understanding mapreduce

When I first read the Mongodb getting started manual, I saw mapreduce. It was so difficult that I ignored it directly. Now, when I see this part of knowledge again, I am determined to learn it. 1. concept description: MongoDB's MapReduce is equivalent to "groupby" in Mysql. It is easy to use mapreduce to execute parallel data statistics on mongodb. When I first r

CounterBecause the counter view is often more convenient than viewing the cluster logSo in some cases the counter information is more efficient than the cluster logUser-definable countersA description of the built-in counters for Hadoop can be found in the Nineth chapter of the build-in counts in MapReduce features, the authoritative guide to HadoopThis is limited to the space no longer explainsMapReduce allows users to customize counters in a program

4.3 Map class Create a map class and a map function. The map function is Org. apache. hadoop. mapreduce. the Mapper class calls the map method once when processing each key-value pair. You need to override this method. The setup and cleanup methods are also available. The map method is called once when the map task starts to run, and the cleanup method is run once when the whole map task ends.4.3.1 introduction to map The ER er Class is a generic clas

Document directory Refer: 1 MapReduce Overview Ii. How MapReduce works Three MapReduce Framework Structure 4. JobClient TaskTracker Note: I wanted to analyze HDFS and Map-Reduce in detail in the Hadoop learning summary series. However, when searching for information, I found this article, we also found that caibinbupt has analyzed the Hadoop source code

Sharing of third-party configuration files for MapReduce jobs In fact, the sharing method for running third-party configuration files in MapReduce jobs is actually the transfer of parameters in MapReduce jobs. In other words, it is actually the application of DistributedCache. Configuration is commonly used to pass parameters in

Hadoop's automated distributed cache Distributedcache (the new version of the API) is often used in the write MapReduce program, but executes in eclipse under Windows, with an error similar to the following:2016-03-03 10:53:21,424 WARN [main] util. Nativecodeloader (nativecodeloader.java:2016-03-03 10:53:22,152 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated (1019))- Session.id is deprecated. Instead, use Dfs.metrics.ses

Prerequisite Preparation: 1.hadoop installation is operating normally. Hadoop installation Configuration Please refer to: Ubuntu under Hadoop 1.2.1 Configuration installation 2. The integrated development environment is normal. Integrated development environment Configuration Please refer to: Ubuntu building Hadoop Source Reading environment MapReduce Programming Examples: MapReduce Programming Example (i)

tenant ID, the caller ID, the invoked service name (the class name of the called method), the called Method name, execution parameters (serialized into JSON), execution time, execution duration (MS) , the client IP, the client computer name, and the exception (if the method throws an exception).For example, with a simple scenario, there is a reusable library (hugger) and an application that uses this library (Hugmachine), and the code is hosted on GitHub.Must-revalidate: Tells the browser, the

machine and reports it to ResourceManager/schedager. The applicationmaster of each application is responsible for negotiating with scheduler appropriate resource containers, tracking their status, and monitoring progress. Mrv2 is compatible with previous stable versions (hadoop-1.x), which means that the desired map-reduce jobs can run on mrv2. #160; #160; Understanding: the yarn framework is built on the previous map-Reduce framework. It spli

() method implemented using keyReduce phaseIn the reduce phase, when the reduce () method accepts all map outputs mapped to this reduce, it also calls the key comparison function class set by the Job.setsortcomparatorclass () method to sort all the data. It then begins to construct a value iterator corresponding to the key. Use the Job.setgroupingcomparatorclass () method to set the Grouping function class. As long as the comparator compares the same two keys, they belong to the same group, the

How do I divide partition when querying Phoenix data with mapreduce/hive?PhoenixInputFormatThe source code at a glance to know: public listgetsplits (jobcontext context) throws IOException, interruptedexception {Configuration configuration = Context. (); Queryplan Queryplan = this . getqueryplan (context, configuration); List allsplits = Queryplan. getsplits (); List splits = this . generatesplits (Queryplan, allsplits);

Reprinted from: http://www.cnblogs.com/fengfenggirl/p/pagerank-introduction.html PageRank on the page ranking algorithm, was the magic of Google's wealth. Although there have been experiments before, but the understanding is not thorough, these days have looked again, here summarizes the basic principle of PageRank algorithm. First, what is PageRank PageRank page is considered a Web page, a page rank, or Larry Page (Google product manager), because h

Part I: How MapReduce worksMapReduce Roleclient: Job submission initiator.Jobtracker: Initializes the job, allocates the job, communicates with Tasktracker, and coordinates the entire job.Tasktracker: Performs a mapreduce task on the allocated data fragment by maintaining jobtracker communication through the heartbeat heartbeat.Submit Job• The job needs to be configured before the job is submitted• program

Transferred from:http://www.cnblogs.com/forfuture1978/archive/2010/11/19/1882279.htmlTransfer note: Originally wanted in the Hadoop Learning Summary series detailed analysis HDFs and map-reduce, but find the information, found this article, and found that Caibinbupt has been the source code of Hadoop has been detailed analysis, recommended everyone read.Transfer from http://blog.csdn.net/HEYUTAO007/archive/2010/07/10/5725379.aspxReference:1 Caibinbupt Source Code Analysis http://caibinbupt.javae

1. mapreduce Mapreduce is a concept that is hard to understand or understand. It is hard to understand because it is really hard to learn and understand theoretically. It is easy to understand because, if you have run several mapreduce jobs on hadoop and learn a little about the working principle of hadoop, you will basically understand the concept of

Mapreduce Mapreduce is a programming model for data processing. The model is simple, yet not too simple to express useful programs in. hadoop can run mapreduce programs writtenIn various versions; In this chapter, we shall look at the same program expressed in Java, Ruby, Python, and C ++. most important, mapreduce pr

We know that if you want to run a mapreduce job on yarn, you only need to implement a applicationmaster component, and Mrappmaster is the implementation of MapReduce applicationmaster on yarn, It controls the execution of the Mr Job on yarn. So, one of the problems that followed was how Mrappmaster controlled the mapreduce operation on yarn, in other words, what

Prerequisite Preparation: 1.hadoop installation is operating normally. Hadoop installation Configuration Please refer to: Ubuntu under Hadoop 1.2.1 Configuration installation 2. The integrated development environment is normal. Integrated development environment Configuration Please refer to: Ubuntu building Hadoop Source Reading environment MapReduce Programming Examples: MapReduce Programming Example (i)

article from my personal blog: MongoDB mapreduce Usage Summary As we all know, MongoDB is a non-relational database, that is, each table in the MongoDB database is independent, there is no dependency between the table and the table. In MongoDB, in addition to the various CRUD statements, we also provide aggregation and mapreduce statistics, this article mainly to talk about MongoDB's

The first 2 blog test of Hadoop code when the use of this jar, then it is necessary to analyze the source code. It is necessary to write a wordcount before analyzing the source code as follows Package mytest; Import java.io.IOException; Import Java.util.StringTokenizer; Import org.apache.hadoop.conf.Configuration; Import Org.apache.hadoop.fs.Path; Import org.apache.hadoop.io.IntWritable; Import Org.apache.hadoop.io.Text; Import Org.apache.hadoop.mapreduce.Job; Import Org.apache.hadoop.mapreduc