catv combiner

generally used for devices with special requirements. Some foreign manufacturers use FC/UPC for internal jumpers of ODF racks, this mainly aims to improve the ODF device's own indicators. In addition, the "APC" model is widely used in radio and television and CATV in the early stages. The pigtails use an end face with an inclination angle, which can improve the quality of TV signals, the main reason is that the TV signal is analog optical modulation.

, and the popularization of endpoint devices also takes time.5. Cable-modem is an ultra-high-speed modem that has been used for trial in the last two years. It uses a ready-made cable TV (CATV) network for data transmission, it is already a mature technology. With the development of cable TV networks and the continuous improvement of people's quality of life, using Cable Modem to access the Internet through cable TV networks has become a high-speed ac

community properties.The Intelligent Community Information system can be divided into two parts of information network subsystem and control network subsystem from function.The Information network subsystem is composed of data system, voice system, CATV system, on the one hand realizes the comprehensive access of data, voice and image, on the other hand provides the transmission channel for the control network subsystem, its transmission way is flexi

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

I. R collect (Supplier Supplier, Biconsumer Accumulator, Biconsumer combiner)Supplier: A way to create an instance of a target type.Accumulator: A method that adds an element to the target.Combiner: A way to combine multiple results from an intermediate state (which is used in concurrency)New ArraylistII, R collect (Collector Collector)The collector is actually the supplier, accumulator, combiner of the abo

; Many of the methods in {@link collectors} is functions that take a collector and produce a new collector. Attach a sentence on the Javadoc, which indicates that the collection operation can be nested.Custom CollectorAs mentioned earlier, collectors itself provides a common aggregation implementation of collector, so programmers themselves can define their own aggregation implementations according to the circumstances.First we look at the structure of the Collector interfacepublic interfac

Org.apache.hadoop.mapred.TextOutputFormat; Import Org.apache.hadoop.util.GenericOptionsParser; The Hadoop data type used by the program import org.apache.hadoop.io.DoubleWritable; Import Org.apache.hadoop.io.Text; Import org.apache.hadoop.io.ArrayWritable; Passing the doublewritable array between master and worker requires modifying the data type, and constructing a new class doublearraywritable to pave the back job. public static class Doublearraywritable extends Arraywritable {public doub

percentage of map output record boundaries, and other caches are used to save data • Io. sort. spill. percent • default value: 0.80 • threshold for starting spill operations by map • Io. sort. factor • 10 by default • Maximum number of streams simultaneously operated during merge operations. • Min. num. spill. for. combine • default value 3 • Minimum number of spill run by the combiner function • mapred. compress. map. output • default value false •

= Curr_key Total = Count # same key ; Accumulate sum else: prev_key = Curr_key Total + = count # emit last Keyif prev_key: print >>sys. STDOUT, "%s\t%i"% (Prev_key, total) The Hadoop stream (streaming) By default divides the key and value (value) with a tab character. Because we also split the fields with the tab character, we have to tell it by passing it to Hadoop three options to show that our data's health (key) is made up of the first three domains. -jobconf stream.num.map.ou

Hadoop is implemented in Java, but we can also write MapReduce programs in other languages, such as Shell, Python, and Ruby. The following describes Hadoop Streaming and uses Python as an example. 1. Hadoop Streaming The usage of Hadoop Streaming is as follows: 1 hadoop jar hadoop-streaming.jar -D property=value -mapper mapper.py -combiner combiner.py -reducer reducer.py -input Input -output Output -file mapper.py -file reducer.py -Mapper-

executes the processing program on the node, improving the efficiency. This chapter mainly introduces the mapreduce programming model and distributed file system.. 2.1SectionIntroduce functional programming FP (functional programming), which is inspired by mapreduce design; 2.2SectionDescribes the basic programming models of Mapper, reducer, and mapreduce; 2.3SectionDiscusses the role of the execution framework in executing mapreduce programs (jobs; 2.4SectionPartitioner and

class, and then publishes kV pairs in the form of In the preceding input, the first map will output: Second map output: In this article, we will go deep into the large number of map outputs in this task and study how to control the output in a more fine-grained manner. Wordcount specifies the combiner in row 46. Therefore, after the output of each map is sorted by key, the local combiner (consistent wit

controlled by user-defined partition functions. The default partition Er (partitioner) Partitions through the hash function. The data flow between a map task and a reduce task is called Shuffle). If there is no reduce task, there may also be no need to execute reduce tasks, that is, data can be completely parallel. Combiner (Merge function) By the way, combiner. When hadoop runs a user, it specifies

buffer is full, Map will be blocked and the direct path spill will be completed. Before writing the data in the buffer to a disk, the spill thread sorts the data in a secondary order. The spill thread sorts the data in the partition order first, and then sorts the data in each partition by the Key. The output includes an index file and data file. If Combiner is set, the output is sorted Based on the output. Combi