r,bash,shell,unix,terminal , Why is Unix/Terminal faster than R?


Why is Unix/Terminal faster than R?

Question:

Tag: r,bash,shell,unix,terminal

I'm new to Unix, however, I have recently realized that very simple Unix commands can do very simple things to large data set very very quickly. My question is why are these Unix commands so fast relative to R?

Let's begin by assuming that the data is big, but not larger than the amount of RAM on your computer.

Computationally, I understand that Unix commands are likely faster than their R counterparts. However, I can't imagine that this would explain the entire time difference. After all basic R functions, like Unix commands, are written in low-level languages like C/C++.

I therefore suspect that the speed gains have to do with I/O. While I only have a basic understanding of how computers work, I do understand that to manipulate data it most first be read from disk (assuming the data is local). This is slow. However, regardless of whether you use R functions or Unix commands to manipulate data both most obtain the data from disk.

Therefore I suspect that how data is read from disk, if that even makes sense, is what is driving the time difference. Is that intuition correct?

Thanks!

UPDATE: Sorry for being vague. This was done on purpose, I was hoping to discuss this idea in general, rather than focus on a specific example.

Regardless, I'll generate an example of counting the number of rows

First I'll generate a big data set.

row = 1e7
col = 50
df<-matrix(rpois(row*col,1),row,col)
write.csv(df,"df.csv")

Doing it with Unix

time wc -l df.csv
real    0m12.261s
user    0m1.668s
sys     0m2.589s

Doing it with R

library(data.table)
system.time({ nrow(fread("df.csv")) })
...
user   system   elapsed
26.77    1.67     47.07

Notice that elapsed/real > user + system. This suggests that the CPU is waiting on the disk.

I suspected the slow speed of R has to do with reading the data in. It appears that I'm right:

system.time(fread("df.csv"))
user  system  elapsed
34.69   2.81    47.41

My question is how is the I/O different for Unix and R. Why?


Answer:

I'm not sure what operations you're talking about, but in general, more complex processing systems like R use more complex internal data structures to represent the data being manipulated, and constructing these data structures can be a big bottleneck, significantly slower than the simple lines, words, and characters that Unix commands like grep tend to operate on.

Another factor (depending on how your scripts are set up) is whether you're processing the data one thing at a time, in "streaming mode", or reading everything into memory. Unix commands tend to be written to operate in pipelines, and to read a small piece of data (usually one line), process it, maybe write out a result, and move on to the next line. If, on the other hand, you read the entire data set into memory before processing it, then even if you do have enough RAM, allocating and organizing all the necessary memory can be very expensive.

[updated in response to your additional information]

Aha. So you were asking R to read the whole file into memory at once. That accounts for much of the difference. Let's talk about a few more things.

I/O. We can think about three ways of reading characters from a file, especially if the style of processing we're doing affects the way that's most convenient to do the reading.

  1. Unbuffered small, random reads. We ask the operating system for 1 or a few characters at a time, and process them as we read them.
  2. Unbuffered large, block-sized reads. We ask the operating for big chunks of memory -- usually of a size like 1k or 8k -- and chew on each chunk in memory before asking for the next chunk.
  3. Buffered reads. Our programming language gives us a way of asking for as many characters as we want out of an intermediate buffer, and code that's built into the language ("library" code) automatically takes care of keeping that buffer full by reading large, block-sized chunks from the operating system.

Now, the important thing to know is that the operating system would much rather read big, block-sized chunks. So #1 can be drastically slower than 2 and 3. (I've seen factors of 10 or 100.) But no well-written programs use #1, so we can pretty much forget about it. As long as you're using 2 or 3, the I/O speed will be roughly the same. (In extreme cases, if you know what you're doing, you can get a little efficiency increase by using 2 instead of 3, if you can.)

