timeblocks 0.1.3

Reusable Django time block & recurrence engine.

Timeblocks

A reusable Django library for creating and managing time blocks using safe, deterministic recurrence rules.

Designed for scheduling systems where correctness, idempotency, and data safety matter.

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Why timeblocks?

Most scheduling implementations break when:

• recurrence rules change
• slots are regenerated
• bookings must be preserved
• timezones drift
• duplicate slots appear
• concurrent actions occur (booking vs updates)

timeblocks solves these problems by enforcing strict invariants:

• slots are generated from immutable templates
• destructive operations are explicit and scoped
• locked (booked) slots are never modified
• regeneration is safe and idempotent
• all datetime values are normalized to UTC
• concurrency is handled explicitly, not implicitly

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Mental Model (Read This First)

timeblocks separates intent from reality.

• SlotSeries represents intent

  “This resource should be available every Mon/Wed/Fri from 10–11”

• Slot represents reality

  A concrete time interval that exists, may be booked, or may be cancelled

This separation is intentional and fundamental.

SlotSeries is the source of truth.
Slots are generated artifacts.

Slots must never be treated as authoritative configuration.

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Core Concepts

SlotSeries (template)

A SlotSeries defines how slots should exist:

• start date
• time window
• recurrence rule
• termination condition

It does not represent bookings or history.

Slot (instance)

A Slot is a concrete time interval generated from a series.

Slots may be:

• open
• locked (e.g. booked)
• soft-deleted (historical)

Once a slot is locked, it becomes immutable.

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Lifecycle Semantics

A typical lifecycle looks like this:

1. A SlotSeries is created
2. Concrete Slot rows are generated
3. Some slots become locked (e.g. booked)
4. The series may be regenerated or cancelled
5. Historical slots are preserved

Important rules:

• Regeneration never modifies locked slots
• Cancellation never deletes historical data
• Slots are soft-deleted, never hard-deleted
• Operations are safe to retry (idempotent)

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Supported Recurrence Types

• NONE — single occurrence
• DAILY — every N days
• WEEKLY — specific weekdays (e.g. Mon/Wed/Fri)
• WEEKDAY_MON_FRI — Monday to Friday

Additional recurrence types can be added safely without breaking existing data.

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Installation

pip install timeblocks

Add to Django settings:

INSTALLED_APPS = [
    ...
    "django.contrib.contenttypes",
    "timeblocks",
]

Run migrations:

python manage.py migrate

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Basic Usage

from datetime import date, time
from timeblocks.services.series_service import SeriesService

series = SeriesService.create_series(
    owner=user,
    data={
        "start_date": date(2025, 1, 1),
        "start_time": time(9, 0),
        "end_time": time(10, 0),
        "timezone": "UTC",
        "recurrence_type": "DAILY",
        "interval": 1,
        "end_type": "AFTER_OCCURRENCES",
        "occurrence_count": 5,
    },
)

This will create:

• one SlotSeries
• five Slot rows
• all timestamps normalized to UTC

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Regenerating Slots

When a recurrence rule changes, regenerate safely:

from timeblocks.services.series_service import SeriesService

SeriesService.regenerate_series(
    series=series,
    scope="future",  # or "all"
)

Regeneration Rules

• locked slots are never touched
• soft-deleted slots are preserved
• scope controls blast radius
• operation is atomic and idempotent

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Cancelling a Series

from timeblocks.services.series_service import SeriesService

SeriesService.cancel_series(series=series)

Effects:

• series is deactivated
• future unlocked slots are soft-deleted
• past and locked slots remain intact

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Invariants & Guarantees

timeblocks enforces the following invariants at all times:

• a slot can never be booked twice
• locked slots are immutable
• regeneration is scoped and deterministic
• cancellation preserves historical data
• destructive operations are explicit
• all writes are transactional
• all datetime values are normalized to UTC

Violation of these invariants is considered a bug.

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Concurrency & Safety

timeblocks is designed to be safe under concurrent access.

Key principles:

• booking must use row-level locking (select_for_update)
• regeneration and cancellation lock affected rows before mutation
• destructive operations never race with bookings

Do not implement booking or regeneration logic outside the provided services unless you fully understand the concurrency implications.

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Common Gotchas & Best Practices

❗ Django Context Required

timeblocks is a Django app. Models and services must be used inside a configured Django environment (e.g. manage.py shell).

❗ Soft Deletes

Slots are soft-deleted. Always query active availability with:

Slot.objects.filter(is_deleted=False)

❗ Do Not Edit Slots Directly

Slots are generated artifacts. Always mutate schedules via SlotSeries and service methods.

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What timeblocks Does NOT Do

• booking logic
• payments
• permissions
• notifications
• UI or API views

These belong in your application layer.

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Public API Stability

The following interfaces are considered stable starting from v1.0:

• Slot, SlotSeries models
• SeriesService public methods
• Published enums and query helpers

Internal modules and helpers are not part of the public API and may change without notice.

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Compatibility

• Django >= 3.2
• Python >= 3.8
• Database-agnostic (PostgreSQL, MySQL, SQLite)

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Versioning & Upgrades

timeblocks follows semantic versioning.

• PATCH releases fix bugs without changing behavior
• MINOR releases add new recurrence types or capabilities
• MAJOR releases may change behavior or contracts

Breaking changes are always documented in the changelog.