Kafka S3 Connector (`kaf-s3-connector`)

A Python library to seamlessly handle large Kafka messages by offloading them to Amazon S3.

This library provides a custom Kafka Producer and Consumer that automatically handle the process of storing large message payloads in an S3 bucket and passing lightweight references through Kafka.

Key Features

Automatic S3 Offloading: Produce messages larger than Kafka's recommended limit without manual intervention.
Transparent Consumption: Consume large messages as if they were directly in Kafka.
Data Integrity: Verifies every S3 object against the SHA-256 and, where S3 provides a usable one, the ETag carried in the reference. References without a verifiable checksum are rejected by default (require_integrity).
Secure by Default: Leverages AWS IAM roles and the default boto3 credential chain, avoiding the need to hardcode secrets.
Flexible Configuration: Built on top of confluent-kafka-python, allowing for full customization of Kafka client settings, including SASL and SSL.
Operational Ready: DLQ support, Prometheus /metrics, Helm chart, non-root image, optional compression, lifecycle-rule TTL tagging, SSE-KMS.
Bounded Resources: max_payload_bytes caps both the download and the gzip expansion, so a small object cannot inflate into an unbounded allocation.
Tested Against Real Infrastructure: an opt-in suite runs the connector against a real Kafka broker and a real S3 service in throwaway containers, on top of the mocked unit tests.

Installation

pip install .

Requires Python 3.10 or newer.

How it Works

A message is treated as an S3 reference only if it is a JSON object carrying both s3_bucket and s3_key. Anything else — plain bytes, or JSON that is simply your own payload — passes through untouched when allow_inline_payloads is enabled.

The S3Producer receives a payload. Anything at or under max_inline_bytes is sent to Kafka unchanged and the rest of these steps do not apply.
Larger payloads are uploaded to the configured bucket under a random key, or a content hash when deterministic_keys is set, optionally gzipped and encrypted.
It produces a small JSON reference to the topic carrying the bucket, the key, a SHA-256 of the payload, the ETag where S3 provides a usable one, and the compression used. If the Kafka publish fails, the S3 object is rolled back.
The S3Consumer recognises a reference by its s3_bucket and s3_key fields, and passes anything else through as an inline payload.
It checks the reference against the configured bucket and prefix, downloads the object under a size cap, and decompresses it under the same cap.
It verifies the payload against the SHA-256 and, where present, the ETag, then returns it. A reference carrying no verifiable checksum is rejected by default.

A multipart upload's ETag is a hash of part hashes rather than of the content, so those references omit it and rely on the SHA-256 instead.

Configuration

Configuration is handled through a single dictionary, with separate keys for kafka and s3 settings.

Security

AWS Credentials: This library is designed to be secure. Do not pass AWS credentials in the configuration. It uses boto3's default credential discovery chain. The recommended and most secure way to provide credentials is by using an IAM Role attached to your compute instance (EC2, ECS, Lambda, etc.). Alternatively, you can use environment variables or a shared credentials file (~/.aws/credentials).
Kafka Security: All standard confluent-kafka-python security settings are supported and should be passed within the kafka dictionary. This includes SASL for authentication (e.g., PLAIN, SCRAM, and GSSAPI for Kerberos) and SSL/TLS for encryption.

