combiners

问题 This question already has answers here : Java8 stream.reduce() with 3 parameters - getting transparency (2 answers) Closed 3 years ago . I am using a simple reduce method with three arguments viz. identity, accumulator and combiner. Here is my code... Integer ageSumComb = persons .stream() .reduce(0, (sum, p) -> { System.out.println("Accumulator: Sum= "+ sum + " Person= " + p); return sum += p.age; }, (sum1, sum2) -> { System.out.format("Combiner: Sum1= " + sum1 + " Sum2= "+ sum2); return

问题 I'm making a Hadoop application in Java with the MapReduce framework. I use only Text keys and values for both input and output. I use a combiner to do an extra step of computations before reducing to the final output. But I have the problem that the keys do not go to the same reducer. I create and add the key/value pair like this in the combiner: public static class Step4Combiner extends Reducer<Text,Text,Text,Text> { private static Text key0 = new Text(); private static Text key1 = new Text

问题 There are notice about what how cascading/scalding optimized map-side evaluation They use so called Partial Aggregation. Is it actually better approach then Combiners? Are there any performance comparison on some common hadoop tasks(word count for example)? If so wether hadoop will support this in future? 回答1: In practice, there are more benefits from partial aggregation than from use of combiners. The cases where combiners are useful are limited. Also, combiners optimize the amount of

问题 Sample of 2 (made-up) example rows in df: userid facultyid courseid schoolid 167 265 NA 1678 167 71111 301 NA Suppose that I have a couple hundred duplicate userid like in the above example. However, the vast majority of userid have different values. How can I combine rows with duplicate userid in such a way as to stick to the column values in the 1st (of the 2) row unless the first value is NA (in which case the NA will be repopulated with whatever value came from the second row)? In essence

问题 In many MapReduce programs, I see a reducer being used as a combiner as well. I know this is because of the specific nature of those programs. But I am wondering if they can be different. 回答1: Yes, a combiner can be different to the Reducer, although your Combiner will still be implementing the Reducer interface. Combiners can only be used in specific cases which are going to be job dependent. The Combiner will operate like a Reducer, but only on the subset of the Key/Values output from each

问题 A Combiner runs after the Mapper and before the Reducer,it will receive as input all data emitted by the Mapper instances on a given node. then emits output to the Reducers. And also,If a reduce function is both commutative and associative , then it can be used as a Combiner. My Question is what does the phrase " commutative and associative " mean in this situation? 回答1: Assume you have a list of numbers, 1 2 3 4 5 6. Associative here means you can take your operation and apply it to any

问题 Last week I posted the following question . The idea was to make a loop that determined the content of a database by randomly combining observations based on the variable "id". For instance: dataset 1: combinations of id 1, 2, 3, 4, 5, 6, 7, 8... dataset 2: combinations of id 1, 2, 3 dataset 3: combinations of id 2, 3, 4, 5 dataset 4: combinations of id 5, 6, 7, 8, 9, 10... I got a perfect answer to the question: for(i in 2:max(o$id)){ combis=combn(unique(o$id),i) for(j in 1:ncol(combis)){

问题 I'm having a bit of an issue. Lets say I have 2 text boxes, one on the left with this content: Win Lose Hello Goodbye And one on the right, with this information: One Two Three Four Now, on button press, I want to combine these two text boxes with colon delimitation, so it would output like this: Win:One Lose:Two Hello:Three Goodbye:Four Any idea how I can accomplish this? Nothing I have tried thus far has worked. This is my current code, sorry. I'm not trying to have you do my work for me, I

问题 This question already has answers here : Combine column to remove NA's (10 answers) Closed 3 years ago . I have a data frame data <- data.frame('a' = c('A','B','C','D','E'), 'x' = c(1,2,NA,NA,NA), 'y' = c(NA,NA,3,NA,NA), 'z' = c(NA,NA,NA,4,NA)) It looks like this: a x y z 1 A 1 NA NA 2 B 2 NA NA 3 C NA 3 NA 4 D NA NA 4 5 E NA NA NA I expect to get a data like this: a N 1 A 1 2 B 2 3 C 3 4 D 4 5 E NA Thank you! 回答1: A dplyr solution using coalesce . library(dplyr) data %>% mutate(N = coalesce

问题 I'm getting confused after reading below article on Hadoop- Definitive guide 4th edition(page-204) Before it writes to disk, the thread first divides the data into partitions corresponding to the reducers that they will ultimately be sent to. Within each partition, the background thread performs an in-memory sort by key, and if there is a combiner function, it is run on the output of the sort. Running the combiner function makes for a more compact map output, so there is less data to write to