An Unusual Performance Optimization

How wondering about an odd benchmark result led to better performance

I regularly run the HexaPDF benchmarks to make sure that HexaPDF gets faster and not slower. One of the benchmarks, the “raw_text” benchmark, always had me wondering why using TrueType fonts was visibly slower. So I decided to investigate.

The Odd Benchmark Result

The “raw_text” benchmarks tests the performance of close-to-metal text output. This is important because it is the limiting factor when creating PDF files, especially big PDF files, with much text content.

What the benchmark does is

  • reading a file line by line,
  • putting each line on a page, without line wrapping and with manual cursor positioning,
  • and creating new pages as necessary.

Here is the main part of the HexaPDF script:

File.foreach(ARGV[0], mode: 'r') do |line|
  if y < bottom_margin
    # Remove the canvas object out of scope for garbage collection
    if canvas
      canvas.context.contents = canvas.context.contents
    canvas = doc.pages.add.canvas
    canvas.font(font, size: 12)
    canvas.leading = 14
    canvas.move_text_cursor(offset: [72, top_margin])
    y = top_margin

  y -= 14

The bottleneck is at the bottom where each line gets decoded into glyph objects (font.decode_utf8) and the characters’ glyphs are shown (canvas.show_glyphs_only).

There is nothing specific here regarding TrueType fonts. The font object either represents one of the built-in PDF fonts like ‘Times Roman’ or a supplied TrueType font. The methods in either version are very similar.

The canvas.show_glyphs_only method of the canvas is also font-agnostic, calling font.encode to encode a certain glyph into the needed PDF content stream operator representation.

What’s more is that the two font methods are using cached values as much as possible. I.e. once a glyph is created for a certain UTF-8 character, it is re-used. And once a glyph is encoded, it doesn’t need to be encoded again.

So from the first look at it there shouldn’t have been much difference, performance-wise, when using a TrueType font instead of built-in font. But there was, as the benchmark results showed:

|                              ||    Time |     Memory |   File size |
| hexapdf     | 1x             |    557ms |  34.160KiB |     452.598 |
| hexapdf     | 5x             |  1.891ms |  45.244KiB |   2.258.904 |
| hexapdf     | 10x            |  3.754ms |  57.364KiB |   4.517.825 |
| hexapdf     | 1x ttf         |    634ms |  33.044KiB |     549.522 |
| hexapdf     | 5x ttf         |  2.335ms |  48.908KiB |   2.687.124 |
| hexapdf     | 10x ttf        |  4.693ms |  63.568KiB |   5.360.947 |

The text file used by the benchmark is the Project Gutenberg text of Homer’s Odyssey (contains about 12.000 lines and about 700.000 characters). The “1x”, “5x” and “10x” indicators show the number of times the text was output.

For the “10x” version the TrueType benchmark ran about 25% slower than the one with the built-in PDF font. Some of the difference can be attributed to the need of subsetting the TrueType font and embedding it in the PDF. Also, when looking at the file sizes there is a difference of about 820KiB. This is because each glyph is encoded using two bytes in the TrueType version and only one byte in the built-in PDF font version. So the serializer also has more work to do.

But still, it felt a bit off…

Investigating the Cause

As I have written before development of HexaPDF is done in a memory and performance conscious way. So most parts of HexaPDF are already heavily optimized with respect to those regards. The next step was to find out the cause - it was time to unpack the trusty profilers!

Try 1 - Run-time Profiling

Since the TrueType version was markedly slower my first thought was that there was some performance problem. This is where ruby-prof shines! It helps us to find out which methods are called how many times and how long processing took in each method.

I have the following simple script that can just be required and it automatically runs ruby-prof:

  require 'ruby-prof'
  $profile = RubyProf::WALL_TIME)

  result = $profile.stop'ruby-prof.graph.html', 'w+'), min_percent: 1)

The script creates an output file ruby-prof.graph.html which contains all the information we need to pin-point performance-hungry method.

I ran the benchmark together with this script, once using a built-in font and once using a TrueType font. The results, alas, were disappointing. The time spent in the top methods as well as the number of calls was nearly identical. So there wasn’t really a clue there.

If a run-time profiler doesn’t show much difference, maybe a look at the memory consumption and the number of created objects helps.

Try 2 - Memory Profiling

There are several great memory profilers available. Most often, however, I use the AllocationTracer gem for this task.

As with ruby-prof I have a small script that can just be required:

require 'set'
require 'forwardable'
require 'allocation_tracer'

ObjectSpace::AllocationTracer.setup(%i{path line type})

  results = ObjectSpace::AllocationTracer.stop
  results.reject {|k, v| v[0] < 10}.sort_by{|k, v| [v[0], k[0]]}.each do |k, v|
    $stderr.puts "#{k[0]}:#{k[1]} - #{k[2]} - #{v[0]}"
  $stderr.puts "Sum: " + results.inject(0) {|sum, (k,v)| sum + v[0]}.to_s
  pp ObjectSpace::AllocationTracer.allocated_count_table
  pp :total => ObjectSpace::AllocationTracer.allocated_count_table.values.inject(:+)

The script outputs the location, type and count of created objects, as well as a summary at the end.

