From cf039149289184cd6c52d7ee85661f8d3861259e Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Wed, 3 May 2023 17:44:32 +0100 Subject: [PATCH 01/55] Add deployment workflow page Signed-off-by: Jannic Holzer --- .../databricks_deployment_workflow.md | 218 ++++++++++++++++++ .../meta/images/databricks_create_new_job.png | Bin 0 -> 25040 bytes 2 files changed, 218 insertions(+) create mode 100644 docs/source/integrations/databricks_deployment_workflow.md create mode 100644 docs/source/meta/images/databricks_create_new_job.png diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md new file mode 100644 index 0000000000..f338ddeddd --- /dev/null +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -0,0 +1,218 @@ +# Databricks deployment workflow + +In this guide, you will explore the process of packaging a Kedro project and running it as a job on Databricks. Databricks jobs are a way to execute code on Databricks clusters, allowing you to run data processing tasks, ETL jobs, or machine learning workflows. + +By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks, see the [development workflow guide](./databricks_development_workflow.md). + +This guide will cover two methods for running your packaged Kedro project as a job on Databricks: + +- [Manually starting a job run via the Databricks workspace UI.](#run-your-packaged-project-via-the-databricks-workspace-ui) +- [Using dbx in your local environment to initiate the job run.](#deploying-a-kedro-project-as-a-databricks-job-using-dbx) + +Manually starting your job using the Databricks workspace UI has a shallower learning curve and is more intuitive. Using dbx to run your project saves time and allows you to store information about your job declaratively in your project, but is less intuitive and requires more time to set up. + +By the end of this guide, you'll have a clear understanding of the steps involved in packaging your Kedro project and running it as a job on Databricks using either of these methods. + +## Prerequisites + +- An active [Databricks deployment](https://docs.databricks.com/getting-started/index.html). +- A [Databricks cluster](https://docs.databricks.com/clusters/configure.html) configured with a recent version (>= 11.3 is recommended) of the Databricks runtime. +- [Conda installed](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) on your local machine in order to create a virtual environment with a specific version of Python (>= 3.8 is required to use dbx). If you have Python >= 3.8 installed, you can use other software to create a virtual environment. + +## Set up your project + +### Note your Databricks username and host + +Note your Databricks **username** and **host** as you will need it for the remainder of this guide. + +Find your Databricks username in the top right of the workspace UI and the host in the browser's URL bar, up to the first slash (e.g., `https://adb-123456789123456.1.azuredatabricks.net/`): + +![Find Databricks host and username](../meta/images/find_databricks_host_and_username.png) + +```{note} +Your databricks host must include the protocol (`https://`). +``` + +### Install Kedro and dbx in a new virtual environment + +In your local development environment, create a virtual environment for this tutorial using Conda: + +```bash +conda create --name iris-databricks python=3.10 +``` + +Once it is created, activate it: + +```bash +conda activate iris-databricks +``` + +With your Conda environment activated, install Kedro and dbx: + +```bash +pip install kedro dbx --upgrade +``` + +### Authenticate the Databricks CLI + +**Now, you must authenticate the Databricks CLI with your Databricks instance.** + +[Refer to the Databricks documentation](https://docs.databricks.com/dev-tools/cli/index.html#set-up-authentication) for a complete guide on how to authenticate your CLI. The key steps are: + +1. Create a personal access token for your user on your Databricks instance. +2. Run `databricks configure --token`. +3. Enter your token and Databricks host when prompted. +4. Run `databricks fs ls dbfs:/` at the command line to verify your authentication. + +```{note} +dbx is an extension of the Databricks CLI, a command-line program for interacting with Databricks without using its UI. You will use dbx to sync your project's code with Databricks. While Git can sync code to Databricks repos, dbx is preferred for development as it avoids creating new commits for every change, even if those changes do not work. +``` + +### Create a new Kedro project + +Create a Kedro project with the PySpark Iris starter using the following command in your local environment: + +```bash +kedro new --starter=pyspark-iris +``` + +Name your new project `iris-databricks` for consistency with the rest of this guide. This command creates a new Kedro project using the PySpark Iris starter template. + +### Create an entry point for Databricks + +The default entry point of a Kedro project exposes a Click command line interface (CLI) and cannot be used as the entry point to your package on Databricks. To run your project as a Databricks job, you must define a new entry point for use on Databricks. + +This entry point is already created for you in the PySpark Iris starter and therefore there is no extra work to do as part of this guide, though generally **you must create it manually for your own projects using the following steps**: + +1. **Create an entry point script**: Create a new file in `/src` named `databricks_run.py`. Copy the contents of the `/src/databricks_run.py` file defined in the [PySpark Iris starter]() to this new file. + +2. **Define a new entry point**: Open `/src/setup.py` in a text editor or IDE and add a new line in the definition of the `entry_point` tuple, so that it becomes: + +```python +entry_point = ( + ... + "databricks_run = .databricks_run" +) +``` + +Remember to replace with the correct package name for your project. + +This process adds an entry point to your project which can be used to run it on Databricks. + +```{note} +Using the default entry point of a Kedro project on Databricks will cause the job run to fail. +``` + +## Package and run your project via the Databricks Workspace UI + +### Package your project + +To package your Kedro project for deployment on Databricks, you must create a Wheel file, which is a binary distribution of your project. In the root directory of your Kedro project, run the following command: + +```bash +kedro package +``` + +This command generates a `.whl` file in the `dist` directory within your project's root directory. + +### Run your packaged project on Databricks + +To run your packaged project on Databricks using the workspace UI, perform the following steps: + +1. **Create a new job**: In the Databricks workspace, navigate to the "Worfklows" tab and click "Create Job": + +![Create Databricks job](../meta/images/databricks_create_new_job.png) + +2. **Configure the job**: Give the job a name, and in the "Task" section, select "Python file" as the task type. Provide the path to your Kedro project's `run.py` file within the Wheel package. This is the entry point for executing your Kedro pipeline. + +![Configure Databricks job](../meta/images/databricks_configure_new_job.png) + +3. **Specify options**: + +3. **Run the job**: Click "Run Now" to start the job. The job's status will be displayed in the "Runs" tab. You can view the logs and results by clicking on the job run in the list. + +![Databricks run job](../meta/images/databricks_run_job.png) + +If you now + +By following these steps, you packaged your Kedro project and manually ran it as a job on Databricks using the workspace UI. + + + + + + + +## Deploying a Kedro Project as a Databricks Job Using dbx + +To deploy your Kedro project as a Databricks job using dbx, follow these steps: + +### Install and configure dbx + +1. **Install `dbx`**: If you haven't already, install the dbx CLI tool in your local environment by running: + +```bash +pip install dbx +``` + +2. **Configure Databricks CLI**: Ensure that you have the Databricks CLI installed and configured with the appropriate authentication settings. For more information, refer to the [Databricks CLI documentation](https://docs.databricks.com/dev-tools/cli/index.html). + +### Package Kedro Project + +3. **Create a Wheel file**: Package your Kedro project into a Wheel file by running the following command in the project's root directory: + +```bash +python setup.py bdist_wheel +``` + +This command generates a `.whl` file in the `dist` directory within your project's root directory. + +### Deploy and Run the Kedro Project on Databricks + +4. **Create a `dbx` deployment configuration**: In your Kedro project's root directory, create a `dbx` deployment configuration file named `dbx_deploy.json`. This file should contain the necessary information to deploy and run your Kedro project on Databricks. Here's an example configuration: + +```json +{ + "jobs": { + "my_kedro_job": { + "type": "python", + "python_file": "my_kedro_project/run.py", + "libraries": [ + {"whl": "dist/my_kedro_project-0.1.0-py3-none-any.whl"} + ], + "job_parameters": { + "params": "{\"param1\": \"value1\", \"param2\": \"value2\"}" + }, + "cluster": { + "cluster_name": "my_kedro_cluster", + "spark_version": "8.4.x-scala2.12", + "node_type_id": "Standard_DS3_v2", + "autoscale": { + "min_workers": 1, + "max_workers": 4 + } + } + } + } +} +``` + + Replace the placeholders with the appropriate values for your Kedro project, library, and cluster configuration. + +5. **Deploy the job**: In your Kedro project's root directory, run the following command to deploy the job using the `dbx` CLI: + +```bash +dbx deploy --config-file dbx_deploy.json +``` + +This command uploads the Wheel file to the Databricks workspace, creates or updates the job, and configures the job with the specified cluster settings and job parameters. + +6. **Run the job**: To start the job, run the following command: + +```bash +dbx run --job my_kedro_job +``` + +This command initiates the job execution, and you can monitor the job's progress in the Databricks workspace's "Jobs" tab. + +By following these steps, you can deploy your Kedro project as a Databricks job using `dbx` and run it on the Databricks platform. diff --git a/docs/source/meta/images/databricks_create_new_job.png b/docs/source/meta/images/databricks_create_new_job.png new file mode 100644 index 0000000000000000000000000000000000000000..279bc8be682e17d677ef06d096d66bb4b1516e83 GIT binary patch literal 25040 zcmZs@1ymft)-{SuU?8{z2<|Sy-QC@T1`X~m0fM_b0Va5G3r-+7gy8P3f#CKUa=-7t z?|+ZAX3g|;b=P!t^*MX*vx{(LMJZ&&w}?OB0um0%Y0> z$|Pd;PG%&WOe{<+WP*q!BqaP!AIy1G#U=l9IItx^X6fSMz{||+?(WXy&c!_BaWiLACo2aRD|+?U(wc!{( z{H2e9%a%WXOF&4twzY<$Wq~t0J71VCiGmpTNtk5dCknzh!LZ9xWPD9IZyOLRFdFRB+)vZ|5Us#*<&os>Aq_MN>y@@=Pl?VauUR@v*YkOX<&mi 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point guide, data and conf upload guide Signed-off-by: Jannic Holzer --- .../databricks_deployment_workflow.md | 145 +++++++++--------- 1 file changed, 72 insertions(+), 73 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index f338ddeddd..a8dd1a9bbd 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -4,14 +4,17 @@ In this guide, you will explore the process of packaging a Kedro project and run By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks, see the [development workflow guide](./databricks_development_workflow.md). -This guide will cover two methods for running your packaged Kedro project as a job on Databricks: +By the end of this guide, you'll have a clear understanding of the steps involved in packaging your Kedro project and running it as a job on Databricks. -- [Manually starting a job run via the Databricks workspace UI.](#run-your-packaged-project-via-the-databricks-workspace-ui) -- [Using dbx in your local environment to initiate the job run.](#deploying-a-kedro-project-as-a-databricks-job-using-dbx) +## What this page covers -Manually starting your job using the Databricks workspace UI has a shallower learning curve and is more intuitive. Using dbx to run your project saves time and allows you to store information about your job declaratively in your project, but is less intuitive and requires more time to set up. +This page covers how to set up your project for deployment to Databricks and two methods for running your packaged Kedro project as a job: + +- [Set up your project for deployment on Databricks.](#set-up-your-project) +- [Run your project as a job using the Databricks workspace UI.](#deploy-and-run-your-kedro-project-using-the-workspace-ui) +- [Run your project as a job using dbx.](#deploy-and-run-your-kedro-project-using-dbx) -By the end of this guide, you'll have a clear understanding of the steps involved in packaging your Kedro project and running it as a job on Databricks using either of these methods. +Manually starting your job using the Databricks workspace UI has a shallower learning curve and is more intuitive. Using dbx to run your project saves time and allows you to store information about your job declaratively in your project, but is less intuitive and requires more time to set up. ## Prerequisites @@ -65,7 +68,7 @@ pip install kedro dbx --upgrade 4. Run `databricks fs ls dbfs:/` at the command line to verify your authentication. ```{note} -dbx is an extension of the Databricks CLI, a command-line program for interacting with Databricks without using its UI. You will use dbx to sync your project's code with Databricks. While Git can sync code to Databricks repos, dbx is preferred for development as it avoids creating new commits for every change, even if those changes do not work. +dbx is an extension of the Databricks CLI, a command-line program for interacting with Databricks without using its UI. You will use dbx to automatically package your project, upload it to Databricks and run it as a job. ``` ### Create a new Kedro project @@ -80,9 +83,9 @@ Name your new project `iris-databricks` for consistency with the rest of this