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Build reactive back end with ease

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"boiler": Backend-Originated Instantly-Loaded Entity Repository

NOTE: This package is not profiled or checked for memory usage. It is recommended that you use Kubernetes to increase fault tolerance.

Flask-boiler manages your application state with Firestore. You can create view models that aggregates underlying data sources and store them immediately and permanently in Firestore. As a result, your front end development will be as easy as using Firestore. Flask-boiler is comparable to Spring Web Reactive.


When you change the attendance status of one of the participants in the meeting, all other participants receive an updated version of the list of people attending the meeting.


Some reasons that you may want to use this framework or architectual practice:

  • You want to build a reactive system and not just a reactive view.
  • You want to build a scalable app that is native to distributed systems.
  • You want a framework with a higher level of abstraction, so you can exchange components such as transportation protocols
  • You want your code to be readable and clear and written mostly in python, while maintaining compatibility to different APIs.
  • You have constantly-shifting requirements, and want to have the flexibility to migrate different layers, for example, switch from REST API to WebSocket to serve a resource.

This framework is at beta testing stage. API is not guaranteed and may change.

Documentations: readthedocs

Quickstart: Quickstart

API Documentations: API Docs

Example of a Project using flask-boiler: gravitate-backend

Related Technologies

Ideal Usage

boiler will compile your python code into flink jobs, web servers, and more to be run on a kubernetes engine (not currently implemented).

Ideal Usage

Connectors supported


  • REST API (Flask and Flasgger)
  • GraphQL (Starlette)
  • Firestore
  • Firebase Functions
  • JsonRPC (flask-jsonrpc)
  • Leancloud Engine
  • WebSocket (flask socketio)

To be supported:

  • Flink Table API
  • Kafka

Design Pattern

flask-boiler abstracts to MVVM (Model-View-ViewModel), where,

  1. Model consists of a transactional database or datastore, and lives in back end.
  2. ViewModel consists of a distributed state consists of Model and aggregator. It is the main part of boiler. For client-read, it receives the streams coming in from the Model layer, and output them as a View to the View layer. For client-write, it receives the change streams from View layer, and operate on Model layer to persist the change. ViewModel lives in the back end, and may be operated as boiler python code, or compiled as flink jobs in the case of big data application (to be implemented).
  3. View is the presentational layer for the back end. It serves 1NF normalized data that are readable to the front end without further aggregation. Client reads and writes to View. View should be ephemeral, and can be rebuilt from ViewModel.
    View may be a remote system, eg. firestore or leancloud.


In your project directory,

pip install flask-boiler

See more in Quickstart.

State Management

You can combine information gathered in domain models and serve them in Firestore, so that front end can read all data required from a single document or collection, without client-side queries and excessive server roundtrip time.

There is a medium article that explains a similar architecture called "reSolve" architecture.

See examples/meeting_room/view_models on how to use flask-boiler to expose a "view model" in firestore that can be queried directly by front end without aggregation.

Processor Modes

flask-boiler is essentially a framework for source-sink operations:

Source(s) -> Processor -> Sink(s)

Take query as an example,

  • Boiler
  • NoSQL
  • Flink
    • staticmethods: converts to UDF
    • classmethods: converts to operators and aggregator's

Declare View Model

class CityView(ViewModel):

    name = attrs.bproperty()
    country = attrs.bproperty()

    def new(cls, snapshot):
        store = CityStore()
        store.add_snapshot("city", dm_cls=City, snapshot=snapshot)
        return cls(store=store)

    def name(self):

    def country(self):

    def doc_ref(self):
        return CTX.db.document(f"cityView/{}")

Document View

class MeetingSessionGet(Mediator):

    from flask_boiler import source, sink

    source = source.domain_model(Meeting)
    sink = sink.firestore()  # TODO: check variable resolution order

    def materialize_meeting_session(self, obj):
        meeting = obj
        assert isinstance(meeting, Meeting)

        def notify(obj):
            for ref in obj._view_refs:
                self.sink.emit(reference=ref, snapshot=obj.to_snapshot())

        _ = MeetingSession.get(
        # mediator.notify(obj=obj)

    def start(cls):

WebSocket View

class Demo(WsMediator):

mediator = Demo(view_model_cls=rainbow_vm,

io = flask_socketio.SocketIO(app=app)


Create Flask View

You can use a RestMediator to create a REST API. OpenAPI3 docs will be automatically generated in <site_url>/apidocs when you run _ = Swagger(app).

app = Flask(__name__)

class MeetingSessionRest(Mediator):

    # from flask_boiler import source, sink

    view_model_cls = MeetingSessionC

    rest = RestViewModelSource()

    @rest.route('/<doc_id>', methods=('GET',))
    def materialize_meeting_session(self, doc_id):

        meeting = Meeting.get(doc_id=doc_id)

        def notify(obj):
            d = obj.to_snapshot().to_dict()
            content = jsonify(d)

        _ = MeetingSessionC.get(

    # @rest.route('/', methods=('GET',))
    # def list_meeting_ids(self):
    #     return [meeting.to_snapshot().to_dict() for meeting in Meeting.all()]

    def start(cls, app):

swagger = Swagger(app)

(currently under implementation)

Object Lifecycle


Object created with -> Object exported with obj.to_view_dict.


