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1.1.2 • Public • Published

Illustrator Parser - pdfcpu

Browser-compatible parser backwards compatible with previous iteration and https://gitlab.avcd.cz/backend/octopus-illustrator.



import {
} from '@opendesign/illustrator-parser-pdfcpu/dist/index'
import { FSContext } from '@opendesign/illustrator-parser-pdfcpu/dist/fs_context'

// Will use embedded binary to extract information onto disk
const ctx = await FSContext({ file: '/path/to/illustrator/file' })

// Parses Illustrator file embedded within PDF
const PrivateData = await PrivateData(ctx)

// Returns list of all artboards
const Artboards = await Promise.all(
  ArtBoardRefs(ctx).map((ref) => ArtBoard(ctx, ref))


import { ArtBoard, ArtBoardRefs, PrivateData } from '@opendesign/illustrator-parser-pdfcpu/dist/index'
import { WASMContext } from '@opendesign/illustrator-parser-pdfcpu/dist/wasm_context'

// file can be obtained from <input type=file>
const contents = new Uint8Array(await file.arrayBuffer())

// will try to run WASM via standard browser/Node.js APIs
const ctx = await WASMContext(data)

// Parses Illustrator file embedded within PDF
const PrivateData = await PrivateData(ctx)

// Returns list of all artboards
const Artboards = await Promise.all(
  ArtBoardRefs(ctx).map((ref) => ArtBoard(ctx, ref))


Code structure


Contains Go code to parse Illustrator file, dump PDF structure and extract private data.

Right now has two commands, to be used for extracting data from .ai file in different contexts:

  • dump-serialized - targeting native code - will create a new folder in TMPDIR and dump extracted information there. Folder structure:

      ├── _contents/
      ├── _private.ai
      ├── bitmaps/
      ├── fonts/
      └── source.json
  • wasm - targeting browser. When run via WebAssembly.instantiateStreaming will allow extracting information from file without server.

Environment variables

  • GOGC - controls how much extra memory will be allocated by Go Garbage Collector. Default is 100 - meaning memory will increase 2x each time. This default works great for most programs, but not for dump-serialized, which allocates lots of chunks. To combat that, it runs GC manually every so often during dumping process. Here 20 works best.
  • TMPDIR - dictates where file will be written. Be advised to move it off RAM when running batch on all test data - there're tens of GBs of files created in that process.


Contains Typescript code to parse outputs of Go code into Octpus-compatible format.

Uses jest for unit tests, contained in __test__.

Public API consists of 3 functions:

  • ArtBoardRefs,
  • ArtBoard,
  • PrivateData,

All of them expect Context as argument. Context has to be obtained by parsing .ai file with either binary or WASM - see examples above.

For ArtBoardRefs and ArtBoard, implementation roughly follows the steps:

  • obtain refs from PDF XRefTable from Context,
  • walk through refs to create a tree,
  • traverse the tree parsing nodes containing raw data,
  • modify the output to resemble poppler results.

For PrivateData Context already has raw bytes representing the data unpacked and ready to scan. In this case, we read it once for two purposes:

  • extract Artboard names (by checking each line for known pattern),
  • parse Text layer data - this requires buffering all lines which contain this data, unpacking it and then parsing.

In total, there are three parsers for raw data:

  • src/contents - parses "XObject" data, described in Section 7.8.2 Content Streams of PDF spec,
  • src/cmap - parses Font description, from Section 9.7.5 CMaps,
  • src/private-data/text-document - parses document inside private data describing text layers,

Fist two parser roughly follow the same pattern: lexer -> operator stacking -> reducer. This works because we don't need tree structre - contents stream is just a list of operators and operands, whilst in cmaps we only need to extract dictionary. Private data implements entirely different lexer and parser, and its past development can be traced in https://gitlab.avcd.cz/backend/ai-private-data-parser/.


Contains some automation for the overall package. Can download test-data, compile Go code and run it on aforementioned test data.


You might need:

  • Node.js - for Typescript and Gulp. Run npm install to download deps.
  • Go - for wasm. npm run build will take care of everything.

If you have nix just run nix-shell and you'll have development shell ready :)

Possible optimizations

  • [x] Use symbols instead of type: number markers on types - will improve readability of IR,
  • [ ] Create symbol for each operator - currently each comparison in index.ts does linear equality check for each string,
  • [x] Avoid duplicate parsing in decoder (i.e. parseFloat on literal string - maybe some intermediate parsing step?)

Release process

  • ensure new description is added to CHANGELOG.md in Unreleased section - kacl will check this in next step :)

  • bump version using npm version,

  • publish new version with npm publish.



  • Transpile all files to JS:

      tsc -p .
  • Use 0x to create a flamegraph:

      0x ./scripts/parse.js ./test-data/996617_f752c559434a4109863b6fda349bd304_LaneWebsite2.0_Resources_Blog.ai


  • point AICPU_DUMP_PPROF to where dump should be created:

      export AICPU_DUMP_PPROF=./prof/
  • run dump:

      ./wasm/cmd/dump-serialized/dump-serialized ./test-data/996617_f752c559434a4109863b6fda349bd304_LaneWebsite2.0_Resources_Blog.ai
  • analyze with

      go tool pprof -http=":8081" ./wasm/cmd/dump-serialized/dump-serialized ./pprof/cpu.pprof




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