A published dataset is a named reference to a Dask collection or list of futures that has been published to the cluster. It is available for any client to see and persists beyond the scope of an individual session.
Publishing datasets is useful in the following cases:
- You want to share computations with colleagues
- You want to persist results on the cluster between interactive sessions
In this example we load a dask.dataframe from S3, manipulate it, and then publish the result.
Connect and Load
from dask.distributed import Client client = Client('scheduler-address:8786') import dask.dataframe as dd df = dd.read_csv('s3://my-bucket/*.csv') df2 = df[df.balance < 0] df2 = client.persist(df2) >>> df2.head() name balance 0 Alice -100 1 Bob -200 2 Charlie -300 3 Dennis -400 4 Edith -500
To share this collection with a colleague we publish it under the name
Load published dataset from different client
Now any other client can connect to the scheduler and retrieve this published dataset.
>>> from dask.distributed import Client >>> client = Client('scheduler-address:8786') >>> client.list_datasets() ['negative_accounts'] >>> df = client.get_dataset('negative_accounts') >>> df.head() name balance 0 Alice -100 1 Bob -200 2 Charlie -300 3 Dennis -400 4 Edith -500
This allows users to easily share results. It also allows for the persistence of important and commonly used datasets beyond a single session. Published datasets continue to reside in distributed memory even after all clients requesting them have disconnected.
Published collections are not automatically persisted. If you publish an un-persisted collection then others will still be able to get the collection from the scheduler, but operations on that collection will start from scratch. This allows you to publish views on data that do not permanently take up cluster memory but can be surprising if you expect “publishing” to automatically make a computed dataset rapidly available.
Any client can publish or unpublish a dataset.
Publishing too many large datasets can quickly consume a cluster’s RAM.