8000 [R-Forge #5297] Speed in rolling joins · Issue #538 · Rdatatable/data.table · GitHub
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[R-Forge #5297] Speed in rolling joins #538

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arunsrinivasan opened this issue Jun 8, 2014 · 1 comment
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[R-Forge #5297] Speed in rolling joins #538

arunsrinivasan opened this issue Jun 8, 2014 · 1 comment
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@mattdowle
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feature request
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v1.9.4

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@arunsrinivasan
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Submitted by: Michele Carriero; Assigned to: Nobody; R-Forge link

Hello,

is the following difference in time expected or is possible to decrease it?

date<-seq.Date(as.Date("2010-01-01"), as.Date("2014-01-01"), "day")
dt1 <- data.table(date=date,
                  var1=rnorm(length(date)),
                  var2=rnorm(length(date)),
                  var3=rnorm(length(date)),
                  key="date")

date<-seq.Date(as.Date("2010-01-01"), as.Date("2014-01-01"), "month")
dt2 <- data.table(date=date,
                  var4=rnorm(length(date),100),
                  key="date")

> microbenchmark(dt2[dt1, roll=T][, list(date, var1, var2, var4)],dt2[dt1, list(var1, var2, var4), roll=T])
Unit: milliseconds
                                               expr      min       lq   median       uq      max neval
 dt2[dt1, roll = T][, list(date, var1, var2, var4)] 1.564117 1.610941 1.646219 1.689355 2.227991   100
         dt2[dt1, list(var1, var2, var4), roll = T] 2.811368 2.943662 3.033141 3.151162 3.453434   100

The expressions are "equal", using all.equal(,check.attributes=F). In this example it's just twice slower but it gets larger as the tables increase in size.
@arunsrinivasan arunsrinivasan added this to the v1.9.4 milestone Jun 19, 2014
@arunsrinivasan
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By implementing FR #371 in commit 2679047 by Matt, this issue has been automatically fixed. Here's the microbenchmark timings now:

Unit: milliseconds
                                               expr      min       lq   median
 dt2[dt1, roll = T][, list(date, var1, var2, var4)] 3.810468 3.953549 4.176619
         dt2[dt1, list(var1, var2, var4), roll = T] 1.958930 2.043809 2.108974
       uq      max neval
 4.663930 8.126353   100
 2.311414 9.342197   100

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