In October 2020, our customers reported response time inflation for our API to scrape Google Shopping Results. We looked into it and sped up our parsers three times by shipping two pull requests to Nokogiri, trying to patch libxml2, and improving algorithms in our code. Below I describe our path from problem to solution, and how to improve it further.

TL;DR. Use flame graphs to detect performance bottlenecks and verify improvements.

HTML parsing speedup

Initially, we aggregated Logflare logs and looked into individual responses. Then we profiled our code and benchmarked assumptions.


We reported 2.23s as total_time_taken but customer reports 5.3577799797058105s, Rails reports 5.129043s (x_runtime), and Cloudflare reports 5.194s (origin_time). Three seconds difference is huge. Probably it was coming from HTML to JSON parsing as it's the main thing not included in our JSON total_time_taken.

50K responses per day took more than 10 seconds according to Logflare

The average successful proxy response time in October 2020 was as usual — 2.3 seconds

Looking further, parsing took about three seconds for searches with one hundred results (num=100&tbm=shop). HTML pages with one hundred Google Shopping results are 1.5 — 2MiB in size.

$ curl -s -A 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.122 Safari/537.36' '' | wc -c | numfmt --to=iec-i --suffix=B


We (naively) profiled our code and found out that image extraction via regexes from the inline JavaScript is slow but Nokogiri::XML::XPathContext#evaluate took a bit more.

$ TEST_RUBY_PROF=1 bundle exec rspec spec/search/google/shopping_results/shopping_results_card_results_spec.rb$ cat tmp/test_prof/ruby-prof-report-flat-wall-total.txt
Measure Mode: wall_time
Thread ID: 9714020
Total: 11.660184
Sort by: self_time

 %self      total      self      wait     child     calls  name                           location
 18.38      2.840     2.143     0.698     0.000       19   <Module::Kernel>#select
 13.88      1.626     1.619     0.005     0.002     2531   Nokogiri::XML::XPathContext#evaluate
  9.28      1.237     1.082     0.139     0.016      187   JSImageExtractor#extract_js_image js_image_extractor.rb:2
  4.22      0.566     0.492     0.000     0.074      488   <Module::Bootsnap::CompileCache::Native>#fetch
  3.97      0.463     0.463     0.000     0.000        5   <Class::Nokogiri::HTML::Document>#read_memory
  3.26      0.381     0.380     0.001     0.000    88163   String#split

* recursively called methods

Then we looked into flame graphs from rbspy and found out that Nokogiri::HTML takes 25% of wall time.

 takes 25% time of data extraction from HTML

Benchmarking Nokogiri with different versions of libxml2

Looked in nokogiri and found out that before v1.11.0 it builds libxml2 without compiler optimizations. We’ve tried Nokogiri with --use-system-libraries and Nokogiri::HTML execution time decreased from 2.7 seconds to 1.5 seconds.

Timing results with system libraries

# Install system libxml2 and libxslt
$ gem install nokogiri --use-system-libraries$ bundle exec rails runner tmp/profiler.rbMeasure Mode: wall_time
Thread ID: 70292512504140
Fiber ID: 70292321749400
Total: 1.507682
Sort by: self_time%self      total      self      wait     child     calls  name                           location
 43.31      0.654     0.653     0.000     0.001     1128   Nokogiri::XML::XPathContext#evaluate
 13.84      0.211     0.209     0.002     0.000        1   <Class::Nokogiri::HTML::Document>#read_memory
  8.40      0.142     0.127     0.008     0.008      100   JSImageExtractor#extract_js_image js_image_extractor.rb:2

Regular nokogiri libraries

# Install system libxml2 and libxslt
$ gem install nokogiri --use-system-libraries$ bundle exec rails runner tmp/profiler.rbMeasure Mode: wall_time
Thread ID: 69919886342480
Fiber ID: 69919692333960
Total: 2.854719
Sort by: self_time%self      total      self      wait     child     calls  name                           location
 41.16      1.190     1.175     0.015     0.000        1   <Class::Nokogiri::HTML::Document>#read_memory
 31.82      0.910     0.908     0.000     0.001     1128   Nokogiri::XML::XPathContext#evaluate
  4.40      0.146     0.125     0.013     0.008      100   JSImageExtractor#extract_js_image js_image_extractor.rb:2

At that time I wasn’t sure how to properly profile Ruby code, so I’ve used everything: rbspy, stackprof, and ruby-prof.

