catv combiner

Inverted index:Before we found the file location---Find the wordRight now:Depending on the word, returns the result of which file it appears in, and how often it is.This is like Baidu Search, you enter a keyword, then the Baidu engine quicklyFind the file with the keyword on its server, and depending on the frequency and some other policies(such as page click Poll Rate), etc. to return your results. In this process, the inverted index plays a key roleCombine multiple text words, break down, coun

abstract void Merge (Aggregationbuffer agg, Object partial) throws hiveexception; Public abstract Object Terminate (Aggregationbuffer agg) throws hiveexception; ......}Before you describe the above method, you need to mention a Genericudafevaluator internal enumeration class modepublic static enum Mode { /** * Corresponds to the map stage, calls iterate () and terminatepartial () */ PARTIAL1, /** * Equivalent to combiner phase, ca

method. In each partition, the data is sorted by key, and if there is a combiner, it will perform a protocol operation on the same key to reduce the write and transfer consumption of the data, but combiner is an optimization function that is not necessarily executed, but may be applied more than once. These overflow files are eventually merged into a partitioned and sorted output file, a process called mer

" In-depth study of MapReduce and its job commissioning and optimization methods Deep mastery of HDFs and system-level operations and performance optimization methods The first part. MapreduceMapReduce Workflow and Basic architecture reviewOperation and Maintenance related Parameter tuning Benchmark Reuse JVM Error awareness and speculative execution Task Log Analysis Tolerance for error percentage setting and skipping bad rec

Content Outline1) The base class Mapper class in MapReduce, customizing the parent class of the Mapper class.2) The base class reducer class in MapReduce, customizing the parent class of the Reducer class.1, Mapper ClassAPI documentation1) inputsplit input shard, InputFormat input format2) sorted sorting and group grouping of mapper output results3) partition the mapper output according to the number of reducer patition4) combiner the mapper output da

= IndexedText(porter, grail)>>> text.concordance('lie')r king ! DENNIS : Listen , strange women lying in ponds distributing swords is no beat a very brave retreat . ROBIN : All lies ! MINSTREL : [ singing ] Bravest of Nay . Nay . Come . Come . You may lie here . Oh , but you are wounded !doctors immediately ! No , no , please ! Lie down . [ clap clap ] PIGLET : Wellere is much danger , for beyond the cave lies the Gorge of Eternal Peril , which you . Oh ... TIM : To the north there lies

to make a specific sort of it yourself? The answer is yes.But first you need to know the default collation before using it. It is sorted by the key value, and if key is the intwritable type that encapsulates int, then MapReduce sorts the key by the number size,If key is a text type encapsulated as String, then mapreduce sorts the strings in dictionary order.Knowing this detail, we know that we should use the intwritable-type data structure that encapsulates int. That is, the data that is read i

this property. For example, if we calculate the average temperature, we cannot use the combination function above. Because: Mean (0, 20, 10, 25, 15) = 14 But: Mean (mean (0, 20), mean (+)) = mean (10) = 15 A composite function cannot replace the reduce function. But it can help reduce the amount of data transferred between map and reduce. For this reason alone, it is worth considering whether you can use a composite function in a mapreduce job. indicates a combination function Back to the Jav

functions because the group operation returns a record for each group, including a bag in each set, so the Exec method iterates through the bag record. Take the Count function for example: Public Long exec (Tuple input) throws IOException { try { databag bag = (databag) input.get (0); if (bag==null) return null; Iterator it = Bag.iterator (); Long cnt = 0; while (It.hasnext ()) { Tuple t = (Tuple) it.next ();

sake of convenience, I alias part of the Hadoop commandAlias stop-dfs='/usr/local/hadoop/sbin/stop-dfs.sh'alias start-dfs=' /usr/local/hadoop/sbin/start-dfs.sh'alias dfs='/usr/local/ Hadoop/bin/hdfs dfs'Once Hadoop is started, create a user directory firstDFS-mkdir -p/user/rootUpload a sample to this directoryDfs-put./sample.csv/user/rootOf course it can be handled more standardized, the difference between the two will sayDFS-mkdir -p/user/root/-put./sample.csv/user/root/inputNext, mapper.py a

