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The python-domino library

This library provides bindings for the Domino APIs. It ships with the Domino Standard Environment (DSE).

See this documentation for details about the APIs:

The latest released version of python-domino is 1.3.1.

Version compatibility matrix

The python-domino library is compatible with different versions of Domino:

Domino Versionspython-domino

3.6.x or lower

0.3.5

4.1.0 or higher

1.0.0 or higher

5.3.0 or higher

1.2.0 or Higher

5.5.0 or higher

1.2.2 or Higher

5.10.0 or higher

1.3.1 or Higher

Development

The current python-domino is based on Python 3.9, which is therefore recommended for development. Pipenv is also recommended to manage the dependencies.

To use the Python binding in a Domino workbook session, include dominodatalab in your project’s requirements.txt file. This makes the Python binding available for each new workbook session (or batch run) started within the project.

To install dependencies from setup.py for development:

pipenv install -e ".[dev]"

Use the same process for Airflow and data:

pipenv install -e ".[data]" ".[airflow]"

Set up the connection

You can set up the connection by creating a new instance of Domino:

_class_ Domino(project, api_key=None, host=None, domino_token_file=None, auth_token=None)
Parameters

  • project: A project identifier (in the form of owner_user_name/projectname).

  • api_proxy: (Optional) Location of the Domino API reverse proxy as host:port.

    If set, this proxy is used to intercept any Domino API requests and insert an authentication token. This is the preferred method of authentication. Alternatively, set the DOMINO_API_PROXY environment variable. In Domino 5.4.0 or later, this variable is set inside a Domino run container.

    Note
    		 This mechanism does not work when connecting to an HTTPS endpoint; it is meant to be used inside Domino runs.

  • api_key: (Optional) An API key to authenticate with.

    See Get API Key. If not provided, the library expects to find one in the DOMINO_USER_API_KEY environment variable.

  • host: (Optional) A host URL.

    If not provided, the library expects to find one in the DOMINO_API_HOST environment variable.

  • domino_token_file: (Optional) Path to the Domino token file containing auth token.

    If not provided, the library expects to find one in the DOMINO_TOKEN_FILE environment variable.

  • auth_token: (Optional) Authentication token.

Authentication

Domino looks for the authentication method in the following order and uses the first one it finds:

  1. api_proxy

  2. auth_token

  3. domino_token_file

  4. api_key

  5. DOMINO_API_PROXY

  6. DOMINO_TOKEN_FILE

  7. DOMINO_USER_API_KEY

The API proxy is the preferred method of authentication. See Use the API Proxy to Authenticate Calls to the Domino API.

Additional environment variables

  • DOMINO_LOG_LEVEL

    The default log level is INFO. You can change the log level by setting DOMINO_LOG_LEVEL, for example to DEBUG.

  • DOMINO_VERIFY_CERTIFICATE

    For testing purposes and issues with SSL certificates, set DOMINO_VERIFY_CERTIFICATE to false. Be sure to unset this variable when not in use.

  • DOMINO_MAX_RETRIES

    Default Retry is set to 4 Determines the number of attempts for the request session in case of a ConnectionError Get more info on request max timeout/error durations based on Retry and backoff factors

Methods

Projects

See example_projects_usage.py for example code.

project_create(project_name, owner_username=None)

Create a new project with given project name.

  • project_name: The name of the project.

  • owner_username: (Optional) The owner username for the project. This parameter is useful when you need to create a project under an organization.

collaborators_get()

Get the list of collaborators on a project.

collaborators_add(username_or_email, message="")

Add collaborators to a project.

  • username_or_email: Name or email of the Domino user to add as collaborator to the current project.

  • message: Optional - Message related to the user’s role or purpose to the project.

Project tags

Project tags are an easy way to add freeform metadata to a project. Tags help colleagues and consumers organize and find the Domino projects that interest them. Tags can be used to describe the subject explored by a project, the packages and libraries it uses, or the source of the data within.

See example_projects_usage.py for example code.

tags_list(*project_id)

List a project’s tags.

  • project_id: The project identifier.

tag_details(tag_id)

Get details about a tag.

  • tag_id: The tag identifier.

tags_add(tags, *project_id)

Create a tag, if it does not exist, and add it to a project.

  • tags (list): One or more tag names.