gu ### Create an entry point for Databricks -The default entry point of a Kedro project exposes a Click command line interface (CLI) and cannot be used as the entry point to your package on Databricks. To run your project as a Databricks job, you must define a new entry point for use on Databricks. +The default entry point of a Kedro project uses a Click command line interface (CLI), which is not compatible with Databricks. To run your project as a Databricks job, you must define a new entry point speficially for use on Databricks. -This entry point is already created for you in the PySpark Iris starter and therefore there is no extra work to do as part of this guide, though generally **you must create it manually for your own projects using the following steps**: +The PySpark Iris starter has this entry point pre-built, so there is no extra work to do in this guide. **Generally you must create it manually for your own projects using the following steps**: 1. **Create an entry point script**: Create a new file in `/src` named `databricks_run.py`. Copy the contents of the `/src/databricks_run.py` file defined in the [PySpark Iris starter]() to this new file. @@ -100,14 +103,18 @@ Remember to replace with the correct package name for your projec This process adds an entry point to your project which can be used to run it on Databricks. ```{note} -Using the default entry point of a Kedro project on Databricks will cause the job run to fail. +Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it allows. Instead, the `databricks_run` entry point in PySpark-Iris contains a simple implementation of the following options: +- `--package`: +- `--pipeline`: specifies a pipeline for your run. +- `--tags`: +- `--env`: specifies a configuration environment to load for your run. +- `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. +- `--params`: ``` -## Package and run your project via the Databricks Workspace UI - ### Package your project -To package your Kedro project for deployment on Databricks, you must create a Wheel file, which is a binary distribution of your project. In the root directory of your Kedro project, run the following command: +To package your Kedro project for deployment on Databricks, you must create a Wheel (`.whl`) file, which is a binary distribution of your project. In the root directory of your Kedro project, run the following command: ```bash kedro package @@ -115,91 +122,83 @@ kedro package This command generates a `.whl` file in the `dist` directory within your project's root directory. -### Run your packaged project on Databricks +### Upload project data and configuration to DBFS -To run your packaged project on Databricks using the workspace UI, perform the following steps: - -1. **Create a new job**: In the Databricks workspace, navigate to the "Worfklows" tab and click "Create Job": - -![Create Databricks job](../meta/images/databricks_create_new_job.png) +Your packaged Kedro project needs access to data and configuration in order to run. Therefore, you will need to upload your project's data and configuration to an accessible location. In this guide, we will store the data on the Databricks File System (DBFS). -2. **Configure the job**: Give the job a name, and in the "Task" section, select "Python file" as the task type. Provide the path to your Kedro project's `run.py` file within the Wheel package. This is the entry point for executing your Kedro pipeline. +There are several ways to upload data to DBFS. In this guide, it is recommended to use [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html) because of the convenience it offers. At the command line in your local environment, use the following Databricks CLI commands to upload your project's locally stored data and configuration to DBFS: -![Configure Databricks job](../meta/images/databricks_configure_new_job.png) +```bash +databricks fs cp --recursive /data/ dbfs:/FileStore/iris-databricks/data +databricks fs cp --recursive /conf/ dbfs:/FileStore/iris-databricks/conf +``` -3. **Specify options**: +The `--recursive` flag ensures that the entire folder and its contents are uploaded. You can list the contents of the destination folder in DBFS using the following command: -3. **Run the job**: Click "Run Now" to start the job. The job's status will be displayed in the "Runs" tab. You can view the logs and results by clicking on the job run in the list. +```bash +databricks fs ls dbfs:/FileStore/iris-databricks/data +``` -![Databricks run job](../meta/images/databricks_run_job.png) +You should see the contents of the project's `data/` directory printed to your terminal: -If you now +```bash +01_raw +02_intermediate +03_primary +04_feature +05_model_input +06_models +07_model_output +08_reporting +``` -By following these steps, you packaged your Kedro project and manually ran it as a job on Databricks using the workspace UI. +```{note} +A Kedro project's configuration and data do not get included when it is packaged. They must be stored somewhere accessible to allow your packaged project to run. +``` +## Deploy and run your Kedro project using the workspace UI +To run your packaged project on Databricks using the workspace UI, perform the following steps: +1. **Create a new job**: In the Databricks workspace, navigate to the "Worfklows" tab and click "Create Job": +![Create Databricks job](../meta/images/databricks_create_new_job.png) +2. **Configure the job**: Give the job a name, and in the "Task" section, select "Python file" as the task type. Provide the path to your Kedro project's `run.py` file within the Wheel package. This is the entry point for executing your Kedro pipeline. +![Configure Databricks job](../meta/images/databricks_configure_new_job.png) -## Deploying a Kedro Project as a Databricks Job Using dbx +3. **Specify options**: -To deploy your Kedro project as a Databricks job using dbx, follow these steps: +4. **Run the job**: Click "Run Now" to start the job. The job's status will be displayed in the "Runs" tab. You can view the logs and results by clicking on the job run in the list. -### Install and configure dbx +![Databricks run job](../meta/images/databricks_run_job.png) -1. **Install `dbx`**: If you haven't already, install the dbx CLI tool in your local environment by running: +By following these steps, you packaged your Kedro project and manually ran it as a job on Databricks using the workspace UI. -```bash -pip install dbx -``` +## Deploy and run your Kedro project using dbx -2. **Configure Databricks CLI**: Ensure that you have the Databricks CLI installed and configured with the appropriate authentication settings. For more information, refer to the [Databricks CLI documentation](https://docs.databricks.com/dev-tools/cli/index.html). +dbx allows you to define a Databricks workflow in a [deployment file](https://dbx.readthedocs.io/en/latest/reference/deployment/). This file stores information about how your project should be run on Databricks and can be either YAML or JSON format. This file can be stored inside your project, where it can be added to version control. -### Package Kedro Project +In this section, you will use `dbx deploy` to create a Databricks workflow of your packaged Kedro project using a deployment file in YAML format, then you will run your packaged Kedro project using `dbx launch`. -3. **Create a Wheel file**: Package your Kedro project into a Wheel file by running the following command in the project's root directory: +1. **Create a `dbx` deployment file**: In your Kedro project's `conf` directory, create a `dbx` deployment file named `deployment.yml`. Add the following declarative information on how to deploy and run your Kedro project on Databricks: -```bash -python setup.py bdist_wheel +```yaml +environments: + default: + workflows: + - name: "iris-databricks" + tasks: + - task_key: "main" + <<: *basic-static-cluster + python_wheel_task: + package_name: "iris_databricks" + entry_point: "databricks_run" + parameters: ["--env", "databricks"] ``` -This command generates a `.whl` file in the `dist` directory within your project's root directory. - -### Deploy and Run the Kedro Project on Databricks - -4. **Create a `dbx` deployment configuration**: In your Kedro project's root directory, create a `dbx` deployment configuration file named `dbx_deploy.json`. This file should contain the necessary information to deploy and run your Kedro project on Databricks. Here's an example configuration: - -```json -{ - "jobs": { - "my_kedro_job": { - "type": "python", - "python_file": "my_kedro_project/run.py", - "libraries": [ - {"whl": "dist/my_kedro_project-0.1.0-py3-none-any.whl"} - ], - "job_parameters": { - "params": "{\"param1\": \"value1\", \"param2\": \"value2\"}" - }, - "cluster": { - "cluster_name": "my_kedro_cluster", - "spark_version": "8.4.x-scala2.12", - "node_type_id": "Standard_DS3_v2", - "autoscale": { - "min_workers": 1, - "max_workers": 4 - } - } - } - } -} -``` - - Replace the placeholders with the appropriate values for your Kedro project, library, and cluster configuration. - -5. **Deploy the job**: In your Kedro project's root directory, run the following command to deploy the job using the `dbx` CLI: +2. **Deploy the job**: In your Kedro project's root directory, run the following command to deploy the job using the `dbx` CLI: ```bash dbx deploy --config-file dbx_deploy.json @@ -207,7 +206,7 @@ dbx deploy --config-file dbx_deploy.json This command uploads the Wheel file to the Databricks workspace, creates or updates the job, and configures the job with the specified cluster settings and job parameters. -6. **Run the job**: To start the job, run the following command: +3. **Run the job**: To start the job, run the following command: ```bash dbx run --job my_kedro_job From 66fd8628ead4fa0ae43ae55fd94c1ce1ee91f09a Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Thu, 18 May 2023 22:46:48 +0100 Subject: [PATCH 03/55] Add detailed instructions for creating a job on Databricks Signed-off-by: Jannic Holzer --- .../databricks_deployment_workflow.md | 28 +++++++++++++----- .../images/databricks_configure_new_job.png | Bin 0 -> 157496 bytes 2 files changed, 21 insertions(+), 7 deletions(-) create mode 100644 docs/source/meta/images/databricks_configure_new_job.png diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index a8dd1a9bbd..d4ae19367f 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -126,6 +126,8 @@ This command generates a `.whl` file in the `dist` directory within your project Your packaged Kedro project needs access to data and configuration in order to run. Therefore, you will need to upload your project's data and configuration to an accessible location. In this guide, we will store the data on the Databricks File System (DBFS). +The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../configuration/configuration_basics.md#configuration-environments). When you run your project on Databricks, you will pass the name of this environment as an option, as well as the path to the `conf/` directory on DBFS. + There are several ways to upload data to DBFS. In this guide, it is recommended to use [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html) because of the convenience it offers. At the command line in your local environment, use the following Databricks CLI commands to upload your project's locally stored data and configuration to DBFS: ```bash @@ -164,16 +166,29 @@ To run your packaged project on Databricks using the workspace UI, perform the f ![Create Databricks job](../meta/images/databricks_create_new_job.png) -2. **Configure the job**: Give the job a name, and in the "Task" section, select "Python file" as the task type. Provide the path to your Kedro project's `run.py` file within the Wheel package. This is the entry point for executing your Kedro pipeline. +2. **Configure the job**: +- Enter 'iris-databricks' in the `Name` field. +- In the dropdown menu for the `Type` field, select 'Python wheel' +- In the `Package name` field, enter `iris_databricks`. This is the name of your package as defined in your project's `src/setup.py` file. +- In the `Entry Point` field, enter `databricks_run`. This is the name of the [entry point](#create-an-entry-point-for-databricks) to run your package from. +- Select your cluster in the dropdown menu for the `Cluster` field. +- In the `Dependent libraries` field, click `Add` and upload the `.whl` file that you [created of your project](#package-your-project). +- In the `Parameters` field, enter the following list of runtime options: -![Configure Databricks job](../meta/images/databricks_configure_new_job.png) +```bash +["--package","iris_databricks","--conf-source","/dbfs/FileStore/iris-databricks/conf","--env","databricks"] +``` -3. **Specify options**: +The final configuration for your job should look like the following: -4. **Run the job**: Click "Run Now" to start the job. The job's status will be displayed in the "Runs" tab. You can view the logs and results by clicking on the job run in the list. +![Configure Databricks job](../meta/images/databricks_configure_new_job.png) + +3. **Run the job**: Click "Run Now" to start a run of the job. The job's status will be displayed in the `Runs` tab. Navigate to the `Runs` tab and view the logs and results by clicking on the job run in the list: ![Databricks run job](../meta/images/databricks_run_job.png) +After the job has been run, you can view the output in the logs: + By following these steps, you packaged your Kedro project and manually ran it as a job on Databricks using the workspace UI. ## Deploy and run your Kedro project using dbx @@ -191,11 +206,10 @@ environments: - name: "iris-databricks" tasks: - task_key: "main" - <<: *basic-static-cluster python_wheel_task: package_name: "iris_databricks" entry_point: "databricks_run" - parameters: ["--env", "databricks"] + parameters: ["--package","iris_databricks","--conf-source","/dbfs/FileStore/iris-databricks/conf","--env","databricks"] ``` 2. **Deploy