Regular expression operations in R, such as
gsub, sometimes have
significant performance overheads due to encoding conversions.
Some R code tries to mitigate this by ignoring input encodings and
pretending it is fine to work on individual bytes (via
This removes such overheads, but produces correct results only in special
cases, e.g. for simple regular expressions in UTF-8. With the current
strsplit in R, this can also silently
introduce invalid strings, which may cause invalid results or errors in
further processing. Changes in R are planned to reduce these risks.
This blog post presents performance improvements implemented in R-devel, the
development version of R, to reduce the incentive for using
I’ve implemented several optimizations based on micro-benchmarks operating on the text of Ulysses by James Joyce, from the Gutenberg project. 18% input lines have some non-ASCII characters (usually fancy quotes).
I’ve used several arbitrary regular expressions: “a” and " " (to get many matches), “XXX” (to get no matches), “[aeiou]” (a group, many matches). I also had an artificial variant with very long lines of input.
I am using different inputs for evaluation in this blog post, for which I’ve not tuned the code.
The Lodger by Marie Belloc Lowndes is available both in English and in Japanese. The English version has 35% non-ASCII input lines, the Japanese 80%.
As regexps, lets use “XXX”, “London”, “the”, “[Tt ]” for the English version and “XXX”, “ロンドン”, “し”, “[し。]” for the Japanese version. Lets use “REP” as replacement. To avoid measuring noise, repeat the text 100x, so use many lines of the original text (“long” data). Alternatively, repeat text on each line 100x (“wide” data).
Regexp engines used by R have a mode supporting Unicode. In this mode, all inputs must be converted to a specific encoding.
TRE, used to implement POSIX Extended regular expressions, works in wide characters (UTF-16LE on Windows and UTF-32LE/BE on Unix). R never stores strings internally in wide characters and hence it always has to convert inputs before passing them to TRE and may have to do some conversions “back” to UTF-8 when producing outputs.
PCRE2, used to implement Perl regular expressions, works in UTF-8 when supporting Unicode (as used in R). As strings in R are normally in UTF-8, and since R 4.2.0 even on Windows, the conversion is almost never needed anymore. This is an advantage for PCRE2 over TRE. Strings are, however, still checked in R whether they are valid UTF-8 before being passed to PCRE2.
To reduce the conversion overheads, R automatically chooses a non-Unicode mode when all of the inputs are ASCII. When even a single element in a possibly long input vector (e.g. lines of text) is non-ASCII, the Unicode mode is used and all inputs have to be converted. With UTF-8, this is a no-op, but conversion to UTF-16 or UTF-32 requires some work.
These additional performance optimizations have now been implemented.
Conversion from ASCII to wide characters is treated specially. R knows which R strings are ASCII (there is a bit for it in the header), so the check is constant-time and the conversion is then a simple tight loop. This helps TRE.
Conversion from wide characters to UTF-8 is specialized for the case when it is expected that the result will be ASCII. The conversion still checks the result is really ASCII and if not, it starts again using the full conversion. Clearly, this could go further by compiling the regular expression twice and using non-Unicode mode on some input elements, if needed. This helps replacement searches with TRE.
It turned out that in addition to R validating that the inputs were valid UTF-8 strings, a duplicate validation was done in PCRE. PCRE has a flag to disable that in these cases, which has now been set, improving performance of searches with PCRE. The improvements were significant in very long vectors with multiple matches, where many unnecessary validations were made.
R also supports “fixed” searches via the regular expression functions, when the patterns (and replacements) are fixed strings. These are implemented directly inside R. So far they have only been optimized for looking for a single ASCII character.
UTF-8 encoding has the nice property that the leading bytes are from a different range than the continuation bytes. Searching for a valid UTF-8 string inside another valid UTF-8 string can hence be done byte-by-byte, because there is no risk that one could match a partial (invalid) substring, and this search is faster than when decoding characters.
Also, counting the number of characters in a valid UTF-8 string is easy: one only needs to count the leading bytes, which are easy to detect, so this is faster than “decoding” and counting characters.
The “fixed” searches have now been extended to take advantage of these properties and speed up searches and replacements in UTF-8.
Additional performance improvement was obtained by switching the main loop
to C function
strstr, which appears to be still faster than what the
compiler could optimize out from the previous code.
First, lets have a look at the “long” data, which should be representable of a real ebook text. The reported values are ratios of execution times, new divided by old, so smaller is better, rounded to a single decimal digit:
The “fixed” search for “XXX” and “London” (strings rarely or never present) shows 0.3, so it took about 30% of the original time, hence the speedup in latency is about 3.3x.
The optimizations of the conversion when the text is ASCII don’t apply in a Japanese text, hence performance with TRE is not improved much, yet some minor simplifications of the conversion help with frequent matches. Speedups for PCRE2 and “fixed” searches apply:
The wide texts are not very realistic, but perhaps resemble regexp searches of some generated data. I included them as a performance issue has been reported to me in such inputs:
The 0.0 for PCRE2 gsub is a result of rounding: the execution time is less than 3% of the original, so the speedup is about 40x. This of course a good demonstration of that a micro-benchmark can arbitrarily inflate a performance change, but the cause of this is primarily that PCRE2 has accidentally been validating the strings repeatedly (even though R knows they are valid).
In a Japanese text, there are no visible improvements for TRE. The improvement for PCRE2/gsub is again substantial (that 0.0 stands for 30x):
The numbers were measured on 64-bit Intel laptop running Ubuntu 20.04.
Summary and recommendations
The numbers above demonstrate that the current overheads with encoding
conversions in regular expression operations in R have been reduced,
particularly for PCRE2 (
perl=TRUE on recent systems) and “fixed” searches.
PCRE2 ended up nearly always faster than TRE and usually several times faster. At least in part this can be due to encoding conversions to wide strings needed only by TRE. In new code, using Perl regular expressions probably makes sense, anyway, due to support for Unicode properties and because PCRE2 is still actively maintained, but TRE is not.
Further, after these optimizations, “fixed” searches are still faster than PCRE2, so these could be used in cases when performance is really critical and when we are looking for a fixed string.
useBytes=TRUE is a dangerous way to improve performance of regular
expression operations and these optimizations have been implemented to
reduce the incentive to use that. Hopefully, now more of these uses may be
removed (switched to default