diff --git a/website/blog/2021-11-29-open-source-community-growth.md b/website/blog/2021-11-29-open-source-community-growth.md index 8a71a504875..98b64cefa3d 100644 --- a/website/blog/2021-11-29-open-source-community-growth.md +++ b/website/blog/2021-11-29-open-source-community-growth.md @@ -57,7 +57,7 @@ For starters, I want to know how much conversation is occurring across the vario There are a ton of metrics that can be tracked in any GitHub project — committers, pull requests, forks, releases — but I started pretty simple. For each of the projects we participate in, I just want to know how the number of GitHub stars grows over time, and whether the growth is accelerating or flattening out. This has become a key performance indicator for open source communities, for better or for worse, and keeping track of it isn't optional. -Finally, I want to know how much Marquez and OpenLineage are being used. It used to be that when you wanted to consume a bit of tech, you'd download a file. Folks like me who study user behavior would track download counts as if they were stock prices. This is no longer the case; today, our tech is increasingly distributed through package managers and image repositories. Docker Hub and PyPI metrics have therefore become good indicators of consumption. Docker image pulls and runs of `pip install` are the modern day download and, as noisy as these metrics are, they indicate a similar level of user commitment. +Finally, I want to know how much Marquez and OpenLineage are being used. It used to be that when you wanted to consume a bit of tech, you'd download a file. Folks like me who study user behavior would track download counts as if they were stock prices. This is no longer the case; today, our tech is increasingly distributed through package managers and image repositories. Docker Hub and PyPI metrics have therefore become good indicators of consumption. Docker image pulls and runs of `python -m pip install` are the modern day download and, as noisy as these metrics are, they indicate a similar level of user commitment. To summarize, here are the metrics I decided to track (for now, anyway): - Slack messages (by user/ by community) diff --git a/website/blog/2022-04-14-add-ci-cd-to-bitbucket.md b/website/blog/2022-04-14-add-ci-cd-to-bitbucket.md index 451013b1572..44346e93741 100644 --- a/website/blog/2022-04-14-add-ci-cd-to-bitbucket.md +++ b/website/blog/2022-04-14-add-ci-cd-to-bitbucket.md @@ -159,7 +159,7 @@ pipelines: artifacts: # Save the dbt run artifacts for the next step (upload) - target/*.json script: - - pip install -r requirements.txt + - python -m pip install -r requirements.txt - mkdir ~/.dbt - cp .ci/profiles.yml ~/.dbt/profiles.yml - dbt deps @@ -208,7 +208,7 @@ pipelines: # Set up dbt environment + dbt packages. Rather than passing # profiles.yml to dbt commands explicitly, we'll store it where dbt # expects it: - - pip install -r requirements.txt + - python -m pip install -r requirements.txt - mkdir ~/.dbt - cp .ci/profiles.yml ~/.dbt/profiles.yml - dbt deps diff --git a/website/blog/2022-05-03-making-dbt-cloud-api-calls-using-dbt-cloud-cli.md b/website/blog/2022-05-03-making-dbt-cloud-api-calls-using-dbt-cloud-cli.md index 2ee774d4f1d..6758a28638c 100644 --- a/website/blog/2022-05-03-making-dbt-cloud-api-calls-using-dbt-cloud-cli.md +++ b/website/blog/2022-05-03-making-dbt-cloud-api-calls-using-dbt-cloud-cli.md @@ -59,7 +59,7 @@ You probably agree that the latter example is definitely more elegant and easier In addition to CLI commands that interact with a single dbt Cloud API endpoint there are composite helper commands that call one or more API endpoints and perform more complex operations. One example of composite commands are `dbt-cloud job export` and `dbt-cloud job import` where, under the hood, the export command performs a `dbt-cloud job get` and writes the job metadata to a file and the import command reads job parameters from a JSON file and calls `dbt-cloud job create`. The export and import commands can be used in tandem to move dbt Cloud jobs between projects. Another example is the `dbt-cloud job delete-all` which fetches a list of all jobs using `dbt-cloud job list` and then iterates over the list prompting the user if they want to delete the job. For each job that the user agrees to delete a `dbt-cloud job delete` is performed. -To install the CLI in your Python environment run `pip install dbt-cloud-cli` and you’re all set. You can use it locally in your development environment or e.g. in a GitHub actions workflow. +To install the CLI in your Python environment run `python -m pip install dbt-cloud-cli` and you’re all set. You can use it locally in your development environment or e.g. in a GitHub actions workflow. ## How the project came to be @@ -310,7 +310,7 @@ The `CatalogExploreCommand.execute` method implements the interactive exploratio I’ve included the app in the latest version of dbt-cloud-cli so you can test it out yourself! To use the app you need install dbt-cloud-cli with extra dependencies: ```bash -pip install dbt-cloud-cli[demo] +python -m pip install dbt-cloud-cli[demo] ``` Now you can the run app: diff --git a/website/blog/2023-04-18-building-a-kimball-dimensional-model-with-dbt.md b/website/blog/2023-04-18-building-a-kimball-dimensional-model-with-dbt.md index 3ca1f6ac2a9..ab364749eff 100644 --- a/website/blog/2023-04-18-building-a-kimball-dimensional-model-with-dbt.md +++ b/website/blog/2023-04-18-building-a-kimball-dimensional-model-with-dbt.md @@ -79,12 +79,12 @@ Depending on which database you’ve chosen, install the relevant database adapt ```text # install adaptor for duckdb -pip install dbt-duckdb +python -m pip install dbt-duckdb # OR # install adaptor for postgresql -pip install dbt-postgres +python -m pip install dbt-postgres ``` ### Step 4: Setup dbt profile diff --git a/website/docs/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md b/website/docs/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md index ffbd78b939c..6e9153a3780 100644 --- a/website/docs/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md +++ b/website/docs/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md @@ -23,8 +23,8 @@ We'll use pip to install MetricFlow and our dbt adapter: python -m venv [virtual environment name] source [virtual environment name]/bin/activate # install dbt and MetricFlow -pip install "dbt-metricflow[adapter name]" -# e.g. pip install "dbt-metricflow[snowflake]" +python -m pip install "dbt-metricflow[adapter name]" +# e.g. python -m pip install "dbt-metricflow[snowflake]" ``` Lastly, to get to the pre-Semantic Layer starting state, checkout the `start-here` branch. diff --git a/website/docs/docs/build/metricflow-commands.md b/website/docs/docs/build/metricflow-commands.md index 4d2477ad2ed..67589c07836 100644 --- a/website/docs/docs/build/metricflow-commands.md +++ b/website/docs/docs/build/metricflow-commands.md @@ -17,7 +17,7 @@ MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11. MetricFlow is a dbt package that allows you to define and query metrics in your dbt project. You can use MetricFlow to query metrics in your dbt project in the dbt Cloud CLI, dbt Cloud IDE, or dbt Core. -**Note** — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs. +**Note** — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs. @@ -54,7 +54,7 @@ You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-star 1. Create or activate your virtual environment `python -m venv venv` 2. Run `pip install dbt-metricflow` - * You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `pip install "dbt-metricflow[snowflake]"` + * You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `python -m pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `python -m pip install "dbt-metricflow[snowflake]"` **Note**, you'll need to manage versioning between dbt Core, your adapter, and MetricFlow. diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md index b945bede160..f3294477611 100644 --- a/website/docs/docs/cloud/cloud-cli-installation.md +++ b/website/docs/docs/cloud/cloud-cli-installation.md @@ -155,9 +155,9 @@ If you already have dbt Core installed, the dbt Cloud CLI may conflict. Here are - Uninstall the dbt Cloud CLI using the command: `pip uninstall dbt` - Reinstall dbt Core using the following command, replacing "adapter_name" with the appropriate adapter name: ```shell - pip install dbt-adapter_name --force-reinstall + python -m pip install dbt-adapter_name --force-reinstall ``` - For example, if I used Snowflake as an adapter, I would run: `pip install dbt-snowflake --force-reinstall` + For example, if I used Snowflake as an adapter, I would run: `python -m pip install dbt-snowflake --force-reinstall` -------- @@ -243,7 +243,7 @@ To update, follow the same process explained in [Windows](/docs/cloud/cloud-cli- To update: - Make sure you're in your virtual environment -- Run `pip install --upgrade dbt`. +- Run `python -m pip install --upgrade dbt`. diff --git a/website/docs/docs/connect-adapters.md b/website/docs/docs/connect-adapters.md index e301cfc237e..6ccc1b4f376 100644 --- a/website/docs/docs/connect-adapters.md +++ b/website/docs/docs/connect-adapters.md @@ -15,7 +15,7 @@ Explore the fastest and most reliable way to deploy dbt using dbt Cloud, a hoste Install dbt Core, an open-source tool, locally using the command line. dbt communicates with a number of different data platforms by using a dedicated adapter plugin for each. When you install dbt Core, you'll also need to install the specific adapter for your database, [connect to