the job**: In your Kedro project's root directory, run the following command to deploy the job using the `dbx` CLI: @@ -209,7 +223,7 @@ This command uploads the Wheel file to the Databricks workspace, creates or upda 3. **Run the 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68082 bytes .../images/databricks_create_job_cluster.png | Bin 0 -> 63745 bytes .../meta/images/databricks_job_status.png | Bin 0 -> 172426 bytes 4 files changed, 90 insertions(+), 52 deletions(-) create mode 100644 docs/source/meta/images/databricks_configure_job_cluster.png create mode 100644 docs/source/meta/images/databricks_create_job_cluster.png create mode 100644 docs/source/meta/images/databricks_job_status.png diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index d4ae19367f..db5f15873d 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -2,27 +2,29 @@ In this guide, you will explore the process of packaging a Kedro project and running it as a job on Databricks. Databricks jobs are a way to execute code on Databricks clusters, allowing you to run data processing tasks, ETL jobs, or machine learning workflows. -By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks, see the [development workflow guide](./databricks_development_workflow.md). +By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_development_workflow.md). + +Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does not **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line. Here are some example use cases for running your packaged Kedro project as a Databricks job: + +- **Machine learning pipeline with MLflow**: your Kedro project runs an ML model and metrics about your experiments are tracked in MLflow. +- **Data engineering pipeline**: the output of your Kedro project is a file or set of files containing cleaned and processed data. +- **Automated, scheduled runs**: your Kedro project should be run on Databricks on an automated schedule. +- **CI/CD integration**: you have a CI/CD pipeline set up that produces a packaged Kedro project. By the end of this guide, you'll have a clear understanding of the steps involved in packaging your Kedro project and running it as a job on Databricks. ## What this page covers -This page covers how to set up your project for deployment to Databricks and two methods for running your packaged Kedro project as a job: - -- [Set up your project for deployment on Databricks.](#set-up-your-project) +- [Set up your Kedro project for deployment on Databricks.](#set-up-your-project-for-deployment-to-databricks) - [Run your project as a job using the Databricks workspace UI.](#deploy-and-run-your-kedro-project-using-the-workspace-ui) -- [Run your project as a job using dbx.](#deploy-and-run-your-kedro-project-using-dbx) - -Manually starting your job using the Databricks workspace UI has a shallower learning curve and is more intuitive. Using dbx to run your project saves time and allows you to store information about your job declaratively in your project, but is less intuitive and requires more time to set up. +- [Resources for automating your Kedro deployments to Databricks.](#resources-for-automating-deployment-to-databricks) ## Prerequisites - An active [Databricks deployment](https://docs.databricks.com/getting-started/index.html). -- A [Databricks cluster](https://docs.databricks.com/clusters/configure.html) configured with a recent version (>= 11.3 is recommended) of the Databricks runtime. -- [Conda installed](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) on your local machine in order to create a virtual environment with a specific version of Python (>= 3.8 is required to use dbx). If you have Python >= 3.8 installed, you can use other software to create a virtual environment. +- [Conda installed](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) on your local machine in order to create a virtual environment with a specific version of Python (>= 3.7 is required). If you have Python >= 3.7 installed, you can use other software to create a virtual environment. -## Set up your project +## Set up your project for deployment to Databricks ### Note your Databricks username and host @@ -36,7 +38,7 @@ Find your Databricks username in the top right of the workspace UI and the host Your databricks host must include the protocol (`https://`). ``` -### Install Kedro and dbx in a new virtual environment +### Install Kedro and the databricks CLI in a new virtual environment In your local development environment, create a virtual environment for this tutorial using Conda: @@ -50,10 +52,10 @@ Once it is created, activate it: conda activate iris-databricks ``` -With your Conda environment activated, install Kedro and dbx: +With your Conda environment activated, install Kedro and the Databricks CLIs: ```bash -pip install kedro dbx --upgrade +pip install kedro databricks-cli --upgrade ``` ### Authenticate the Databricks CLI @@ -67,10 +69,6 @@ pip install kedro dbx --upgrade 3. Enter your token and Databricks host when prompted. 4. Run `databricks fs ls dbfs:/` at the command line to verify your authentication. -```{note} -dbx is an extension of the Databricks CLI, a command-line program for interacting with Databricks without using its UI. You will use dbx to automatically package your project, upload it to Databricks and run it as a job. -``` - ### Create a new Kedro project Create a Kedro project with the PySpark Iris starter using the following command in your local environment: @@ -104,14 +102,21 @@ This process adds an entry point to your project which can be used to run it on ```{note} Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it allows. Instead, the `databricks_run` entry point in PySpark-Iris contains a simple implementation of the following options: -- `--package`: -- `--pipeline`: specifies a pipeline for your run. -- `--tags`: +- `--package`: the name - `--env`: specifies a configuration environment to load for your run. - `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. -- `--params`: ``` +### Plan your project's output + +You will not be able to directly see your project's log output when it is run as a Databricks job. You will need to decide on and implement a strategy for storing your project's results. Some examples are: + +- Write the result of a data engineering pipeline to a managed delta table. +- Upload metrics from a machine learning model to MLflow. +- Save a dataset as a CSV file in DBFS. + +In this guide, your project will save its logs to a location in DBFS. The `databricks` environment of the PySpark Iris starter is already configured to do this. The configuration used to achieve this can be found in the `conf/databricks/logging.yml` file. + ### Package your project To package your Kedro project for deployment on Databricks, you must create a Wheel (`.whl`) file, which is a binary distribution of your project. In the root directory of your Kedro project, run the following command: @@ -162,12 +167,28 @@ A Kedro project's configuration and data do not get included when it is packaged To run your packaged project on Databricks using the workspace UI, perform the following steps: -1. **Create a new job**: In the Databricks workspace, navigate to the "Worfklows" tab and click "Create Job": +1. **Create a new job**: In the Databricks workspace, navigate to the `Worfklows` tab and click `Create Job`: ![Create Databricks job](../meta/images/databricks_create_new_job.png) -2. **Configure the job**: -- Enter 'iris-databricks' in the `Name` field. +2. **Create a new job cluster**: Create a dedicated [job cluster](https://docs.databricks.com/clusters/index.html) to run your job by clicking on the drop-down menu in the `Cluster` field: + +![Create Databricks job cluster](../meta/images/databricks_create_job_cluster.png) + +Configure the job cluster with the following settings: + +- In the `name` field enter `kedro_deployment_demo`. +- Select the radio button for `Single node`. +- Select the runtime `12.2 LTS` in the `Databricks runtime version` field. +- Leave all other settings with their default values in place. + +The final configuration for the job cluster should look the same as the following: + +![Configure Databricks job cluster](../meta/images/databricks_configure_job_cluster.png) + +3. **Configure the job**: Configure the job with the following settings: + +- Enter `iris-databricks` in the `Name` field. - In the dropdown menu for the `Type` field, select 'Python wheel' - In the `Package name` field, enter `iris_databricks`. This is the name of your package as defined in your project's `src/setup.py` file. - In the `Entry Point` field, enter `databricks_run`. This is the name of the [entry point](#create-an-entry-point-for-databricks) to run your package from. @@ -179,53 +200,70 @@ To run your packaged project on Databricks using the workspace UI, perform the f ["--package","iris_databricks","--conf-source","/dbfs/FileStore/iris-databricks/conf","--env","databricks"] ``` -The final configuration for your job should look like the following: +The final configuration for your job should look the same as the following: ![Configure Databricks job](../meta/images/databricks_configure_new_job.png) -3. **Run the job**: Click "Run Now" to start a run of the job. The job's status will be displayed in the `Runs` tab. Navigate to the `Runs` tab and view the logs and results by clicking on the job run in the list: +4. **Run the job**: Click "Run Now" to start a run of the job. The job's status will be displayed in the `Runs` tab. Navigate to the `Runs` tab and view the logs and results by clicking on the job run in the list: ![Databricks run job](../meta/images/databricks_run_job.png) -After the job has been run, you can view the output in the logs: +After you run the job: -By following these steps, you packaged your Kedro project and manually ran it as a job on Databricks using the workspace UI. +- The job cluster will start. +- The packaged Kedro project and all its dependencies will be installed. +- The packaged Kedro project will be run from the specified `databricks_run` entry point. -## Deploy and run your Kedro project using dbx +You can view the status of the job run by clicking on the `Runs` tab of the `Workflow` panel: -dbx allows you to define a Databricks workflow in a [deployment file](https://dbx.readthedocs.io/en/latest/reference/deployment/). This file stores information about how your project should be run on Databricks and can be either YAML or JSON format. This file can be stored inside your project, where it can be added to version control. +![Databricks job status](../meta/images/databricks_job_status.png) -In this section, you will use `dbx deploy` to create a Databricks workflow of your packaged Kedro project using a deployment file in YAML format, then you will run your packaged Kedro project using `dbx launch`. +A run will take roughly six to seven minutes. -1. **Create a `dbx` deployment file**: In your Kedro project's `conf` directory, create a `dbx` deployment file named `deployment.yml`. Add the following declarative information on how to deploy and run your Kedro project on Databricks: +When the status of your run is `Succeeded`, your job has successfully finished executing. You can now proceed to view the logs that your project produced while it was running. To retrieve the logs file from the [location in DBFS where it was saved](#plan-your-projects-output), use the following `databricks CLI` command at your local command line with the `iris-databrick` conda environment activated: -```yaml -environments: - default: - workflows: - - name: "iris-databricks" - tasks: - - task_key: "main" - python_wheel_task: - package_name: "iris_databricks" - entry_point: "databricks_run" - parameters: ["--package","iris_databricks","--conf-source","/dbfs/FileStore/iris-databricks/conf","--env","databricks"] +```bash +databricks fs cp dbfs:/FileStore/iris-databricks/logs/info.log . ``` -2. **Deploy the job**: In your Kedro project's root directory, run the following command to deploy the job using the `dbx` CLI: +This will download the logs file from DBFS to your local environment. You can view the contents of the `info.log` file in any text editor, they will look the same as the following: ```bash -dbx deploy --config-file dbx_deploy.json +... +2023-05-19 14:38:06,333 - kedro.io.data_catalog - INFO - Loading data from 'example_iris_data' (SparkDataSet)... +2023-05-19 14:38:12,077 - kedro.io.data_catalog - INFO - Loading data from 'parameters' (MemoryDataSet)... +2023-05-19 14:38:12,079 - kedro.pipeline.node - INFO - Running node: split: split_data([example_iris_data,parameters]) -> [X_train@pyspark,X_test@pyspark,y_train@pyspark,y_test@pyspark] +... ``` -This command uploads the Wheel file to the Databricks workspace, creates or updates the job, and configures the job with the specified cluster settings and job parameters. +By following these steps, you packaged your Kedro project and manually ran it as a job on Databricks using the workspace UI. -3. **Run the job**: To start the job, run the following command: +## Resources for automating deployment to Databricks -```bash -dbx launch --job my_kedro_job -``` +This page describes a manual workflow for deploying and running project on Databricks. This process can be automated in two ways: + +- [Use the Databricks API.](#databricks-api) +- [Use the Databricks CLI.](#databricks-cli) + +Both of these methods allows you to store information about your job declaratively in the same version control system as the rest of your project. For each method, the information stored declaratively is the same as what is entered manually in the [above section on creating and running a job in Databricks](#deploy-and-run-your-kedro-project-using-the-workspace-ui). + +These methods can be integrated into a CI/CD pipeline to automatically deploy a packaged Kedro project to Databricks as a job. + +### Databricks API + +The Databricks API enables you to programmatically interact with Databricks services, including job creation and execution. You can use the Jobs API to automate the deployment of your Kedro project to Databricks. The following steps outline how to use the Databricks API to do this: + +1. [Set up your Kedro project for deployment on Databricks.](#set-up-your-project-for-deployment-to-databricks) +2. Create a JSON file containing your job's configuration. +3. Use the Jobs API's [`/create` endpoint](https://docs.databricks.com/workflows/jobs/jobs-api-updates.html#create) to create a new job. +4. Use the Jobs API's [`/runs/submit` endpoint](https://docs.databricks.com/workflows/jobs/jobs-api-updates.html#runs-submit) to run your newly created job. + +### Databricks CLI -This command initiates the job execution, and you can monitor the job's progress in the Databricks workspace's "Jobs" tab. +The Databricks Command Line Interface (CLI) is another way to automate deployment of your Kedro project. The following steps outline how to use the Databricks CLI to automate the deployment of a Kedro project: -By following these steps, you can deploy your Kedro project as a Databricks job using `dbx` and run it on the Databricks platform. +1. [Set up your Kedro project for deployment on Databricks.](#set-up-your-project-for-deployment-to-databricks) +2. Install the Databricks CLI and authenticate it with your workspace. +3. Create a JSON file containing your job's configuration. +4. Use the [`jobs create` command](https://docs.databricks.com/dev-tools/cli/jobs-cli.html#create-a-job) to create a new job. +5. 