I ran the benchmark again, together with this script and for both versions. And… eureka! The non-TrueType version allocated around 1.679 million objects whereas the TrueType version allocated around 2.390 million objects. And the detailed output of the TrueType result also showed us where this happened:

/home/thomas/hexapdf/lib/hexapdf/serializer.rb:272 - T_MATCH - 202480
/home/thomas/hexapdf/lib/hexapdf/serializer.rb:270 - T_STRING - 208762
/home/thomas/hexapdf/lib/hexapdf/serializer.rb:272 - T_STRING - 567231

The non-TrueType version only had the second line (as last line, so with the most allocations) and the other two added up to a bit more than the difference in allocated objects.

Now I knew where to look! Following is the code for the method in question, with one irrelevant statement removed:

def serialize_string(obj)
  obj.gsub!(/[()\\\r]/n, STRING_ESCAPE_MAP)

This method serializes a string into the format used by the PDF spec. The first line was the culprit allocating the many String and Match objects. It replaces all special characters with backslash-escaped versions using the STRING_ESCAPE_MAP hash.

This meant that, somehow, the TrueType version generated many strings for which String#gsub! needed to do something, i.e. ones that included a special character.

Finding the Root Cause

The next obvious step was to look for font related code that generates many strings for the benchmark. This happens during text output in the canvas.show_glyphs_only method. As mentioned before this method delegates to font.encode for retrieving the character codes that get put into a PDF content stream.

Here is the relevant method:

def encode(glyph)
  (@encoded_glyphs[] ||=
      if glyph.kind_of?(InvalidGlyph)
        raise HexaPDF::Error, "Glyph for #{glyph.str.inspect} missing"
      if @subsetter
        [[@subsetter.use_glyph(].pack('n'), glyph]
        [[].pack('n'), glyph]

This method takes a glyph object and returns the character code that is needed for the respective PDF text showing operators. As the HexaPDF default is to subset TrueType fonts, @subsetter.use_glyph( is invoked and returns the mapped glyph index for the given glyph. The returned glyph index is just packed into two bytes and returned.

As there was not further clue here I went further down the rabbit hole and inspected the #use_glyph method of the subsetter class:

def use_glyph(glyph_id)
  return @glyph_map[glyph_id] if @glyph_map.key?(glyph_id)
  @last_id += 1
  @glyph_map[glyph_id] = @last_id

The method returns an already mapped glyph index or, if not already mapped, increases the counter for the last used glyph index, stores it for the given glyph index and returns it. Not shown is that the initial @last_id value is 0.

And here I found the root cause: The counter was increased for every newly encountered glyph. One of those glyphs would be mapped to 13 (if at least thirteen different glyphs were used which is the usual case) and this is the decimal value for \r and one of the special characters that needs to be escaped! The other special characters have the decimal values 40, 41 and 92. So we can assume that in most cases three glyphs are mapped to strings that need to be escaped when serialized.

Implementing a Solution

Now that I knew the why I needed to find a solution.

My first instinct was to set the initial @last_id value to 93. Then the problematic values would never be encountered. However, it turned out that making the necessary adjustments for this to create a valid PDF font object was not that easy.

Skipping the problematic values was also not an option as that would again mean adjustments in other places. But what if we made sure that the problematic values were just never used?

The solution I came up with is to use invalid keys for the @glyph_map hash whenever a problematic value is reached:

if @last_id == 13 || @last_id == 40 || @last_id == 41 || @last_id == 92
  @glyph_map[:"s#{@last_id}"] = @last_id
  @last_id += 1

These invalid keys are then always specially handled in the same manner as the glyph index 0 which represents an undefined glyph. See the commit for all details of the change.

Benchmark Results

After implementing the changes I ran the memory profiler again and the number of allocated objects went down to about 1.680 million. That looked promising from a memory point of view. However, memory savings not always translate into time savings.

So I also ran the benchmarks again:

|                              ||    Time |     Memory |   File size |
| hexapdf     | 1x             |    572ms |  34.680KiB |     452.598 |
| hexapdf     | 5x             |  1.840ms |  45.352KiB |   2.258.904 |
| hexapdf     | 10x            |  3.504ms |  57.464KiB |   4.517.827 |
| hexapdf     | 1x ttf         |    542ms |  33.540KiB |     546.390 |
| hexapdf     | 5x ttf         |  2.099ms |  43.600KiB |   2.670.953 |
| hexapdf     | 10x ttf        |  4.016ms |  63.584KiB |   5.328.382 |

That also looked better! The TrueType benchmark of the “10x” version was now only about 14% slower than the one with the built-in PDF font.


Most optimizations I already did for HexaPDF involved things like in-place modifications of strings, i.e. using better tools that Ruby provides.

In this case, however, the solution to the performance problem was a bit unusual in that we actually had to use deeper TrueType font and PDF format knowledge to modify how strings were created in the first place.

If such a modification was done in a general purpose font handling library, it would probably not be merged. However, HexaPDF includes its own font handling library because of such reasons.