$ cat tmp/profiler.rb
require "ruby-prof"profile = RubyProf.profile do
  search_params = {engine: "google", q: "roller blades", location: "Austin, United States", google_domain: "", hl: "en", gl: "us", num: "500", device: "desktop", tbm: "shop", tbs: "p_ord:rv", file_path: "tmp/roller-blades.html"}!
endprinter =
printer.print($stdout, min_percent: 2)


Julien Khaleghy also tried Oga gem instead of Nokogiri. It was about six times faster than Nokogiri.

Warming up --------------------------------------
            Nokogiri     1.000  i/100ms
                 Oga     1.000  i/100ms
Calculating -------------------------------------
            Nokogiri      0.913  (± 0.0%) i/s -     28.000  in  30.688232s
                 Oga      6.048  (±16.5%) i/s -    176.000  in  30.016426sComparison:
                 Oga:        6.0 i/s
            Nokogiri:        0.9 i/s - 6.62x  (± 0.00) slower

But some tests were failing with LL::ParserError from ruby-ll that is used in Oga.

$ bundle exec rspec specAn error occurred while loading ./spec/search/google/local_results/local_results_for_specific_place_spec.rb.
Failure/Error: if query_displayed = doc.at_css('input[name=q]')LL::ParserError:
  Unexpected T_IDENT for rule 24
# /Library/Ruby/Gems/2.6.0/gems/ruby-ll-2.1.2/lib/ll/driver.rb:15:in `parser_error'An error occurred while loading ./spec/search/google/sports_results/milwaukee_bucks_spec.rb.
Failure/Error: if query_displayed = doc.at_css('input[name=q]')LL::ParserError:
  Unexpected T_IDENT for rule 24
# /Library/Ruby/Gems/2.6.0/gems/ruby-ll-2.1.2/lib/ll/driver.rb:15:in `parser_error'An error occurred while loading ./spec/search/yahoo/organic_results/organic_results_coffee_spec.rb.
Failure/Error: if ad_result_node.classes.include? 'AdTop'NoMethodError:
  undefined method `classes' for #<Oga::XML::Element:0x00007fa7bbd8aa58>
  Did you mean?  classFinished in 0.00004 seconds (files took 6.79 seconds to load)
0 examples, 0 failures, 6 errors occurred outside of examples

We profiled our code once again. at_css took more time than extract_js_image for these shopping results. I guessed that was because we use multiple CSS selectors to support old and new Google layouts.

Alt Text

Solution #1

I’ve compiled nokogiri with -O2 compiler optimization and now its parsing performance was close to oga. I’ve added this workaround to the Nokogiri issue on GitHub.

$ CFLAGS="-O2 -pipe" gem install nokogiri# Run benchmarkWarming up --------------------------------------
            Nokogiri     1.000  i/100ms
                 Oga     1.000  i/100ms
Calculating -------------------------------------
            Nokogiri      4.706  (± 0.0%) i/s -    140.000  in  30.039766s
                 Oga      5.585  (±17.9%) i/s -    166.000  in  30.030045sComparison:
                 Oga:        5.6 i/s
            Nokogiri:        4.7 i/s - same-ish: difference falls within error

It worked because CFLAGS are passed here and there in ext/nokogiri/extconf.rb.

We also tried different optimization levels. -Ofast was a bit faster than -O2 and -Ofast for me. -O2 and -O3 are almost the same. 20ms speedup forNokogiri::HTML not worth the risks of aggressive optimizations.

We reinstalled Nokogiri with -O2 on production servers. In production, -O2 gave about 600 ms (3.3 seconds vs 3.9 seconds) speed up for large search results pages.

Pull request to Nokogiri

So we’ve opened a PR to Nokogiri to compile libxml2 with -O2 -g flags. After some discussion, Mike Dalessio came up with heavy refactoring of extconf.rb which also included changes from my PR. I have enjoyed communication with Mike.

Images extraction from inline JavaScript with regular expressions

extract_js_image took 49% wall time of data extraction from Google Shopping HTML with num=100 parameter. The entire parsing took two seconds.

