insights/.claude/commands/conventions.md

description

description
Insights coding conventions for frontend and backend development

Insights Coding Conventions

When writing code, follow these Insights coding conventions.

Source of truth: These conventions are maintained at:

• https://hanzo.ai/handbook/engineering/conventions/frontend-coding
• https://hanzo.ai/handbook/engineering/conventions/backend-coding

If this file gets out of sync, update it from the source.

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Frontend Coding Conventions

Two layers: Kea -> React

Our frontend webapp is written with Kea and React as two separate layers. Kea is used to organise the app's data for rendering (we call this the data or state layer), and React is used to render the computed state (this is the view or template layer).

We try to be very explicit about this separation, and avoid local React state wherever possible, with exceptions for the lib/ folder. Having all our data in one layer makes for code that's easier to test, and observe. Basically, getting your data layer right is hard enough. We aim to not make it harder by constraining your data to a DOM-style hierarchy.

Hence the explicit separation between the data and view layers.

General tips

• Think data first: get your mental model of the data flowing through the app right, and then everything else will be simpler.
• Be practical, yet remember that you are balancing speed of delivery with ease of maintainability. If you have to choose: code should be easier to understand than it was to write.

Do-s & Don't-s

• General
  • Write all new code with TypeScript and proper typing.
  • Write your frontend data handling code first, and write it in a Kea logic.
  • Don't use useState or useEffect to store local state. It's false convenience. Take the extra 3 minutes and change it to a logic early on in the development.
  • Logics still have a tiny initialization cost. Hence this rule doesn't apply to library components in the lib/ folder, which might be rendered hundreds of times on a page with different sets of data. Still feel free to write a logic for a complicated lib/ component when needed.
  • Use named exports (export const DashboardMenu = () => <div />), and avoid default exports.
• Naming things:
  • Always look around the codebase for naming conventions, and follow the best practices of the environment (e.g. use camelCase variables in JS, snake_case in Python).
  • Use clear, yet functional names (searchResults vs data).
  • Logics are camelCase (dashboardLogic)
  • React components are PascalCase (DashboardMenu).
  • Props for both logics and components are PascalCase and end with Props (DashboardLogicProps & DashboardMenuProps)
  • Name the .ts file according to its main export: DashboardMenu.ts or DashboardMenu.tsx or dashboardLogic.ts or Dashboard.scss. Pay attention to the case.
  • Avoid index.ts, styles.css, and other generic names, even if this is the only file in a directory.
• Scenes
  • Our app is built of scenes, managed through a scene router in sceneLogic.
  • A scene is the smallest unit in the router and for code splitting. Usually we split scenes by resource type (dashboard, insight) and function (edit, index).
  • Each scene (e.g. Dashboards) exports an object of type SceneExport, containing the scene's root logic and its React component.
  • The scene's logic is automatically mounted and receives the scene's URL params as props (via paramsToProps).
  • Use urlToAction and actionToUrl on the scene's logic to sync state with the URL. Try to only use them on the scene's logic, not in any deeper logics.
  • Logics mounted by React components through the view layer unmount when the component unmounts. Use useAttachedLogic(dataNodeLogic(propsFromComponent), mySceneLogic()) to attach a logic to the scene's logic so it persists until the scene's logic is unmounted, surviving React component remounts.
  • You can control what's shown on the tab via the breadcrumbs selector in your scene's logic. The last breadcrumb controls the title and the icon, the one before that controls the back button. If there are more breadcrumbs, they will be ignored.
• Kea
  • It's worth repeating: think of the data flow. Then work to simplify it. Derive as much state as possible via selectors, update the source via cascading actions, and avoid complex loops where a value triggers a subscription which calls an action which changes the value which triggers the subscription, ...
  • Use subscriptions and propsChanged sparingly, only if you can't find any other way. These have a high chance of leading to messy, cyclic or slow data flows.
  • Try to write your code such that you only use urlToAction in your scene's logic (e.g. insightSceneLogic), and never deeper down in e.g. propertyFilterLogic.
  • Take the time and read through the Kea docs until you can explain how all the various operations (actions, reducers, selectors, listeners, subscriptions, props, events, hooks, etc) work behind the scenes. It's worth knowing your tools.
• CSS
  • We use Tailwind CSS wherever possible
  • Where it's not possible
    • We use regular SCSS files for styling to keep things simple and maintainable in the long run, as opposed to supporting the CSS-in-JS flavour of the month.
    • Inside MyBlogComponent.tsx import MyBlogComponent.scss
    • Namespace all your CSS rules under globally unique classes that match the component's name and case, for example .DashboardMenu { put everything here }
    • We loosely follow BEM conventions. If an element can't be namespaced inside a container class (e.g. modals that break out of the containing DOM element), use BEM style names like .DashboardMenu__modal to keep things namespaced.
  • Keep an eye out for custom styles in SCSS files that can be easily replaced with Tailwind classes and replace them with Tailwind when you see them
• Testing
  • Before adding a test, make sure it earns its place and sits as low on the test pyramid as it can — the value/cost rubric in Testing below is language-agnostic.
  • Write logic tests for all logic files.
  • react testing library tests are particularly useful for components with complex interactions or to guide future humans or agents when they're changing components without full context of the uses and edge cases
  • Add all new presentational elements and scenes to our storybook. Run pnpm storybook locally.

