swh.indexer.indexer module

swh.indexer.indexer.write_to_temp(filename: str, data: bytes, working_directory: str) → Iterator[str]

Write the sha1’s content in a temporary file.

Parameters:

• filename – one of sha1’s many filenames

• data – the sha1’s content to write in temporary file

• working_directory – the directory into which the file is written

Returns:

The path to the temporary file created. That file is filled in with the raw content’s data.

class swh.indexer.indexer.TId

type of the ids of index()ed objects.

alias of TypeVar(‘TId’)

class swh.indexer.indexer.TData

type of the objects passed to index().

alias of TypeVar(‘TData’)

class swh.indexer.indexer.TResult

return type of index()

alias of TypeVar(‘TResult’)

class swh.indexer.indexer.BaseIndexer(config=None, **kw)

Bases: Generic[TId, TData, TResult]

Base class for indexers to inherit from.

The main entry point is the run() function which is in charge of triggering the computations on the batch dict/ids received.

Indexers can:

• filter out ids whose data has already been indexed.

• retrieve ids data from storage or objstorage

• index this data depending on the object and store the result in storage.

Indexers are kafka journal client so they consumes specific topics. Topics will be inferred from the required class attribute “object_types”.

To implement a new object type indexer, inherit from the BaseIndexer and implement indexing:

run():

object_ids are different depending on object. For example: sha1 for content, sha1_git for revision, directory, release, and id for origin

To implement a new concrete indexer, inherit from the object level classes: ContentIndexer, DirectoryIndexer, OriginIndexer.

Then you need to implement the following functions:

filter():

filter out data already indexed (in storage).

index_object():

compute index on id with data (retrieved from the storage or the objstorage by the id key) and return the resulting index computation.

persist_index_computations():

persist the results of multiple index computations in the storage.

The new indexer implementation can also override the following functions:

prepare():

Configuration preparation for the indexer. When overriding, this must call the super().prepare() instruction.

check():

Configuration check for the indexer. When overriding, this must call the super().check() instruction.

register_tools():

This should return a dict of the tool(s) to use when indexing or filtering.

Prepare and check that the indexer is ready to run.

results: List[TResult]

object_types: List[str]

USE_TOOLS = True

catch_exceptions = True

Prevents exceptions in index() from raising too high. Set to False in tests to properly catch all exceptions.

storage: StorageInterface

objstorage: Any

idx_storage: IndexerStorageInterface

prepare(**kw) → None

Prepare the indexer’s needed runtime configuration. Without this step, the indexer cannot possibly run.

property tool: Dict

check() → None

Check the indexer’s configuration is ok before proceeding. If ok, does nothing. If not raise error.

register_tools(tools: Dict[str, Any] | List[Dict[str, Any]]) → List[Dict[str, Any]]

Permit to register tools to the storage.

Add a sensible default which can be overridden if not sufficient. (For now, all indexers use only one tool)

Expects the self.config[‘tools’] property to be set with one or more tools.

Parameters:

tools – Either a dict or a list of dict.

Returns:

List of dicts with additional id key.

Return type:

list

Raises:

ValueError – if not a list nor a dict.

index(id: TId, data: TData | None, **kwargs) → List[TResult]

Index computation for the id and associated raw data.

Parameters:

• id – identifier or Dict object

• data – id’s data from storage or objstorage depending on object type

Returns:

a dict that makes sense for the persist_index_computations() method.

Return type:

dict

filter(ids: List[TId]) → Iterator[TId]

Filter missing ids for that particular indexer.

Parameters:

ids – list of ids

Yields:

iterator of missing ids

abstractmethod persist_index_computations(results: List[TResult]) → Dict[str, int]

Persist the computation resulting from the index.

Parameters:

results – List of results. One result is the result of the index function.

Returns:

a summary dict of what has been inserted in the storage

class swh.indexer.indexer.ContentIndexer(config=None, **kw)

Bases: BaseIndexer[HashDict, bytes, TResult], Generic[TResult]

A content indexer working on the journal (method process_journal_objects) or on a list of ids directly (method run).

Note: ContentIndexer is not an instantiable object. To use it, one should inherit from this class and override the methods mentioned in the BaseIndexer class.

Prepare and check that the indexer is ready to run.

object_types: List[str] = ['content']

run(objects: List[Dict], **kwargs) → Tuple[Dict, List]

Given a list of ids:

• retrieve the content from the storage

• execute the indexing computations

• store the results

Parameters:

• objects – Objects read from a kafka topics

• **kwargs – passed to the index method

Returns:

A summary Dict of the task’s status

class swh.indexer.indexer.OriginIndexer(config=None, **kw)

Bases: BaseIndexer[str, None, TResult], Generic[TResult]

An object type indexer, inherits from the BaseIndexer and implements Origin indexing using the run method

Note: the OriginIndexer is not an instantiable object. To use it in another context one should inherit from this class and override the methods mentioned in the BaseIndexer class.

Implementation wise, the default behavior of this class is to consume origin_visit_status objects. Override the process_journal_objects() if you need to consume more topics.

Prepare and check that the indexer is ready to run.

object_types: List[str] = ['origin_visit_status']

filter_objects(objects: Iterable[Dict]) → Iterator[Dict]

Index origin on origin visit status with status ‘full’.

map_objects_to_typed_objects(objects: Iterable[Dict]) → Iterator[Origin]

Transform iterable of objects into an Origin iterator.

run(objects: List[Dict], **kwargs) → Tuple[Dict, List]

Given a list of dict representing either origin or origin visit status:

• retrieve the corresponding origins from storage

• execute the indexing computations on those origins

• finally store the results

• return a summary of such computations

Parameters:

• objects – list of origin visit status read from a kafka topics.

• **kwargs – passed to the index method

Returns:

Tuple of summary and indexation results

index_list(origins: Iterable[Origin], **kwargs) → List[TResult]

Default implementation which simply iterates over origins to index

class swh.indexer.indexer.DirectoryIndexer(config=None, **kw)

Bases: BaseIndexer[bytes, Directory, TResult], Generic[TResult]

An object type indexer, inherits from the BaseIndexer and implements Directory indexing using the run method

Note: the DirectoryIndexer is not an instantiable object. To use it in another context one should inherit from this class and override the methods mentioned in the BaseIndexer class.

Prepare and check that the indexer is ready to run.

object_types: List[str] = ['directory']

run(objects: List[Dict], **kwargs) → Tuple[Dict, List]

Given a list of sha1_gits:

• retrieve directories from storage

• execute the indexing computations

• store the results

Parameters:

ids – sha1_git’s identifier list