$ bundle exec stackprof tmp/stackprof.dump --text --limit 20
  Mode: wall(1000)
  Samples: 986 (50.00% miss rate)
  GC: 18 (1.83%)
     TOTAL    (pct)     SAMPLES    (pct)     FRAME
       490  (49.7%)         467  (47.4%)     extract_js_image
       334  (33.9%)         334  (33.9%)     Nokogiri::XML::Document#decorate
        23   (2.3%)          23   (2.3%)     Nokogiri::XML::XPathContext#register_namespaces
        23   (2.3%)          23   (2.3%)     JSUtils.unescape
        15   (1.5%)          13   (1.3%)     Nokogiri::HTML::Document.parse
        15   (1.5%)          13   (1.3%)     Mongoid::Fields::ClassMethods#database_field_name
         9   (0.9%)           9   (0.9%)     (sweeping)
         8   (0.8%)           8   (0.8%)     (marking)
        13   (1.3%)           7   (0.7%)     #<Module:0x00007fb3c0edab68>.xpath_for
         7   (0.7%)           7   (0.7%)     block (3 levels) in class_attribute
        10   (1.0%)           6   (0.6%)     Nokogiri::XML::Searchable#extract_params
         4   (0.4%)           4   (0.4%)     Nokogiri::XML::Node#namespaces
       890  (90.3%)           4   (0.4%)     get_shopping_results

We found out with Žilvinas Kučinskas, that extract_js_image matched the entire HTML string against seven regular expressions on each method call. get_thumbnail calls extract_js_image. get_thumbnail is called fifty times from different parsers. The parser for Google Images API called get_thumbnail one hundred times.

extract_js_image looked like this:

if html =~ REGEX_1
  JSUtils.unescape $1
elsif html =~ REGEX_2
  JSUtils.unescape $1
elsif html =~ REGEX_3
  JSUtils.unescape $1
elsif html =~ REGEX_4
  JSUtils.unescape $1
elsif html =~ REGEX_5
  JSUtils.unescape $1
elsif html =~ REGEX_6
  JSUtils.unescape $1
elsif html =~ REGEX_7
  JSUtils.unescape $1

Solution #2

The first assumption was to call String#scan once per parsing and cache extracted matches. We worked more on this assumption and came up with regular expressions with named captures.







We used code from this StackOverflow answer to convert all named captures from the String#scan to the single dictionary of { “thumbnail_id” => “thumbnail” }.

def extracted_thumbnails
  return @extracted_thumbnails if @extracted_thumbnails.present?

  js_image_regexes = JS_IMAGE_REGEXES.detect { |key, _| engine.starts_with?(key.to_s) }&.last || JS_IMAGE_REGEXES[:all]@extracted_thumbnails = js_image_regexes.collect { |regex|
    regex_capture_names = regex.namesthumbnail_index = regex_capture_names.index(THUMBNAIL_CAPTURE_NAME)
    thumbnail_id_index = regex_capture_names.index(THUMBNAIL_ID_CAPTURE_NAME)

    html.scan(regex).collect do |match|
      found_thumbnail = match[thumbnail_index]
      found_thumbnail_id = match[thumbnail_id_index]

      found_thumbnail_id.split(",").map { |thumb| Hash["'", "").squish, found_thumbnail] }
  }.flatten.inject(:merge) || {}

extracted_thumbnails allocates about 2.25 MB of memory for large HTML like Google Shopping results. That’s 0.2 MB more comparing to the previous slow implementation.

Usage of extracted_thumbnails is straightforward.

def extract_js_image(image_node)
  return unless image_node thumbnail_id = (image_node["id"])

  return unless thumbnail_id if (found_thumbnail = extracted_thumbnails[thumbnail_id])

extract_js_image takes 17.9% wall time of data extraction from Google shopping HTML with num=100 parameter. The entire parsing takes 1.2 seconds.

$ bundle exec stackprof tmp/stackprof.dump --text --limit 20
  Mode: wall(1000)
  Samples: 532 (58.18% miss rate)
  GC: 15 (2.82%)
     TOTAL    (pct)     SAMPLES    (pct)     FRAME
       253  (47.6%)         253  (47.6%)     Nokogiri::XML::Document#decorate
       107  (20.1%)          95  (17.9%)     extract_js_image
        23   (4.3%)          23   (4.3%)     Nokogiri::XML::XPathContext#register_namespaces
        12   (2.3%)          10   (1.9%)     JSUtils.unescape
        11   (2.1%)           9   (1.7%)     Nokogiri::HTML::Document.parse


Then we ran rbspy in production and found out that at_css (Nokogiri::XML::Document#decorate) took much time because of performance problem in libxml2 (the underlying lib that nokogiri uses to parse and traverse XML).