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a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 6fe184090f..d32ca9f730 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -147,7 +147,17 @@ You will not be able to directly see your project's log output when it is run as - Upload metrics from a machine learning model to MLflow. - Save a dataset as a CSV file in DBFS. -In this guide, your project will save its logs to a location in DBFS. **The `databricks` environment of the PySpark Iris starter is already configured to do this.** The configuration used to achieve this can be found in the `conf/databricks/logging.yml` file. +In this guide, your project will save its logs to a location in DBFS. + +- **Create a directory to save your logs into on DBFS**: at the command line in your local environment, run the following Databricks CLI command: + +```bash +databricks fs mkdirs dbfs:/FileStore/iris-databricks/logs +``` + +This command creates a directory on DBFS (`/dbfs/FileStore/iris-databricks/logs`) where your project can save its logs. + +**The `databricks` environment of the PySpark Iris starter is already configured to save logs in this location.** The configuration used to achieve this is part of the [`databricks` environment](../configuration/configuration_basics.md#configuration-environments) defined for PySpark-Iris and can be found in the `conf/databricks/logging.yml` file. ### Package your project @@ -165,7 +175,9 @@ Your packaged Kedro project needs access to data and configuration in order to r The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../configuration/configuration_basics.md#configuration-environments). When you run your project on Databricks, you will pass the name of this environment as an option, as well as the path to the `conf/` directory on DBFS. -There are several ways to upload data to DBFS. In this guide, it is recommended to use [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html) because of the convenience it offers. At the command line in your local environment, use the following Databricks CLI commands to upload your project's locally stored data and configuration to DBFS: +There are several ways to upload data to DBFS. In this guide, it is recommended to use [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html) because of the convenience it offers. + +- **Upload your project's data and config**: at the command line in your local environment, use the following Databricks CLI commands to upload your project's locally stored data and configuration to DBFS: ```bash databricks fs cp --recursive /data/ dbfs:/FileStore/iris-databricks/data From 8b246d143f97e42b2610a72632e324f6fa683237 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Tue, 23 May 2023 16:18:13 +0100 Subject: [PATCH 12/55] Lint Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index d32ca9f730..b2dc6f86e5 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -123,10 +123,7 @@ if __name__ == "__main__": 2. **Define a new entry point**: Open `/src/setup.py` in a text editor or IDE and add a new line in the definition of the `entry_point` tuple, so that it becomes: ```python -entry_point = ( - ..., - "databricks_run = .databricks_run:main" -) +entry_point = (..., "databricks_run = .databricks_run:main") ``` Remember to replace with the correct package name for your project. From 25da49dfc16f2856224f94af0544048c1ed615c5 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Tue, 23 May 2023 16:48:06 +0100 Subject: [PATCH 13/55] Lint databricks_run Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index b2dc6f86e5..71025cf134 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -92,6 +92,7 @@ The PySpark Iris starter has this entry point pre-built, so there is no extra wo ```python import argparse import logging + from kedro.framework.project import configure_project from kedro.framework.session import KedroSession From c83107b8427876c7810d5902dfec6d48c0022898 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Tue, 23 May 2023 22:09:42 +0100 Subject: [PATCH 14/55] Minor wording change in reference to logs Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 71025cf134..30aed19872 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -269,7 +269,7 @@ When the status of your run is `Succeeded`, your job has successfully finished e databricks fs cp dbfs:/FileStore/iris-databricks/logs/info.log . ``` -This will download the logs file from DBFS to your local environment. You can view the contents of the `info.log` file in any text editor, they will look the same as the following: +This will download the logs file from DBFS to your local environment. You can view the contents of the `info.log` file in any text editor, they will look similar to the following: ```bash ... From cfbe80b75b33a317d21f531bd30b46df696ad132 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Tue, 23 May 2023 22:12:16 +0100 Subject: [PATCH 15/55] Modify reference to Pyspark-Iris Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 30aed19872..151c99e9df 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -155,7 +155,7 @@ databricks fs mkdirs dbfs:/FileStore/iris-databricks/logs This command creates a directory on DBFS (`/dbfs/FileStore/iris-databricks/logs`) where your project can save its logs. -**The `databricks` environment of the PySpark Iris starter is already configured to save logs in this location.** The configuration used to achieve this is part of the [`databricks` environment](../configuration/configuration_basics.md#configuration-environments) defined for PySpark-Iris and can be found in the `conf/databricks/logging.yml` file. +**The `databricks` environment of the PySpark Iris starter is already configured to save logs in this location.** The configuration used to achieve this is part of the [`databricks` environment](../configuration/configuration_basics.md#configuration-environments) defined for PySpark Iris and can be found in the `conf/databricks/logging.yml` file. ### Package your project From 7e5406a6abdf13db66ad8b07b6b92ec27f036cf8 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 10:33:20 +0100 Subject: [PATCH 16/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 151c99e9df..7d25ed2c51 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -1,6 +1,6 @@ # Databricks deployment workflow -In this guide, you will explore the process of packaging a Kedro project and running it as a job on Databricks. Databricks jobs are a way to execute code on Databricks clusters, allowing you to run data processing tasks, ETL jobs, or machine learning workflows. +Databricks jobs are a way to execute code on Databricks clusters, allowing you to run data processing tasks, ETL jobs, or machine learning workflows. In this guide we explore the process of packaging a Kedro project and running it as a job on Databricks. By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_development_workflow.md). From fdfe925d7d7c411622aa1024176d2c5fbccb3c76 Mon Sep 17 00:00:00 2001 From: Jo Stichbury Date: Wed, 24 May 2023 10:58:41 +0100 Subject: [PATCH 17/55] Fix linter errors to enable docs build for inspection Signed-off-by: Jo Stichbury --- docs/build-docs.sh | 2 +- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/build-docs.sh b/docs/build-docs.sh index d55076e118..cd68029918 100755 --- a/docs/build-docs.sh +++ b/docs/build-docs.sh @@ -10,5 +10,5 @@ action=$1 if [ "$action" == "linkcheck" ]; then sphinx-build -WETan -j auto -D language=en -b linkcheck -d docs/build/doctrees docs/source docs/build/linkcheck elif [ "$action" == "docs" ]; then - sphinx-build -WETa -j auto -D language=en -b html -d docs/build/doctrees docs/source docs/build/html + sphinx-build -ETa -j auto -D language=en -b html -d docs/build/doctrees docs/source docs/build/html fi diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 7d25ed2c51..8bb4814834 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -1,6 +1,6 @@ # Databricks deployment workflow -Databricks jobs are a way to execute code on Databricks clusters, allowing you to run data processing tasks, ETL jobs, or machine learning workflows. In this guide we explore the process of packaging a Kedro project and running it as a job on Databricks. +Databricks jobs are a way to execute code on Databricks clusters, allowing you to run data processing tasks, ETL jobs, or machine learning workflows. In this guide we explore the process of packaging a Kedro project and running it as a job on Databricks. By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_development_workflow.md). From 3ac02cca00e5d64d7380df4f7e32e26dfe334dda Mon Sep 17 00:00:00 2001 From: Jo Stichbury Date: Wed, 24 May 2023 11:37:32 +0100 Subject: [PATCH 18/55] Update build-docs.sh --- docs/build-docs.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/build-docs.sh b/docs/build-docs.sh index cd68029918..d55076e118 100755 --- a/docs/build-docs.sh +++ b/docs/build-docs.sh @@ -10,5 +10,5 @@ action=$1 if [ "$action" == "linkcheck" ]; then sphinx-build -WETan -j auto -D language=en -b linkcheck -d docs/build/doctrees docs/source docs/build/linkcheck elif [ "$action" == "docs" ]; then - sphinx-build -ETa -j auto -D language=en -b html -d docs/build/doctrees docs/source docs/build/html + sphinx-build -WETa -j auto -D language=en -b html -d docs/build/doctrees docs/source docs/build/html fi From 04ca9c0184b34dd534a0a2291809dcac40683e31 Mon Sep 17 00:00:00 2001 From: Jo Stichbury Date: Wed, 24 May 2023 11:53:36 +0100 Subject: [PATCH 19/55] Fix broken link Signed-off-by: Jo Stichbury --- docs/source/deployment/airflow_astronomer.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/deployment/airflow_astronomer.md b/docs/source/deployment/airflow_astronomer.md index b3e4d7385d..307e9ab903 100644 --- a/docs/source/deployment/airflow_astronomer.md +++ b/docs/source/deployment/airflow_astronomer.md @@ -30,7 +30,7 @@ The general strategy to deploy a Kedro pipeline on Apache Airflow is to run ever To follow this tutorial, ensure you have the following: -* An Airflow cluster: you can follow [Astronomer's quickstart guide](https://docs.astronomer.io/astro/category/install-astro) to set one up. +* An Airflow cluster: you can follow [Astronomer's quickstart guide](https://docs.astronomer.io/astro/create-deployment) to set one up. * The [Astro CLI installed](https://docs.astronomer.io/astro/install-cli) * `kedro>=0.17` installed From 93997e267c5c5820f29e0412e3222b2637eac883 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 12:49:38 +0100 Subject: [PATCH 20/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 8bb4814834..65ae0c81f8 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -4,7 +4,9 @@ Databricks jobs are a way to execute code on Databricks clusters, allowing you t By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_development_workflow.md). -Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does not **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. Here are some example use cases for running a packaged Kedro project as a Databricks job: +Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does not **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. + +Here are some typical use cases for running a packaged Kedro project as a Databricks job: - **Data engineering pipeline**: the output of your Kedro project is a file or set of files containing cleaned and processed data. - **Machine learning with MLflow**: your Kedro project runs an ML model and metrics about your experiments are tracked in MLflow. From 74ab283ce0866311c8eae53b159d3a9a87402562 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 12:49:52 +0100 Subject: [PATCH 21/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 65ae0c81f8..48f329a1f5 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -81,7 +81,7 @@ Create a Kedro project with the PySpark Iris starter using the following command kedro new --starter=pyspark-iris ``` -Name your new project `iris-databricks` for consistency with the rest of this guide. This command creates a new Kedro project using the PySpark Iris starter template. +This command creates a new Kedro project using the PySpark Iris starter template. Name your new project `iris-databricks` for consistency with the rest of this guide. ### Create an entry point for Databricks From caa9eccb2f8c61111f6304a1a3114c8e07733d06 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 12:50:14 +0100 Subject: [PATCH 22/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 48f329a1f5..429d4d6cbe 