Metadata workflow

Intrinsic metadata

Indexing intrinsic metadata requires extracting information from the lowest levels of the Merkle DAG (directories, files, and content blobs) and associate them to the highest ones (origins). In order to deduplicate the work between origins, we split this work between multiple indexers, which coordinate with each other and save their results at each step in the indexer storage.

Indexer architecture

Origin-Head Indexer

First, the Origin-Head indexer gets called externally, with an origin as argument (or multiple origins, that are handled sequentially). For now, its tasks are scheduled manually via recurring Scheduler tasks; but in the near future, the journal will be used to do that.

It first looks up the last snapshot and determines what the main branch of origin is (the “Head branch”) and what revision it points to (the “Head”). Intrinsic metadata for that origin will be extracted from that revision.

It schedules a Revision Metadata Indexer task for that revision, with a hint that the revision is the Head of that particular origin.

Revision and Content Metadata Indexers

These two indexers do the hard part of the work. The Revision Metadata Indexer fetches the root directory associated with a revision, then extracts the metadata from that directory.

To do so, it lists files in that directory, and looks for known names, such as codemeta.json, package.json, or pom.xml. If there are any, it runs the Content Metadata Indexer on them, which in turn fetches their contents and runs them through extraction dictionaries/mappings. See below for details.

Their results are saved in a database (the indexer storage), associated with the content and revision hashes.

If it received a hint that this revision is the head of an origin, the Revision Metadata Indexer then schedules the Origin Metadata Indexer to run on that origin.

Origin Metadata Indexer

The job of this indexer is very simple: it takes an origin identifier and a revision hash, and copies the metadata of the former to a new table, to associate it with the latter.

The reason for this is to be able to perform searches on metadata, and efficiently find out which origins matched the pattern. Running that search on the revision_metadata table would require either a reverse lookup from revisions to origins, which is costly.

Translation from language-specific metadata to CodeMeta

Intrinsic metadata are extracted from files provided with a project’s source code, and translated using CodeMeta’s crosswalk table.

All input formats supported so far are straightforward dictionaries (eg. JSON) or can be accessed as such (eg. XML); and the first part of the translation is to map their keys to a term in the CodeMeta vocabulary. This is done by parsing the crosswalk table’s CSV file and using it as a map between these two vocabularies; and this does not require any format-specific code in the indexers.

The second part is to normalize values. As language-specific metadata files each have their way(s) of formatting these values, we need to turn them into the data type required by CodeMeta. This normalization makes up for most of the code of swh.indexer.metadata_dictionary.

Supported intrinsic metadata

The following sources of intrinsic metadata are supported:

Supported CodeMeta terms

The following terms may be found in the output of the metadata translation (other than the codemeta mapping, which is the identity function, and therefore supports all terms):

http://schema.org/author:
	gemspec, npm, pkg-info
http://schema.org/codeRepository:
	gemspec, npm
http://schema.org/description:
	gemspec, maven, npm, pkg-info
http://schema.org/email:
	gemspec, pkg-info
http://schema.org/identifier:
	maven
http://schema.org/keywords:
	npm, pkg-info
http://schema.org/license:
	gemspec, npm, pkg-info
http://schema.org/name:
	gemspec, maven, npm, pkg-info
http://schema.org/url:
	npm, pkg-info
http://schema.org/version:
	gemspec, maven, npm, pkg-info
https://codemeta.github.io/terms/issueTracker:
	npm

Adding support for additional ecosystem-specific metadata

This section will guide you through adding code to the metadata indexer to detect and translate new metadata formats.

First, you should start by picking one of the CodeMeta crosswalks. Then create a new file in swh-indexer/swh/indexer/metadata_dictionary/, that will contain your code, and create a new class that inherits from helper classes, with some documentation about your indexer:

from .base import DictMapping, SingleFileMapping
from swh.indexer.codemeta import CROSSWALK_TABLE

class MyMapping(DictMapping, SingleFileMapping):
        """Dedicated class for ..."""
        name = 'my-mapping'
        filename = b'the-filename'
        mapping = CROSSWALK_TABLE['Name of the CodeMeta crosswalk']

Then, add a string_fields attribute, that is the list of all keys whose values are simple text values. For instance, to translate Python PKG-INFO, it’s:

string_fields = ['name', 'version', 'description', 'summary',
                 'author', 'author-email']

These values will be automatically added to the above list of supported terms.

Last step to get your code working: add a translate method that will take a single byte string as argument, turn it into a Python dictionary, whose keys are the ones of the input document, and pass it to _translate_dict.

For instance, if the input document is in JSON, it can be as simple as:

def translate(self, raw_content):
    raw_content = raw_content.decode()  # bytes to str
    content_dict = json.loads(raw_content)  # str to dict
    return self._translate_dict(content_dict)  # convert to CodeMeta

_translate_dict will do the heavy work of reading the crosswalk table for each of string_fields, read the corresponding value in the content_dict, and build a CodeMeta dictionary with the corresponding names from the crosswalk table.

One last thing to run your code: add it to the list in swh-indexer/swh/indexer/metadata_dictionary/__init__.py, so the rest of the code is aware of it.

Now, you can run it:

python3 -m swh.indexer.metadata_dictionary MyMapping path/to/input/file

and it will (hopefully) returns a CodeMeta object.

If it works, well done!

You can now improve your translation code further, by adding methods that will do more advanced conversion. For example, if there is a field named license containing an SPDX identifier, you must convert it to an URI, like this:

def normalize_license(self, s):
    if isinstance(s, str):
        return {"@id": "https://spdx.org/licenses/" + s}

This method will automatically get called by _translate_dict when it finds a license field in content_dict.