Evolve tag responses without breaking old clients

Application background

A reading-list app shows tags beside each bookmark. Its existing mobile client receives strings such as "databases" and displays them directly. The new web interface needs a stable tag ID and a color so a tag can be renamed without losing its identity.

Replacing the strings with objects in the existing response would surprise old mobile code. Customers may keep that old app for months. The service needs to support both representations while clients move to the new one.

Example walkthrough

An API contract is the agreed request and response behavior that callers rely on. Compatibility means the supported older callers can continue to work during the change.

Your assignment

Deliver: Implement old and new tag response formats from one stored model. Define how callers select a format and when support for the old one can end.

Required behavior: Keep tags as string[] for the supported old contract and add a separately named tagObjects field or explicit API version. Reject incompatible input clearly. Both representations derive from one canonical stored model.

The required first milestone is a working local implementation of the behavior above. The numbered implementation steps define the scope. The cloud architecture is a later extension, not something the starter has already provisioned.

Get the code and run the supplied example

The code is in the public junior-to-staff repository. Install Git and Python 3.12+. No AWS account or Python packages are required for this first run. If you already have a checkout, use it and skip cloning.

git clone https://github.com/Soulful-Iris/junior-to-staff.git
cd junior-to-staff
python3 examples/architecture-starts/04_the_api_that_does_not_break_its_callers.py

Supplied file: examples/architecture-starts/04_the_api_that_does_not_break_its_callers.py. You can also read or download the source here (download file, source below).

"""Local mechanism demonstration for 04-the-api-that-does-not-break-its-callers. No AWS resources are created."""
tags=[{'id':'t7','label':'databases','color':'blue'}]
def response():
    return {'tags':[t['label'] for t in tags],'tagObjects':[dict(t) for t in tags]}
body=response(); print(body)
print('Old client:',', '.join(body['tags']))
print('New client:',body['tagObjects'][0]['id'])

This program is a mechanism demonstration: it runs the small scenario in one process and prints the result. It is not an HTTP service, a complete application, or an AWS deployment. A successful run demonstrates this mechanism only. It does not establish the workload or failure guarantees of the application you will build.

Example output from the supplied run:

Generated IDs and timestamps may differ. Compare the state transitions and outcomes.

{'tags': ['databases'], 'tagObjects': [{'id': 't7', 'label': 'databases', 'color': 'blue'}]}
Old client: databases
New client: t7

Run the application you will extend

The reading-list API setup guide gives you a real local HTTP server, SQLite database, save/list/edit requests and controlled title success/timeout behavior. Start it in one terminal and send the documented curl requests from another. Read that setup before following the implementation steps below. The demo above isolates this lesson's mechanism. The server is where you integrate it.

For a first run, start this in terminal 1 from the repository root:

python3 examples/reading-list-starter/app.py --db /tmp/reading-list.sqlite3

In terminal 2, save one bookmark with a controlled title timeout:

curl -i http://127.0.0.1:8080/bookmarks \
  -H 'X-Demo-User: alice' -H 'Content-Type: application/json' \
  -d '{"url":"https://example.com/docs","title_mode":"timeout"}'

Expect 201 Created, a bookmark id and title_status: "timeout". The URL is persisted despite the title failure. This is the supplied baseline. The assignment adds the behavior described above. The lookup is a fixture, so no external website is contacted. For members Bob or Ben in a scenario, use the starter's second demo identity bob. Alice or Ana corresponds to alice.

Work in your own branch or copy examples/reading-list-starter/ to work/04-the-api-that-does-not-break-its-callers/. app.py exists in that directory. Add the modules named below there as you separate HTTP, storage and background work. The server has demo membership, not production authentication.

Local components and state to implement

This table names the records, interfaces or decision inputs for your deliverable. Unless a name is explicitly linked to supplied source above, it is something you create. Implement the local state transitions first, then connect the HTTP, storage or worker boundaries required by the steps.

Implement the assignment

1. Capture actual caller behavior

Write down request/response examples used by supported clients, including nulls, empty lists, unknown fields and error shapes. Do not assume every client ignores additional fields. Use an explicit version if strict decoders require it.

2. Add a canonical model and adapters

Store tag IDs and metadata once. Build response serializers for the old and new contracts. Keep legacy tags as strings. Do not overload the same field with mixed types. Decide how old clients create or rename tags without stable IDs.

3. Run both client paths

Use a small old-client script that joins tag strings and a new-client script that reads IDs. Send both through the same application state. Check a real error response too. Compatible success bodies do not protect callers from changed error semantics.

4. Retire deliberately

Measure requests by explicit client/API version, publish the support window and keep an owner for the adapter. Remove it only after the agreed condition. Database migration and API retirement are separate steps.

Demonstrate the completed local result

Handoff: In your implementation README, include the start command, one successful operation, the failure case above and the resulting stored state or decision. State which dependencies are simulated. Someone with a fresh checkout should be able to reproduce this without your chat history.

Workload assumptions and capacity decisions

These are constructed exercise assumptions. The stated workload is a design target. The local demonstration does not establish that throughput. Use the estimation constants to check units before choosing capacity.

Map the local implementation to AWS

Deployment status: local only. Running the supplied command creates no AWS resources and configures no cloud connections. The diagram is a proposed deployment of the completed application. Each box needs either a deployed runtime, a provisioned service or an explicitly external dependency.

Read the diagram by following the arrows from the entry point: application code accepts the request or event, the state owner commits it, and any worker produces the later result. The table ties those roles to code and adapter work. Multiple boxes do not imply multiple Python files already exist.

Compatibility belongs at the application boundary. A managed gateway can route versions, but it cannot infer the meaning of tags or repair a changed JSON type for old callers.

Provision resources, then connect the application

Use one disposable AWS environment for the cloud exercise. Put the named resources in infra/template.yaml or your existing IaC tool, pass resource IDs through configuration, and scope each runtime role to its own tables, buckets and queues. The diagram is a design to implement. It is not a claim that these resources have been deployed. Record the commands you used to deploy and remove the exercise resources.

For concrete provisioning commands, configuration wiring and cleanup, use the AWS foundation guide. It includes a deployable table/queue/object-storage foundation and explains which application and service adapters you still implement.

A provisioned queue or table does not make the local program use it. Configure resource IDs in the deployed runtime, replace the local adapter, and replay the same successful and failing operation against that runtime. Record the deployed commit and observable result, then remove the disposable resources using your infrastructure tool.

Extend the design after the baseline works

Change units from milliseconds to seconds. Use a new field name or version and explicit conversion. Keeping a JSON number type does not preserve its semantic contract.