The flame graph shows that there’s something wrong with the C function. Interesting…

c function is not very helpful to find the performance problem, so we dug deeper.

perf Linux profiler

I searched over the web how to profile C extensions for Ruby and C code in general, and found out Brendan Gregg’s tutorial on Linux perf. That was my first usage of Linux perf profiler. I’ve also tried gperftools and pprof, because I've seen its usage. And flamescope, because it was made by Brendan Gregg. There are many similar tools and it was hard to figure out what to use during two weeks or so.

I reinstalled Nokogiri with debugging info, not-stripped just in case.

CFLAGS="-O2 -ggdb3 -gdwarf -pipe -lprofiler -fno-omit-frame-pointer" gem install nokogiri

Executing perf record and perf report shows thatxmlXPathCompOpEval and xmlXPathNodeCollectAndTest took most of the time, and are being called recursively.

Alt Text

The flame graph for the single search.parse! shown is basically the same.

cargo install flamegraph
flamegraph -- bundle exec rails runner ' "pc game", tbm: "shop", file_path: "tmp/pc_game_big_shopping.html").parse!;

Alt Text

Chart from pprof shows that objspace_malloc_increase.constprop.0, atomic_sub_nounderflow and xmlStrlen have the biggest total and self time. xmlXPathNodeCollectAndTest has the biggest self time which means the body of this function is a potential performance bottleneck.

I’ve installed perf_data_converter to be able to use report with pprof.

perf record -F99 -e cycles:u -g -- bundle exec rails runner ' "pc game", tbm: "shop", file_path: "tmp/pc_game_big_shopping.html").parse!;'

pprof -web

Alt Text

flamescope shows the same as flamegraph. Both of these tools use the same tools to generate chart probably.

Alt Text

Returning back to Nokogiri

I’ve opened an issue in the Nokogiri repository. I had ideas for improving xmlStrlen performance but it was better to get help from someone who knows C and has experience with libxml2.

In the big document, xmlStrlen is called 15K times for the same string. In the small document, it’s being called for 5K times. Three times bigger document and three times more calls. But the strings are the same. My assumption as a person who doesn’t know C: the unique string pointer address means that the function was called with the same argument.

Given this Ruby script that searches an element in the container.

html =[0])

doc = Nokogiri::HTML.parse(html)

10.times do
  doc.css(".sh-dlr__list-result, .sh-dgr__grid-result").each do |sh_r|
    10.times do
      sh_r.at_css(".na4ICd:not(.rOwove):not(.TxCHDf):nth-of-type(1), .hBUZL:not(.Rv2Cae):nth-of-type(2), .hBUZL:not(.Rv2Cae):not(.Fxxvzc):not(:has(span)):not(:has(div)), .dWRflb, .p7n7Ze:not(:has(a))")

I’ve calculated xmlStrlen calls by attaching dynamic breakpoint in gdb. To do so, I recompiled nokogiri with debug information.

CFLAGS="-O -ggdb3 -pipe -fno-omit-frame-pointer" gem install nokogiri

Then attached the breakpoint via gdb to get the line to attach dprintf later on.

bundle exec gdb -q -ex 'set breakpoint pending on' --ex 'thread apply all bt full' --ex 'b xmlStrlen' --ex run --args ruby ./tmp/slow_search_parse.rb tmp/pc_game_big_shopping.html

Then attached [dprintf][41] via GDB to the xmlStrlen function of libxml2 to print pointer of str variable and log output to the file. I executed this script with a big and small HTML document.

bundle exec gdb -q -ex 'set breakpoint pending on' --ex 'thread apply all bt full' --ex 'dprintf xmlstring.c:425, "str: %pn", str' --ex run --ex quit --args ruby ./tmp/slow_search_parse.rb tmp/pc_game_big_shopping.html |& tee xmlStrlen_big.log

bundle exec gdb -q -ex 'set breakpoint pending on' --ex 'thread apply all bt full' --ex 'dprintf xmlstring.c:425, "str: %pn", str' --ex run --ex quit --args ruby ./tmp/slow_search_parse.rb tmp/pc_game_small_shopping.html |& tee xmlStrlen_small.log

Then examined logs

$ grep str: xmlStrlen_big.log | sort | uniq -c | sort -rn | head -20
  15446 str: 0x112e760
  15405 str: 0x112e6d0
  13405 str: 0x15ddb80
   4006 str: 0x7fffe6c615ad
   1962 str: 0x112e740
    381 str: 0x7fffe69de010
    374 str: 0x15b5520
    321 str: 0x1649d60
    306 str: 0x174c7a0
    306 str: 0x1734430
    306 str: 0x15db410
    306 str: 0x15b63b0
    303 str: 0x174c830
    303 str: 0x15db4a0
    303 str: 0x15b64a0
    298 str: 0x161a8b0
    297 str: 0x16e3fa0
    291 str: 0x160fd50
    287 str: 0x163c320
    285 str: 0x16feef0

