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# Kanta Database Format and Design Principles
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This document describes the on-disk format and design principles of Kanta.
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It is intentionally focused on the current standalone package behavior.
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## Core Principles
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1. Append-only durability
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- State changes are persisted as appended JSON lines.
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- Existing lines are never edited in place.
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2. Differential persistence
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- Kanta stores diffs (patches), not full state, for normal writes.
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- This keeps write volume small and preserves a clear change history.
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3. Deterministic replay
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- Current state is reconstructed by replaying log records in order.
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- Snapshot records accelerate replay while preserving deterministic results.
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4. Transactional in-memory writes
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- Application code mutates in-memory data inside `kanta.transaction(...)`.
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- On success, Kanta computes and queues a diff record.
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- On failure, in-memory data is rolled back.
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5. Explicit schema evolution
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- Schema migration functions are versioned (`migrate_vN`).
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- Migrations run at open time and advance the stored version.
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## On-Disk Record Types
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Kanta uses a newline-delimited stream where each line is either a change
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record or a snapshot record.
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### Change record
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One JSON object per line:
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```json
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{"ts":"2026-06-10T02:55:00Z","a":"update","v":5,"u":"user-id","m":"2026-06-10T02:55:00Z","diff":{"users":{"alice":{"age":31}}}}
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```
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Fields:
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- `ts`: UTC timestamp of the record.
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- `a`: action name.
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- `v`: schema version after this change.
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- `u`: optional actor identifier.
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- `m`: optional domain modification timestamp.
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- `diff`: jsondiff patch payload.
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### Snapshot record
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Snapshot lines are prefixed with `SNAPSHOT `, followed by JSON:
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```text
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SNAPSHOT {"ts":"2026-06-10T00:00:00Z","v":5,"state":{"users":{}},"m":"2026-06-10T00:00:00Z"}
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```
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Fields:
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- `ts`: snapshot creation time.
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- `v`: schema version represented by the snapshot.
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- `state`: full state dictionary.
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- `m`: optional domain modification timestamp.
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## Replay Model
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1. Find the last snapshot in the file, if present.
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2. Initialize replay state from snapshot state (or `{}` if none).
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3. Replay subsequent change records in order using patch application.
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4. The final replay state becomes in-memory `kanta.data`.
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This model provides fast startup for large logs while retaining append-only
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history.
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## Serialization Semantics
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- In-memory data is defined by an application `msgspec.Struct` type.
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- Kanta round-trips through plain builtins for persistence and diffing.
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- Dict keys are serialized as strings (`str_keys=True`) for stable JSON form.
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- Normalization changes introduced by struct decode/encode are logged as
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`migrate:msgspec` when they produce a diff.
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## Transaction Semantics
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- `kanta.transaction(action=...)` captures a pre-transaction snapshot dict.
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- On success:
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- compute diff between previous builtins and current builtins,
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- queue a `ChangeRecord` if non-empty.
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- On exception:
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- restore in-memory data from snapshot,
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- re-raise the exception.
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Nested transactions are rejected.
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## Flush and Lifecycle
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- Writes are queued in memory.
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- `kanta.flush()` appends queued records to disk.
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- A background async task can flush periodically.
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- `kanta.close()` performs final flush and releases file resources.
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- `async with Kanta(...)` guarantees open/close lifecycle management.
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## Migrations
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- Migration source is configured on `Kanta(...)` via `migrations=`.
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- Accepted values:
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- imported module object,
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- import path string.
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- Migrations mutate replayed dict state in-place and return the new version.
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## Safety Invariants
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- Any detected out-of-transaction mutation is treated as a fatal consistency
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violation.
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- Flush failures mark the instance as failed and trigger shutdown behavior.
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- Object identity of `kanta.data` is preserved across rollback when possible,
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minimizing stale-reference hazards for callers.
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