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Backend Coding Conventions

Coding standards

• Always place imports at the top of the file (module level), never inside functions or methods (local imports)

Dataclasses

• Prefer a small dataclass over a tuple when returning or passing multiple values: always when two or more elements share a type (callers can silently swap them, e.g. (start, end)), and when there are roughly 3+ elements, where positional access hurts readability
• Use @frozen from insights.dataclasses (applies frozen=True, kw_only=True, slots=True; every flag overridable, e.g. @frozen(slots=False) for functools.cached_property)
• Consume results with dot notation (result.field), never by unpacking into positional locals
• Name dataclasses after the domain concept (BillingPeriod), never *Info/*Data/*Tuple
• Mark secret fields with field(repr=False)
• A bare @dataclass without an explicit frozen= choice fails the insights/test/test_dataclass_defaults.py ratchet and is flagged by the prefer-frozen-dataclasses semgrep rule

Logging

As a general rule, we should have logs for every expected and unexpected actions of the application, using the appropriate log level.

We should also be logging these exceptions to Insights. Python exceptions should almost always be captured automatically without extra instrumentation, but custom ones (such as failed requests to external services, query errors, or Celery task failures) can be tracked using capture_exception().

Levels

A log level or log severity is a piece of information telling how important a given log message is:

• DEBUG: should be used for information that may be needed for diagnosing issues and troubleshooting or when running application in the test environment for the purpose of making sure everything is running correctly
• INFO: should be used as standard log level, indicating that something happened
• WARN: should be used when something unexpected happened but the code can continue the work
• ERROR: should be used when the application hits an issue preventing one or more functionalities from properly functioning

Format

django-structlog is the default logging library we use (see docs). It's a structured logging framework that adds cohesive metadata on each logs that makes it easier to track events or incidents.

Structured logging means that you don't write hard-to-parse and hard-to-keep-consistent prose in your logs but that you log events that happen in a context instead.

import structlog
logger = structlog.get_logger(__name__)
logger.debug("event_sent_to_kafka", event_uuid=str(event_uuid), kafka_topic=topic)

will produce:

2021-10-28T13:46:40.099007Z [debug] event_sent_to_kafka [insights.api.capture] event_uuid=017cc727-1662-0000-630c-d35f6a29bae3 kafka_topic=default

As you can see above, the log contains all the information needed to understand the app behaviour.

Security

Don't log sensitive information. Make sure you never log:

• authorization tokens
• passwords
• financial data
• health data
• PII (Personal Identifiable Information)

Testing

A test suite is a shared, permanent liability: every test runs on every PR forever, costs CI time, can flake and block unrelated work, and is code someone has to maintain as the system changes. So judge a new test on two independent axes — value (does it catch a realistic regression we actually make?) and cost (how far down the test pyramid does it sit?). Maximize value and minimize cost; this never means "write fewer tests", it means drop the ones that catch nothing and push the rest as far down the pyramid as they go.

