Architecture

Here there is info about main components in Superdesk and how these interact. To run superdesk we use honcho to define processes for each of components:

rest: gunicorn -c gunicorn_config.py wsgi
wamp: python3 -u ws.py
work: celery -A worker worker
beat: celery -A worker beat --pid=

REST API Server

The entry point is Superdesk REST API. This is a python application built on top of eve and flask frameworks. Clients communicate with this api to authenticate, fetch and modify data, upload new content etc.

There is an app factory which you can use to create apps for production/testing:

superdesk.factory.get_app(config=None, media_storage=None, config_object=None, init_elastic=None)

App factory.

Parameters:
  • config – configuration that can override config from default_settings.py
  • media_storage – media storage class to use
  • config_object – config object to load (can be module name, module or an object)
  • init_elastic – obsolete config - kept there for BC
Returns:

a new SuperdeskEve app instance

It can use different wsgi servers, we use Gunicorn.

Notifications

There is also websockets server where both API server and celery workers can push notifications to clients, which use that information to refresh views or otherwise keep in sync. In the background it’s using celery queue and from there it sends everything to clients. There is no communication from client to server, all changes are done via API server.

There is also a factory to create notification server:

superdesk.ws.create_server(config)

Create websocket server and run it until it gets Ctrl+C or SIGTERM.

Parameters:config – config dictionary

Celery Workers

Tasks that involve communication with external services (ingest update, publishing), do some binary files manipulation (image cropping, file metadata extraction) or happen periodically (content expiry) are executed using celery.

It uses same app factory like API server.

Data Layer

In short - main data storage is mongoDB, content items are also indexed using elastic. This logic is implemented via custom eve data layer, superdesk service layer and data backend.

class superdesk.datalayer.SuperdeskDataLayer(app)

Superdesk Data Layer.

Implements eve data layer interface, is used to make eve work with superdesk service layer. It handles app initialization and later it forwards eve calls to respective service.

get_elastic_resources()

Get set of available elastic resources.

init_elastic(app)

Init elastic index.

It will create index and put mapping. It should run only once so locks are in place. Thus mongo must be already setup before running this.

class superdesk.services.BaseService(datasource=None, backend=None)

Base service for all endpoints, defines the basic implementation for CRUD datalayer functionality.

find(where, **kwargs)

Find items in service collection using mongo query.

Parameters:where (dict) –
is_authorized(**kwargs)

Subclass should override if the resource handled by the service has intrinsic privileges.

Parameters:kwargs – should have properties which help in authorizing the request
Returns:False if unauthorized and True if authorized

Remove item from search.

Parameters:item (dict) – item
search(source)

Search using search backend.

Parameters:source – query source param
class superdesk.eve_backend.EveBackend

Superdesk data backend, handles mongodb/elastic data storage.

create(endpoint_name, docs, **kwargs)

Insert documents into given collection.

Parameters:
  • endpoint_name – api resource name
  • docs – list of docs to be inserted
create_in_mongo(endpoint_name, docs, **kwargs)

Create items in mongo.

Parameters:
  • endpoint_name – resource name
  • docs – list of docs to create

Create items in elastic.

Parameters:
  • endpoint_name – resource name
  • docs – list of docs
delete(endpoint_name, lookup)

Delete method to delete by using mongo query syntax.

Parameters:
  • endpoint_name – Name of the endpoint
  • lookup – User mongo query syntax. example 1. {'_id':123}, 2. {'item_id': {'$in': [123, 234]}}
Returns:

Returns the mongo remove command response. {‘n’: 12, ‘ok’: 1}
delete_ids_from_mongo(endpoint_name, ids)

Delete the passed ids from mongo without searching or checking

Parameters:ids –
Returns:
find(endpoint_name, where, max_results=0)

Find items for given endpoint using mongo query in python dict object.

It handles request creation here so no need to do this in service.

:param string endpoint_name :param dict where :param int max_results

find_and_modify(endpoint_name, **kwargs)

Find and modify in mongo.

Parameters:
  • endpoint_name – resource name
  • kwargs – kwargs for pymongo find_and_modify
find_one(endpoint_name, req, **lookup)

Find single item.

Parameters:
  • endpoint_name – resource name
  • req – parsed request
  • lookup – additional filter
get(endpoint_name, req, lookup)

Get list of items.

Parameters:
  • endpoint_name – resource name
  • req – parsed request
  • lookup – additional filter
get_from_mongo(endpoint_name, req, lookup)

Get list of items from mongo.

No matter if there is elastic configured, this will use mongo.

Parameters:
  • endpoint_name – resource name
  • req – parsed request
  • lookup – additional filter

Remove document from search backend.

:param endpoint_name :param dict doc: Document to delete

replace(endpoint_name, id, document, original)

Replace an item.

