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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 toftoolsbut 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!sumupprovides fast summary statistics, and includes thefasttabstatcommand, a faster version oftabstat.egenmiscintroduces the egen functionsfastxtile,fastwpctile, etc. that provide much faster alternatives toxtileandpctile. Also see thefastxtilepackage, which provides similar functionality.randomtagis a much faster alternative tosample.reghdfeprovides a faster alternative toxtregandareg, as well as multi-way clustering and IV regression.parallelallows for easier parallel computing in Stata (useful when running simulations, reshaping, etc.)boottest, for efficiently running wild bootstraps.- The
rangerun,runbyandrangestatcommands 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.
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 groupreplacingegen groupfcollapsereplacingcollapse,contractand most ofegen(through the, mergeoption)join(and its wrapperfmerge) replacingmergefisidreplacingisidflevelsofreplacinglevelsoffsortreplacingsort(although it is rarely faster than sort)
* 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().
(see the test folder for the details of the tests and benchmarks)
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
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)
compressing 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.
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.
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% |
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.
Provides the same results as levelsof.
In large datasets, takes up to 20% of the time of levelsof.
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)
Within Stata, type:
cap ado uninstall ftools
ssc install ftools
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.
In case of a Mata error, try typing ftools to create the Mata library (lftools.mlib).
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)
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
- 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.
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).
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).
2.49.0 06may2022: fixed a bug infcollapsewith quantiles (p**, median, and iqr stats).ftoolscomputes these statistics usingmoremataand had failed to update its function arguments as required by recent changes in moremata.
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