  • project_id: (Defaults to current project ID) The project identifier.

tag_get_id(tag_name, *project_id)

Get the tag ID using the tag string name.

  • tag_name (string): The tag name.

  • project_id: (Defaults to current project id) The project ID.

tags_remove(tag_name, project_id=None)

Remove a tag from a project.

  • tag_name (string): The tag name.

  • project_id: (Defaults to current project id) The project ID.

Executions

See these code example files:

runs_list()

List the executions on the selected project.

runs_start(command, isDirect, commitId, title, tier, publishApiEndpoint)

Start a new execution on the selected project.

  • command: The command to execution as an array of strings where members of the array represent arguments of the command. For example: ["main.py", "hi mom"]

  • isDirect: (Optional) Whether this command should be passed directly to a shell.

  • commitId: (Optional) The commitId to launch from. If not provided, the project launches from the latest commit.

  • title: (Optional) A title for the execution.

  • tier: (Optional) The hardware tier to use for the execution. This is the human-readable name of the hardware tier, such as "Free", "Small", or "Medium". If not provided, the project’s default tier is used.

  • publishApiEndpoint: (Optional) Whether to publish an API endpoint from the resulting output.

runs_start_blocking(command, isDirect, commitId, title, tier, publishApiEndpoint, poll_freq=5, max_poll_time=6000)

Start a new execution on the selected project and make a blocking request that waits until job is finished.

  • command: The command to execution as an array of strings where members of the array represent arguments of the command. For example: ["main.py", "hi mom"]

  • isDirect: (Optional) Whether this command should be passed directly to a shell.

  • commitId: (Optional) The commitId to launch from. If not provided, the project launches from the latest commit.

  • title: (Optional) A title for the execution.

  • tier: (Optional) The hardware tier to use for the execution. Will use project’s default tier if not provided. If not provided, the project’s default tier is used.

  • publishApiEndpoint: (Optional) Whether to publish an API endpoint from the resulting output.

  • poll_freq: (Optional) Number of seconds between polling of the Domino server for status of the task that is running.

  • max_poll_time: (Optional) Maximum number of seconds to wait for a task to complete. If this threshold is exceeded, an exception is raised.

  • retry_count: (Optional) Maximum number of polling retries (in case of transient HTTP errors). If this threshold is exceeded, an exception is raised.

run_stop(runId, saveChanges=True):

Stop an existing execution in the selected project.

  • runId: String that identifies the execution.

  • saveChanges: (Defaults to True) If false, execution results are discarded.

runs_stdout(runId)

Get stdout emitted by a particular execution.

  • runId: string that identifies the execution

Files and blobs

See these code example files:

files_list(commitId, path)

List the files in a folder in the Domino project.

  • commitId: The commitId to list files from.

  • path: (Defaults to "/") The path to list from.

files_upload(path, file)

Upload a Python file object into the specified path inside the project. See examples/upload_file.py for an example. All parameters are required.

  • path: The path to save the file to. For example, /README.md writes to the root directory of the project while /data/numbers.csv saves the file to a sub folder named data. If the specified folder does not yet exist, it is created.

  • file: A Python file object. For example: f = open("authors.txt","rb")

blobs_get(key)

Deprecated Use get_blobs_v2. Retrieve a file from the Domino server by blob key.

  • key: The key of the file to fetch from the blob server.

blobs_get_v2(path, commit_id, project_id)

Retrieve a file from the Domino server in a project from its path and commit id.

  • path: The path to the file in the Domino project.

  • commit_id: ID of the commit to retrieve the file from.

  • project_id: ID of the project to retrieve the file from.

Apps

app_publish(unpublishRunningApps=True, hardwareTierId=None)

Publish an app within a project, or republish an existing app.

  • unpublishRunningApps: (Defaults to True) Check for an active app instance in the current project and unpublish it before re/publishing.

  • hardwareTierId: (Optional) Launch the app on the specified hardware tier.

app_unpublish()

Stop the running app in the project.

Jobs

job_start(command, commit_id=None, hardware_tier_name=None, environment_id=None, on_demand_spark_cluster_properties=None, compute_cluster_properties=None, external_volume_mounts=None, title=None):

Start a new job (execution) in the project.

  • command (string): Command to execute in Job. For example: domino.job_start(command="main.py arg1 arg2")

  • commit_id (string): (Optional) The commitId to launch from. If not provided, the job launches from the latest commit.