dbt Core](/docs/core/about-core-setup), and set up a `profiles.yml` file. -With a few exceptions [^1], you can install all [Verified adapters](/docs/supported-data-platforms) from PyPI using `pip install adapter-name`. For example to install Snowflake, use the command `pip install dbt-snowflake`. The installation will include `dbt-core` and any other required dependencies, which may include both other dependencies and even other adapter plugins. Read more about [installing dbt](/docs/core/installation). +With a few exceptions [^1], you can install all [Verified adapters](/docs/supported-data-platforms) from PyPI using `python -m pip install adapter-name`. For example to install Snowflake, use the command `python -m pip install dbt-snowflake`. The installation will include `dbt-core` and any other required dependencies, which may include both other dependencies and even other adapter plugins. Read more about [installing dbt](/docs/core/installation). [^1]: Here are the two different adapters. Use the PyPI package name when installing with `pip` diff --git a/website/docs/docs/core/connect-data-platform/hive-setup.md b/website/docs/docs/core/connect-data-platform/hive-setup.md index 92210162324..33e45e28a0d 100644 --- a/website/docs/docs/core/connect-data-platform/hive-setup.md +++ b/website/docs/docs/core/connect-data-platform/hive-setup.md @@ -131,7 +131,7 @@ you must install the `dbt-hive` plugin. The following commands will install the latest version of `dbt-hive` as well as the requisite version of `dbt-core` and `impyla` driver used for connections. ``` -pip install dbt-hive +python -m pip install dbt-hive ``` ### Supported Functionality diff --git a/website/docs/docs/core/connect-data-platform/spark-setup.md b/website/docs/docs/core/connect-data-platform/spark-setup.md index 3cface96440..e8d65153058 100644 --- a/website/docs/docs/core/connect-data-platform/spark-setup.md +++ b/website/docs/docs/core/connect-data-platform/spark-setup.md @@ -35,15 +35,15 @@ If connecting to a Spark cluster via the generic thrift or http methods, it requ ```zsh # odbc connections -$ pip install "dbt-spark[ODBC]" +$ python -m pip install "dbt-spark[ODBC]" # thrift or http connections -$ pip install "dbt-spark[PyHive]" +$ python -m pip install "dbt-spark[PyHive]" ``` ```zsh # session connections -$ pip install "dbt-spark[session]" +$ python -m pip install "dbt-spark[session]" ```

Configuring {frontMatter.meta.pypi_package}

diff --git a/website/docs/docs/core/connect-data-platform/starrocks-setup.md b/website/docs/docs/core/connect-data-platform/starrocks-setup.md index e5c1abac037..485e1d18fb7 100644 --- a/website/docs/docs/core/connect-data-platform/starrocks-setup.md +++ b/website/docs/docs/core/connect-data-platform/starrocks-setup.md @@ -34,7 +34,7 @@ meta: pip is the easiest way to install the adapter: -pip install {frontMatter.meta.pypi_package} +python -m pip install {frontMatter.meta.pypi_package}

Installing {frontMatter.meta.pypi_package} will also install dbt-core and any other dependencies.

diff --git a/website/docs/docs/core/connect-data-platform/trino-setup.md b/website/docs/docs/core/connect-data-platform/trino-setup.md index 199a6da8510..a7dc658358f 100644 --- a/website/docs/docs/core/connect-data-platform/trino-setup.md +++ b/website/docs/docs/core/connect-data-platform/trino-setup.md @@ -255,7 +255,7 @@ The only authentication parameter to set for OAuth 2.0 is `method: oauth`. If yo For more information, refer to both [OAuth 2.0 authentication](https://trino.io/docs/current/security/oauth2.html) in the Trino docs and the [README](https://github.com/trinodb/trino-python-client#oauth2-authentication) for the Trino Python client. -It's recommended that you install `keyring` to cache the OAuth 2.0 token over multiple dbt invocations by running `pip install 'trino[external-authentication-token-cache]'`. The `keyring` package is not installed by default. +It's recommended that you install `keyring` to cache the OAuth 2.0 token over multiple dbt invocations by running `python -m pip install 'trino[external-authentication-token-cache]'`. The `keyring` package is not installed by default. #### Example profiles.yml for OAuth diff --git a/website/docs/docs/core/connect-data-platform/upsolver-setup.md b/website/docs/docs/core/connect-data-platform/upsolver-setup.md index 6b2f410fc07..8e4203e0b0c 100644 --- a/website/docs/docs/core/connect-data-platform/upsolver-setup.md +++ b/website/docs/docs/core/connect-data-platform/upsolver-setup.md @@ -33,7 +33,7 @@ pagination_next: null pip is the easiest way to install the adapter: -pip install {frontMatter.meta.pypi_package} +python -m pip install {frontMatter.meta.pypi_package}

Installing {frontMatter.meta.pypi_package} will also install dbt-core and any other dependencies.

