datallog run

The datallog run command executes a specific automation locally within the current project. This is the primary way to test and debug your automation's logic before pushing it to the cloud.

Usage

datallog run [options] <automation_name>

Arguments

<automation_name>

• Type: string
• Required: Yes

The name of the automation to run. This must match the name of an automation created with datallog create-automation.

Options

-s, --seed <seed>

• Type: string
• Default: None

An optional seed value to pass as the initial input to the automation's @core_task. This is useful for providing a small piece of data directly from the command line. This option cannot be used with --seed-file.

-f, --seed-file <seed_file>

• Type: string (file path)
• Default: seed.json in the automation's directory. If this file exists, its content will be passed as the initial data to the @core_task when running the automation locally.

The path to a file containing the seed data for the automation. This is ideal for providing larger or more complex JSON data as the initial input. This option cannot be used with --seed.

-p, --parallelism <n>

• Type: integer
• Default: 1

The number of parallel workers to use when executing the automation. If a task returns a list, Datallog will process the items in that list using this many parallel workers.

-l, --log-to-dir <log_to_dir>

• Type: string (directory path)
• Default: None

Specifies a directory where the output logs of the automation run should be stored. This is useful for capturing detailed execution logs for later analysis.

Examples

Run the hello-automation automation:

datallog run hello-automation

Run the automation with a simple string as the seed data:

datallog run hello-automation -s "user-id-123"

Run the automation using a JSON file as the seed data:

datallog run hello-automation -f ./initial-data.json

Run the automation with 8 parallel workers and save the logs to a run_logs directory:

datallog run hello-automation -p 8 -l ./run_logs