  • hardware_tier_name (string): (Optional) The hardware tier NAME to launch job in. If not provided, the project’s default tier is used.

  • environment_id (string): (Optional) The environment ID with which to launch the job. If not provided, the project’s default environment is used.

  • on_demand_spark_cluster_properties (dict): (Optional) On demand spark cluster properties. The following properties can be provided in the Spark cluster:

    {
    "computeEnvironmentId": "<Environment ID configured with spark>"
    "executorCount": "<Number of Executors in cluster>"
     (optional defaults to 1)
    "executorHardwareTierId": "<Hardware tier ID for Spark Executors>"
     (optional defaults to last used historically if available)
    "masterHardwareTierId":  "<Hardware tier ID for Spark master"
     (optional defaults to last used historically if available)
    "executorStorageMB": "<Executor's storage in MB>"
     (optional defaults to 0; 1GB is 1000MB Here)
}

  • param compute_cluster_properties (dict): (Optional) The compute-cluster properties definition contains parameters for launching any Domino supported compute cluster for a job. Use this to launch a job that uses a compute-cluster instead of the deprecated on_demand_spark_cluster_properties field. If on_demand_spark_cluster_properties and compute_cluster_properties are both present, on_demand_spark_cluster_properties is ignored. compute_cluster_properties contains the following fields:

    {
    "clusterType": <string, one of "Ray", "Spark", "Dask", "MPI">,
    "computeEnvironmentId": <string, The environment ID for the cluster's nodes>,
    "computeEnvironmentRevisionSpec": <one of "ActiveRevision", "LatestRevision",
    {"revisionId":"<environment_revision_id>"} (optional)>,
    "masterHardwareTierId": <string, the Hardware tier ID for the cluster's master node (required unless clusterType is MPI)>,
    "workerCount": <number, the total workers to spawn for the cluster>,
    "workerHardwareTierId": <string, The Hardware tier ID for the cluster workers>,
    "workerStorage": <{ "value": <number>, "unit": <one of "GiB", "MB"> },
    The disk storage size for the cluster's worker nodes (optional)>
    "maxWorkerCount": <number, The max number of workers allowed. When
    this configuration exists, autoscaling is enabled for the cluster and
    "workerCount" is interpreted as the min number of workers allowed in the cluster
    (optional)>
}

  • external_volume_mounts (List[string]): (Optional) External volume mount IDs to mount to execution. If not provided, the job launches with no external volumes mounted.

  • title (string): (Optional) Title for Job.

job_stop(job_id, commit_results=True):

Stop the Job (execution) in the project.

  • job_id (string): Job identifier.

  • commit_results (boolean): (Defaults to true) If false, the job results are not committed.

job_status(job_id):

Get the status of a job.

  • job_id (string): Job identifier.

job_start_blocking(poll_freq=5, max_poll_time=6000, **kwargs):

Start a job and poll until the job is finished. Additionally, this method supports all the parameters in the job_start method.

  • poll_freq: Poll frequency interval in seconds.

  • max_poll_time: Max poll time in seconds.

Datasets

A Domino dataset is a collection of files that are available in user executions as a filesystem directory. A dataset always reflects the most recent version of the data. You can modify the contents of a dataset through the Domino UI or through workload executions.

See Domino Datasets for more details, and example_dataset.py for example code.

datasets_list(project_id=None)

Provide a JSON list of all the available datasets.

  • project_id (string): (Defaults to None) The project identifier. Each project can hold up to 5 datasets.

datasets_ids(project_id)

List the IDs the datasets for a particular project.

  • project_id: The project identifier.

datasets_names(project_id)

List the names the datasets for a particular project.

  • project_id: The project identifier.

datasets_details(dataset_id)

Provide details about a dataset.

  • dataset_id: The dataset identifier.

datasets_create(dataset_name, dataset_description)

Create a new dataset.

  • dataset_name: Name of the new dataset. NOTE: The name must be unique.

  • dataset_description: Description of the dataset.

datasets_update_details(dataset_id, dataset_name=None, dataset_description=None)

Update a dataset’s name or description.

  • dataset_id: The dataset identifier.

  • dataset_name: (Optional) New name of the dataset.