Example Configuration

producer_config = {
    "kafka": {
        "bootstrap.servers": "localhost:9092",
        # Optional DLQ for delivery failures
        # "dlq_topic": "my-s3-dlq",
    },
    "s3": {
        "bucket": "my-large-messages-bucket",
        # Optional toggles
        # "max_inline_bytes": 900_000,     # inline small payloads on Kafka, offload larger ones
        # "max_payload_bytes": 5 * 1024 * 1024 * 1024,  # hard cap on payload size
        # "prefix": "kafka/topic",         # prefix keys for organization/enforcement
        # "deterministic_keys": False,     # use payload hash for idempotent keys;
                                           #   marks references so consumers with
                                           #   delete_after_consume leave them alone
        # "multipart_threshold": 8388608,  # switch to multipart above this size;
                                           #   S3 caps a single PUT at 5 GiB
        # "produce_timeout": 30.0,         # how long to apply backpressure when
                                           #   librdkafka's local queue is full
        # "compression": "gzip",           # compress before S3 upload (gzip or None)
        # "ttl_seconds": 86400,            # tags the object kaf-s3-ttl-seconds=<n> so an
                                           #   S3 lifecycle rule can expire it
        # "server_side_encryption": "aws:kms", # SSE, optionally with KMS key below
        # "sse_kms_key_id": "<kms-key-id>",
        # "region_name": "eu-central-1",   # otherwise the boto3 default chain applies
        # "endpoint_url": "https://minio.local:9000",  # S3-compatible endpoints
    }
}

consumer_config = {
    "kafka": {
        "bootstrap.servers": "localhost:9092",
        "group.id": "my-s3-consumers",
        "auto.offset.reset": "earliest",
        # Optional: send failures to a DLQ topic
        # "dlq_topic": "my-s3-dlq",
    },
    "s3": {
        "bucket": "my-large-messages-bucket",
        # Optional toggles
        # "max_inline_bytes": 900_000,     # inline small payloads on Kafka, offload larger ones
        # "max_payload_bytes": 5 * 1024 * 1024 * 1024,  # hard cap on payload size
        # "delete_after_consume": False,   # discouraged: single-consumer-group only and
                                           #   needs manual commits. Prefer a lifecycle
                                           #   rule. See "Reclaiming S3 storage"
        # "allow_inline_payloads": True,   # allow non-reference payloads to pass through unchanged
        # "dlq_max_raw_bytes": 16384,      # raw bytes echoed into a DLQ record
        # "dlq_max_record_bytes": 524288,  # hard cap on the encoded DLQ record
        # "prefix": "kafka/topic",         # enforce prefix on incoming references
        # "require_integrity": True,       # reject references carrying no etag/sha256
        # "compression": "gzip",           # decompress automatically on consume
        # "deterministic_keys": False,     # use payload hash for idempotent keys;
                                           #   marks references so consumers with
                                           #   delete_after_consume leave them alone
        # "multipart_threshold": 8388608,  # switch to multipart above this size;
                                           #   S3 caps a single PUT at 5 GiB
        # "produce_timeout": 30.0,         # how long to apply backpressure when
                                           #   librdkafka's local queue is full (producer)
        # "ttl_seconds": 86400,            # tags objects for an S3 lifecycle rule (producer)
        # "server_side_encryption": "aws:kms", # SSE, optionally with KMS key below (producer)
        # "sse_kms_key_id": "<kms-key-id>",    # (producer)
    }
}

Usage

Producer

from s3_connector import S3Producer

# Initialize producer with the config
producer = S3Producer(config=producer_config)

# Read a sample file
with open("examples/sample_payload.txt", "rb") as f:
    payload_data = f.read()

# Produce the data to a topic
producer.produce(topic="large-messages-topic", payload=payload_data)

# produce() is asynchronous. Flush before exiting or queued messages are lost.
producer.flush(timeout=30.0)
producer.close()
print("Produced message to Kafka via S3.")

S3Producer is also a context manager, which closes (and therefore flushes) on exit:

with S3Producer(config=producer_config) as producer:
    producer.produce(topic="large-messages-topic", payload=payload_data)

Consumer

from s3_connector import S3Consumer

# Initialize consumer with the config
consumer = S3Consumer(config=consumer_config)
consumer.subscribe(["large-messages-topic"])
# subscribe() also accepts on_assign / on_revoke / on_lost rebalance callbacks.