(text key, text value, int numreducetasks){TODO auto-generated Method Stubstring[] Nameagescore = value.tostring (). Split ("\ t");String age = nameagescore[1];//Studentint ageint = Integer.parseint (age);//Partitioning by agesDefault specified partition 0if (Numreducetasks = = 0)return 0;Age less than or equal to 20, specify partition 0if (Ageint return 0;}Age greater than 20, less than or equal to 50, specifying partition 1if (Ageint > ageint return 1% Numreducetasks;}Remaining age, specify

Static classReduceextendsMapreducebaseImplementsReducer { Public voidReduce (Text key, iteratorvalues, Outputcollectoroutput, Reporter Reporter)throwsIOException {intsum = 0; while(Values.hasnext ()) {sum+=Values.next (). get (); } output.collect (Key,Newintwritable (sum)); } } Public Static voidMain (string[] args)throwsException {//1. Initializing a MapReduce job with the Jobconf classjobconf conf =NewJobconf (WordCount.class); //Call the Setjobname () method to name the j

set the hive parameter, which will start an additional Mr Job to package small filesHive.merge.mapredfiles = False if the Reduce output file is merged, the default is FalseHive.merge.size.per.task = 256*1000*1000 the size of the merged file(3) Note Data SkewA more common approach in hiveFirst, two Mr Jobs are generated through hive.groupby.skewindata=true control, and the output of the first Mr Job map is randomly assigned to reduce for pre-summarization, reducing the data skew problem caused b

What is the role of 1.Combiner? 2. How are job level parameters tuned? 3. What are the tasks and administrator levels that can be tuned? Hadoop provides a variety of configurable parameters for user jobs to allow the user to adjust these parameter values according to the job characteristics to optimize the operational efficiency.an application authoring specification1. Set CombinerFor a large number of MapReduce programs, if you can set a

Map-side Tuning parameters Property name Type Default value Description Io.sort.mb Int 100 The size of the memory buffer used when sorting the map output, in M. When the node memory is large, the parameter can be increased to reduce the number of disk writes. Io.sort.record.percent Float 0.05 Used as a scale for storing

processing results ==============>> mapreduce !!! 2. Basic Node Hadoop has the following five types of nodes: (1) jobtracker (2) tasktracker (3) namenode (4) datanode (5) secondarynamenode 3. Fragmentation theory (1) hadoop divides mapreduce input into fixed-size slices, which are called input split. In most cases, the slice size is equal to the HDFS block size (64 MB by default ). (2) 4. Local data is preferred Hadoop tends to perform Map Processing on the nodes that store data, which is ca

. Then run combiner (if set). The essence of combiner is also a reducer, which aims to process the files to be written to the disk first, the amount of data written to the disk is reduced. Finally, write the data to the local disk to generate a spill file (the spill file is saved in the directory specified by {mapred. Local. dir} and will be deleted after the map task is completed ). Finally, each map task

): setting this parameter to true helps display the text on the LCD screen. SetTextAlign(Paint. Align align): sets the alignment direction of the drawn text. SetTextScaleX(Float scaleX): sets the scale ratio of the X axis of the drawn text to achieve the text stretching effect. SetTextSize(Float textSize): Set the font size of the drawn text. SetTextSkewX(Float skewX): Set italic text. skewX is a skewed radian. SetTypeface(Typeface typeface): Set the Typeface object, that is, the font style

aug-val (func reduced-x)))))eg,(defun count-slices (x) (cond ((null x) 0) (t (+ 1 (count-slices (rest x))))))8.12 variations on the basic templates Consing (DEFUN func (N) (COND (end-test NIL) (T (CONS new-element (func reduced-n))))) Multiple variables change at the same time (DEFUN func (N X) (COND (end-test end-value) (T (func reduced-n reduced-x)))) Conditional Augmentation (DEFUN func (X) (COND (end-test end-value) (aug-test (aug-fun aug-val (func reduced-x)) (T (func reduced-x)))

overflow file on the disk. If the buffer is not large enough or the map output result is large enough, the overwrite file will be executed multiple times. Therefore, you need to merge these overwrite files into a file, which is called merge. Merge's operation is to merge the K-V with the same key from different map task results into a group to form k-[V1, V2, V…]. Because multiple files are merged into one file, the same key may also exist. If a combiner