Large payload storage - Python SDK

The Temporal Service enforces a 2 MB per payload limit. When your Workflows or Activities handle data larger than the limit, you can offload payloads to external storage, such as S3, and pass a small reference token through the Event History instead. This is called the claim check pattern. This page shows you how to set up External Storage with AWS S3 and how to implement a custom storage driver.

For more information about how External Storage fits in the data pipeline, see External Storage.

Store and retrieve large payloads with AWS S3

The Python SDK includes an S3 storage driver. Follow these steps to set it up:

Install the S3 driver dependency:
```
pip install temporalio[s3driver]
```
Import the classes S3StorageDriverClient and S3StorageDriver and create an instance of each. When you create the driver instance, pass the client argument to the constructor as well as the name of your S3 bucket:
```
from temporalio.contrib.aws.s3driver import S3StorageDriver, S3StorageDriverClient

driver_client = S3StorageDriverClient()
driver = S3StorageDriver(client=driver_client, bucket="my-temporal-payloads")
```
The driver generates S3 keys using a SHA-256 hash of the payload content. Identical payloads produce the same key, so the driver skips the upload if the object already exists. The driver includes the namespace and Workflow ID in the S3 key to group related payloads in your bucket. For example: v0/ns/my-namespace/wfi/my-workflow/d/sha256/{hash}.

Configure the driver on your DataConverter and pass the converter to your Client and Worker:

from temporalio.converter import DataConverter, ExternalStorage

converter = DataConverter(
    external_storage=ExternalStorage(
        drivers=[driver],
        payload_size_threshold=256 * 1024,  # 256 KiB (default)
    ),
)

client = await Client.connect("localhost:7233", data_converter=converter)

worker = Worker(
    client,
    task_queue="my-task-queue",
    workflows=[MyWorkflow],
    activities=[my_activity],
)

All Workflows and Activities running on the Worker use the storage driver automatically without changes to your business logic. The driver uploads and downloads payloads concurrently and validates payload integrity on retrieve.

(Optional) To route payloads to different buckets, pass a function as the bucket parameter instead of a string. The function takes the store context and the payload as arguments, and returns a bucket name, so you can route based on the Activity Task Queue or other context:

from temporalio.converter import StorageDriverStoreContext, ActivitySerializationContext
from temporalio.api.common.v1 import Payload

QUEUE_BUCKETS = {
    "queue-a": "bucket-queue-a",
    "queue-b": "bucket-queue-b",
}

def bucket_selector(ctx: StorageDriverStoreContext, payload: Payload) -> str:
    if isinstance(ctx.serialization_context, ActivitySerializationContext):
        queue = ctx.serialization_context.activity_task_queue
        if queue and queue in QUEUE_BUCKETS:
            return QUEUE_BUCKETS[queue]
    return "default-bucket"

driver = S3StorageDriver(client=driver_client, bucket=bucket_selector)

Implement a custom storage driver

If you need a storage system other than S3, you can implement your own storage driver. The following example shows a complete custom driver implementation that uses local disk as the backing store:

import os
import uuid
from typing import Sequence

from temporalio.api.common.v1 import Payload
from temporalio.converter import (
    StorageDriver,
    StorageDriverClaim,
    StorageDriverStoreContext,
    StorageDriverRetrieveContext,
)


class LocalDiskStorageDriver(StorageDriver):
    def __init__(self, store_dir: str = "/tmp/temporal-payload-store") -> None:
        self._store_dir = store_dir

    def name(self) -> str:
        return "local-disk"

    async def store(
        self,
        context: StorageDriverStoreContext,
        payloads: Sequence[Payload],
    ) -> list[StorageDriverClaim]:
        os.makedirs(self._store_dir, exist_ok=True)

        prefix = self._store_dir
        sc = context.serialization_context
        if sc is not None and hasattr(sc, "workflow_id"):
            prefix = os.path.join(self._store_dir, sc.namespace, sc.workflow_id)
            os.makedirs(prefix, exist_ok=True)

        claims = []
        for payload in payloads:
            key = f"{uuid.uuid4()}.bin"
            file_path = os.path.join(prefix, key)
            with open(file_path, "wb") as f:
                f.write(payload.SerializeToString())
            claims.append(StorageDriverClaim(claim_data={"path": file_path}))
        return claims

    async def retrieve(
        self,
        context: StorageDriverRetrieveContext,
        claims: Sequence[StorageDriverClaim],
    ) -> list[Payload]:
        payloads = []
        for claim in claims:
            file_path = claim.claim_data["path"]
            with open(file_path, "rb") as f:
                data = f.read()
            payload = Payload()
            payload.ParseFromString(data)
            payloads.append(payload)
        return payloads

Extend the StorageDriver class

A custom driver extends the StorageDriver abstract class and implements three methods:

name() returns a unique string that identifies the driver.
store() receives a list of payloads and returns one StorageDriverClaim per payload. A claim is a set of string key-value pairs that the driver uses to locate the payload later.
retrieve() receives the claims that store() produced and returns the original payloads.

Store payloads

In store(), serialize each payload to bytes with payload.SerializeToString(), upload the bytes to your storage system, and return a StorageDriverClaim with enough information to find the payload later. Using a content-addressable key like a SHA-256 hash gives you deduplication for free.

Retrieve payloads

In retrieve(), download the bytes using the claim data, then reconstruct the payload with payload.ParseFromString(data).

Configure the Data Converter

Pass an ExternalStorage instance to your DataConverter and use the converter when creating your Client and Worker:

from temporalio.converter import DataConverter, ExternalStorage

converter = DataConverter(
    external_storage=ExternalStorage(
        drivers=[MyStorageDriver()],
        payload_size_threshold=256 * 1024,  # 256 KiB (default)
    ),
)

The payload_size_threshold controls which payloads get offloaded. Payloads smaller than this value stay inline in the Event History. If you don't provide a value, the default value is 256 KiB. Set it to None to externalize all payloads regardless of size.

Use multiple storage drivers

When you have multiple drivers, such as for hot and cold storage tiers, pass a driver_selector function that chooses which driver handles each payload:

hot_driver = MyStorageDriver("hot-bucket")
cold_driver = MyStorageDriver("cold-bucket")

ExternalStorage(
    drivers=[hot_driver, cold_driver],
    driver_selector=lambda context, payload: (
        cold_driver if payload.ByteSize() > 1_000_000 else hot_driver
    ),
    payload_size_threshold=100 * 1024,
)

Return None from the selector to keep a specific payload inline.

Store and retrieve large payloads with AWS S3​

Implement a custom storage driver​

Extend the StorageDriver class​

Store payloads​

Retrieve payloads​

Configure the Data Converter​

Use multiple storage drivers​