catv combiner

1, comparator try not to let Mr Generate serialization and deserialization conversion, reference Writablecomparable class2,reducer severe data skew, you can consider a custom partitionerBut before you can try using combiner to compress the data to see if it solves the problemRegular expressions are not used in the 3,map phase4,split use StringUtils, the test performance is much higher than (String,scanner,stringtokenizer), writableutils and other tool

value is passed, or false to pass by reference. The output of the initial mapper is saved in memory. Assuming that the incoming value is no longer called at a later stage, it can be efficient and generally set to trueThe reduce function receives the input data and crosses its values, and reduce generates all the merged results for those values.Each merge result obtained by the cross product is fed into the function combine () (not combiner) to genera

;/*** Hello world!**/public class WordCount1 {public static class Map extends Mapper Private final static longwritable one = new longwritable (1);Private text Word = new text ();@Overridepublic void Map (longwritable key, Text value, context context)Throws IOException, Interruptedexception {String line = value.tostring ();StringTokenizer tokenizer = new StringTokenizer (line);while (Tokenizer.hasmoretokens ()) {Word.set (Tokenizer.nexttoken ());Context.write (Word, one);}}}public static class Re

cluster should be slightly smaller than the number of reducer task slotscombiner use : Fully use the merge function to reduce the amount of data passed between map and reduce, combiner run after mapmedian compression : compressing the map output value reduces the amount of conf.setcompressmapoutput (true) before reducing to reduce Setmapoutputcompressorclass (Gzipcodec.class)Custom Writable: If you use a custom writable object or a custom comparator,

Hadoop: Data flow graph (based on Hadoop 0.18.3): A simple example of how data flows in Hadoop.Hadoop: Data flow graph (based on Hadoop 0.18.3):Here is an example of the process of data flow in Hadoop, an example of how the total number of words in some articles is counted. First, files represent these articles that require statistical vocabulary. first, Hadoop allocates the initial data to the mapper task of each machine, and the figures in the figure represent the sequential flow of data. 1.

become a complete data file; In order to provide a data storage fault tolerance mechanism, The file system also provides a multi-backup storage management capability for data blocks? Combiner and Partitioner: In order to reduce data communication overhead, intermediate results are required to be merged (combine) before they enter the reduce node, and data with the same primary key can be combined to avoid duplicate transmission; The data processed by

(Score >Maxscore) {Name= Valtokens[0]; Age= Valtokens[1]; Gender=key.tostring (); Maxscore=score; }} context.write (NewText (name),NewText ("Age:" + age + Tab_separator + "Gender:" + gender + Tab_separator + "score:" +maxscore)); }} @SuppressWarnings ("Deprecation") @Override Public intRun (string[] args)throwsException {//reading configuration FilesConfiguration conf =NewConfiguration (); Path MyPath=NewPath (args[1]); FileSystem HDFs=mypath.getfilesystem (conf); if(Hdfs.isdirectory (MyPath)) {

I. Overview of the MapReduce job processing processWhen users are dealing with a problem using the MapReduce computational model of Hadoop, they only need to design mapper and reducer processing functions, and possibly include combiner functions. After that, create a new Job object and configure the job's run environment, and finally call the job's waitforcompletion or the Submit method to submit the job. The code is as follows:1 //Create a new defaul

before the merge is completed46. The direct communication protocol between task and Tasktracker isA. JobsubmissionprotocolB. ClientProtocolC. TaskumbilicalprotocolD. Intertrackerprotocol Interdatanodeprotocol:datanode interface for internal interaction to update block metadata;Innertrackerprotocol:tasktracker and Jobtracker interface, function and Datanodeprotocol are similar;Jobsubmissionprotocol:jobclient interface with Jobtracker, used to submit job, job and other job-related operat

the shuffle process in MapReduce is divided into two processes, map and reduce. Map End:1. (hash partitioner) after executing the map function, hash according to key, and the result of reduce the number of modulus (the key value pair will be processed by a reduce side) to get a partition number.2. (Sort combiner) writes the byte after the key-value pair and the partition number to the memory buffer (size 100M, loading factor 0.8), when the memory buff