print("Waiting for messages...")
while True:
    try:
        # Poll for a message
        payload = consumer.poll(timeout=10.0)

        if payload:
            print(f"Received message of size: {len(payload)} bytes")
            # Save the received file
            with open("examples/received_payload.txt", "wb") as f:
                f.write(payload)
            break # Exit after one message for this example

    except KeyboardInterrupt:
        break

consumer.close()

poll() returns None both when no message arrived and when a message was skipped as unusable. Register the skipped hook to tell the two apart:

consumer_config["hooks"] = {
    "skipped": lambda reason, data: print(f"dropped a message: {reason}"),
}

Threads

A connector instance is not safe to share across threads. Give each thread its own S3Producer or S3Consumer, which is also what confluent-kafka expects for consumers.

Reclaiming S3 storage

Offloaded objects outlive the Kafka messages that reference them, so something has to remove them. There are two mechanisms, and they are not equivalent.

S3 lifecycle rules (recommended). Expiry belongs to the bucket, not to a consumer. A rule works no matter how many consumer groups read the topic, keeps working when a consumer is down, and costs nothing at runtime. Set ttl_seconds on the producer and the object is tagged kaf-s3-ttl-seconds=<n>; lifecycle rules can filter on tags, so a rule can then expire it. config/s3-lifecycle.json is ready to apply:

aws s3api put-bucket-lifecycle-configuration \
  --bucket my-large-messages-bucket \
  --lifecycle-configuration file://config/s3-lifecycle.json

Set the expiry longer than the topic's retention, or a consumer that falls behind will find its objects gone. S3 expiry is day-granular, so objects live a little past their TTL. The file also carries a prefix-based rule and an AbortIncompleteMultipartUpload rule, which is worth enabling on any bucket this library writes to.

delete_after_consume (discouraged). Deletes each object once its message is consumed. It reclaims storage immediately rather than a day later, which matters at high volume, but it is only correct when exactly one consumer group reads the topic. Kafka is a fan-out log: any other group still needing that object loses the message, and no consumer can detect that another group exists. The connector logs a warning at startup, and that is the most it can do.

It also requires enable.auto.commit: False and is refused otherwise — under auto-commit the object is removed before the payload is processed, so a crash loses the record.

It remains supported and there is no plan to remove it, but a lifecycle rule is the recommended mechanism. Reach for this one only when you own every consumer of the topic and sub-day reclamation genuinely matters.

How deferred deletion behaves

Deletion waits for the commit that makes the offset durable, so the object always outlives the offset:

deletions are tracked per partition and offset, so commit(message=...) deletes only what that commit covers;
a commit with deletions pending is forced synchronous, since an asynchronous commit has not reached the broker yet;
partitions lost to a rebalance drop their pending deletions, so the member that takes them over still finds the objects;
objects written with deterministic_keys are never deleted, because deduplication means several messages can reference one object;
close() drops anything uncommitted rather than deleting it.

For at-least-once delivery, disable auto-commit and commit after processing:

consumer_config["kafka"]["enable.auto.commit"] = False
...
payload = consumer.poll(timeout=10.0)
if payload:
    handle(payload)
    consumer.commit()

Testing

Unit tests mock Kafka and S3 and need nothing running:

pip install ".[test]"
pytest

Integration tests run the connector against a real Kafka broker and a real S3 service, started as throwaway containers. They need Docker, are marked integration, and are excluded from the default run:

pip install ".[integration]"
pytest -m integration

They exist because a mock encodes the same assumptions the code does, so it cannot catch a wrong one. Each test in tests/integration/test_real_semantics.py pins a behaviour that a released bug got wrong — multipart ETags are not content hashes, gzip embeds an mtime, librdkafka rejects unknown configuration keys, produce() raises BufferError under load — alongside end-to-end coverage of deferred deletion, rebalance handover, DLQ publication, tampering and missing objects. Without Docker, or without the extra installed, the suite skips.

Docker & Operations

Build locally:

docker build -t kaf-s3-connector:local .