$ grep str: xmlStrlen_small.log | sort | uniq -c | sort -rn | head -20
   5046 str: 0xf3fbc0
   5038 str: 0xf3fcb0
   1687 str: 0xf40440
    906 str: 0xf3fc30
    806 str: 0x7fffe6c615ad
    431 str: 0xfe0460
    420 str: 0x11b53e0
    384 str: 0x1067bb0
    353 str: 0xfdc760
    328 str: 0x11e5ab0
    322 str: 0x11e5a40
    306 str: 0x11ccea0
    303 str: 0x11ccf30
    303 str: 0x11bc040
    301 str: 0x11a57d0
    291 str: 0x11e7740
    289 str: 0x1200a70
    288 str: 0x11d5660
    284 str: 0x11e6d80
    284 str: 0x11c3fd0

I had a simple idea about caching the length of strings in memory in xmlStrlen and compare the performance, but Mike Dalessio said it’s not safe to cache string lengths across xmlStrlen calls.

Then Mike came up with a 2x speedup of node lookups which decreased the time to extract data from big HTML files to one second.

Improvement of xmlStrlen from libxml2

We’ve moved further with an idea to cache string lengths in xmlStrlen and compared its performance with strlen from glibc.

Given the simplest benchmark, xmlStrlen is two times slower than strlen from glibc on small strings, ten times slower on average strings, and thirty times slower on big strings and an entire HTML file.

The performance of xmlStrlen resulted in the 0.7 - 1.5 seconds to parse and search through 2 MB HTML document by using Nokogiri. I described this issue in the Nokogiri repository which led to 2x speedup on the Nokogiri side, but xmlStrlen still could be faster.

$ ./slow_parsing_benchmark
xmlStrlen (entire HTML file): 926171.936981 μs
glibc_xmlStrlen (entire HTML file): 36905.903992 μs
delta (xmlStrlen ÷ glibc_xmlStrlen): 25.094584 timesxmlStrlen (average string): 57479.204010 μs
glibc_xmlStrlen (average string): 5802.069000 μs
delta (xmlStrlen ÷ glibc_xmlStrlen): 9.905937 timesxmlStrlen (bigger string): 388056.315979 μs
glibc_xmlStrlen (bigger string): 12797.856995 μs
delta (xmlStrlen ÷ glibc_xmlStrlen): 30.318382 timesxmlStrlen (smallest string): 15870.046021 μs
glibc_xmlStrlen (smallest string): 6282.208984 μs
delta (xmlStrlen ÷ glibc_xmlStrlen): 2.527903 times

So I’ve opened an issue in libxml2. The naive approach to simply reuse strlen in xmlStrlen speed up our document parsing and searching from 1.4 seconds to about 800 ms.

diff --git a/xmlstring.c b/xmlstring.c
index e8a1e45d..df247dff 100644
--- a/xmlstring.c
+++ b/xmlstring.c
@@ -423,14 +423,9 @@ xmlStrsub(const xmlChar *str, int start, int len) {

 xmlStrlen(const xmlChar *str) {
-    int len = 0;
     if (str == NULL) return(0);
-    while (*str != 0) { /* non input consuming */
-        str++;
-        len++;
-    }
-    return(len);
+    return strlen((const char*)str);


Mike Dalessio noted that xmlStrlen() has remained unchanged since the commit it was introduced, 260a68fd, in 1998, and is equivalent to the K&R version.

glibc’s implementation is faster because it’s implemented in assembly customized for common/modern CPUs. Some background here.

With some help, I’ve posted a very verbose comment of 10% speedup with the xmlStrlen patch on the C program that used libxml2 directly.