Parameters:
  • endpoint_name – resource name
  • id – item id
  • document – next version of item
  • original – current version of document
replace_in_mongo(endpoint_name, id, document, original)

Replace item in mongo.

Parameters:
  • endpoint_name – resource name
  • id – item id
  • document – next version of item
  • original – current version of item

Replace item in elastic.

Parameters:
  • endpoint_name – resource name
  • id – item id
  • document – next version of item
  • original – current version of item
search(endpoint_name, source)

Search for items using search backend

:param string endpoint_name :param dict source

set_default_dates(doc)

Helper to populate _created and _updated timestamps.

system_update(endpoint_name, id, updates, original)

Only update what is provided, without affecting etag.

This is useful when you want to make some changes without affecting users.

Parameters:
  • endpoint_name – api resource name
  • id – document id
  • updates – changes made to document
  • original – original document
update(endpoint_name, id, updates, original)

Update document with given id.

Parameters:
  • endpoint_name – api resource name
  • id – document id
  • updates – changes made to document
  • original – original document
update_in_mongo(endpoint_name, id, updates, original)

Update item in mongo.

Modifies _updated timestamp and _etag.

Parameters:
  • endpoint_name – resource name
  • id – item id
  • updates – updates to item to be saved
  • original – current version of the item

Media Storage

By default uploaded/ingested files are stored in mongoDB GridFS.

class superdesk.storage.SuperdeskGridFSMediaStorage(app=None)
find(folder=None, upload_date=None, resource=None)

Search for files in the GridFS

Searches for files in the GridFS using a combination of folder name and/or upload date comparisons. The upload date comparisons uses the same mongodb BSON comparison operators, i.e. $eq, $gt, $gte, $lt, $lte and $ne, and can be combined together.

Parameters:
  • folder (str) – Folder name
  • upload_date (dict) – Upload date with comparison operator (i.e. $lt, $lte, $gt or $gte)
  • resource – The resource type to use
Return list:

List of files that matched the provided parameters
put(content, filename=None, content_type=None, metadata=None, resource=None, folder=None, **kwargs)

Store content in gridfs.

Parameters:
  • content – binary stream
  • filename – unique filename
  • content_type – mime type
  • metadata – file metadata
  • resource – type of resource
  • folder (str) – Folder that the file will be stored in
Return str:

The ID that was generated for this object
remove_unreferenced_files(existing_files, resource=None)

Get the files from Grid FS and compare against existing files and delete the orphans.

url_for_download(media_id, content_type=None)

Return url for download.

Parameters:media_id – media id from media_id method
url_for_media(media_id, content_type=None)

Return url for given media id.

Parameters:media_id – media id from media_id method

There is also Amazon S3 implementation, which is used when Amazon is configured via settings.

class superdesk.storage.AmazonMediaStorage(app=None)
delete_objects(ids)

Delete the objects with given list of ids.

exists(id_or_filename, resource=None)

Test if given name or unique id already exists in storage system.

find(folder=None, upload_date=None, resource=None)

Search for files in the S3 bucket

Searches for files in the S3 bucket using a combination of folder name and/or upload date comparisons. Also uses the superdesk.utc.query_datetime method to compare the upload_date provided and the upload_date of the file.

Parameters:
  • folder (str) – Folder name
  • upload_date (dict) – Upload date with comparison operator (i.e. $lt, $lte, $gt or $gte)
  • resource – The resource type to use
Return list:

List of files that matched the provided parameters
get(id_or_filename, resource=None)

Open the file given by name or unique id.

Note that although the returned file is guaranteed to be a File object, it might actually be some subclass. Returns None if no file was found.

get_all_keys()

Return the list of all keys from the bucket.

media_id(filename, content_type=None, version=True)

Get the media_id path for the given filename.

if filename doesn’t have an extension one is guessed, and additional version option to have automatic version or not to have, or to send a string one.

put(content, filename=None, content_type=None, resource=None, metadata=None, _id=None, version=True, folder=None)

Save a new file using the storage system, preferably with the name specified.

If there already exists a file with this name name, the storage system may modify the filename as necessary to get a unique name. Depending on the storage system, a unique id or the actual name of the stored file will be returned. The content type argument is used to appropriately identify the file when it is retrieved.

Parameters:
  • content (ByteIO) – Data to store in the file object
  • filename (str) – Filename used to store the object
  • content_type (str) – Content type of the data to be stored
  • resource – Superdesk resource, i.e. ‘upload’ or ‘download’
  • metadata – Not currently used with Amazon S3 storage
  • _id (str) – ID to be used as the key in the bucket
  • version – If True the timestamp will be prepended to the key else a string can be used to prepend the key
  • folder (str) – The folder to store the object in
Return str:

The ID that was generated for this object
remove_unreferenced_files(existing_files)

Get the files from S3 and compare against existing and delete the orphans.