Datasets best practices¶
This article describes how to use Domino Datasets to solve problems, improve collaboration, and open new workflow possibilities in Domino.
You can use Datasets to…¶
When you start a Run or launch a Workspace, Domino copies your project files to an executor. When working with large volumes of data, this presents three potential problems:
The number of files that can be stored in Domino project files may exceed the configurable limit. By default, the limit is 10,000 files.
There is a limit to the size of any individual file that can be transferred to and from your Domino project files. By default, this limit is 8GB.
The time required to transfer data to and from the executor is proportional to the size of the data. It can take a long time if the size of the data is very large, leading to long startup and shutdown times for Runs and Workspaces.
You can solve these problems with Domino Datasets:
There is no limit to the number of files that can be stored in a Domino Dataset.
There is no limit to the size of any individual file stored in a Domino Dataset.
Domino Datasets are attached to executors as networked filesystems, removing the need to transfer their contents to the executor when starting a Run or Workspace.
If you have a Dataset that is being used by downstream consumers for critical work, tagging allows you to continue to improve, process, and experiment with new Snapshots without impacting those consumers. When you have improved data ready for use, you can switch which Snapshot is tagged, and your tag consumers will automatically start getting your new data.
Consider the Dataset shown below.
The Dataset has three active Snapshots. If you decide that you want
consumers of this Datasets to work from Snapshot 1, since Snapshot 2
represents an experimental modification of the data that you are not yet
confident in, you can apply a tag like
prod to Snapshot 1.
When your consumers mount the Dataset in their projects, they have the option to mount whichever Snapshot is marked with a given tag. When they choose the Pin your snapshot at a certain tag update behavior, they will see a dropdown menu of available tags.
When you are confident that your experimentation has produced a new
Snapshot that is ready for production use, you can remove the
tag from Snapshot 1, and apply it to the new Snapshot. Your consumers
will then automatically see the newly tagged Snapshot mounted in their
Runs and Workspaces. Note that trying to apply the tag again without
first removing it from the previously tagged Snapshot will result in an
If you use the Domino CLI to work with projects to your local machine, you may find that storing large data files slows down your download and sync operations, and fills up a lot of your local disk storage. You can prevent this by storing data in a Domino Dataset, and reserving your project files for the scripts and documents you want to work with locally.
Follow these steps to simplify your local workflow:
Once the files have been written to the Dataset, you can remove them from your project files.
Update your code to reference your data files in their new location, at:
When everything is working smoothly, you can delete any copies of the project from your local machine that have the large data files in them.
Suppose you have data stored in an external data source that is periodically updated. If you wanted to fetch the latest state of that file once per week and load it into a Domino Dataset, you could use the following process to set up a scheduled Run.
Create a Dataset to store the data from the external source.
Write a script that fetches the data and writes it to the Dataset.
Set up an advanced mode configuration to bridge between your script and your Dataset.
Create a scheduled job to run your script with the new Dataset configuration.
Below is a detailed example showing how to fetch a large, dynamic data file from a private S3 bucket with a scheduled Run once per week.
First, create a Dataset to hold the file. This example shows the Dataset being named fetched-from-S3.
After clicking Upload Contents, the Dataset will be created. However, instead of using one of the UI options to perform an upload, you should instead click Files from the project menu, then click Add File to start creating the script for your scheduled Run.
For this example, assume the S3 bucket is named my_bucket and the file
you want is named
some_data.csv. In that case, you can set up your
script like this:
import boto3 import io # create new S3 client client = boto3.client('s3') # download some_data.csv from my_bucket and write to latest-S3 output mount file = client.download_file('my_bucket', 'some_data.csv', '/domino/datasets/latest-S3/some_data.csv')
It’s important to note that the latest-S3 part of the path in the last line of the script is a folder you need to set up as part of your Datasets advanced mode configuration. To set that up, create another new file in your project, and name it domino.yaml.
To match the script shown above, its contents should be the following:
datasetConfigurations: - name: "pipe-in" outputs: - path: "latest-S3" dataset: "fetched-from-S3"
That configuration sets up the fetched-from-S3 Dataset created earlier for new input at the latest-S3 path used by the fetch-data.py script. The last step is to set up a scheduled Job that executes this script once per week with the correct Dataset configuration.