FTOOLS: A faster Stata for large datasets
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usage
benchmarks
install
Introduction
Some of the most common Stata commands (collapse, merge, sort, etc.) are not designed for large datasets. This package provides alternative implementations that solves this problem, speeding up these commands by 3x-10x:
Other user commands that are very useful for speeding up Stata with large datasets include:
gtools
, a package similar toftools
but written in C. In most cases it's much faster than both ftools and the standard Stata commands, as shown in the graph above. Try it out!sumup
provides fast summary statistics, and includes thefasttabstat
command, a faster version oftabstat
.egenmisc
introduces the egen functionsfastxtile
,fastwpctile
, etc. that provide much faster alternatives toxtile
andpctile
. Also see thefastxtile
package, which provides similar functionality.randomtag
is a much faster alternative tosample
.reghdfe
provides a faster alternative toxtreg
andareg
, as well as multi-way clustering and IV regression.parallel
allows for easier parallel computing in Stata (useful when running simulations, reshaping, etc.)boottest
, for efficiently running wild bootstraps.- The
rangerun
,runby
andrangestat
commands are useful for running commands and collecting statistics on rolling windows of observations.
ftools
can also be used to speed up your own commands. For more information, see this presentation from the 2017 Stata Conference (slides 14 and 15 show how to create faster alternatives to unique
and xmiss
with only a couple lines of code). Also, see help ftools
for the detailed documentation.
Details
ftools is two things:
- A list of Stata commands optimized for large datasets, replacing commands such as: collapse, contract, merge, egen, sort, levelsof, etc.
- A Mata class (Factor) that focuses on working with categorical variables. This class is what makes the above commands fast, and is also what powers
reghdfe
Currently the following commands are implemented:
fegen group
replacingegen group
fcollapse
replacingcollapse
,contract
and most ofegen
(through the, merge
option)join
(and its wrapperfmerge
) replacingmerge
fisid
replacingisid
flevelsof
replacinglevelsof
fsort
replacingsort
(although it is rarely faster than sort)
Usage
* Stata usage:
sysuse auto
fsort turn
fegen id = group(turn trunk)
fcollapse (sum) price (mean) gear, by(turn foreign) freq
* Advanced: creating the .mlib library:
ftools, compile
* Mata usage:
sysuse auto, clear
mata: F = factor("turn")
mata: F.keys, F.counts
mata: sorted_price = F.sort(st_data(., "price"))
Other features include:
- Add your own functions to -fcollapse-
- View the levels of each variable with
mata: F.keys
- Embed -factor()- into your own Mata program. For this, you can
use
F.sort()
and the built-inpanelsubmatrix()
.
Benchmarks
(see the test folder for the details of the tests and benchmarks)
egen group
Given a dataset with 20 million obs. and 5 variables, we create the following variable, and create IDs based on that:
gen long x = ceil(uniform()*5000)
Then, we compare five different variants of egen group:
Method | Min | Avg |
---|---|---|
egen id = group(x) | 49.17 | 51.26 |
fegen id = group(x) | 1.44 | 1.53 |
fegen id = group(x), method(hash0) | 1.41 | 1.60 |
fegen id = group(x), method(hash1) | 8.87 | 9.35 |
fegen id = group(x), method(stata) | 34.73 | 35.43 |
Our variant takes roughly 3% of the time of egen group.
If we were to choose a more complex hash method, it would take 18% of the time.
We also report the most efficient method based in Stata (that uses bysort
),
which is still significantly slower than our Mata approach.
Notes:
- The gap is larger in systems with two or less cores, and smaller in systems with many cores (because our approach does not take much advantage of multicore)
- The gap is larger in datasets with more observations or variables.
- The gap is larger with fewer levels
collapse
On a dataset of similar size, we ran collapse (sum) y1-y15, by(x3)
where x3
takes 100 different values:
Method | Time | % of Collapse |
---|---|---|
collapse … , fast | 81.87 | 100% |
sumup | 56.18 | 69% |
fcollapse … , fast | 38.54 | 47% |
fcollapse … , fast pool(5) | 28.32 | 35% |
tab ... | 9.39 | 11% |
We can see that fcollapse
takes roughly a third of the time of collapse
(although it uses more memory when moving data from Stata to Mata).
As a comparison, tabulating the data (one of the most efficient Stata operations) takes 11% of the time of collapse
.
Alternatively, the pool(#)
option will use very little memory (similar to collapse
) at also very good speeds.
