# PCloud
PCloud is a set of distributed infrastructure tools meant for setting up a personal cloud on commodity hardware such as Raspberry Pi.
The goal of the project is to provide:
* Object and file storage: distributed storage with configurable replication for durability and redundancy
* Knowledge graph: storing graph shaped data representing user/application generated content and relations between them
* Application infrastructure: to easily set up and run third-party applications, where applications can communicate with each other and produce/consume knowledge graph nodes
* Search infrastructure: indexing knowledge graph and making it searchable

To prove the viability of the project first milestone will be to build fully functional image storage and indexing infrastructure. User experience setting it up will look something like this:
* Set up PCloud on 3 or more servers and pair a mobile device with it
* Configure IFTTT (if this then that) like the workflow to automatically:
  * Back up every newly taken picture on PCloud
  * Run face detection app on backed up pictures and store this information in Metadata service
  * Index face annotations and make them searchable

The user must be able to configure all of these from the previously paired mobile device.

# Status
Three core infrastructure services have been prototyped:
* Knowledge Graph API: GraphQL based API with extensible schema
  * Provides CRUD operations
  * Auto-generates appropriate events upon data modification and includes them within mutation transaction
  * Application installed by Application Manager (see below) can extend Knowledge Graph schema
* Application Manager: supports installing third-party applications by uploading configuration archives via web UI
  * Application configuration consists of:
   * Schema extension (optional): if provided Knowledge Graph schema will be extended with new types and relations
   * Actions (optional): application can define any number of actions that can be invoked from other applications. Actions are parametrized
   * Initialization action (optional): application can configure the action, provided possibly by another application, to be run post-installation
   * Triggers (optional): applications can set up triggers on Knowledge Graph mutations. Triggers run actions
  * Applications are installed into separate namespaces for isolation
* Event Processor: monitors changes in Knowledge Graph and triggers actions registered by applications installed using Application Manager
  * It is a state machine moving events from NEW to IN_PROGRESS to DONE states

On top of this we are running five "third-party" applications:
* Random Puppy:
  * Does not use any PCloud features
  * Deploys web server with ingress
* Object Store:
  * Provides AWS S3 compatible API
  * Exposes create-bucket-with-webhook action so other applications can create buckets and receive notifications when new objects are created
  * An important detail here is that the object store itself is installed as a third-party app. This means other storage solutions can be integrated with PCloud infrastructure without changing PCloud itself
* Image importer:
  * Registers new Knowledge Graph node type: Image
  * Using Object Store create-bucket-with-webhook action to create new images bucket and register itself as a listener
  * For every new object creates a new Image node in Knowledge Graph
* Face Detector:
  * Registers new Knowledge Graph node type ImageSegment and extends previously created Image type with their relation
  * Registers trigger on new Image nodes with action running face detection algorithm, which upon completion creates ImageSegment node for each face and attaches them to source Image
* Photos UI:
  * Web-based photo gallery application consuming Image and ImageSegment nodes via Knowledge Graph API

Note that since Object Store is a standalone application with its API, new third-party applications can be developed to import data from cloud providers, such as Google Drive, where users might currently be storing their data.

# Next steps:
* To further isolate applications from each other, the namespaces that are created must be configured so they can communicate only with core PCloud services. PCloud API will take responsibility for forwarding action requests to applications
* Once the previous step is implemented, PCloud API can enforce ACLs and make application to application communication transparent to the PCloud owner. Application during installation will list actions, provided by other applications, it wants to use. And all of these can be reviewed and possibly rejected by the owner
* Make Knowledge Graph types namespaced. Right now applications add new types into the global namespace which can cause conflicts. To avoid this Knowledge Graph API can build namespaces on top of Dgraph GraphQL schemas
* Support replaying events so applications can "react" to events created before installation. This way Face Detection app will be able to process images created before the face detector was installed
* Make Tensorflow Facenet model work on Raspberry Pi