Pull from GHCR:

docker pull ghcr.io/novatechflow/kaf-s3-connector:latest
# or a specific tag (git tag or commit SHA)
docker pull ghcr.io/novatechflow/kaf-s3-connector:v2.0.0

Run as consumer (replace envs as needed):

docker run --rm -p 8000:8000 \
  -e MODE=consumer \
  -e TOPIC=large-messages-topic \
  -e KAFKA_BOOTSTRAP_SERVERS=broker:9092 \
  -e KAFKA_GROUP_ID=my-group \
  -e S3_BUCKET=my-large-messages-bucket \
  kaf-s3-connector:local

Run as producer reading stdin:

echo "hello" | docker run --rm -i \
  -e MODE=producer \
  -e TOPIC=large-messages-topic \
  -e KAFKA_BOOTSTRAP_SERVERS=broker:9092 \
  -e S3_BUCKET=my-large-messages-bucket \
  kaf-s3-connector:local

Configuration is driven by env vars:

Kafka: KAFKA_BOOTSTRAP_SERVERS, KAFKA_GROUP_ID (consumer), DLQ_TOPIC, KAFKA_SECURITY_PROTOCOL, KAFKA_SASL_*, KAFKA_SSL_*, etc.
S3: S3_BUCKET, S3_PREFIX, S3_DELETE_AFTER_CONSUME, S3_ALLOW_INLINE_PAYLOADS, S3_MAX_INLINE_BYTES, S3_MAX_PAYLOAD_BYTES, S3_DETERMINISTIC_KEYS, S3_COMPRESSION (gzip), S3_TTL_SECONDS, S3_SSE, S3_SSE_KMS_KEY_ID.
App: MODE (producer|consumer), TOPIC, POLL_TIMEOUT, METRICS_PORT (default 8000; the image healthcheck follows it), METRICS_ADDRESS (default all interfaces), LOG_LEVEL.
Also: AWS_REGION, S3_ENDPOINT_URL, S3_REQUIRE_INTEGRITY, KAFKA_ENABLE_AUTO_COMMIT.
Boolean env vars accept true/1/yes/on (case-insensitive); anything else is false.
With KAFKA_ENABLE_AUTO_COMMIT=false the container commits after each payload, giving at-least-once delivery.

Metrics

/metrics exposes Prometheus text format on METRICS_PORT (default 8000).
Only low-cardinality labels (topic, partition, reason) become series. Payload sizes are aggregated into <event>_bytes counters rather than one series per size, and S3 keys are never used as labels. reason is always a fixed code, never a formatted message.
Skip reason object_missing covers objects that are gone: a TTL lifecycle rule fired, another consumer group deleted them, or an auto-committed message is being replayed. Other S3 errors (AccessDenied, NoSuchBucket, throttling) propagate to the caller.
Integrity reasons: etag_mismatch, sha256_mismatch, missing_checksum, missing_etag, prefix_violation, unexpected_bucket, object_too_large, decompressed_too_large, decompression_failed, unsupported_compression. Skip reasons: tombstone, malformed_message, missing_key.
The endpoint is unauthenticated. Keep it on an internal network, or bind it explicitly with METRICS_ADDRESS.
Sample Prometheus scrape config: config/prometheus.yml
Sample Grafana dashboard JSON: config/grafana-dashboard.json
Sample S3 lifecycle policy: config/s3-lifecycle.json

Helm

Chart: charts/kaf-s3-connector
Override values (examples):

helm upgrade --install kaf-s3 charts/kaf-s3-connector \
  --set image.repository=ghcr.io/novatechflow/kaf-s3-connector \
  --set image.tag=latest \
  --set env.MODE=consumer \
  --set env.TOPIC=large-messages-topic \
  --set env.KAFKA_BOOTSTRAP_SERVERS=broker:9092 \
  --set env.KAFKA_GROUP_ID=my-group \
  --set env.S3_BUCKET=my-large-messages-bucket

service.port drives the Service, container port, both probes, the scrape annotation and the app's METRICS_PORT — set it alone to move the metrics endpoint, and do not put METRICS_PORT in env.

