the basis of the hadoop cluster built from scratch in Articles 1 and 2. We will use the spark 1.0.0 version released in May 30, 2014, that is, the latest version of spark, to build a spark Cluster Based
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
Start and view the cluster status
Step 1: Start the hadoop cluster, which is explained in detail in the second lecture. I will not go into details here:
After the JPS command is run on the master machine, the following process information is displayed:
When JPS is used on slave1 and slave2, the following process information is displayed:
Step 2: Start the spark Cluster
On the basis of the successful start of the hadoop cluster, to start the
Step 4: build and test the spark development environment through spark ide
Step 1: Import the package corresponding to spark-hadoop, select "file"> "project structure"> "Libraries", and select "+" to import the package corresponding to spark-hadoop:
Click "OK" to confirm:
Click "OK ":
After idea
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 TCP sockets, after acquiring data from a data source, you can
Open idea under the SRC under main under Scala right click to create a Scala class named Simpleapp, the content is as followsImportOrg.apache.spark.SparkContextImportOrg.apache.spark.sparkcontext._ImportOrg.apache.spark.SparkConfObjectSimpleapp{defMain(Args:array[string]) {ValLogFile ="/home/spark/opt/spark-1.2.0-bin-hadoop2.4/readme.md"//should be some file on your system Valconf =NewSparkconf (). Setap
Zhou Zhihu L.Holiday, finally can spare time to update the blog ....1. Get DataThis article provides a detailed introduction to Sparksql's content by using the Spark project git log on GitHub as the data.The Data Acquisition command is as follows:[[emailprotected] spark]# git log --pretty=format:‘{"commit":"%H","author":"%an","author_email":"%ae","date":"%ad","message":"%f"}‘ > sparktest.jsonThe output of
Open idea under the SRC under main under Scala right click to create a Scala class named Simpleapp, the content is as followsOrg.apache.spark.SparkContext org.apache.spark.sparkcontext._ org.apache.spark.SparkConf"a"). Count () numbs = logdata.filter (line = Line.contains ("B")). Count () println ("Lines with a:%s, Lines with B:%s". Format (Numas, numbs))}}
Packaging files:File-->>projectstructure-click artificats-->> click the Green Plus-click jar-->> Select from module with Depe
Step 2: Use the spark cache mechanism to observe the Efficiency Improvement
Based on the above content, we are executing the following statement:
650) This. width = 650; "src =" http://s3.51cto.com/wyfs02/M00/49/AF/wKioL1QY8tmiGO95AAG6MKKe5vI885.jpg "style =" float: none; "Title =" 1.png" alt = "wkiol1qy8tmigo95aag6mkke5vi885.jpg"/>
650) This. width = 650; "src =" http://s3.51cto.com/wyfs02/M00/49/AD/wKiom1QY8sLjnB_KAAHXbDhuD_I646.jpg "style =" float
Tags: spark books spark hotspot Spark Technology spark tutorial
The command to end historyserver is as follows:
Step 4: Verify the hadoop distributed Cluster
First, create two directories on the HDFS file system. The creation process is as follows:
/Data/wordcount in HDFS is used to store the data f
Step 2: Use the spark cache mechanism to observe the Efficiency Improvement
Based on the above content, we are executing the following statement:
It is found that the same calculation result is 15.
In this case, go to the Web console:
The console clearly shows that we performed the "count" Operation twice.
Now we will execute the "Sparks" variable for the "cache" Operation:
Run the Count operation to view the Web console:
At this tim
Step 2: Use the spark cache mechanism to observe the Efficiency Improvement
Based on the above content, we are executing the following statement:
It is found that the same calculation result is 15.
In this case, go to the Web console:
The console clearly shows that we performed the "count" Operation twice.
Now we will execute the "Sparks" variable for the "cache" Operation:
Run the Count operation to view the Web console:
At this time, we found
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