diff --git a/website/docs/docs/core/docker-install.md b/website/docs/docs/core/docker-install.md index dfb2a669e34..8de3bcb5c06 100644 --- a/website/docs/docs/core/docker-install.md +++ b/website/docs/docs/core/docker-install.md @@ -5,7 +5,7 @@ description: "You can use Docker to install dbt and adapter plugins from the com dbt Core and all adapter plugins maintained by dbt Labs are available as [Docker](https://docs.docker.com/) images, and distributed via [GitHub Packages](https://docs.github.com/en/packages/learn-github-packages/introduction-to-github-packages) in a [public registry](https://github.com/dbt-labs/dbt-core/pkgs/container/dbt-core). -Using a prebuilt Docker image to install dbt Core in production has a few benefits: it already includes dbt-core, one or more database adapters, and pinned versions of all their dependencies. By contrast, `pip install dbt-core dbt-` takes longer to run, and will always install the latest compatible versions of every dependency. +Using a prebuilt Docker image to install dbt Core in production has a few benefits: it already includes dbt-core, one or more database adapters, and pinned versions of all their dependencies. By contrast, `python -m pip install dbt-core dbt-` takes longer to run, and will always install the latest compatible versions of every dependency. You might also be able to use Docker to install and develop locally if you don't have a Python environment set up. Note that running dbt in this manner can be significantly slower if your operating system differs from the system that built the Docker image. If you're a frequent local developer, we recommend that you install dbt Core via [Homebrew](/docs/core/homebrew-install) or [pip](/docs/core/pip-install) instead. diff --git a/website/docs/docs/core/pip-install.md b/website/docs/docs/core/pip-install.md index 44fac00e493..e1a0e65312c 100644 --- a/website/docs/docs/core/pip-install.md +++ b/website/docs/docs/core/pip-install.md @@ -39,7 +39,7 @@ alias env_dbt='source /bin/activate' Once you know [which adapter](/docs/supported-data-platforms) you're using, you can install it as `dbt-`. For example, if using Postgres: ```shell -pip install dbt-postgres +python -m pip install dbt-postgres ``` This will install `dbt-core` and `dbt-postgres` _only_: @@ -62,7 +62,7 @@ All adapters build on top of `dbt-core`. Some also depend on other adapters: for To upgrade a specific adapter plugin: ```shell -pip install --upgrade dbt- +python -m pip install --upgrade dbt- ``` ### Install dbt-core only @@ -70,7 +70,7 @@ pip install --upgrade dbt- If you're building a tool that integrates with dbt Core, you may want to install the core library alone, without a database adapter. Note that you won't be able to use dbt as a CLI tool. ```shell -pip install dbt-core +python -m pip install dbt-core ``` ### Change dbt Core versions @@ -79,13 +79,13 @@ You can upgrade or downgrade versions of dbt Core by using the `--upgrade` optio To upgrade dbt to the latest version: ``` -pip install --upgrade dbt-core +python -m pip install --upgrade dbt-core ``` To downgrade to an older version, specify the version you want to use. This command can be useful when you're resolving package dependencies. As an example: ``` -pip install --upgrade dbt-core==0.19.0 +python -m pip install --upgrade dbt-core==0.19.0 ``` ### `pip install dbt` @@ -95,7 +95,7 @@ Note that, as of v1.0.0, `pip install dbt` is no longer supported and will raise If you have workflows or integrations that relied on installing the package named `dbt`, you can achieve the same behavior going forward by installing the same five packages that it used: ```shell -pip install \ +python -m pip install \ dbt-core \ dbt-postgres \ dbt-redshift \ diff --git a/website/docs/docs/core/source-install.md b/website/docs/docs/core/source-install.md index 42086159c03..d17adc13c53 100644 --- a/website/docs/docs/core/source-install.md +++ b/website/docs/docs/core/source-install.md @@ -17,10 +17,10 @@ To install `dbt-core` from the GitHub code source: ```shell git clone https://github.com/dbt-labs/dbt-core.git cd dbt-core -pip install -r requirements.txt +python -m pip install -r requirements.txt ``` -This will install `dbt-core` and `dbt-postgres`. To install in editable