• All new packages and most new significant functionality should come with unit tests
• Significant features should come with integration and/or end-to-end tests
• Analytics-related queries should be covered by snapshot tests for ease of reviewing
• For pytest use the assert x == y instead of the self.assertEqual(x, y) format of tests
  • it's recommended in the pytest docs
  • and you get better output when the test fails
• prefer assertions like assert ['x', 'y'] == response.json()["results"] over assert len(response.json()["results"]) == 2
  • that's because you want test output to give you the information you need to fix a failure
  • and because you want your assertions to be as concrete as possible it shouldn't be possible to break the code and the test pass

Does this test earn its place?

Before writing a test, answer in one sentence: what realistic regression does this catch that no existing test already catches? Name the bug, the code path, and the input that would break — "increases coverage", "good practice", and "the function exists" are not answers. A good answer sounds like "if someone makes parse_filters drop the team_id clause, this fails".

Most low-value tests are one of these — recognize them and extend an existing test (or delete the code) instead:

• Trivial / framework behavior: getters, setters, constants, that Django saved a row or that DRF serialized a field. You're testing someone else's code, not yours.
• Change-detector tests: asserting which private methods were called, with mocks wired to match the current code. They fail on every refactor and catch no real bug. Assert observable behavior through the public interface (return value, persisted state, emitted event, HTTP response), not the choreography that produces it. See Change-Detector Tests Considered Harmful.
• Redundant coverage: a new test that's a variation of an existing one is a @parameterized case (Python) or a test.each row (Jest), not a new test function.
• Coverage-chasing: an uncovered line is information, not a defect — don't add a test just to move the number.

Weight tests down the pyramid

Each rung is roughly an order of magnitude slower and flakier than the one below:

pure function  →  kea logic test  →  Django TestCase  →  Datastore-backed test  →  Playwright e2e
   cheapest                                                                          most expensive

Aim for a ratio, not a cap: many tests at the bottom, very few at the top — if you want more coverage, add it at the bottom. When logic is hard to test cheaply, that's a design signal: extract it into a pure function (or a kea logic) and test that directly rather than standing up a database, a request, and a render. Escalating to the next rung is the last resort, not the default.

• Use TestCase, not TransactionTestCase, unless you truly need it. TransactionTestCase flushes the DB between tests instead of rolling back a transaction — dramatically slower, and a common source of cross-test interference. For transaction.on_commit side effects use self.captureOnCommitCallbacks(execute=True); reaching Datastore is not a reason to switch (DatastoreTestMixin runs on a plain TestCase).
• Mock only true boundaries — network, external APIs, the clock, queues. Don't mock your own internal helpers; that's how change-detector tests are born.
• Frontend: prefer a kea logic test (logic.actions / logic.values) over a full component render whenever the behavior lives in the logic, and don't snapshot large rendered trees — assert specific fields instead.
• Keep tests deterministic and isolated: no time.sleep or arbitrary waits (use freeze_time or wait on a real condition), no real network or live external services, and they must pass in any order. Don't leave a @skip/xfail/.only without a one-line reason and a linked issue.

Fast developer ("unit") tests

A good test should:

• focus on a single use-case at a time
• have a minimal set of assertions per test
• explain itself well
• help you understand the system
• make good use of parameterized testing to show behavior with a range of inputs
• help us have confidence that the impossible is unrepresentable
• help us have confidence that the system will work as expected

Integration tests

• Integration tests should ensure that the feature works in the running system
• They give greater confidence (because you avoid the mistake of just testing a mock) but they're slower
• They are generally less brittle in response to changes because they test at a higher level than developer tests (e.g. they test a Django API not a class used inside it)

To ee or not to ee?

We default to open but when adding a new feature we should consider if it should be MIT licensed or Enterprise edition licensed. Everything in the ee folder is covered by a different license. It's easy to move things from ee to open, but not the other way.

All the open source code is copied to the insights-foss repo with the ee code stripped out. You need to consider whether your code will work if imports to ee are unavailable.