Notes:
- The gap is larger if you want to collapse fewer variables
- The gap is larger if you want to collapse to fewer levels
- The gap is larger for more complex stats. (such as median)
compress
ing the by() identifiers beforehand might lead to significant improvements in speed (by allowing the use of the internal hash0 function instead of hash1).- In a computer with less memory, it seems
pool(#)
might actually be faster.
collapse: alternative benchmark
We can run a more complex query, collapsing means and medians instead of sums, also with 20mm obs.:
Method | Time | % of Collapse |
---|---|---|
collapse … , fast | 81.06 | 100% |
sumup | 67.05 | 83% |
fcollapse … , fast | 30.93 | 38% |
fcollapse … , fast pool(5) | 33.85 | 42% |
tab | 8.06 | 10% |
(Note: sumup
might be better for medium-sized datasets, although some benchmarking is needed)
And we can see that the results are similar.
join (and fmerge)
Similar to merge
but avoids sorting the datasets. It is faster than merge
for datasets larger than ~ 100,000 obs., and for datasets above 1mm obs. it
takes a third of the time.
Method | Time | % of merge |
---|---|---|
merge | 28.89 | 100% |
join/fmerge | 8.69 | 30% |
fisid
Similar to isid
, but allowing for if in
and on the other hand not allowing for using
and sort
.
In very large datasets, it takes roughly a third of the time of isid
.
flevelsof
Provides the same results as levelsof
.
In large datasets, takes up to 20% of the time of levelsof
.
fsort
At this stage, you would need a significantly large dataset (50 million+) for fsort
to be faster than sort
.
Method | Avg. 1 | Avg. 2 |
---|---|---|
sort id | 62.52 | 71.15 |
sort id, stable | 63.74 | 65.72 |
fsort id | 55.4 | 67.62 |
The table above shows the benchmark
on a 50 million obs. dataset.
The unstable sorting is slightly slower (col. 1) or slighlty faster (col. 2)
than the fsort
approach. On the other hand, a stable sort is clearly
slower than fsort
(which always produces a stable sort)
Installation
Stable Version
Within Stata, type:
cap ado uninstall ftools
ssc install ftools
Dev Version
With Stata 13+, type:
cap ado uninstall ftools
net install ftools, from(https://github.com/sergiocorreia/ftools/raw/master/src/)
For older versions, first download and extract the zip file, and then run
cap ado uninstall ftools
net install ftools, from(SOME_FOLDER)
Where SOME_FOLDER is the folder that contains the stata.toc and related files.
Compiling the mata library
In case of a Mata error, try typing ftools
to create the Mata library (lftools.mlib).
Installing local versions
To install from a git fork, type something like:
cap ado uninstall ftools
net install ftools, from("C:/git/ftools/src")
ftools, compile
(Changing "C:/git/" to your own folder)
Dependencies
The fcollapse
function requires the moremata
package for some the median and percentile stats:
ssc install moremata
Users of Stata 11 and 12 need to install the boottest
package:
ssc install boottest
FAQ:
"What features is this missing?"
- You can create levels based on one or more variables, and on numeric or string variables, but not on combinations of both. Thus, you can't do something like
fcollapse price, by(make foreign)
because make is string and foreign is numeric. This is due to a limitation in Mata and is probably a hard restriction. As a workaround, just run something likefegen id = group(make)
, to create a numeric ID. - Support for weights is incomplete (datasets that use weights are often relatively small, so this feature has less priority)
- Some commands could also gain large speedups (merge, reshape, etc.)
- Since Mata is ~4 times slower than C, rewriting this in a C plugin should lead to a large speedup.
"How can this be faster than existing commands?"
Existing commands (e.g. sort) are often compiled and don't have to move data from Stata to Mata and viceversa. However, they use inefficient algorithms, so for datasets large enough, they are slower. In particular, creating identifiers can be an ~O(N) operation if we use hashes instead of sorting the data (see the help file). Similarly, once the identifiers are created, sorting other variables by these identifiers can be done as an O(N) operation instead of O(N log N).
asarray
and it was much slower"
"But I already tried to use Mata's Mata's asarray()
has a key problem: it is very slow with hash collisions (which you see a lot in this use case). Thus, I avoid using asarray()
and instead use hash1()
to create a hash table with open addressing (see a comparision between both approaches here).
Updates
2.49.0 06may2022
: fixed a bug infcollapse
with quantiles (p**, median, and iqr stats).ftools
computes these statistics usingmoremata
and had failed to update its function arguments as required by recent changes in moremata.