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On the basis of the previous chapter, "Environment Construction", this chapter makes a test for each module.Mysql Test 1. mysql Node preparation For easy testing, in MySQL node, add point data Go to the master node docker exec -it hadoop-maste /bin/bash Enter the database node ssh hadoop-mysql Create a database create database zeppelin_test; Create a data table create table user_info(id INT NOT NULL AUTO_IN
When it comes to big data, I believe you are not unfamiliar with the two names of Hadoop and Apache Spark. But we tend to understand that they are simply reserved for the literal, and do not think deeply about them, the following may be a piece of me to see what the similarities and differences between them.The problem-solving dimension is different.First, Hadoop
When it comes to big data, I believe you are not unfamiliar with the two names of Hadoop and Apache Spark. But we tend to understand that they are simply reserved for the literal, and do not think deeply about them, the following may be a piece of me to see what the similarities and differences between them.1, the problem-solving level is not the sameFirst, Hadoop
In the previous article we implemented Java+spark+hive+maven implementation and exception handling, the test instance is packaged to run in the Linux environment, but when the Windows system runs directly, there will be Hive related exception output, This article will help you integrate the Hadoop+spark+hive development environment on a Windows system. I. Develop
Hardware environment:Hddcluster1 10.0.0.197 REDHAT7Hddcluster2 10.0.0.228 Centos7 this one as masterHddcluster3 10.0.0.202 REDHAT7Hddcluster4 10.0.0.181 Centos7Software Environment:Turn off all firewalls firewallOpenssh-clientsOpenssh-serverJava-1.8.0-openjdkJava-1.8.0-openjdk-develHadoop-2.7.3.tar.gzProcess: Select a machine as Master Configure Hadoop users on the master node, install SSH server, install the Java environment
needs to be started separately, it's worth the time to stop alone 4. on [nn1], format it, and start: bin/hdfs namenode-start namenode5. On [NN2], Synchronize nn1 metadata information: bin/hdfs Namenode-bootstrapstandby 6. start [nn2]:sbin/hadoop-daemon.sh start Namenode after the above four steps, NN1 and nn2 are processed standby status 7. switch [NN1] to active There's a problem with how to configure automatic switching, this does not, forc
When it comes to big data frameworks, Hadoop and Spark are the hottest, but we tend to understand them literally, without thinking deeply about them, and what technologies are being used in the industry now? What are the similarities and differences between the two? What problems have they solved? Let's see what the difference is between them.the problem-solving dimension is different.First,
The mapreduce process, spark, and Hadoop shuffle-centric comparative analysisThe map-shuffle-reduce process of mapreduce and sparkMapReduce Process Parsing (MapReduce uses sort-based shuffle)The obtained data shard partition is parsed, the k/v pair is obtained, and then the map () is processed.After the map function is processed, it enters the collect stage, collects the processed k/v pairs, and stores them
1. Download Spark and run wget http://apache.fayea.com/apache-mirror/spark/spark-1.0.0/spark-1.0.0-bin-hadoop2.tgz I'm the download here is version 1.0.0, because we just test the use of spark, so do not need to configure the spark
deployment, fast, but operation of their own operations, Ansible is also a choice yo, after all, pure ssh.3, after the first Hadoop, how to happily copy to other nodes? This script is not very convenient, may be related to the directory to customize ... If all the things can be unified into a directory ... :), and Scp-r $var _folder [e-mail protected]$1:/usr/local/, this write ugly, then only fast.#!/bin/BashEcho "Usage:./init_hadoop_spark-f demo-dat
Our Hadoop production environment has two versions, one of which is 1.0.3, and in order to support log compression and split, we have added feature about BZIP2 compression in hadoop-1.2. Everything works well.To meet the company's needs for iterative computing (complex Hivesql, ad recommendation algorithms, machine learning etc), we built our own spark cluster, i
1. What is storm and how do you do it better?Storm is an open-source distributed real-time computing system that can handle a large amount of data flow simply and reliably. Storm has many application scenarios, such as real-time analytics, online machine learning, continuous computing, distributed RPC, ETL, and so on.Storm supports horizontal scaling with high fault tolerance, guaranteeing that every message will be processed and processed quickly (in a small cluster, each node can process milli
Apache Hadoop and Apache SparkNext talk about multicore machines, petabytes of data, and tasks, similar to all of the Java or overloaded machine learning algorithms that Twitter mentions. When it comes to Hadoop, it has to say the broad framework and its components: Hadoop Distributed File System (HDFS), Resource management Platform (YARN), Data processing module
--name Spark_scala--class WordCount--executor-memory 1G--total-executor-cores 2 ~/sparktest/spark_scala.jar/home/jiahong/jia.txtEnter Hadoop, then use the Spark-submit command to submit the jar package, if you do not understand the above command, you can use Spark-submit--help to view the HelpSpark://jiahong-optiplex-7010:7077 address of the primary nodeAddress
scala> val file = Sc.textfile ("Hdfs://9.125.73.217:9000/user/hadoop/logs") Scala> val count = file.flatmap (line = Line.split ("")). Map (Word = = (word,1)). Reducebykey (_+_) Scala> Count.collect () Take the classic wordcount of Spark as an example to verify that spark reads and writes to the HDFs file system 1. Start the S
being resolved). Be sure to create a connection in the inside Yo, otherwise there will be a serialization problem. Hadoo Integrated MongoDB Update:Val mongoconfig = new Configuration ()Mongoconfig.set ("Mongo.output.uri", "mongodb://master:27017/db.table")Saverdd.saveasnewapihadoopfile ("", classof[Object], classof[mongoupdatewritable],classof[mongooutputformat[object,mongoupdatewritable]],mongoconfig). It can be used in conjunction with MongoDB's nu
Just make sure your computer is installed in the Java environment, and you're ready to start.I. Preparatory work1. Download the Hadoop2.7.1 version (write Spark and Hadoop are mostly used yarn, so Hadoop must be installed): HTTP://APACHE.FAYEA.COM/HADOOP/COMMON/HADOOP-2.7.1/
My 64-bit machine, when Hadoop started with this problem because the local library that Hadoop itself comes with is 32-bit, I now hadoop2.2.0 have replaced the local library with 64 bits, and the corresponding version was used when compiling spark:spark_hadoop_version=2.2.0 spark_yarn=true./SBT/SBT AssemblyBut now when you get into the spark shell, there's still
Hadoop Shell does not report this error when running, because I have re-compiled the source files on the 64-bit machine and copied so files to the native directory of Hadoop, and the environment variables are set correctly, so Hadoop itself is not a problem.However, this issue will be reported when launching the spark-
Reference documents:http://blog.csdn.net/lxhandlbb/article/details/54410644Each time a spark task is submitted to yarn, uploading resource (package spark jars and upload) is always present on HDFs.In bad condition, it will be stuck here for a long time.Solve:To create a directory on HDFs:HDFs Dfs-mkdir/spark_jarsUpload Spark's jars (spark1.6 only need to upload Spark
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