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -13,7 +13,6 @@ Here are some typical use cases for running a packaged Kedro project as a Databr - **Automated and scheduled runs**: your Kedro project should be run on Databricks on an [automated schedule](https://docs.databricks.com/workflows/jobs/schedule-jobs.html#add-a-job-schedule). - **CI/CD integration**: you have a CI/CD pipeline set up that produces a packaged Kedro project. -By the end of this guide, you'll have a clear understanding of the steps involved in packaging your Kedro project and running it as a job on Databricks. ## What this page covers From 9901b40fdd20f2be6046f681b7bb516206dbe32f Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 12:50:30 +0100 Subject: [PATCH 23/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 429d4d6cbe..467d17f0ff 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -74,7 +74,7 @@ pip install kedro databricks-cli --upgrade ### Create a new Kedro project -Create a Kedro project with the PySpark Iris starter using the following command in your local environment: +Create a Kedro project by using the following command in your local environment: ```bash kedro new --starter=pyspark-iris From dd9b4a05d3f59307244d7fac27b37d36df772a16 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Wed, 24 May 2023 12:53:21 +0100 Subject: [PATCH 24/55] Remove spurious word Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 467d17f0ff..8cd7f12af7 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -4,7 +4,7 @@ Databricks jobs are a way to execute code on Databricks clusters, allowing you t By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_development_workflow.md). -Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does not **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. +Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. Here are some typical use cases for running a packaged Kedro project as a Databricks job: From bd2ce14691ec622224b75afb258ad60cce226ac5 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 12:53:51 +0100 Subject: [PATCH 25/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 467d17f0ff..41de051565 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -84,7 +84,7 @@ This command creates a new Kedro project using the PySpark Iris starter template ### Create an entry point for Databricks -The default entry point of a Kedro project uses a Click command line interface (CLI), which is not compatible with Databricks. To run your project as a Databricks job, you must define a new entry point speficially for use on Databricks. +The default entry point of a Kedro project uses a Click command line interface (CLI), which is not compatible with Databricks. To run your project as a Databricks job, you must define a new entry point specifically for use on Databricks. The PySpark Iris starter has this entry point pre-built, so there is no extra work to do in this guide. **Generally you must create it manually for your own projects using the following steps**: From f5cd5be3a4e911474c16710af41000dd64a49393 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 12:54:02 +0100 Subject: [PATCH 26/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 41de051565..5faf58f2ca 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -86,7 +86,7 @@ This command creates a new Kedro project using the PySpark Iris starter template The default entry point of a Kedro project uses a Click command line interface (CLI), which is not compatible with Databricks. To run your project as a Databricks job, you must define a new entry point specifically for use on Databricks. -The PySpark Iris starter has this entry point pre-built, so there is no extra work to do in this guide. **Generally you must create it manually for your own projects using the following steps**: +The PySpark Iris starter has this entry point pre-built, so there is no extra work to do here, but generally you must **create an entry point manually for your own projects using the following steps**: 1. **Create an entry point script**: Create a new file in `/src/iris_databricks` named `databricks_run.py`. Copy the following code to this file: From f3cf2614ae1ac6a9961d19c58298233cfce23c7c Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 12:54:15 +0100 Subject: [PATCH 27/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 5faf58f2ca..76c3aa8eb9 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -160,7 +160,7 @@ This command creates a directory on DBFS (`/dbfs/FileStore/iris-databricks/logs` ### Package your project -To package your Kedro project for deployment on Databricks, you must create a Wheel (`.whl`) file, which is a binary distribution of your project. In the root directory of your Kedro projec t, run the following command: +To package your Kedro project for deployment on Databricks, you must create a Wheel (`.whl`) file, which is a binary distribution of your project. In the root directory of your Kedro project, run the following command: ```bash kedro package From edbee92dfc3f90977bafa8988dfa18a086172e07 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:33:26 +0100 Subject: [PATCH 28/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 76c3aa8eb9..7ccbc33368 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -208,7 +208,7 @@ A Kedro project's configuration and data do not get included when it is packaged ## Deploy and run your Kedro project using the workspace UI -To run your packaged project on Databricks, login to your Databricks account and, perform the following steps in the workspace: +To run your packaged project on Databricks, login to your Databricks account and perform the following steps in the workspace: 1. **Create a new job**: In the Databricks workspace, navigate to the `Worfklows` tab and click `Create Job`: From f67d7f4e694451ba599b3a3999dea4ee1f38ddcf Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:33:34 +0100 Subject: [PATCH 29/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 7ccbc33368..f1f73809fa 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -259,7 +259,7 @@ The following things happen when you run your job: - The job cluster will be provisioned and started (job status: `Pending`). - The packaged Kedro project and all its dependencies will be installed (job status: `Pending`) -- The packaged Kedro project will be run from the specified `databricks_run` entry point (job status: `In Progress`). +- The packaged Kedro project is run from the specified `databricks_run` entry point (job status: `In Progress`). - The packaged code will finish executing and the job cluster will be stopped (job status: `Succeeded`). A run will take roughly six to seven minutes. From 5c60a4b8129befd47192274a73aa6139254c38dd Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:33:43 +0100 Subject: [PATCH 30/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index f1f73809fa..64e6ccc14e 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -260,7 +260,7 @@ The following things happen when you run your job: - The job cluster will be provisioned and started (job status: `Pending`). - The packaged Kedro project and all its dependencies will be installed (job status: `Pending`) - The packaged Kedro project is run from the specified `databricks_run` entry point (job status: `In Progress`). -- The packaged code will finish executing and the job cluster will be stopped (job status: `Succeeded`). +- The packaged code finishes executing and the job cluster is stopped (job status: `Succeeded`). A run will take roughly six to seven minutes. From 1b457577cbba3d1055e579e51f417e6a18d662cb Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:34:11 +0100 Subject: [PATCH 31/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 64e6ccc14e..18a840c7d7 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -257,7 +257,7 @@ This page also shows an overview of all past runs of your job. As you only just The following things happen when you run your job: -- The job cluster will be provisioned and started (job status: `Pending`). +- The job cluster is provisioned and started (job status: `Pending`). - The packaged Kedro project and all its dependencies will be installed (job status: `Pending`) - The packaged Kedro project is run from the specified `databricks_run` entry point (job status: `In Progress`). - The packaged code finishes executing and the job cluster is stopped (job status: `Succeeded`). From a7aacb53c3d2a88f61dc2c47abd1580eeea49488 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:34:20 +0100 Subject: [PATCH 32/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 18a840c7d7..8c033c5b10 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -258,7 +258,7 @@ This page also shows an overview of all past runs of your job. As you only just The following things happen when you run your job: - The job cluster is provisioned and started (job status: `Pending`). -- The packaged Kedro project and all its dependencies will be installed (job status: `Pending`) +- The packaged Kedro project and all its dependencies are installed (job status: `Pending`) - The packaged Kedro project is run from the specified `databricks_run` entry point (job status: `In Progress`). - The packaged code finishes executing and the job cluster is stopped (job status: `Succeeded`). From a11624c1c13184b8b987d9fa8c7921d1036ceaf0 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:34:38 +0100 Subject: [PATCH 33/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 8c033c5b10..29374a491b 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -293,7 +293,7 @@ Both of these methods enable you to store information about your job declarative These methods can be integrated into a CI/CD pipeline to automatically deploy a packaged Kedro project to Databricks as a job. -### Databricks API +### How to use the Databricks API to automate deployment of a Kedro project The Databricks API enables you to programmatically interact with Databricks services, including job creation and execution. You can use the Jobs API to automate the deployment of your Kedro project to Databricks. The following steps outline how to use the Databricks API to do this: From 40c6e627827365b04cd9e025b5744a0ebaeadb8a Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:34:46 +0100 Subject: [PATCH 34/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 29374a491b..281e828d25 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -302,7 +302,7 @@ The Databricks API enables you to programmatically interact with Databricks serv 3. Use the Jobs API's [`/create` endpoint](https://docs.databricks.com/workflows/jobs/jobs-api-updates.html#create) to create a new job. 4. Use the Jobs API's [`/runs/submit` endpoint](https://docs.databricks.com/workflows/jobs/jobs-api-updates.html#runs-submit) to run your newly created job. -### Databricks CLI +### How to use the Databricks CLI to automate deployment of a Kedro project The Databricks Command Line Interface (CLI) is another way to automate deployment of your Kedro project. The following steps outline how to use the Databricks CLI to automate the deployment of a Kedro project: From f37a8e837e1a97e79d82a2f75e096c63c93bb00d Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:42:19 +0100 Subject: [PATCH 35/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 281e828d25..2c7aa234a5 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -284,7 +284,7 @@ By following these steps, you packaged your Kedro project and manually ran it as ## Resources for automating deployment to Databricks -This page describes a manual workflow for deploying and running project on Databricks. This process can be automated in two ways: +Up to this point, this page has described a manual workflow for deploying and running a project on Databricks. The process can be automated in two ways: - [Use the Databricks API.](#databricks-api) - [Use the Databricks CLI.](#databricks-cli) From dc8c9ed0023795ffaba07f9b0c0b04580258f98d Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:42:28 +0100 Subject: [PATCH 36/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 2c7aa234a5..971ba80f2d 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -270,7 +270,7 @@ When the status of your run is `Succeeded`, your job has successfully finished e databricks fs cp dbfs:/FileStore/iris-databricks/logs/info.log . ``` -This will download the logs file from DBFS to your local environment. You can view the contents of the `info.log` file in any text editor, they will look similar to the following: +This will download the log file from DBFS to your local environment. You can view the contents of the `info.log` file in any text editor, they will look similar to the following: ```bash ... From 28c7b87561abbaa4dc16d74ca04ccc7f7430cdd5 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:42:45 +0100 Subject: [PATCH 37/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 971ba80f2d..9866d259db 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -264,7 +264,7 @@ The following things happen when you run your job: A run will take roughly six to seven minutes. -When the status of your run is `Succeeded`, your job has successfully finished executing. You can now proceed to view the logs that your project produced while it was running. To retrieve the logs file from the [location in DBFS where it was saved](#plan-your-projects-output), use the following `databricks CLI` command at your local command line with the `iris-databrick` conda environment activated: +When the status of your run is `Succeeded`, your job has successfully finished executing. You can now view the logs that your project produced while it was running. To retrieve the log file from the [location in DBFS where it was saved](#plan-your-projects-output), use the following `databricks CLI` command at your local command line with the `iris-databrick` conda environment activated: ```bash databricks fs cp dbfs:/FileStore/iris-databricks/logs/info.log . From 1e9a01d33447615b507c0006e3638627015115de Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Wed, 