  • dataset_description: (Optional) New description of the dataset.

datasets_remove(dataset_ids)

Delete a set of datasets.

  • dataset_ids (list[string]): List of IDs of the datasets to delete. NOTE: Datasets are first marked for deletion, then deleted after a grace period (15 minutes, configurable). A Domino admin may also need to complete this process before the name can be reused.

datasets_upload_files(dataset_id, local_path_to_file_or_directory, file_upload_setting, max_workers, target_chunk_size, target_relative_path)

Uploads a file or entire directory to a dataset.

  • dataset_id: The dataset identifier.

  • local_path_to_file_or_directory: The path to the file or directory in local machine.

  • file_upload_setting: (Optional) The setting to resolve naming conflict, must be one of Overwrite, Rename, Ignore (default).

  • max_workers: (Optional) The max amount of threads (default: 10).

  • target_chunk_size: (Optional) The max chunk size for multipart upload (default: 8MB).

  • target_relative_path: (Optional) The path on the dataset to upload the file or directory to. Note that the path must exist or the upload will fail.

Example

from domino import Domino

# By and large your commands will run against a single project,
# so you must specify the full project name
domino = Domino("chris/canon")

# List all runs in the project, most-recently queued first
all_runs = domino.runs_list()['data']

latest_100_runs = all_runs[0:100]

print(latest_100_runs)

# all runs have a commitId (the snapshot of the project when the
# run starts) and, if the run completed, an "outputCommitId"
# (the snapshot of the project after the run completed)
most_recent_run = all_runs[0]

commitId = most_recent_run['outputCommitId']

# list all the files in the output commit ID -- only showing the
# entries under the results directory.  If not provided, will
# list all files in the project.  Or you can say path=“/“ to
# list all files
files = domino.files_list(commitId, path='results/')['data']

for file in files:
print file['path'], '->', file['url']

print(files)

# Get the content (i.e. blob) for the file you're interested in.
# blobs_get_v2 returns a connection rather than the content, because
# the content can get quite large and it's up to you how you want
# to handle it
print(domino.blobs_get_v2(files[0]['path'], commitId, domino.project_id).read())

# Start a run of file main.py using the latest copy of that file
domino.runs_start(["main.py", "arg1", "arg2"])

# Start a "direct" command
domino.runs_start(["echo 'Hello, World!'"], isDirect=True)

# Start a run of a specific commit
domino.runs_start(["main.py"], commitId="aabbccddee")

Airflow

The python-domino client comes bundled with an Operator for use with Apache Airflow as an extra.

When installing the client from PyPI, add the airflow flag to extras:

pip install "dominodatalab[airflow]"

Similarly, when installing the client from GitHub, use the following command:

pip install -e git+{python-domino-repo}.git@1.0.6#egg="dominodatalab[airflow]"

See also example_airflow_dag.py for example code.

DominoOperator

from domino.airflow import DominoOperator

Allows a user to schedule Domino executions via Airflow. Follows the same function signature as domino.runs_start with two extra arguments:

startup_delay: Optional[int] = 10

Add a startup delay to your job, useful if you want to delay execution until after other work finishes.

include_setup_log: Optional[bool] = True

Determine whether to publish the setup log of the job as the log prefix before stdout.

DominoSparkOperator

from domino.airflow import DominoSparkOperator

Allows a user to schedule Domino executions via the v4 API, which supports onDemandSparkClusters. Follows the same function signature as domino.job_start, with the addition of startup_delay from above.

Manual installation

Because python-domino ships with the DSE, normally you do not need to install it. This section provides instructions for installing it in another environment or updating it to a newer version.

Starting from version 1.0.6, python-domino is available on PyPI as dominodatalab:

pip install dominodatalab

If you are adding install instructions for python-domino to your Domino Environment Dockerfile Instructions field, you must add RUN to the beginning:

RUN pip install dominodatalab

To install a specific version of the library from PyPI, such as 1.0.6:

pip install dominodatalab==1.0.6

To install a specific version of the library from GitHub, such as 1.0.6:

pip install {python-domino-repo}/archive/1.0.6.zip

License

This library is made available under the Apache 2.0 License. This is an open-source project of Domino Data Lab.

Visit the python-domino repository

You can find the complete library, with documentation and example code, in the public repository at https://github.com/dominodatalab/python-domino.