hive (version 0.1-13)

hive_stream: Hadoop Streaming with package hive

Description

High-level R function for using Hadoop Streaming.

Usage

hive_stream( mapper, reducer, input, output, henv = hive(),
             mapper_args = NULL, reducer_args = NULL, cmdenv_arg = NULL,
streaming_args = NULL)

Arguments

mapper

a function which is executed on each worker node. The so-called mapper typically maps input key/value pairs to a set of intermediate key/value pairs.

reducer

a function which is executed on each worker node. The so-called reducer reduces a set of intermediate values which share a key to a smaller set of values. If no reducer is used leave empty.

input

specifies the directory holding the data in the DFS.

output

specifies the output directory in the DFS containing the results after the streaming job finished.

henv

Hadoop local environment.

mapper_args

additional arguments to the mapper.

reducer_args

additional arguments to the reducer.

cmdenv_arg

additional arguments passed as environment variables to distributed tasks.

streaming_args

additional arguments passed to the Hadoop Streaming utility. By default, only the number of reducers will be set using "-D mapred.reduce.tasks=".

Details

The function hive_stream() starts a MapReduce job on the given data located on the HDFS.

References

Apache Hadoop Streaming (http://hadoop.apache.org/common/docs/current/streaming.html).

Examples

## A simple word count example

## Put some xml files on the HDFS:
DFS_put(system.file("defaults/core/", package = "hive"), "/tmp/input")
DFS_put(system.file("defaults/hdfs/hdfs-default.xml", package = "hive"), "/tmp/input")
DFS_put(system.file("defaults/mapred/mapred-default.xml", package = "hive"), "/tmp/input")
## Define the mapper and reducer function to be applied:
## Note that a Hadoop map or reduce job retrieves data line by line from stdin.
mapper <- function(x){
    con <- file( "stdin", open = "r" )
    while (length(line <- readLines(con, n = 1L, warn = FALSE)) > 0) {
        terms <- unlist(strsplit(line, " "))
        terms <- terms[nchar(terms) > 1 ]
        if( length(terms) )
            cat( paste(terms, 1, sep = "\t"), sep = "\n")
    }
}
reducer <- function(x){
    env <- new.env( hash = TRUE )
    con <- file( "stdin", open = "r" )
    while (length(line <- readLines(con, n = 1L, warn = FALSE)) > 0) {
        keyvalue <- unlist( strsplit(line, "\t") )
        if( exists(keyvalue[1], envir = env, inherits = FALSE) ){
            assign( keyvalue[1], get(keyvalue[1], envir = env) + as.integer(keyvalue[2]), envir = env )
        } else {
            assign( keyvalue[1], as.integer(keyvalue[2]), envir = env )
        }
    }
    env <- as.list(env)
    for( term in names(env) )
        writeLines( paste(c(term, env[[term]]), collapse ="\t") )
}
hive_set_nreducer(1)
hive_stream( mapper = mapper, reducer = reducer, input = "/tmp/input", output = "/tmp/output" )
DFS_list("/tmp/output")
head( DFS_read_lines("/tmp/output/part-00000") )
## Don't forget to clean file system
DFS_dir_remove("/tmp/input")
DFS_dir_remove("/tmp/output")