Object created when a new domain model is created in database -> Object changed when underlying datasource changes -> Object calls self.notify

Typical ViewMediator Use Cases

Data flow direction is described as Source -> Sink. "Read" describes the flow of data where front end would find data in Sink useful. "Write" describes the flow of data where the Sink is the single source of truth.


Read: Request -> Response
Write: Request -> Document

  1. Front end sends HTTP request to Server
  2. Server queries datastore
  3. Server returns response


Read: Document -> Document
Write: Document -> Document

  1. Datastore triggers update function
  2. Server rebuilds ViewModel that may be changed as a result
  3. Server saves newly built ViewModel to datastore


Read: Document -> Document
Write: Document -> Document

  1. Datastore triggers update function for document d at time t
  2. Server starts a transaction
  3. Server sets write_option to only allow commit if documents are last updated at time t (still under design)
  4. Server builds ViewModel with transaction
  5. Server saves ViewModel with transaction
  6. Server marks document d as processed (remove document or update a field)
  7. Server retries up to MAX_RETRIES from step 2 if precondition failed


Read: Document -> WebSocket Event
Write: WebSocket Event -> Document

  1. Front end subscribes to a ViewModel by sending a WebSocket event to server
  2. Server attaches listener to the result of the query
  3. Every time the result of the query is changed and consistent:
    1. Server rebuilds ViewModel that may be changed as a result
    2. Server publishes newly built ViewModel
  4. Front end ends the session
  5. Document listeners are released


Read: Document -> Document
Write: Document -> Document


Rest Query Query+Task WebSocket Document
Guarantees ≤1 (At-Most-Once) ≥ 1 (At-Least-Once) =1[^1] (Exactly-Once) ≤1 (At-Most-Once) ≥ 1 (At-Least-Once)
Idempotence If Implemented No Yes, with transaction[^1] If Implemented No
Designed For Stateless Lambda Stateful Container Stateless Lambda Stateless Lambda Stateful Container
Latency Higher Higher Higher Lower Higher
Throughput Higher when Scaled Lower[^2] Lower Higher when Scaled Lower[^2]
Stateful No If Implemented If Implemented Yes Yes
Reactive No Yes Yes Yes Yes

[^1]: A message may be received and processed by multiple consumer, but only one consumer can successfully commit change and mark the event as processed. [^2]: Scalability is limited by the number of listeners you can attach to the datastore.


Decoupled Domain Model and View Model

Using Firebase Firestore sometimes require duplicated fields across several documents in order to both query the data and display them properly in front end. Flask-boiler solves this problem by decoupling domain model and view model. View model are generated and refreshed automatically as domain model changes. This means that you will only have to write business logics on the domain model without worrying about how the data will be displayed. This also means that the View Models can be displayed directly in front end, while supporting real-time features of Firebase Firestore.

One-step Configuration

Rather than configuring the network and different certificate settings for your database and other cloud services. All you have to do is to enable related services on Google Cloud Console, and add your certificate. Flask-boiler configures all the services you need, and expose them as a singleton Context object across the project.


Since all View Models are persisted in Firebase Firestore. Even if your App Instance is offline, the users can still access a view of the data from Firebase Firestore. Every View is also a Flask View, so you can also access the data with auto-generated REST API, in case Firebase Firestore is not viable.

Added Safety

By separating business data from documents that are accessible to the front end, you have more control over which data is displayed depending on the user's role.

One-step Documentation

All ViewModels have automatically generated documentations (provided by Flasgger). This helps AGILE teams keep their documentations and actual code in sync.


When you need better performance or relational database support, you can always refactor a specific layer by adding modules such as flask-sqlalchemy.



In GraphQL, the fields are evaluated with each query, but flask-boiler evaluates the fields if and only if the underlying data source changes. This leads to faster read for data that has not changed for a while. Also, the data source is expected to be consistent, as the field evaluation are triggered after all changes made in one transaction to firestore is read.

GraphQL, however, lets front-end customize the return. You must define the exact structure you want to return in flask-boiler. This nevertheless has its advantage as most documentations of the request and response can be done the same way as REST API.

REST API / Flask

REST API does not cache or store the response. When a view model is evaluated by flask-boiler, the response is stored in firestore forever until update or manual removal.

Flask-boiler controls role-based access with security rules integrated with Firestore. REST API usually controls these access with a JWT token.


Redux is implemented mostly in front end. Flask-boiler targets back end and is more scalable, since all data are communicated with Firestore, a infinitely scalable NoSQL datastore.

Flask-boiler is declarative, and Redux is imperative. The design pattern of REDUX requires you to write functional programming in domain models, but flask-boiler favors a different approach: ViewModel reads and calculates data from domain models and exposes the attribute as a property getter. (When writing to DomainModel, the view model changes domain model and exposes the operation as a property setter). Nevertheless, you can still add function callbacks that are triggered after a domain model is updated, but this may introduce concurrency issues and is not perfectly supported due to the design tradeoff in flask-boiler.

Architecture Diagram:

Architecture Diagram


Pull requests are welcome.

Please make sure to update tests as appropriate.



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