24 May 2023 14:57:09 +0100 Subject: [PATCH 38/55] Add advantages subheading Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 4c19b059a9..325982ab29 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -2,9 +2,11 @@ Databricks jobs are a way to execute code on Databricks clusters, allowing you to run data processing tasks, ETL jobs, or machine learning workflows. In this guide we explore the process of packaging a Kedro project and running it as a job on Databricks. +## What are the advantages of packaging a Kedro project to run on Databricks? + By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_development_workflow.md). -Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. +Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. Here are some typical use cases for running a packaged Kedro project as a Databricks job: @@ -13,7 +15,6 @@ Here are some typical use cases for running a packaged Kedro project as a Databr - **Automated and scheduled runs**: your Kedro project should be run on Databricks on an [automated schedule](https://docs.databricks.com/workflows/jobs/schedule-jobs.html#add-a-job-schedule). - **CI/CD integration**: you have a CI/CD pipeline set up that produces a packaged Kedro project. - ## What this page covers - [Set up your Kedro project for deployment on Databricks.](#set-up-your-project-for-deployment-to-databricks) From c2b406e2160a8d5324f7222c643646bd68256cda Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Wed, 24 May 2023 14:59:08 +0100 Subject: [PATCH 39/55] Update docs/source/integrations/databricks_deployment_workflow.md Co-authored-by: Jo Stichbury --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 9866d259db..f0f4362d2d 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -170,7 +170,7 @@ This command generates a `.whl` file in the `dist` directory within your project ### Upload project data and configuration to DBFS -Your packaged Kedro project needs access to data and configuration in order to run. Therefore, you will need to upload your project's data and configuration to an accessible location. In this guide, we will store the data on the Databricks File System (DBFS). +Your packaged Kedro project needs access to data and configuration in order to run. Therefore, you will need to upload your project's data and configuration to a location accessible to Databricks. In this guide, we will store the data on the Databricks File System (DBFS). The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../configuration/configuration_basics.md#configuration-environments). When you run your project on Databricks, you will pass the name of this environment as an option, as well as the path to the `conf/` directory on DBFS. From 040799170de31bd0a3d0f27dac20cdf5fee38cc3 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Wed, 24 May 2023 15:32:05 +0100 Subject: [PATCH 40/55] Add alternative ways to upload data to DBFS Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index ea7c89e4ec..50c81a0906 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -175,7 +175,7 @@ Your packaged Kedro project needs access to data and configuration in order to r The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../configuration/configuration_basics.md#configuration-environments). When you run your project on Databricks, you will pass the name of this environment as an option, as well as the path to the `conf/` directory on DBFS. -There are several ways to upload data to DBFS. In this guide, it is recommended to use [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html) because of the convenience it offers. +There are several ways to upload data to DBFS: you can use the [DBFS API](https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/dbfs), the [`dbutils` module](https://docs.databricks.com/dev-tools/databricks-utils.html) in a Databricks notebook or the [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html). In this guide, it is recommended to use the Databricks CLI because of the convenience it offers. - **Upload your project's data and config**: at the command line in your local environment, use the following Databricks CLI commands to upload your project's locally stored data and configuration to DBFS: From 2149bfdebecfc6bce726c6153bb283f637ceb769 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Wed, 24 May 2023 15:34:03 +0100 Subject: [PATCH 41/55] Move note on unpackaged config and data Signed-off-by: Jannic Holzer --- .../source/integrations/databricks_deployment_workflow.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 50c81a0906..371058fe6b 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -171,6 +171,10 @@ This command generates a `.whl` file in the `dist` directory within your project ### Upload project data and configuration to DBFS +```{note} +A Kedro project's configuration and data do not get included when it is packaged. They must be stored somewhere accessible to allow your packaged project to run. +``` + Your packaged Kedro project needs access to data and configuration in order to run. Therefore, you will need to upload your project's data and configuration to a location accessible to Databricks. In this guide, we will store the data on the Databricks File System (DBFS). The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../configuration/configuration_basics.md#configuration-environments). When you run your project on Databricks, you will pass the name of this environment as an option, as well as the path to the `conf/` directory on DBFS. @@ -203,10 +207,6 @@ You should see the contents of the project's `data/` directory printed to your t 08_reporting ``` -```{note} -A Kedro project's configuration and data do not get included when it is packaged. They must be stored somewhere accessible to allow your packaged project to run. -``` - ## Deploy and run your Kedro project using the workspace UI To run your packaged project on Databricks, login to your Databricks account and perform the following steps in the workspace: From 672c3d057576814e7c99b0f84f4cae930f47158b Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Wed, 24 May 2023 15:56:59 +0100 Subject: [PATCH 42/55] Fix broken links Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 371058fe6b..d42e306203 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -287,8 +287,8 @@ By following these steps, you packaged your Kedro project and manually ran it as Up to this point, this page has described a manual workflow for deploying and running a project on Databricks. The process can be automated in two ways: -- [Use the Databricks API.](#databricks-api) -- [Use the Databricks CLI.](#databricks-cli) +- [Use the Databricks API.](#how-to-use-the-databricks-api-to-automate-deployment-of-a-kedro-project) +- [Use the Databricks CLI.](#how-to-use-the-databricks-cli-to-automate-deployment-of-a-kedro-project) Both of these methods enable you to store information about your job declaratively in the same version control system as the rest of your project. For each method, the information stored declaratively is the same as what is entered manually in the [above section on creating and running a job in Databricks](#deploy-and-run-your-kedro-project-using-the-workspace-ui). From d939fbaa6add1a2f63cf0d495daa295fb9e4024b Mon Sep 17 00:00:00 2001 From: Jo Stichbury Date: Thu, 25 May 2023 14:18:16 +0100 Subject: [PATCH 43/55] Move databricks back into deployment section Signed-off-by: Jo Stichbury --- docs/source/deployment/databricks.md | 4 ---- .../deployment/databricks/databricks.md | 11 ++++++++++ .../databricks_deployment_workflow.md | 0 .../databricks_development_workflow.md | 0 .../databricks}/databricks_visualisation.md | 0 .../databricks}/databricks_workspace.md | 0 docs/source/deployment/index.md | 4 ++-- docs/source/index.rst | 2 +- docs/source/integrations/index.md | 22 ------------------- .../integrations/pyspark_integration.md | 2 +- 10 files changed, 15 insertions(+), 30 deletions(-) delete mode 100644 docs/source/deployment/databricks.md create mode 100644 docs/source/deployment/databricks/databricks.md rename docs/source/{integrations => deployment/databricks}/databricks_deployment_workflow.md (100%) rename docs/source/{integrations => deployment/databricks}/databricks_development_workflow.md (100%) rename docs/source/{integrations => deployment/databricks}/databricks_visualisation.md (100%) rename docs/source/{integrations => deployment/databricks}/databricks_workspace.md (100%) delete mode 100644 docs/source/integrations/index.md diff --git a/docs/source/deployment/databricks.md b/docs/source/deployment/databricks.md deleted file mode 100644 index 5f19f88b70..0000000000 --- a/docs/source/deployment/databricks.md +++ /dev/null @@ -1,4 +0,0 @@ -# Databricks - - -[Please visit the documentation section specifically covering integration of Kedro projects with Databricks](../integrations/index.md#databricks-integration). diff --git a/docs/source/deployment/databricks/databricks.md b/docs/source/deployment/databricks/databricks.md new file mode 100644 index 0000000000..ba64fe4b95 --- /dev/null +++ b/docs/source/deployment/databricks/databricks.md @@ -0,0 +1,11 @@ +# Databricks + + +```{toctree} +:maxdepth: 1 + +databricks_workspace.md +databricks_development_workflow +databricks_deployment_workflow +databricks_visualisation +``` \ No newline at end of file diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md similarity index 100% rename from docs/source/integrations/databricks_deployment_workflow.md rename to docs/source/deployment/databricks/databricks_deployment_workflow.md diff --git a/docs/source/integrations/databricks_development_workflow.md b/docs/source/deployment/databricks/databricks_development_workflow.md similarity index 100% rename from docs/source/integrations/databricks_development_workflow.md rename to docs/source/deployment/databricks/databricks_development_workflow.md diff --git a/docs/source/integrations/databricks_visualisation.md b/docs/source/deployment/databricks/databricks_visualisation.md similarity index 100% rename from docs/source/integrations/databricks_visualisation.md rename to docs/source/deployment/databricks/databricks_visualisation.md diff --git a/docs/source/integrations/databricks_workspace.md b/docs/source/deployment/databricks/databricks_workspace.md similarity index 100% rename from docs/source/integrations/databricks_workspace.md rename to docs/source/deployment/databricks/databricks_workspace.md diff --git a/docs/source/deployment/index.md b/docs/source/deployment/index.md index daaf643046..0afdcd75e7 100644 --- a/docs/source/deployment/index.md +++ b/docs/source/deployment/index.md @@ -30,7 +30,7 @@ This following pages provide information for deployment to, or integration with, * [AWS Step functions](aws_step_functions.md) * [Azure](azure.md) * [Dask](dask.md) -* [Databricks](../integrations/databricks_workspace.md) +* [Databricks](./databricks/databricks.md) * [Kubeflow Workflows](kubeflow.md) * [Prefect](prefect.md) * [Vertex AI](vertexai.md) @@ -55,7 +55,7 @@ amazon_sagemaker aws_step_functions azure dask -databricks +databricks/databricks kubeflow prefect vertexai diff --git a/docs/source/index.rst b/docs/source/index.rst index b4481e6897..3e43153604 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -125,7 +125,7 @@ Welcome to Kedro's documentation! .. toctree:: :maxdepth: 2 - integrations/index.md + integrations/pyspark_integration.md .. toctree:: :maxdepth: 2 diff --git a/docs/source/integrations/index.md b/docs/source/integrations/index.md deleted file mode 100644 index 7618eb81dd..0000000000 --- a/docs/source/integrations/index.md +++ /dev/null @@ -1,22 +0,0 @@ -# Integrations - - -## Databricks integration - -```{toctree} -:maxdepth: 1 - -databricks_workspace.md -databricks_development_workflow.md -databricks_deployment_workflow.md -databricks_visualisation.md -``` - - -## PySpark integration - -```{toctree} -:maxdepth: 1 - -pyspark_integration.md -``` diff --git a/docs/source/integrations/pyspark_integration.md b/docs/source/integrations/pyspark_integration.md index c900243961..3afaf084c7 100644 --- a/docs/source/integrations/pyspark_integration.md +++ b/docs/source/integrations/pyspark_integration.md @@ -1,4 +1,4 @@ -# Build a Kedro pipeline with PySpark +# PySpark integration This page outlines some best practices when building a Kedro pipeline with [`PySpark`](https://spark.apache.org/docs/latest/api/python/index.html). It assumes a basic understanding of both Kedro and `PySpark`. From 89760f15bd4641170612dab059a85f71662fbb8b Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Thu, 25 May 2023 14:56:06 +0100 Subject: [PATCH 44/55] Remove references to PySpark Iris (pyspark-iris) starter Signed-off-by: Jannic Holzer --- .../databricks_deployment_workflow.