Reason behind speed of fread in data.table package in R

Question

I am amazed by the speed of the fread function in data.table on large data files but how does it manages to read so fast? What are the basic implementation differences between fread and read.csv?

Interoceptor

Answer 1

I assume we are comparing to read.csv with all known advice applied such as setting colClasses, nrows etc. read.csv(filename) without any other arguments is slow mainly because it first reads everything into memory as if it were character and then attempts to coerce that to integer or numeric as a second step.

So, comparing fread to read.csv(filename, colClasses=, nrows=, etc) ...

They are both written in C so it's not that.

There isn't one reason in particular, but essentially, fread memory maps the file into memory and then iterates through the file using pointers. Whereas read.csv reads the file into a buffer via a connection.

If you run fread with verbose=TRUE it will tell you how it works and report the time spent in each of the steps. For example, notice that it skips straight to the middle and the end of the file to make a much better guess of the column types (although in this case the top 5 were enough).

> fread("test.csv",verbose=TRUE)
Input contains no \n. Taking this to be a filename to open
File opened, filesize is 0.486 GB
File is opened and mapped ok
Detected eol as \n only (no \r afterwards), the UNIX and Mac standard.
Using line 30 to detect sep (the last non blank line in the first 'autostart') ... sep=','
Found 6 columns
First row with 6 fields occurs on line 1 (either column names or first row of data)
All the fields on line 1 are character fields. Treating as the column names.
Count of eol after first data row: 10000001
Subtracted 1 for last eol and any trailing empty lines, leaving 10000000 data rows
Type codes (   first 5 rows): 113431
Type codes (+ middle 5 rows): 113431
Type codes (+   last 5 rows): 113431
Type codes: 113431 (after applying colClasses and integer64)
Type codes: 113431 (after applying drop or select (if supplied)
Allocating 6 column slots (6 - 0 dropped)
Read 10000000 rows and 6 (of 6) columns from 0.486 GB file in 00:00:44
  13.420s ( 31%) Memory map (rerun may be quicker)
   0.000s (  0%) sep and header detection
   3.210s (  7%) Count rows (wc -l)
   0.000s (  0%) Column type detection (first, middle and last 5 rows)
   1.310s (  3%) Allocation of 10000000x6 result (xMB) in RAM
  25.580s ( 59%) Reading data
   0.000s (  0%) Allocation for type bumps (if any), including gc time if triggered
   0.000s (  0%) Coercing data already read in type bumps (if any)
   0.040s (  0%) Changing na.strings to NA
  43.560s        Total

NB: these timings on my very slow netbook with no SSD. Both the absolute and relative times of each step will vary widely from machine to machine. For example if you rerun fread a second time you may notice the time to mmap is much less because your OS has cached it from the previous run.

$ lscpu
Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                2
On-line CPU(s) list:   0,1
Thread(s) per core:    1
Core(s) per socket:    2
Socket(s):             1
NUMA node(s):          1
Vendor ID:             AuthenticAMD
CPU family:            20
Model:                 2
Stepping:              0
CPU MHz:               800.000         # i.e. my slow netbook
BogoMIPS:              1995.01
Virtualisation:        AMD-V
L1d cache:             32K
L1i cache:             32K
L2 cache:              512K
NUMA node0 CPU(s):     0,1

Meitner

Also read.csv() reads everything into a big character matrix and then modifies that, does fread() do the same thing? In fastread we guess column types and then coerce as we go to avoid a complete copy of the df. – Cytosine

@Cytosine No. Why are you suggesting it might? What's fastread? – Meitner

@Cytosine Ok I see your repo. Why are you doing that? – Meitner

Seemed like an obvious way (to us!) to reduce memory usage - i.e. instead of creating complete character vector then coercing to numeric vector, coerce as you go. – Cytosine

@Cytosine Why do you write "(to us!)"? What are you saying creates a complete character vector? Are are claiming fread doesn't coerce as it goes? – Meitner

Obvious to us != obvious to everyone != correct. I'm not suggesting anything about fread(). – Cytosine