Kafka SASL/SSL passwords belong in a Secret, not in values.env, which renders into the Deployment in plain text:

kubectl create secret generic kaf-s3-kafka-credentials \
  --from-literal=KAFKA_SASL_USERNAME=svc-kaf-s3 \
  --from-literal=KAFKA_SASL_PASSWORD=...

helm upgrade --install kaf-s3 charts/kaf-s3-connector \
  --set 'envFrom[0].secretRef.name=kaf-s3-kafka-credentials'

The chart sets prometheus.io/scrape pod annotations; config/prometheus.yml includes a matching Kubernetes discovery job.

Case Study

This connector was architected as part of a consulting engagement to solve a common Apache Kafka® challenge: handling large messages without destabilizing the cluster.

Full case study: Kafka-to-S3 Connector: Large Message Offloading and Scalable ETL

The Problem

Kafka is designed for high-throughput streaming but has strict limits on message sizes. When workloads produce large payloads (documents, JSON blobs, binary exports, telemetry batches), teams face a choice: raise broker limits (risky), chunk messages (complex), or find a better pattern.

The Solution

kaf-s3-connector offloads large payloads to S3 while keeping Kafka responsible only for lightweight references:

Producer                          Consumer
   │                                 │
   ▼                                 ▼
┌─────────┐    S3 key + ETag    ┌─────────┐
│ Payload │ ──────────────────► │  Kafka  │
│  (big)  │                     │  topic  │
└────┬────┘                     └────┬────┘
     │                               │
     ▼                               ▼
┌─────────┐                     ┌─────────┐
│   S3    │ ◄─────────────────► │ Fetch & │
│ bucket  │    retrieve blob    │ verify  │
└─────────┘                     └─────────┘

Benefits

Removes message size limits: payloads up to 5GB
Stabilizes Kafka clusters: brokers never see large messages
Enables lakehouse integration: S3 objects ready for Iceberg, Spark, Flink
Production-ready: DLQ, compression, ETag integrity, Prometheus metrics, Helm chart

If you want all of your Kafka data in S3, not only the large payloads, see KafScale: Kafka-compatible streaming with stateless brokers that keeps data in S3.

Use Cases

Ingesting files, documents, or binary data into streaming pipelines
Pre-processing before writing to Apache Iceberg® or Parquet
Integrating Kafka with S3-based data lakes

Building data pipelines with Kafka, S3, or lakehouse architectures?

→ Consulting Services
→ Book a call

License

MIT. See LICENSE.

Name		Name	Last commit message	Last commit date
.github		.github
charts/kaf-s3-connector		charts/kaf-s3-connector
config		config
examples		examples
src/s3_connector		src/s3_connector
tests		tests
.dockerignore		.dockerignore
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
setup.py		setup.py

Navigation Menu

Repository navigation

About

Repository files navigation

Kafka S3 Connector (`kaf-s3-connector`)

Key Features

Installation

How it Works

Configuration

Security

Example Configuration

Usage

Producer

Consumer

Threads

Reclaiming S3 storage

How deferred deletion behaves

Testing

Docker & Operations

Metrics

Helm

Case Study

The Problem

The Solution

Benefits

Use Cases

License

About

Releases

Packages

Used by

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Kafka S3 Connector (kaf-s3-connector)

Key Features

Installation

How it Works

Configuration

Security

Example Configuration

Usage

Producer

Consumer

Threads

Reclaiming S3 storage

How deferred deletion behaves

Testing

Docker & Operations

Metrics

Helm

Case Study

The Problem

The Solution

Benefits

Use Cases

License

About

Topics

Resources

Contributing

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages

Kafka S3 Connector (`kaf-s3-connector`)