First, prepareUpload apache-hive-1.2.1.tar.gz and Mysql--connector-java-5.1.6-bin.jar to NODE01Cd/toolsTAR-ZXVF apache-hive-1.2.1.tar.gz-c/ren/Cd/renMV apache-hive-1.2.1 hive-1.2.1This cluster uses MySQL as the hive metadata storeVI Etc/profileExport hive_home=/ren/hive-1.2.1Export path= $PATH: $HIVE _home/binSource/etc/profileSecond, install MySQLYum-y install MySQL mysql-server mysql-develCreating a hive Database Create databases HiveCreate a hive user grant all privileges the hive.* to [e-mai
The task scheduling system for Spark is as follows:From the Chinese Academy of Sciences to see the cause rddobject generated DAG, and then entered the Dagscheduler stage, Dagscheduler is the state-oriented high-level scheduler, Dagscheduler the DAG split into a lot of tasks, Each group of tasks is a state, whenever encountering shuffle will produce a new state, you can see a total of three state;dagscheduler need to record those rdd is deposited into
You are welcome to reprint it. Please indicate the source, huichiro.Summary
This article will give a brief review of the origins of the quasi-Newton method L-BFGS, and then its implementation in Spark mllib for source code reading.Mathematical Principles of the quasi-Newton Method
Code Implementation
The regularization method used in the L-BFGS algorithm is squaredl2updater.
The breezelbfgs function in the breeze library of the scalanlp member
You can see the initialization UI code in Sparkcontext://Initialize the Spark UIPrivate[Spark]ValUI: Option[sparkui] =if(conf. Getboolean ("Spark.ui.enabled", true)) {Some(Sparkui.Createliveui( This, conf, Listenerbus, Jobprogresslistener, Env. SecurityManager,AppName)) }Else{//For tests, does not enable the UI None}//Bind the UI before starting the Task Scheduler to communicate//The bound port to
Hadoop until reduce is actually the constant merge, file-based multiplexing and sequencing, and the same partition merge on the map side, at the reduce side, Merge the data files from the mapper-side copy to use for the finally reduceMulti-merge sorting, reaching two goals.Merge, put the value of the same key into a ArrayList; sort, and finally the result is sorted by key.This method is very good extensibility, the face of big data is not a problem, of course, the problem in efficiency, after a
Contents of this issue:1. A thorough study of the relationship between Dstream and Rdd2. Thorough research on the streaming of Rddathorough study of the relationship between Dstream and Rdd Pre-Class thinking:How is the RDD generated?What does the rdd rely on to generate? According to Dstream.What is the basis of the RDD generation?is the execution of the RDD in spark streaming different from the Rdd execution in
Introduction to spark Core conceptsA spark application initiates various concurrent operations on the cluster by the drive program, and a drive program typically contains multiple executor nodes, and the drive program accesses the SAPRK through a Saprkcontext object. The Rdd (Elastic distributed DataSet)----A distributed collection of elements, and the RDD supports two operations: conversion operations, act
Provides various official and user release code examples. For code reference, you are welcome to exchange and learn about spark grassland system development, spark grassland system source code, distribution system micro-distribution, it is a three-level distribution mall based on the public platform. The three-level distribution should achieve an infinite loop model, and an innovation of the enterprise mark
3, hands-on generics in Scalageneric generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scalaimplicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala:Let's take a look at the example of hidden parameters:
The
3, hands-on generics in Scala generic generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scala Implicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala: Let's take a look at the example of hidden parameters:
Http://spark.apache.org/docs/1.2.1/streaming-programming-guide.htmlHow to shard data in sparkstreamingLevel of Parallelism in Data processingCluster resources can be under-utilized if the number of parallel tasks used on any stage of the computation are not high E Nough. For example, for distributed reduce operations like reduceByKey reduceByKeyAndWindow and, the default number of parallel tasks are controlled by The spark.default.parallelism configuration property. You can pass the level of par
configuration file are:
Run the ": WQ" command to save and exit.
Through the above configuration, we have completed the simplest pseudo-distributed configuration.
Next, format the hadoop namenode:
Enter "Y" to complete the formatting process:
Start hadoop!
Start hadoop as follows:
Use the JPS command that comes with Java to query all daemon processes:
Start hadoop !!!
Next, you can view the hadoop running status on the Web page used to monitor the cluster status in hadoop. The specific pa
There is a simple demo of spark-streaming, and there are examples of Kafka successful running, where the combination of both, is also commonly used one.
1. Related component versionFirst confirm the version, because it is different from the previous version, so it is necessary to record, and still do not use Scala, using Java8,spark 2.0.0,kafka 0.10.
2. Introduction of MAVEN PackageFind some examples of a c
The Spark standalone uses the Master/slave architecture, which includes the following classes:
Class: Org.apache.spark.deploy.master.Master Description: Responsible for the entire cluster of resource scheduling and application management. Message type: Receives messages sent by worker 1. Registerworker 2. Executorstatechanged 3. Workerschedulerstateresponse 4. Heartbeat messages sent to the worker 1. Registeredworker 2. Registerworkerfailed 3. Reco
This time we start Spark-shell by specifying the Executor-memory parameter:The boot was successful.On the command line we have specified that the memory of executor on each machine Spark-shell run take up is 1g in size, and after successful launch see Web page:To read files from HDFs:The Mappedrdd returned in the command line, using todebugstring, can view its lineage relationship:You can see that Mappedrdd
The output from the WordCount in a previous article shows that the results are unsorted and how do you sort the output of spark?The result of Reducebykey is Key,value position permutation (number, character), then the number is sorted, and then the key,value position is replaced by the sorted result, and finally the result is stored in HDFsWe can find out that we have successfully sorted out the results!Spark
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.