catv combiner

process has a very big impact on the total time of the job operation, the general MapReduce tuning is mainly to adjust the parameters of the shuffle stage.such as: Data flow for multiple reduce tasksIv. How to reduce the amount of data from map to reduceThe available bandwidth on the cluster limits the number of MapReduce jobs because the intermediate results of the map are passed to reduce for transmission over the network, so the most important point is to minimize the amount of data transfe

jobconf, and a combiner class in some applications, it is also the implementation of reducer. 2.1.2 jobtracker and tasktracker They are all scheduled by one master service jobtracker and multiple slaver service tasktracker running on multiple nodes. The master is responsible for scheduling each sub-task of a job on slave, and monitoring them. If a failed task is found, the master re-runs it. Slave is responsible for directly executing each task. Task

check is done here. If it is not a native type (that is, it complies with the type, array, map class), an exception is thrown, and Operator Overloading is also implemented. For integer types, use genericudafsumlong to implement the UDAF logic. For floating point types, use genericudafsumdouble to implement the UDAF logic. Implement Evaluator AllEvaluators must inherit from the abstract class org. Apache. hadoop. hive. QL. UDF. Generic. genericudafevaluator. Subclass must implement some o

The sum process and the product process that we have completed before are described in section 1.32, indicating that they are special cases of the Process named accumulate. Conversely, we need to abstract the sum and product processes to form a more general and general process. We have discussed in the problem-solving summary in exercise 1.31. In fact, the sum process differs a little from the product process, that is, the cumulative operations are different, and the initialization values are

is directly output to the multipleer without the video server. C. Video Server The video server uses a large array disk to store audio and video streaming files, effectively manage the program editing process and material library, prepare program series orders, and broadcast multiple programs through the broadcast control platform. 　　　　 D. DTS The multi-channel MPEG-2 data transmission stream (SP, single-program TS) from the video server and real-time Encoding Equipment is reused as one-way t

, thousands of households are connected to catv TV stations on the same axis of copper wires. Recently, Internet connections have been started and used in local telephones. The higher the speed, the more channels, and two-way communication has been started. It is difficult to transmit high-frequency electrical signals from the same axis over a long distance. Therefore, to increase the communication distance, a large number of amplifiers must be used,

. DMT divides available bandwidth into N sub-channels. Based on the data transmission capability of sub-channels, the data is dynamically allocated to each sub-channel, greatly improving the bandwidth utilization and minimizing the error code and noise, this increases the system transmission capacity. In addition, ADSL uses the New Adaptive Filtering Technology, grid encoding, and the forward correction of intertwined methods to overcome Gaussian noise interference and increase the channel capac

, and DMT modulation is a form of multi-carrier modulation. DMT divides available bandwidth into N sub-channels. Based on the data transmission capability of sub-channels, the data is dynamically allocated to each sub-channel, greatly improving the bandwidth utilization and minimizing the error code and noise, this increases the system transmission capacity. In addition, ADSL uses the New Adaptive Filtering Technology, grid encoding, and the forward correction of intertwined methods to overcome

narrowband services, combined with Cable Modem and other technologies, it can economically access user-side broadband services. This is one of the commercial broadband service access technologies for users, namely, FTTC + HFCs ). The goal of Broadband Service Access Technology for users is to achieve FTTH at home through optical fiber, combined with the networking technology of PON in Passive Optical Networks and transmission technologies such as ATM and sub-carrier multiplexing SCM) and Dense

, uplink using QPSK modulation. Using these techniques, the downlink rate of the asymmetric cable modem system can be as high as 30Mbps and the uplink speed can reach 2.56Mbps. It includes a plurality of cable digital modems Cablelink cm2000d and one or more cable modem front-end system cablelinkhs2000d on the front end of the CATV. The hs2000d includes an RF modulation and demodulation module and a channel control module. The minimal configuration of

, thousands of households are connected to catv TV stations on the same axis of copper wires. Recently, Internet connections have been started and used in local telephones. The higher the speed, the more channels, and two-way communication has been started. It is difficult to transmit high-frequency electrical signals from the same axis over a long distance. Therefore, to increase the communication distance, a large number of amplifiers must be used,

achieve our original goal, because the map output will become a.txt-> words. Words.. WordsThis is obviously not the result we want.So the format of the map output should beText 1 with single wordSuch as:Hello->a.txt 1This is used here as a separation between the word and the text where it residesThis will not affect our results when merging according to Key.The map code is as follows:public static class Mymapper extends MapperAfter map execution is completeWe need a

MapReduce design Pattern (mapreduce)The entire MapReduce operation stage can be divided into the following four types:1, Input-map-reduce-output2, Input-map-output3, Input-multiple Maps-reduce-output4, Input-map-combiner-reduce-outputI'll show you which design patterns to use in each scenario.Input-map-reduce-outputInput? Map? Reduce? OutputIf we need to do some aggregation operations (aggregation), we need to use this pattern. Scene

buffer ratio of start spill defaults to 0.80, which can be mapreduce.map.sort.spill.percent configured. While the background thread writes, map continues to write the output to this ring buffer, and if the buffer pool is full, the map blocks until the spill process completes without overwriting the existing data in the buffer pool.Before writing, the background thread divides the data according to the reducer that they will send to, and by invoking Partitioner the getPartition() method it knows

adjustment.Note: The result of the merge sort is two files, one is index and the other is a data file, and the index file records the offset of each different key in the data file (that is, partition).On the map node, if you find that the child node of the map is heavier than the machine IO, the reason may be io.sort.factor This setting is relatively small, io.sort.factor set smallWords, if the spill file is more, merge into a file for a lot of read operations, which increases the load of IO. I

and I/O. Sort. factor is increased, it is helpful to reduce the number of merge operations and the read/write frequency of map to the disk, which may achieve the goal of optimizing the job. When a job specifies a combiner, we all know that after the introduction of map, the map results will be merged on the map end based on the functions defined by combiner. The time to run the

learned earlier, collector functions must meet identity constraints and dependency constraints. When creating collectors based on Collector implementation simplification, such as Stream.collect (Collector), the following constraints must be observed: The first parameter is passed to the accumulator () function, and two parameters are passed to the combiner () function. The arguments passed to the Finisher () function must be the result of the last ca

components and relationships of the MAP/reduce framework.2.1 Overall Structure 2.1.1 Mapper and reducer The most basic components of mapreduce applications running on hadoop include a er and a reducer class, as well as an execution program for creating jobconf, and a combiner class in some applications, it is also the implementation of reducer.2.1.2 jobtracker and tasktracker They are all scheduled by one master service jobtracker and multiple slaver

is that simplicity prevails over everything. Why is there a very simple implementation that requires a complicated one. The reason is that, if it looks pretty, it often carries a thorn and a simple output implementation. Each time collect is called, a file is written. Frequent hard disk operations may lead to inefficiency of this solution. In order to solve this problem, this complicated version is available. It should first enable a piece of memory CacheAnd then create a ratio to do Threshold,

, according to the map output. Then, in each partition, press key to sort inside. If you have combiner action , it will be done on the output after sorting. When the above steps are complete, the overflow thread begins to write to the disk. Note : compressing the map output while writing a disk can not only speed up the write disk, save disk space, but also reduce the amount of data passed to reduce. The default is no compression, boot compression