Monte Carlo CLI
Monte Carlo's Alpha CLI!
Installation
Requires Python 3.9 or greater. Normally you can install and update using pip. For instance:
pip install virtualenv
virtualenv venv
. venv/bin/activate
pip install -U montecarlodata
Developers of the CLI can use:
pip install virtualenv
make install
. venv/bin/activate
pre-commit install
Either way confirm the installation by running:
montecarlo --version
If the Python requirement does not work for you please reach out to support@montecarlodata.com. Docker is an option.
Quick start
First time users can configure the tool by following the onscreen prompts:
montecarlo configure
MCD tokens can be generated from the dashboard.
Use the --help flag for details on any advanced options (e.g. creating multiple montecarlo profiles) or
see docs here.
Non-interactive / scripted use: when stdin is not a terminal (CI, scheduled jobs), montecarlo configure
defaults to API key auth and prints a notice to stderr. Pass --api-key --mcd-id <ID> --mcd-token <TOKEN>
to configure without prompts, or --oauth to use OAuth client credentials.
That's it! You can always validate your connection with:
montecarlo validate
User settings
Any configuration set by montecarlo configure can be found in ~/.mcd/ by default.
The MCD ID and Token can be overwritten, or even set, by the environment:
MCD_DEFAULT_API_IDMCD_DEFAULT_API_TOKEN
These two are required either as part of configure or as environment variables.
The following values can also be set by the environment:
MCD_API_ENDPOINT- Overwrite the default API endpointMCD_VERBOSE_ERRORS- Enable verbose logging on errors (default=false)
Help
Documentation for commands, options, and arguments can be found here.
You can also use montecarlo help to echo all help text or use the --help flag on any command.
Examples
Using Docker from a local installation
docker build -t montecarlo .
docker run -e MCD_DEFAULT_API_ID='<ID>' -e MCD_DEFAULT_API_TOKEN='<TOKEN>' montecarlo --version
Replace --version with any sub-commands or options. If interacting with files those directories will probably need to be mounted too.
Configure a named profile with custom config-path
$ montecarlo configure --profile-name zeus --config-path .
Key ID: 1234
Secret:
$ cat ./profiles.ini
[zeus]
mcd_id = 1234
mcd_token = 5678
List active integrations
$ montecarlo integrations list
╒══════════════════╤══════════════════════════════════════╤══════════════════════════════════╕
│ Integration │ ID │ Created on (UTC) │
╞══════════════════╪══════════════════════════════════════╪══════════════════════════════════╡
│ Odin │ 58005657-2914-4701-9a11-260ac425b14e │ 2021-01-02T01:30:52.806602+00:00 │
├──────────────────┼──────────────────────────────────────┼──────────────────────────────────┤
│ Thor │ 926816bd-ab17-4f95-a953-fa14482c59de │ 2021-01-02T01:31:19.892205+00:00 │
├──────────────────┼──────────────────────────────────────┼──────────────────────────────────┤
│ Loki │ 1cf1dc0d-d8ec-4c85-8e64-57ab2ad8e023 │ 2021-01-02T01:32:37.709747+00:00 │
╘══════════════════╧══════════════════════════════════════╧══════════════════════════════════╛
Apply monitors configuration
$ montecarlo monitors apply --namespace my-monitors
Gathering monitor configuration files.
- models/customer_success/schema.yml - Embedded monitor configuration found.
- models/customer_success/schema.yml - Monitor configuration found.
- models/lineage/schema.yml - Embedded monitor configuration found.
Modifications:
- ResourceModificationType.UPDATE - Monitor: type=stats, table=analytics:prod.customer_360
- ResourceModificationType.UPDATE - Monitor: type=categories, table=analytics:prod.customer_360
- ResourceModificationType.UPDATE - Monitor: type=stats, table=analytics:prod_lineage.lineage_nodes
- ResourceModificationType.UPDATE - Freshness SLI: table=analytics:prod.customer_360, freshness_threshold=30
Import DBT manifest
$ montecarlo import dbt-manifest --dbt-manifest-file target/manifest.json
Importing DBT objects into Monte Carlo catalog. please wait...
Imported a total of 51 DBT objects into Monte Carlo catalog.
Tests and Releases
Locally make test will run all tests. CircleCI manages all testing for deployment.
To publish a new release, navigate to Releases in the GitHub repo and then:
- Click "Draft a new release"
- In the "Choose a tag" dropdown, type the new version number, for example
v1.2.3and click "Create a new tag" - Follow the format from previous releases for the description
- Leave "Set as the latest release" checked
- Click "Publish release"
- CircleCI will take care of publishing a new package to PyPI and generating documentation.
License
Apache 2.0 - See the LICENSE for more information.
4 maintainers
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