mode (includes your local changes as you make them), use `pip install -e editable-requirements.txt` instead. +This will install `dbt-core` and `dbt-postgres`. To install in editable mode (includes your local changes as you make them), use `python -m pip install -e editable-requirements.txt` instead. ### Installing adapter plugins @@ -29,12 +29,12 @@ To install an adapter plugin from source, you will need to first locate its sour ```shell git clone https://github.com/dbt-labs/dbt-redshift.git cd dbt-redshift -pip install . +python -m pip install . ``` You do _not_ need to install `dbt-core` before installing an adapter plugin -- the plugin includes `dbt-core` among its dependencies, and it will install the latest compatible version automatically. -To install in editable mode, such as while contributing, use `pip install -e .` instead. +To install in editable mode, such as while contributing, use `python -m pip install -e .` instead. diff --git a/website/docs/docs/dbt-versions/core-upgrade/08-upgrading-to-v1.0.md b/website/docs/docs/dbt-versions/core-upgrade/08-upgrading-to-v1.0.md index 3f45e44076c..c0ba804cd78 100644 --- a/website/docs/docs/dbt-versions/core-upgrade/08-upgrading-to-v1.0.md +++ b/website/docs/docs/dbt-versions/core-upgrade/08-upgrading-to-v1.0.md @@ -45,7 +45,7 @@ Global project macros have been reorganized, and some old unused macros have bee ### Installation - [Installation docs](/docs/supported-data-platforms) reflects adapter-specific installations -- `pip install dbt` is no longer supported, and will raise an explicit error. Install the specific adapter plugin you need as `pip install dbt-`. +- `python -m pip install dbt` is no longer supported, and will raise an explicit error. Install the specific adapter plugin you need as `python -m pip install dbt-`. - `brew install dbt` is no longer supported. Install the specific adapter plugin you need (among Postgres, Redshift, Snowflake, or BigQuery) as `brew install dbt-`. - Removed official support for python 3.6, which is reaching end of life on December 23, 2021 diff --git a/website/docs/faqs/Core/install-pip-best-practices.md b/website/docs/faqs/Core/install-pip-best-practices.md index e36d58296ec..72360a52acc 100644 --- a/website/docs/faqs/Core/install-pip-best-practices.md +++ b/website/docs/faqs/Core/install-pip-best-practices.md @@ -30,6 +30,6 @@ Before installing dbt, make sure you have the latest versions: ```shell -pip install --upgrade pip wheel setuptools +python -m pip install --upgrade pip wheel setuptools ``` diff --git a/website/docs/guides/adapter-creation.md b/website/docs/guides/adapter-creation.md index aa4819e73d0..8bf082b04a0 100644 --- a/website/docs/guides/adapter-creation.md +++ b/website/docs/guides/adapter-creation.md @@ -799,7 +799,7 @@ dbt-tests-adapter ```sh -pip install -r dev_requirements.txt +python -m pip install -r dev_requirements.txt ``` ### Set up and configure pytest diff --git a/website/docs/guides/codespace-qs.md b/website/docs/guides/codespace-qs.md index 7712ed8f8e8..b28b0ddaacf 100644 --- a/website/docs/guides/codespace-qs.md +++ b/website/docs/guides/codespace-qs.md @@ -61,7 +61,7 @@ If you'd like to work with a larger selection of Jaffle Shop data, you can gener 1. Install the Python package called [jafgen](https://pypi.org/project/jafgen/). At the terminal's prompt, run: ```shell - /workspaces/test (main) $ pip install jafgen + /workspaces/test (main) $ python -m pip install jafgen ``` 1. When installation is done, run: diff --git a/website/docs/guides/custom-cicd-pipelines.md b/website/docs/guides/custom-cicd-pipelines.md index 672c6e6dab8..bd6d7617623 100644 --- a/website/docs/guides/custom-cicd-pipelines.md +++ b/website/docs/guides/custom-cicd-pipelines.md @@ -336,7 +336,7 @@ lint-project: rules: - if: $CI_PIPELINE_SOURCE == "push" && $CI_COMMIT_BRANCH != 'main' script: - - pip install sqlfluff==0.13.1 + - python -m pip install sqlfluff==0.13.1 - sqlfluff lint models --dialect snowflake --rules L019,L020,L021,L022 # this job calls the dbt Cloud API to run a job @@ -379,7 +379,7 @@ steps: displayName: 'Use Python 3.7' - script: | - pip install requests + python -m pip install requests displayName: 'Install python dependencies' - script: | @@ -434,7 +434,7 @@ pipelines: - step: name: Lint dbt project script: - - pip install sqlfluff==0.13.1 + - python -m pip install sqlfluff==0.13.1 - sqlfluff lint models --dialect snowflake --rules L019,L020,L021,L022 'main': # override if your default branch doesn't run on a branch named "main" diff --git a/website/docs/guides/set-up-ci.md b/website/docs/guides/set-up-ci.md index 83362094ec6..89d7c5a14fa 100644 --- a/website/docs/guides/set-up-ci.md +++ b/website/docs/guides/set-up-ci.md @@ -167,7 +167,7 @@ jobs: with: python-version: "3.9" - name: Install SQLFluff - run: "pip install sqlfluff" + run: "python -m pip install sqlfluff" - name: Lint project run: "sqlfluff lint models --dialect snowflake" @@ -204,7 +204,7 @@ lint-project: rules: - if: $CI_PIPELINE_SOURCE == "push" && $CI_COMMIT_BRANCH != 'main' script: - - pip install sqlfluff + - python -m pip install sqlfluff - sqlfluff lint models --dialect snowflake ``` @@ -235,7 +235,7 @@ pipelines: - step: name: Lint dbt project script: - - pip install sqlfluff==0.13.1 + - python -m pip install sqlfluff==0.13.1 - sqlfluff lint models --dialect snowflake --rules L019,L020,L021,L022 'main': # override if your default branch doesn't run on a branch named "main" diff --git a/website/docs/guides/sl-migration.md b/website/docs/guides/sl-migration.md index 0cfde742af2..c3cca81f68e 100644 --- a/website/docs/guides/sl-migration.md +++ b/website/docs/guides/sl-migration.md @@ -25,10 +25,10 @@ dbt Labs recommends completing these steps in a local dev environment (such as t 1. Create new Semantic Model configs as YAML files in your dbt project.* 1. Upgrade the metrics configs in your project to the new spec.* 1. Delete your old metrics file or remove the `.yml` file extension so they're ignored at parse time. Remove the `dbt-metrics` package from your project. Remove any macros that reference `dbt-metrics`, like `metrics.calculate()`. Make sure that any packages you’re using don't have references to the old metrics spec. -1. Install the CLI with `pip install "dbt-metricflow[your_adapter_name]"`. For example: +1. Install the CLI with `python -m pip install "dbt-metricflow[your_adapter_name]"`. For example: ```bash - pip install "dbt-metricflow[snowflake]" + python -m pip install "dbt-metricflow[snowflake]" ``` **Note** - The MetricFlow CLI is not available in the IDE at this time. Support is coming soon. diff --git a/website/snippets/_setup-pages-intro.md b/website/snippets/_setup-pages-intro.md index 44cbbb1a0c2..5ded5ba5ebc 100644 --- a/website/snippets/_setup-pages-intro.md +++ b/website/snippets/_setup-pages-intro.md @@ -13,7 +13,7 @@

Installing {props.meta.pypi_package}

Use `pip` to install the adapter, which automatically installs `dbt-core` and any additional dependencies. Use the following command for installation: -pip install {props.meta.pypi_package} +python -m pip install {props.meta.pypi_package}

Configuring {props.meta.pypi_package}

diff --git a/website/snippets/_sl-test-and-query-metrics.md b/website/snippets/_sl-test-and-query-metrics.md index 43ebd929cb3..2e9490f089d 100644 --- a/website/snippets/_sl-test-and-query-metrics.md +++ b/website/snippets/_sl-test-and-query-metrics.md @@ -48,8 +48,8 @@ The dbt Cloud CLI is strongly recommended to define and query metrics for your d 1. Install [MetricFlow](/docs/build/metricflow-commands) as an extension of a dbt adapter from PyPI. 2. Create or activate your virtual environment with `python -m venv venv` or `source your-venv/bin/activate`. -3. Run `pip install dbt-metricflow`. - - You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. As an example for a Snowflake adapter, run `pip install "dbt-metricflow[snowflake]"`. +3. Run `python -m pip install dbt-metricflow`. + - You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `python -m pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. As an example for a Snowflake adapter, run `python -m pip install "dbt-metricflow[snowflake]"`. - You'll need to manage versioning between dbt Core, your adapter, and MetricFlow. 4. Run `dbt parse`. This allows MetricFlow to build a semantic graph and generate a `semantic_manifest.json`. - This creates the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt run` before proceeding to ensure the data exists in your warehouse.