lucian
06/29/2025, 3:04 AMMarvin
06/29/2025, 3:04 AMMarvin
06/29/2025, 3:05 AMbash
pip install prefect-redis
2. Create and configure a Redis storage block. You can do this either via Python or the Prefect UI:
python
from prefect_redis import RedisStorageContainer
# Create the Redis block
redis_block = RedisStorageContainer.from_connection_string(
"<redis://hostname:port>" # Replace with your Redis connection string
)
# Save the block
redis_block.save("my-redis-cache")
3. Use the Redis block in your flow:
python
from prefect import flow
from prefect_redis import RedisStorageContainer
@flow(cache_key_fn="my-cache-key")
def my_flow():
# Your flow logic here
pass
if __name__ == "__main__":
# Load the saved Redis block
redis_block = RedisStorageContainer.load("my-redis-cache")
# Run the flow with Redis caching
my_flow.with_options(cache_storage=redis_block)()
The Redis connection can be configured in several ways:
- Using a connection string (`redis://hostname:port`)
- Using SSL (`rediss://hostname:port`)
- Using Unix Domain Socket (<unix://path>
)
For more details and configuration options, check out:
- Prefect Redis Integration Documentation
- Prefect Redis API Reference
Would you like me to explain any specific aspect of Redis caching configuration in more detail?lucian
06/29/2025, 3:32 AMMarvin
06/29/2025, 3:37 AMMarvin
06/29/2025, 3:38 AMbash
pip install prefect-redis
2. Use Redis as a storage block for your flows and tasks.
However, I notice there's some flux in the Redis integration implementation. To ensure I give you the most accurate guidance: could you tell me specifically what you're trying to achieve with Redis caching? Are you looking to:
- Cache task/flow results?
- Store intermediate data?
- Something else?
This will help me provide the most current and appropriate solution for your use case.Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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