splitter combiner

I also saw some columns on the net effect, there is a jquery plug-in jquery.splitter.js, but they basically did not solve a problem: if there is an IFRAME on the page, when dragging the split line through the IFRAME, the mouse does not listen to the boss, I have had a post to discuss this issue. This example uses a small trick to solve the problem and make the drag flow smooth. Copy Code code as follows: Left panel Right panel --> **********************************************

, writablecomparable W2)Another approach is to implement Interface Rawcomparator.Set up to use Setsortcomparatorclass in the job.2.3 Grouping function classes. In the reduce phase, when constructing a value iterator corresponding to a key, as long as first is identical, it belongs to the same group and is placed in a value iterator. This is a comparator that needs to inherit writablecomparator.public static class Groupingcomparator extends WritablecomparatorWith the key comparison function class

setpartitionerclasss in the job to set partitioner.(2.2) key comparison function class. This is the second comparison of the key. This is a comparator that inherits writablecomparator. public static class KeyComparator extends WritableComparator There must be a constructor and the Public int compare (writablecomparable W1, writablecomparable W2) must be overloaded) Another method is to implement the interface rawcomparator.Use setsortcomparatorclass in the job to set the key comparison function

for a reduce task. This is done to avoid some of the reduce tasks being allocated to large amounts of data, while some reduce tasks have little or no data embarrassment. In fact, partitioning is the process of hashing data. The data in each partition is then sorted, and if combiner is set at this point, the sorted result is combiner and the purpose is to have as little data as possible to write to the disk

function after the rotation of the functions, iterate and terminatepartial similar to Hadoop combiner ( Iterate--mapper;terminatepartial--reducer) Merge (): Receive terminatepartial return result, data merge operation with a return type of Boolean terminate () : Returns the result of the final aggregation function Java code "Font-size:x-small;"> Packagecom.alibaba.hive; Import Org.apache.hadoop.hive.ql.exec.UDAF; Import Org.apa

division ), you can also obtain the implementation of the recordreader interface from inputformat and generate pairs from the input. With , you can start the map operation. The map operation passes context. Collect (Outputcollector.Collect) write the result to context. When mapper outputs are collected, they are output to the output file in a specified way by the partitioner class. We can provide combiner for Map

Use to Combiner programming (pluggable)At the map end of the output to merge first, the most basic is to implement local key merge, with local reduce functionIf you do not have combiner, all results are reduce, efficiency will be underThe input and output types of the combiner should be exactly the same (implement functions such as cumulative, maximum, etc.)Job.s

set the key comparison classJob.setsortcomparatorclass (Keycomparator.class);Note: If you do not use the custom Sortcomparator class, the key is used by default in the CompareTo () method to sort keys.4. Define the Grouping class functionIn the reduce phase, a value iterator corresponding to the Key is constructed, as long as first is the same group and placed in a value iterator. There are two ways to define this comparer.1) Inherit Writablecomparatorpublic static class Groupingcomparator exte

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file first; 1 final long size = (bufend >= bufstart3 ? bufend - bufstart5 : (bufvoid - bufend) + bufstart) +7 partitions * APPROX_HEADER_LENGTH; Step 2: Obtain the name of the file written to the local (non-HDFS) file with a serial number, for example, output/spill2.out. The code corresponding to the naming format is: 1 return lDirAlloc.getLocalPathForWrite(MRJobConfig.OUTPUT + "/spill"2 3 + spillNumber + ".out", size, getConf()); Step 3: Sort the

set at this time, such as python MRWordCounter. py-r inline input1 input2 input3. Use the python MRWordCounter. py-r inline input1 input2 input3> out command to output the results of processing multiple files to out. Locally simulate hadoop running: python MRWordCounter-r local This will output the result to the output, which must be written. Run on the hadoop cluster: python MRWordCounter-r hadoop 3. mrjob usage The usage of mrjob is comprehensive in its official documents. The most basic pa

bit of a look around the code:intValue = Stream.of (1, 2, 3, 4). Reduce (sum, item), Sum +item); Assert.assertsame (Value,110);/*or use a method reference*/value= Stream.of (1, 2, 3, 4). Reduce (100, integer::sum); In this example 100 is the calculation of the initial value, each time the addition of the calculated value will be passed to the next calculation of the first parameter. Reduce also has two other overloaded methods: Optionalreduce (Binaryoperatoraccumulator): As defined above, no in

partitioned according to their business requirements, for example, saving different types of results in different file medium. Several partitions are set up here, and there will be several reducer to handle the contents of the corresponding partition.After 1.4 partitioning, the data for each partition is sorted, grouped-the sort is sorted from small to large, and after sorting, the value of the option with the same key value is merged. For example, all key-value pairs may existHello 1Hello 1The

frequencies. class Mapper method Map(docid id, doc d) for all term t in doc d do Emit(term t, count 1)class Reducer method Reduce(term t, counts [c1, c2,...]) sum = 0 for all count c in [c1, c2,...] do sum = sum + c Emit(term t, count sum) The disadvantage of this method is obvious. Mapper submits too many meaningless counts. It can count the words in each document to reduce the amount of data transferred to Cer CER: class Mapp

Hadooppipes::runtask and connects to the parent process and marshals data connected to Java from Mapper or reducer. The RunTask () method is passed in a factory parameter so that it can create an instance of mapper or reducer. One of its creation will be controlled by the Java parent process in the socket connection. We can use the overloaded template factory method to set up a combiner (combiner), a parti

117,838,546 117,838,546 235,677,092 Split_raw_bytes 8,576 0 8,576 Combine Input RecordsCombiner is to minimize the data that needs to be pulled and moved, so the number of combine input bars is consistent with the number of output bars in the map.Combine Output RecordsAfter combiner, the data of the same key is compressed, and many duplicate data are resolved at the map end, indicating the fi

offset of the row in the file, value is the line content Wakayuki is truncated, the first few characters of the next block are read2. Split and Block:BlockThe smallest data storage unit in HDFs,default is 64MBSpitThe smallest compute unit in MapReduce corresponds to block one by one by defaultBlock and SplitSplit vs. block is arbitrary and can be controlled by the user3. Combiner (local reduce) Combiner ca

the contents of the text in parallel and then makes a mapreduce operation. 　　 Map process: Read the text in parallel, the read Word map operation, each word is generated in the form of My understanding:A file with three lines of text for a mapreduce operation.Read the first line Hello World Bye world, dividing the word into a map.Read the second line Hello Hadoop Bye Hadoop, split the word to form a map.Read the third line bye Hadoop Hello Hadoop and split the word to form

: (bufvoid - bufend) + bufstart) +7 partitions * APPROX_HEADER_LENGTH; Step 2: Obtain the name of the file written to the local (non-HDFS) file with a serial number, for example, output/spill2.out. The code corresponding to the naming format is: 1 return lDirAlloc.getLocalPathForWrite(MRJobConfig.OUTPUT + "/spill"2 3 + spillNumber + ".out", size, getConf()); Step 3: Sort the data in the [bufstart, bufend) interval in the buffer zone kvbuffe in the ascending