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index d42e306203..abebbd566b 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -78,16 +78,16 @@ pip install kedro databricks-cli --upgrade Create a Kedro project by using the following command in your local environment: ```bash -kedro new --starter=pyspark-iris +kedro new --starter=databricks-iris ``` -This command creates a new Kedro project using the PySpark Iris starter template. Name your new project `iris-databricks` for consistency with the rest of this guide. +This command creates a new Kedro project using the Databricks Iris starter template. Name your new project `iris-databricks` for consistency with the rest of this guide. ### Create an entry point for Databricks The default entry point of a Kedro project uses a Click command line interface (CLI), which is not compatible with Databricks. To run your project as a Databricks job, you must define a new entry point specifically for use on Databricks. -The PySpark Iris starter has this entry point pre-built, so there is no extra work to do here, but generally you must **create an entry point manually for your own projects using the following steps**: +The Databricks Iris starter has this entry point pre-built, so there is no extra work to do here, but generally you must **create an entry point manually for your own projects using the following steps**: 1. **Create an entry point script**: Create a new file in `/src/iris_databricks` named `databricks_run.py`. Copy the following code to this file: @@ -134,8 +134,9 @@ Remember to replace with the correct package name for your projec This process adds an entry point to your project which can be used to run it on Databricks. ```{note} -Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in the above code and in the PySpark Iris starter contains a simple implementation of two options: -- `--env`: specifies a configuration environment to load for your run. +Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in the above code and in the Databricks Iris starter contains a simple implementation of two options: +- `--package_name` (required): the package name (defined in `setup.py`) of your packaged project. +- `--env`: specifies a [Kedro configuration environment](../configuration/configuration_basics.md#configuration-environments) to load for your run. - `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. ``` @@ -157,7 +158,7 @@ databricks fs mkdirs dbfs:/FileStore/iris-databricks/logs This command creates a directory on DBFS (`/dbfs/FileStore/iris-databricks/logs`) where your project can save its logs. -**The `databricks` environment of the PySpark Iris starter is already configured to save logs in this location.** The configuration used to achieve this is part of the [`databricks` environment](../configuration/configuration_basics.md#configuration-environments) defined for PySpark Iris and can be found in the `conf/databricks/logging.yml` file. +**The Databricks Iris starter is already configured to save logs in this location.** The configuration used to achieve this can be found in the `conf/base/logging.yml` file. ### Package your project @@ -177,7 +178,7 @@ A Kedro project's configuration and data do not get included when it is packaged Your packaged Kedro project needs access to data and configuration in order to run. Therefore, you will need to upload your project's data and configuration to a location accessible to Databricks. In this guide, we will store the data on the Databricks File System (DBFS). -The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../configuration/configuration_basics.md#configuration-environments). When you run your project on Databricks, you will pass the name of this environment as an option, as well as the path to the `conf/` directory on DBFS. +The Databricks Iris starter contains a [catalog](../data/data_catalog.md#the-data-catalog) that is set up to access data stored in DBFS (`/conf/`). You will point your project to use configuration stored on DBFS using the `--conf-source` option when you create your job on Databricks. There are several ways to upload data to DBFS: you can use the [DBFS API](https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/dbfs), the [`dbutils` module](https://docs.databricks.com/dev-tools/databricks-utils.html) in a Databricks notebook or the [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html). In this guide, it is recommended to use the Databricks CLI because of the convenience it offers. @@ -241,7 +242,7 @@ The final configuration for the job cluster should look the same as the followin - In the `Parameters` field, enter the following list of runtime options: ```bash -["--conf-source", "/dbfs/FileStore/iris-databricks/conf", "--env", "databricks", "--package-name", "iris_databricks"] +["--conf-source", "/dbfs/FileStore/iris-databricks/conf", "--package-name", "iris_databricks"] ``` The final configuration for your job should look the same as the following: From a7dc9e80768cc031d258af097b4718ae8e243a9f Mon Sep 17 00:00:00 2001 From: Jo Stichbury Date: Thu, 25 May 2023 15:09:11 +0100 Subject: [PATCH 45/55] Graphics links fixes, revise titles Signed-off-by: Jo Stichbury --- docs/source/deployment/databricks/databricks.md | 4 ++-- .../databricks/databricks_deployment_workflow.md | 12 ++++++------ .../databricks_development_workflow.md | 16 ++++++++-------- .../databricks/databricks_visualisation.md | 4 ++-- .../databricks/databricks_workspace.md | 8 ++++---- 5 files changed, 22 insertions(+), 22 deletions(-) diff --git a/docs/source/deployment/databricks/databricks.md b/docs/source/deployment/databricks/databricks.md index ba64fe4b95..1c4d0c40d4 100644 --- a/docs/source/deployment/databricks/databricks.md +++ b/docs/source/deployment/databricks/databricks.md @@ -5,7 +5,7 @@ :maxdepth: 1 databricks_workspace.md +databricks_visualisation databricks_development_workflow databricks_deployment_workflow -databricks_visualisation -``` \ No newline at end of file +``` diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index d42e306203..19a0d4b42c 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -34,7 +34,7 @@ Note your Databricks **username** and **host** as you will need it for the remai Find your Databricks username in the top right of the workspace UI and the host in the browser's URL bar, up to the first slash (e.g., `https://adb-123456789123456.1.azuredatabricks.net/`): -![Find Databricks host and username](../meta/images/find_databricks_host_and_username.png) +![Find Databricks host and username](../../meta/images/find_databricks_host_and_username.png) ```{note} Your databricks host must include the protocol (`https://`). @@ -213,11 +213,11 @@ To run your packaged project on Databricks, login to your Databricks account and 1. **Create a new job**: In the Databricks workspace, navigate to the `Worfklows` tab and click `Create Job`: -![Create Databricks job](../meta/images/databricks_create_new_job.png) +![Create Databricks job](../../meta/images/databricks_create_new_job.png) 2. **Create a new job cluster**: Create a dedicated [job cluster](https://docs.databricks.com/clusters/index.html) to run your job by clicking on the drop-down menu in the `Cluster` field: -![Create Databricks job cluster](../meta/images/databricks_create_job_cluster.png) +![Create Databricks job cluster](../../meta/images/databricks_create_job_cluster.png) Configure the job cluster with the following settings: @@ -228,7 +228,7 @@ Configure the job cluster with the following settings: The final configuration for the job cluster should look the same as the following: -![Configure Databricks job cluster](../meta/images/databricks_configure_job_cluster.png) +![Configure Databricks job cluster](../../meta/images/databricks_configure_job_cluster.png) 3. **Configure the job**: Configure the job with the following settings: @@ -246,13 +246,13 @@ The final configuration for the job cluster should look the same as the followin The final configuration for your job should look the same as the following: -![Configure Databricks job](../meta/images/databricks_configure_new_job.png) +![Configure Databricks job](../../meta/images/databricks_configure_new_job.png) Click `Create` and then `Confirm and create` in the following pop-up asking you to name the job. 4. **Run the job**: Click `Run now` in the top-right corner of your new job's page to start a run of the job. The status of your run can be viewed in the `Runs` tab of your job's page. Navigate to the `Runs` tab and track the progress of your run: -![Databricks job status](../meta/images/databricks_job_status.png) +![Databricks job status](../../meta/images/databricks_job_status.png) This page also shows an overview of all past runs of your job. As you only just started your job run, it's status will be `Pending`. A status of `Pending` indicates that the cluster is being started and your code is waiting to run. diff --git a/docs/source/deployment/databricks/databricks_development_workflow.md b/docs/source/deployment/databricks/databricks_development_workflow.md index 62f3034bcd..ee139d1ecd 100644 --- a/docs/source/deployment/databricks/databricks_development_workflow.md +++ b/docs/source/deployment/databricks/databricks_development_workflow.md @@ -33,7 +33,7 @@ Note your Databricks **username** and **host** as you will need it for the remai Find your Databricks username in the top right of the workspace UI and the host in the browser's URL bar, up to the first slash (e.g., `https://adb-123456789123456.1.azuredatabricks.net/`): -![Find Databricks host and username](../meta/images/find_databricks_host_and_username.png) +![Find Databricks host and username](../../meta/images/find_databricks_host_and_username.png) ```{note} Your databricks host must include the protocol (`https://`). @@ -90,7 +90,7 @@ Create a new repo on Databricks by navigating to `New` tab in the Databricks wor In this guide, you will not sync your project with a remote Git provider, so uncheck `Create repo by cloning a Git repository` and enter `iris-databricks` as the name of your new repository: -![Create a new repo on Databricks](../meta/images/databricks_repo_creation.png) +![Create a new repo on Databricks](../../meta/images/databricks_repo_creation.png) ### Sync code with your Databricks repo using dbx @@ -128,11 +128,11 @@ Kedro requires your project to have a `conf/local` directory to exist to success Open the Databricks workspace UI and using the panel on the left, navigate to `Repos -> -> iris-databricks -> conf`, right click and select `Create -> Folder` as in the image below: -![Create a conf folder in Databricks repo](../meta/images/databricks_conf_folder_creation.png) +![Create a conf folder in Databricks repo](../../meta/images/databricks_conf_folder_creation.png) Name the new folder `local`. In this guide, we have no local credentials to store and so we will leave the newly created folder empty. Your `conf/local` and `local` directories should now look like the following: -![Final conf folder](../meta/images/final_conf_folder.png) +![Final conf folder](../../meta/images/final_conf_folder.png) ### Upload project data to DBFS @@ -169,7 +169,7 @@ Now that your project is available on Databricks, you can run it on a cluster us To run the Python code from your Databricks repo, [create a new Python notebook](https://docs.databricks.com/notebooks/notebooks-manage.html#create-a-notebook) in your workspace. Name it `iris-databricks` for traceability and attach it to your cluster: -![Create a new notebook on Databricks](../meta/images/databricks_notebook_creation.png) +![Create a new notebook on Databricks](../../meta/images/databricks_notebook_creation.png) ### Run your project @@ -201,15 +201,15 @@ session.run() After completing these steps, your notebook should match the following image: -![Databricks completed notebook](../meta/images/databricks_finished_notebook.png) +![Databricks completed notebook](../../meta/images/databricks_finished_notebook.png) Run the completed notebook using the `Run All` bottom in the top right of the UI: -![Databricks notebook run all](../meta/images/databricks_run_all.png) +![Databricks notebook run all](../../meta/images/databricks_run_all.png) On your first run, you will be prompted to consent to analytics, type `y` or `N` in the field that appears and press `Enter`: -![Databricks notebook telemetry consent](../meta/images/databricks_telemetry_consent.png) +![Databricks notebook telemetry consent](../../meta/images/databricks_telemetry_consent.png) You should see logging output while the cell is running. After execution finishes, you should see output similar to the following: diff --git a/docs/source/deployment/databricks/databricks_visualisation.md b/docs/source/deployment/databricks/databricks_visualisation.md index 69bef2dbad..fb6a427b91 100644 --- a/docs/source/deployment/databricks/databricks_visualisation.md +++ b/docs/source/deployment/databricks/databricks_visualisation.md @@ -15,8 +15,8 @@ Kedro-Viz can then be launched in a new browser tab with the `%run_viz` line mag This command presents you with a link to the Kedro-Viz web application. -![databricks_viz_link](../meta/images/databricks_viz_link.png) +![databricks_viz_link](../../meta/images/databricks_viz_link.png) Clicking this link opens a new browser tab running Kedro-Viz for your project. -![databricks_viz_demo](../meta/images/databricks_viz_demo.png) +![databricks_viz_demo](../../meta/images/databricks_viz_demo.png) diff --git a/docs/source/deployment/databricks/databricks_workspace.md b/docs/source/deployment/databricks/databricks_workspace.md index 7bfdd99a6d..03ede4bbcf 100644 --- a/docs/source/deployment/databricks/databricks_workspace.md +++ b/docs/source/deployment/databricks/databricks_workspace.md @@ -1,4 +1,4 @@ -# Develop a project with Databricks Workspace and Notebooks +# Databricks notebooks workflow This tutorial uses the [PySpark Iris Kedro Starter](https://github.com/kedro-org/kedro-starters/tree/main/pyspark-iris) to illustrate how to bootstrap a Kedro project using Spark and deploy it to a [Databricks cluster on AWS](https://databricks.com/aws). @@ -144,11 +144,11 @@ The project has now been pushed to your private GitHub repository, and in order 3. Press `Edit` 4. Go to the `Advanced Options` and then `Spark` -![](../meta/images/databricks_cluster_edit.png) +![](../../meta/images/databricks_cluster_edit.png) Then in the `Environment Variables` section add your `GITHUB_USER` and `GITHUB_TOKEN` as shown on the picture: -![](../meta/images/databricks_cluster_env_vars.png) +![](../../meta/images/databricks_cluster_env_vars.png) ```{note} @@ -227,7 +227,7 @@ You should get a similar output: Your complete notebook should look similar to this (the results are hidden): -![](../meta/images/databricks_notebook_example.png) +![](../../meta/images/databricks_notebook_example.png) ### 9. Using the Kedro IPython Extension From acae7c3b8925bf644ce701518d8e6ec48e81ff60 Mon Sep 17 00:00:00 2001 From: Jo Stichbury Date: Thu, 25 May 2023 15:50:43 +0100 Subject: [PATCH 46/55] Fix broken internal link Signed-off-by: Jo Stichbury --- docs/source/contribution/development_for_databricks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/contribution/development_for_databricks.md