ktables 0.1.1

Materialize a Kafka topic into an in-memory, compacted dict — a GlobalKTable for asyncio Python.

ktables

Materialize a Kafka topic into an in-memory, compacted dict — a GlobalKTable for asyncio Python.

Every process that opens a KafkaTable replays the topic from the beginning into a local read-only mapping, then keeps consuming for live updates; a KafkaTableWriter maintains the topic with keyed upserts and tombstones. Built for small, broadly-needed reference data — service registries, capability advertisements, feature flags, config maps — not for large or high-churn state.

Table of Contents

• Background
• Install
• Usage
• Consistency contract
• API
• Contributing
• License

Background

Kafka Streams (JVM) has two table abstractions over changelog topics: the partition-sharded KTable, where each application instance holds a slice of the keys, and GlobalKTable, where every instance bootstraps and maintains a full local copy — the right shape for lookup data that any instance may need at any moment. The Python ecosystem has several maintained stream-processing frameworks, but all of them implement only the sharded shape, with framework-owned changelog topics and their own process runtimes. ktables fills the gap with just the global-table piece, as a plain asyncio library over aiokafka: your topic, your message format, your event loop.

The Kafka semantics the implementation relies on (group-less consumers, catch-up gating against end offsets, compaction independence) are documented, with provenance, in the module docstring of kafka_table.py.

Install

pip install ktables

Requires Python 3.10+. Pydantic is not required — the .json() presets accept any class with pydantic-v2's JSON methods.

Usage

Maintain a registry from one service:

from ktables import KafkaTableWriter

writer = KafkaTableWriter.json(
    bootstrap_servers="localhost:9092", topic="my.registry", model=ServiceRecord
)
async with writer:
    await writer.set("billing", record)      # upsert (broker-acked)
    ...
    await writer.delete("billing")           # tombstone: removes the key

Consume it from any other process:

from ktables import KafkaTable

table = KafkaTable.json(
    bootstrap_servers="localhost:9092", topic="my.registry", model=ServiceRecord
)
async with table:               # replays the topic; returns once caught up
    record = table.get("billing")
    if table.status != "caught_up":   # "degraded": catch-up timed out
        ...

Non-pydantic payloads: construct directly with your own codecs — KafkaTable(..., value_decoder=bytes_to_value) / KafkaTableWriter(..., value_encoder=value_to_bytes).

Removing a key on clean shutdown

There is deliberately no delete_on_close option (shutdown-time deletion is application policy, and no library can promise it on a crash). Compose it:

async with writer:
    await writer.set(my_key, my_record)
    try:
        ...  # serve
    finally:
        await writer.delete(my_key)   # acked before the producer stops

Locked-down clusters

Both classes ensure their topic exists at start (idempotent create, compacted). If the application lacks topic-create ACLs, pass ensure_topic=False and create the topic out-of-band (the module-level ensure_topic() function is the deploy-time primitive).

Consistency contract

KafkaTable is eventually consistent. Precisely:

1. When start() / async with returns, contents are complete as of the topic's end offsets at start time — unless status == "degraded" (catch-up timed out; data may be partial).
2. Thereafter, updates appear within milliseconds of the broker write — but there is no read-your-own-writes: after await writer.set(k, v), a table in the same process may briefly still return the old value.
3. Contents are stable between your awaits (single event loop; only the reader task mutates). Use snapshot() for a copy held across awaits.
4. Correctness does not depend on broker-side compaction: last-write-wins over the full log yields the same dict; compaction only bounds replay time.

A tombstone is a record with a null value (b"" is data, not a tombstone). If the background reader dies (non-retriable error, e.g. authorization), contents freeze at the last applied state: status becomes "failed" and failure holds the exception — gate liveness decisions on status, never on reads alone. Transient broker outages do not kill the reader; it resumes.

API

KafkaTable[V] — read-only Mapping[str, V]

Member	Description
KafkaTable(*, bootstrap_servers, topic, value_decoder, key_decoder=utf-8, catchup_timeout=30.0, poll_timeout_ms=200, ensure_topic=True, topic_configs=None)	Construct (does not connect).
KafkaTable.json(*, bootstrap_servers, topic, model, **kwargs)	Preset wiring model.model_validate_json as the decoder.
start() / stop() / async with	Lifecycle. start() raises on double-start, missing topic, or reader death during catch-up; on catch-up timeout it serves degraded.
table[key], key in table, iter, len, .get(key, default=None)	Mapping reads. Raise RuntimeError before start().
snapshot()	Shallow-copy dict, safe to hold across awaits.
status	"unstarted" | "loading" | "caught_up" | "degraded" | "failed".
failure	Exception that killed the reader, else None.
is_caught_up / wait_until_caught_up(timeout=None)	Catch-up gate; the wait returns False on timeout or reader death.
stats	Frozen ViewStats snapshot (see below).

Equality is identity and instances are hashable: a running table is a resource handle, not a value.

KafkaTableWriter[V]

Member	Description
KafkaTableWriter(*, bootstrap_servers, topic, value_encoder, key_encoder=utf-8, ensure_topic=True, topic_configs=None, enable_idempotence=True)	Construct. Idempotence implies acks=all (registry-grade durability); opt out for throwaway data.
KafkaTableWriter.json(*, bootstrap_servers, topic, model=None, **kwargs)	Preset encoding via model_dump_json() (model is typing-only).
set(key, value)	Keyed upsert; awaits broker ack. Re-set periodically as a heartbeat.
delete(key)	Publishes a null-value tombstone; awaits broker ack.
start() / stop() / async with	Lifecycle; set/delete before start raise RuntimeError.

The key encoder must be deterministic and stable across processes — on a multi-partition topic, per-key ordering holds only if a key always hashes to the same partition.

Module level

Member	Description
ensure_topic(bootstrap_servers, topic, *, num_partitions=1, replication_factor=1, topic_configs=None) -> bool	Idempotent explicit create; True if this call created it. Defaults are dev-grade — production registries want RF≥3, min.insync.replicas=2, acks=all.
DEFAULT_TOPIC_CONFIGS	{"cleanup.policy": "compact"} (read-only mapping).
ViewStats	Frozen counters: records_applied, tombstones_applied, keyless_records, key_decode_errors, value_decode_errors, catch_up_seconds, replayed_at_catch_up.
SupportsJsonModel	Protocol the .json() presets require (model_dump_json / model_validate_json).
TableStatus	The status literal type.

Contributing

Questions and bug reports are welcome as issues, and PRs are accepted. The repo is developed with uv; please run the test suite before submitting:

$ uv run pytest tests

Unit tests always run; integration tests need a Kafka broker on localhost:9092 and skip otherwise (docker run -d -p 9092:9092 apache/kafka:3.9.0).

License

MIT