chunk.apply

Rで学ぶスケーラブルなデータ処理

Simon Urbanek

Member of R-Core, Lead Inventive Scientist, AT&T Labs Research

chunk.apply()

ループ処理を抽象化する
並列実行が可能
iotools は hmr の基盤であり、Apache Hadoop インフラ上でデータ処理ができる

mstrsplit() でチャンクを行列として読み込む

# Use chunk.apply to get chunks of rows from foo.csv
chunk_col_sums <- chunk.apply("foo.csv",

 # A function to process each of the chunk
 function(chunk) {
   # Turn the chunk into a matrix
   m <- mstrsplit(chunk, type = "numeric", sep = ",")
   # Return the column sums
   colSums(m)
 }, 
 # Maximum chunk size in bytes
 CH.MAX.SIZE = 1e5)

# Get the total sum
colSums(chunk_col_sums)

dstrsplit() でチャンクをデータフレームとして読み込む

# Use chunk.apply to get chunks of rows from foo.csv
chunk_col_sums <- chunk.apply("foo.csv",

 # A function to process each of the chunk
 function(chunk) {
   # Turn the chunk into a data frame
   d <- dstrsplit(chunk, col_types = rep("numeric", 3), sep = ",")
   # Return the column sums
   colSums(d)
 }, 
 # Maximum chunk size in bytes
 CH.MAX.SIZE = 1e5)

# Get the total sum
colSums(chunk_col_sums)

chunk.apply() の並列化

# Use chunk.apply to get chunks of rows from foo.csv
chunk_col_sums <- chunk.apply("foo.csv",

 # A function to process each of the chunk
 function(chunk) {

   # Turn the chunk into a data frame
   d <- dstrsplit(chunk, col_types = rep("numeric", 3), sep = ",")
   colSums(d)
 }, 
 # 2 processors read and process data
 CH.PARALLEL = 2)

# Get the total sum
colSums(chunk_col_sums)

並列化に関する注意点

プロセッサ数を増やしても、必ずしも処理が速くなるわけではない
単一マシン上でプロセッサを追加しても、収穫逓減が生じることが多い

練習しましょう！

Rで学ぶスケーラブルなデータ処理