data.table new column := slower than base R (?)

[szilard]

While data.table new column := used to be >100x faster than base R, base R (>=3.1) updates now data.frames in place and caught up. I wonder why data.table is slower for example in this case:

library(data.table)

dt <- data.table(x = runif(100e6))
df <- as.data.frame(dt)

system.time( df$y <- 2*df$x )
system.time( dt[,y := 2*x] )

I get:

base R: 0.272 0.300 0.572 (user system elapsed)
data.table: 0.696 0.744 1.444

R 3.1.1 data.table 1.9.4.

[arunsrinivasan]

@szilard thanks! R3.1 doesn't update in-place. It shallow copies other columns and adds the new column..

As to why this happens in data.table, just ran a debugonce() on [.data.table. The issue comes down to the same for-loop as shown in #727. This change seems to have happened during the by=.EACHI implementation.

[szilard]

Thanks @arunsrinivasan!

Yes, I meant base R >=3.1 is not copying the existing columns. So df$y <- 2*df$x takes now approx the same as y <- 2*df$x.

I wonder if data.table can be made faster. First, I increase a bit the data size:

library(data.table)

dt <- data.table(x = runif(500e6))
df <- as.data.frame(dt)

system.time( df$y <- 2*df$x )
system.time( dt[,y := 2*x] )

I get now ~ 2 sec for base R and ~ 5 sec for data.table.

Then I clean up (dt[,y := NULL] which takes suprisingly ~ 3 sec), and finally I run this:

Rprof()
invisible(dt[,y := 2*x])
Rprof(NULL)
summaryRprof()

and get this:

$by.self
          self.time self.pct total.time total.pct
".Call"        2.10    40.70       2.10     40.70
"*"            2.02    39.15       2.02     39.15
"seq_len"      1.04    20.16       1.04     20.16

$by.total
               total.time total.pct self.time self.pct
"["                  5.16    100.00      0.00     0.00
"[.data.table"       5.16    100.00      0.00     0.00
".Call"              2.10     40.70      2.10    40.70
"copy"               2.10     40.70      0.00     0.00
"*"                  2.02     39.15      2.02    39.15
"eval"               2.02     39.15      0.00     0.00
"seq_len"            1.04     20.16      1.04    20.16

$sample.interval
[1] 0.02

$sampling.time
[1] 5.16

Any potential sources for speed-up?

[arunsrinivasan]

I've already linked to the other issue, where I've listed what I think is the cause - there's an unwanted copy in this case. Maybe something else in addition as well, from the timings you show. It's very much fixable. Thanks again.

[szilard]

Awesome, thanks (I've seen the other issue, just was not obvious for me it's an easy fix). Also wondering about the speedup, but I guess ~ 2 sec in this case (see profile above).

What about the seq_len? That's another ~ 1 sec, there must be a seq_len(500e6) call which takes precisely that much (~ 1 sec)? Is that avoidable?

(With those 2 changes now it would be on par with base R ~ 2 sec total.)

[arunsrinivasan]

Right. Don't really understand why there's a seq_len as well.

[szilard]

I think the seq_len call that takes ~ 1 sec is this SDenv$.I = seq_len(SDenv$.N) here https://github.com/Rdatatable/data.table/blob/master/R/data.table.R#L1075

[mattdowle]

Thanks a lot. Will take a look.
We could really do with a timing regression framework! Like Python's vbench.

[alexcpsec]

I can also confirm a lot of performance impact by migrating to 1.9.4.

Is there a proposed timeframe for this bugfix? I suppose I could revert back to 1.9.2 for the time being.

[arunsrinivasan]

@alexcpsec performance impact with respect to just :=? If something else, it'd be great to have a new post with an example.

[alexcpsec]

It is a very convoluted function I have that makes heavy use of := where I had a significant hold-up.

I could try to Rprofile the whole thing with both 1.9.2 and 1.9.4 versions so I can give you a better idea of how the versions compare to each other in a larger piece of code. Would this be useful for you?

[arunsrinivasan]

@alexcpsec, yes, that would be incredibly useful, in the absence of a reproducible example. Thanks.

[arunsrinivasan]

Now I get this:

# clean session
library(data.table)
dt = data.table(x = runif(100e6))
system.time(dt[,y := 2*x])
#  user  system elapsed
#  0.384   0.563   0.956

df = data.frame(x = runif(100e6))
system.time( df$y <- 2*df$x )
#  user  system elapsed
#  0.376   0.554   0.933

The timings are more or less the same, and we can't avoid a copy here. But there are cases where we can delay the copy like R v3.1.0+ does, by shallow copying. That'll be taken care of in #617.