Now let's talk about the way each program processes the data. wc has basically 5 steps:

  1. Read characters one at a time. (I can assure you it uses method 3.)
  2. For each character read, add one to the character count.
  3. If the character read was a newline, add one to the line count.
  4. If the character read was or wasn't a word-separator character, update the word count.
  5. At the very end, print out the counts of lines, words, and/or characters, as requested.

So as you can see it's all I/O and very simple, character-based processing. (The only step that's at all complicated is 4. As an exercise, I once wrote a version of wc that contrived not to do all of steps 2, 3, and 4 inside the read loop if the user didn't ask for all the counts. My version did indeed run significantly faster if you invoked wc -c or wc -l. But obviously the code was significantly more complicated.)

In the case of R, on the other hand, things are quite a bit more complicated. First, you told it to read a CSV file. So as it reads, it has to find the newlines separating lines and the commas separating columns. That's roughly equivalent to the processing that wc has to do. But then, for each number that it finds, it has to convert it into an internal number that it can work with efficiently. For example, if somewhere in the CSV file occurs the sequence

...,12345,...

R is going to have to read those digits (as individual characters) and then do the equivalent of the math problem

 1 * 10000 + 2 * 1000 + 3 * 100 + 4 * 10 + 5 * 1

to get the value 12345.

But there's more. You asked R to build a table. A table is a specific, highly regular data structure which orders all the data into rigid rows and columns for efficient lookup. To see how much work that can be, let's use a slightly far-fetched hypothetical real-world example.

Suppose you're a survey company and it's your job to ask people walking by on the street certain questions. But suppose that the questions are complicated enough that you need all the people seated in a classroom at once. (Suppose further that the people don't mind this inconvenience.)

But first you have to build that classroom. You're not sure how many people are going to walk by, so you build an ordinary classroom, with room for 5 rows of 6 desks for 30 people, and you haul in the desks, and the people start filing in, and after 30 people file in you notice there's a 31st, so what do you do? You could ask him to stand in the back, but you're kind of fixated on the rigid-rows-and-columns idea, so you ask the 31st person to wait, and you quickly call the builders and ask them to build a second 30-person classroom right next to the first, and now you can accept the 31st person and in fact 29 more for a total of 60, but then you notice a 61st person.

So you ask him to wait, and you call the builders back again, and you have them build two more classrooms, so now you've got a nice 2x2 grid of 30-person classrooms, but the people keep coming and soon enough the 121st person shows up and there's not enough room and you still haven't even started asking your survey questions yet.

So you call some fancier builders that know how to do steelwork and you have them build a big 5-story building next door with 50-person classrooms, 5 on each floor, for a total of 50 x 5 x 5 = 1,250 desks, and you have the first 120 people (who've been waiting patiently) file out of the old rooms into the new building, and now there's room for the 121st person and quite a few more behind him, and you hire some wreckers to demolish the old classrooms and recycle some of the materials, and the people keep coming and pretty sure there's 1,250 people in your new building waiting to be surveyed and the 1,251st has just showed up.

So you build a giant new skyscraper with 1,000 desks on each floor and 100 floors, and you demolish the old 5-story building, but the people keep coming, and how big did you say your big data set was? 1e7 x 50? So I don't think the 100-story building is going to be big enough, either. (And when you're all done with all this, the only "survey question" you're going to ask is "How many rows are there?")

Contrived as it may seem, this is actually not too bad an analogy for what R is having to do internally to build the table to store that data set in.

Meanwhile, Bob's discount survey company, who can only tell you how many people he surveyed and how many were men and women and in which age brackets, is down there on the streetcorner, and the people are filing by, and Bob is jotting down tally marks on his clipboards, and the people, once surveyed, are walking away and going about their business, and Bob isn't wasting time and money building any classrooms at all.

I don't know anything about R, but see if there's a way to construct an empty 1e7 x 50 matrix up front, and read the CSV file into it. You might find that significantly quicker. R will still have to do some building, but at least it won't have any false starts.


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