Before the patch

$ cd ~/code/libxml2
$ git checkout origin/master
$ CFLAGS="-O2 -pipe -g" ../configure --host=x86_64-pc-linux-gnu --enable-static --disable-shared --with-iconv=yes --without-python --without-readline --with-c14n --with-debug --with-threads && make clean && make -j
$ sudo make install
$ cd ~/code/benchmark && make && ./slow_parsing_benchmark ./pc_game_big_shopping.html "//*[contains(concat(' ', @class, ' '), ' sh-dlr__list-result ')]//*[contains(concat(' ', @class, ' '), ' hBUZL ')]"
# Some parsing errors like
./pc_game_big_shopping.html:62: HTML parser error : Tag path invalid
4h2c0-1.1.9-2 2-2s2 .9 2 2c0 2-3 1.75-3 5h2c0-2.25 3-2.5 3-5 0-2.21-1.79-4-4-4z"searching './pc_game_big_shopping.html' with '//*[contains(concat(' ',@class,' '), ' sh-dlr__list-result ')]//*[contains(concat(' ', @class, ' '), ' hBUZL ')]' 1000 times
NODESET with 260 results
67855 ms

After the patch

$ cd ~/code/libxml2
$ git checkout xmlStrlen-patch
$ CFLAGS="-O2 -pipe -g" ../configure --host=x86_64-pc-linux-gnu --enable-static --disable-shared --with-iconv=yes --without-python --without-readline --with-c14n --with-debug --with-threads && make clean && make -j
$ sudo make install
$ cd ~/code/benchmark && make && ./slow_parsing_benchmark ./pc_game_big_shopping.html "//*[contains(concat(' ',@class,' '), ' sh-dlr__list-result ')]//*[contains(concat(' ', @class, ' '), ' hBUZL ')]"
searching './pc_game_big_shopping.html' with '//*[contains(concat(' ',@class,' '), ' sh-dlr__list-result ')]//*[contains(concat(' ', @class, ' '), ' hBUZL ')]' 1000 times
NODESET with 260 results
59767 ms

I was not sure why it ran faster with the patch. The flame graph of the sample program from this post looked the same for nokogiri on master and on the branch of this PR. xmlXPathCompOpEval is dominating before and after the patch.

In both cases,  and  are taking most of the time of

Nick Wellnhofer hasn’t responded to my comment and in the PR to libxml2, but our PR to Nokogiri was merged to master and shipped in v1.11.0.rc4, so the solution was good enough for us.

Update February 21, 2022: Mike's PR with the xmlStrlen optimization was merged to Libxml2.


Data extraction from big HTML files decreased from three seconds to one second and two of our PRs were shipped in Nokogiri v1.11.0.

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Two of our PRs were shipped to Nokogiri v1.11.0

Things we haven’t tried

  1. Haven’t tried to use bcc for tracing and profiling.
  2. Haven’t read the entire documentation about perf.

What’s next

I’m glad to have the opportunity to contribute to an open-source project that is used by thousands of people. Hopefully, we will speed up Nokogiri (or XML parser it uses) to match the performance of html5ever or lexbor at some point in the future. 800 ms to extract data from HTML is still too much.

As of an experiment, I’ve made an FFI wrapper around the Rust scraper crate. at_css.text calls of proof of concept are 60 times faster than Nokogiri ones.

$ ruby benchmarks/nokogiri_benchmark.rb
Warming up --------------------------------------
      Nokogiri::HTML     2.000  i/100ms
  NokogiriRust::HTML     3.000  i/100ms
Calculating -------------------------------------
      Nokogiri::HTML     27.195  (±14.7%) i/s -    132.000  in   5.042868s
  NokogiriRust::HTML     40.319  (± 5.0%) i/s -    201.000  in   5.001218sComparison:
  NokogiriRust::HTML:       40.3 i/s
      Nokogiri::HTML:       27.2 i/s - 1.48x  (± 0.00) slowerWarming up --------------------------------------
                         5.000  i/100ms
                       394.000  i/100ms
Calculating -------------------------------------
                         61.027  (± 3.3%) i/s -    305.000  in   5.002827s
                          3.900k (± 2.9%) i/s -     19.700k in   5.056373sComparison:
NokogiriRust::HTML.at_css.text:     3899.6 i/s
Nokogiri::HTML.at_css.text:       61.0 i/s - 63.90x  (± 0.00) slower

No plans at the moment, but I think about making an adapter between html5ever and Nokogiri in a similar way to Nokogumbo. In this case, the project may become more long-term and even be used as the main HTML parsing library for Nokogiri like gumbo is going to become in 2021.

2023 Update

I’m glad to have the opportunity to contribute to an open-source project that is used by thousands of people. Yicheng, a software engineer at SerpApi, open sourced nokolexbor, a performance-focused HTML parser for Ruby. Here's a blog post introduction of it.