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mapreduce using NIO on HDFS


mapreduce using NIO on HDFS

By : Heba Hamdan
Date : November 21 2020, 07:31 AM
this will help
I would love to use ByteBuffer and file locks (NIO). is this possible in this framework ?
code :
part-r-00001
part-r-00002
part-r-..n..


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How to input HDFS file into R mapreduce for processing and get the result into HDFS file

How to input HDFS file into R mapreduce for processing and get the result into HDFS file


By : user2158160
Date : March 29 2020, 07:55 AM
Does that help I have a question similar to the below link in stackoverflow , Basically found a solution to the problem that I stated above.
code :
r.file <- hdfs.file(hdfsFilePath,"r")
from.dfs(
    mapreduce(
         input = as.matrix(hdfs.read.text.file(r.file)),
         input.format = "csv",
         map = ...
))
transfer.csvfile.hdfs.to.hdfs.reduced =
                function(hdfsFilePath, hdfsWritePath, reducedCols=1) {
                        hdfs.init()
                        #local.df = data.frame()
                        #hdfs.get(hdfsFilePath, local.df)
                        #to.dfs(local.df)
                        r.file <- hdfs.file(hdfsFilePath,"r")
                        transfer.reduced.map =
                                        function(.,M) {
                                                numRows <- length(M)
                                                M.vec.elems <-unlist(lapply(M,
                                                                                function(x) strsplit(x, ",")))
                                                M.matrix <- matrix(M.vec.elems, nrow=numRows, byrow=TRUE)
                                                label <- M.matrix[,dim(M.matrix)[2]]
                                                reduced.predictors <- M.matrix[,1:reducedCols]
                                                reduced.M <- cbind(reduced.predictors, label)
                                                keyval(
                                                     1,
                                                     as.numeric(reduced.M))
                                        }
                        reduced.values =
                             values(
                                     from.dfs(
                                        mapreduce(
                                          input = as.matrix(hdfs.read.text.file(r.file)),
                                          input.format = "csv",
                                          map = function(.,M) {
                                                numRows <- length(M)
                                                M.vec.elems <-unlist(lapply(M,
                                                       function(x) strsplit(x, ",")))
                                                M.matrix <- matrix(M.vec.elems, nrow=numRows, byrow=TRUE)
                                                label <- M.matrix[,dim(M.matrix)[2]]
                                                reduced.predictors <- M.matrix[,1:reducedCols]
                                                reduced.M <- cbind(reduced.predictors, label)
                                                keyval(
                                                     1,
                                                     as.numeric(reduced.M)) }
                        )))
                        write.table(reduced.values, file="/root/somnath/reduced.values.csv")
                        w.file <- hdfs.file(hdfsWritePath,"w")
                        hdfs.write(reduced.values,w.file)
                        hdfs.close(r.file)
                        hdfs.close(w.file)
                        #to.dfs(reduced.values)
                }
Does MapReduce need to be use with HDFS

Does MapReduce need to be use with HDFS


By : Mayur
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Hadoop is a framework which includes Map Reduce programming model for computation and HDFS for storage.
HDFS stands for hadoop distributed file system which is inspired from Google File System. The overall Hadoop project is inspired based on the research paper published by Google.
Mapreduce logs - File vs HDFS

Mapreduce logs - File vs HDFS


By : Tejashri
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , FILE - gives the amount of I/O performed on intermediate files, which are maintained internally between map and reduce phase (sort & shuffle phase)
HDFS - Amount of data read by mapper and data written by reducer.
Why is the final reduce step extremely slow in this MapReduce? (HiveQL, HDFS MapReduce)

Why is the final reduce step extremely slow in this MapReduce? (HiveQL, HDFS MapReduce)


By : Dan
Date : March 29 2020, 07:55 AM
wish help you to fix your issue If final reducer is a join then it looks like skew in join key. First of all check two things:
check that b.f1 join key has no duplicates:
code :
select b.f1, count(*) cnt from B b 
 group by b.f1 
having count(*)>1 order by cnt desc;
select a.f1, count(*) cnt from A a
 group by a.f1  
order by cnt desc
limit 10;
SELECT a.f1, f2, ..., fn
  FROM ( select * from A where f1 = skewed_value) as a --skewed
  LEFT JOIN B as b
  ON a.f1 = b.f1
WHERE {PARTITION_FILTER}
UNION ALL
SELECT a.f1, f2, ..., fn
  FROM ( select * from A where f1 != skewed_value) as a --all other
  LEFT JOIN B as b
  ON a.f1 = b.f1
WHERE {PARTITION_FILTER}
set hive.exec.reducers.bytes.per.reducer=67108864;
How can I directly calculate the magnetlink of a file on hdfs through hdfs MapReduce?

How can I directly calculate the magnetlink of a file on hdfs through hdfs MapReduce?


By : Vamsi
Date : March 29 2020, 07:55 AM
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