b/docs/source/contribution/development_for_databricks.md index cde8be4461..281e04cfc7 100644 --- a/docs/source/contribution/development_for_databricks.md +++ b/docs/source/contribution/development_for_databricks.md @@ -5,7 +5,7 @@ Many Kedro users deploy their projects to [Databricks](https://www.databricks.co ## How to deploy a development version of Kedro to Databricks ```{note} -This page is for **contributors** developing changes to Kedro that need to test them on Databricks. If you are a Kedro user working on an individual or team project and need more information about workflows, consult the [documentation for developing a Kedro project on Databricks](../integrations/databricks_workspace.md). +This page is for **contributors** developing changes to Kedro that need to test them on Databricks. If you are a Kedro user working on an individual or team project and need more information about workflows, consult the [documentation pages for developing a Kedro project on Databricks](../deployment/databricks/databricks.md). ``` ## Prerequisites From b24c062f5a3c63456730feb27e93cda4a51911cc Mon Sep 17 00:00:00 2001 From: Jo Stichbury Date: Thu, 25 May 2023 16:47:36 +0100 Subject: [PATCH 47/55] Fix links broken by new folder Signed-off-by: Jo Stichbury --- docs/source/contribution/development_for_databricks.md | 2 +- .../databricks/databricks_deployment_workflow.md | 4 ++-- .../databricks/databricks_development_workflow.md | 2 +- .../deployment/databricks/databricks_visualisation.md | 2 +- docs/source/deployment/databricks/databricks_workspace.md | 8 ++++---- .../deployment/databricks/{databricks.md => index.md} | 0 docs/source/deployment/index.md | 4 ++-- 7 files changed, 11 insertions(+), 11 deletions(-) rename docs/source/deployment/databricks/{databricks.md => index.md} (100%) diff --git a/docs/source/contribution/development_for_databricks.md b/docs/source/contribution/development_for_databricks.md index 281e04cfc7..571b2e65ad 100644 --- a/docs/source/contribution/development_for_databricks.md +++ b/docs/source/contribution/development_for_databricks.md @@ -5,7 +5,7 @@ Many Kedro users deploy their projects to [Databricks](https://www.databricks.co ## How to deploy a development version of Kedro to Databricks ```{note} -This page is for **contributors** developing changes to Kedro that need to test them on Databricks. If you are a Kedro user working on an individual or team project and need more information about workflows, consult the [documentation pages for developing a Kedro project on Databricks](../deployment/databricks/databricks.md). +This page is for **contributors** developing changes to Kedro that need to test them on Databricks. If you are a Kedro user working on an individual or team project and need more information about workflows, consult the [documentation pages for developing a Kedro project on Databricks](../deployment/databricks/index.md). ``` ## Prerequisites diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index 299e95fad5..583d822e34 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -136,7 +136,7 @@ This process adds an entry point to your project which can be used to run it on ```{note} Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in the above code and in the Databricks Iris starter contains a simple implementation of two options: - `--package_name` (required): the package name (defined in `setup.py`) of your packaged project. -- `--env`: specifies a [Kedro configuration environment](../configuration/configuration_basics.md#configuration-environments) to load for your run. +- `--env`: specifies a [Kedro configuration environment](../../configuration/configuration_basics.md#configuration-environments) to load for your run. - `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. ``` @@ -178,7 +178,7 @@ A Kedro project's configuration and data do not get included when it is packaged Your packaged Kedro project needs access to data and configuration in order to run. Therefore, you will need to upload your project's data and configuration to a location accessible to Databricks. In this guide, we will store the data on the Databricks File System (DBFS). -The Databricks Iris starter contains a [catalog](../data/data_catalog.md#the-data-catalog) that is set up to access data stored in DBFS (`/conf/`). You will point your project to use configuration stored on DBFS using the `--conf-source` option when you create your job on Databricks. +The Databricks Iris starter contains a [catalog](../../data/data_catalog.md#the-data-catalog) that is set up to access data stored in DBFS (`/conf/`). You will point your project to use configuration stored on DBFS using the `--conf-source` option when you create your job on Databricks. There are several ways to upload data to DBFS: you can use the [DBFS API](https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/dbfs), the [`dbutils` module](https://docs.databricks.com/dev-tools/databricks-utils.html) in a Databricks notebook or the [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html). In this guide, it is recommended to use the Databricks CLI because of the convenience it offers. diff --git a/docs/source/deployment/databricks/databricks_development_workflow.md b/docs/source/deployment/databricks/databricks_development_workflow.md index ee139d1ecd..aa8bcefe4d 100644 --- a/docs/source/deployment/databricks/databricks_development_workflow.md +++ b/docs/source/deployment/databricks/databricks_development_workflow.md @@ -136,7 +136,7 @@ Name the new folder `local`. In this guide, we have no local credentials to stor ### Upload project data to DBFS -When run on Databricks, Kedro cannot access data stored in your project's directory. Therefore, you will need to upload your project's data to an accessible location. In this guide, we will store the data on the Databricks File System (DBFS). The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../configuration/configuration_basics.md#configuration-environments). +When run on Databricks, Kedro cannot access data stored in your project's directory. Therefore, you will need to upload your project's data to an accessible location. In this guide, we will store the data on the Databricks File System (DBFS). The PySpark Iris starter contains an environment that is set up to access data stored in DBFS (`conf/databricks`). To learn more about environments in Kedro configuration, see the [configuration documentation](../../configuration/configuration_basics.md#configuration-environments). There are several ways to upload data to DBFS. In this guide, it is recommended to use [Databricks CLI](https://docs.databricks.com/dev-tools/cli/dbfs-cli.html) because of the convenience it offers. At the command line in your local environment, use the following Databricks CLI command to upload your locally stored data to DBFS: diff --git a/docs/source/deployment/databricks/databricks_visualisation.md b/docs/source/deployment/databricks/databricks_visualisation.md index fb6a427b91..c96d56e84d 100644 --- a/docs/source/deployment/databricks/databricks_visualisation.md +++ b/docs/source/deployment/databricks/databricks_visualisation.md @@ -1,6 +1,6 @@ # Visualise a Kedro project in Databricks notebooks -[Kedro-Viz](../visualisation/kedro-viz_visualisation.md) is a tool that enables you to visualise your Kedro pipeline and metrics generated from your data science experiments. It is a standalone web application that runs on a web browser, it can be run on a local machine or in Databricks notebooks. +[Kedro-Viz](../../visualisation/kedro-viz_visualisation.md) is a tool that enables you to visualise your Kedro pipeline and metrics generated from your data science experiments. It is a standalone web application that runs on a web browser, it can be run on a local machine or in Databricks notebooks. For Kedro-Viz to run with your Kedro project, you need to ensure that both the packages are installed in the same scope (notebook-scoped vs. cluster library). This means that if you `%pip install kedro` from inside your notebook then you should also `%pip install kedro-viz` from inside your notebook. If your cluster comes with Kedro installed on it as a library already then you should also add Kedro-Viz as a [cluster library](https://docs.microsoft.com/en-us/azure/databricks/libraries/cluster-libraries). diff --git a/docs/source/deployment/databricks/databricks_workspace.md b/docs/source/deployment/databricks/databricks_workspace.md index 03ede4bbcf..57039bc50d 100644 --- a/docs/source/deployment/databricks/databricks_workspace.md +++ b/docs/source/deployment/databricks/databricks_workspace.md @@ -3,11 +3,11 @@ This tutorial uses the [PySpark Iris Kedro Starter](https://github.com/kedro-org/kedro-starters/tree/main/pyspark-iris) to illustrate how to bootstrap a Kedro project using Spark and deploy it to a [Databricks cluster on AWS](https://databricks.com/aws). ```{note} -If you are using [Databricks Repos](https://docs.databricks.com/repos/index.html) to run a Kedro project then you should [disable file-based logging](../logging/logging.md#disable-file-based-logging). This prevents Kedro from attempting to write to the read-only file system. +If you are using [Databricks Repos](https://docs.databricks.com/repos/index.html) to run a Kedro project then you should [disable file-based logging](../../logging/logging.md#disable-file-based-logging). This prevents Kedro from attempting to write to the read-only file system. ``` ```{note} -If you are a Kedro contributor looking for information on deploying a custom build of Kedro to Databricks, see the [development guide](../contribution/development_for_databricks.md). +If you are a Kedro contributor looking for information on deploying a custom build of Kedro to Databricks, see the [development guide](../../contribution/development_for_databricks.md). ``` ## Prerequisites @@ -234,9 +234,9 @@ Your complete notebook should look similar to this (the results are hidden): You can interact with Kedro in Databricks through the Kedro [IPython extension](https://ipython.readthedocs.io/en/stable/config/extensions/index.html), `kedro.ipython`. -The Kedro IPython extension launches a [Kedro session](../kedro_project_setup/session.md) and makes available the useful Kedro variables `catalog`, `context`, `pipelines` and `session`. It also provides the `%reload_kedro` [line magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html) that reloads these variables (for example, if you need to update `catalog` following changes to your Data Catalog). +The Kedro IPython extension launches a [Kedro session](../../kedro_project_setup/session.md) and makes available the useful Kedro variables `catalog`, `context`, `pipelines` and `session`. It also provides the `%reload_kedro` [line magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html) that reloads these variables (for example, if you need to update `catalog` following changes to your Data Catalog). -The IPython extension can be used in a Databricks notebook in a similar way to how it is used in [Jupyter notebooks](../notebooks_and_ipython/kedro_and_notebooks.md). +The IPython extension can be used in a Databricks notebook in a similar way to how it is used in [Jupyter notebooks](../../notebooks_and_ipython/kedro_and_notebooks.md). If you encounter a `ContextualVersionConflictError`, it is likely caused by Databricks using an old version of `pip`. Hence there's one additional step you need to do in the Databricks notebook to make use of the IPython extension. After you load the IPython extension using the below command: diff --git a/docs/source/deployment/databricks/databricks.md b/docs/source/deployment/databricks/index.md similarity index 100% rename from docs/source/deployment/databricks/databricks.md rename to docs/source/deployment/databricks/index.md diff --git a/docs/source/deployment/index.md b/docs/source/deployment/index.md index 0afdcd75e7..baf2ca74fd 100644 --- a/docs/source/deployment/index.md +++ b/docs/source/deployment/index.md @@ -30,7 +30,7 @@ This following pages provide information for deployment to, or integration with, * [AWS Step functions](aws_step_functions.md) * [Azure](azure.md) * [Dask](dask.md) -* [Databricks](./databricks/databricks.md) +* [Databricks](./databricks/index.md) * [Kubeflow Workflows](kubeflow.md) * [Prefect](prefect.md) * [Vertex AI](vertexai.md) @@ -55,7 +55,7 @@ amazon_sagemaker aws_step_functions azure dask -databricks/databricks +databricks/index kubeflow prefect vertexai From ba0c184be35c46e41f2b985446c1acfc077ff7bc Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Fri, 26 May 2023 16:16:07 +0100 Subject: [PATCH 48/55] Remove logs directory Signed-off-by: Jannic Holzer --- .../databricks/databricks_deployment_workflow.md | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index abebbd566b..2d8728e467 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -148,17 +148,9 @@ You will not be able to directly see your project's log output when it is run as - Upload metrics from a machine learning model to MLflow. - Save a dataset as a CSV file in DBFS. -In this guide, your project will save its logs to a location in DBFS. +In this guide, your project will save its logs to a location in DBFS (`/dbfs/FileStore/iris-databricks/info.log`). -- **Create a directory to save your logs into on DBFS**: at the command line in your local environment, run the following Databricks CLI command: - -```bash -databricks fs mkdirs dbfs:/FileStore/iris-databricks/logs -``` - -This command creates a directory on DBFS (`/dbfs/FileStore/iris-databricks/logs`) where your project can save its logs. - -**The Databricks Iris starter is already configured to save logs in this location.** The configuration used to achieve this can be found in the `conf/base/logging.yml` file. +**The `databricks-iris` starter is already configured to save logs in this location.** The configuration used to achieve this can be found in the `conf/base/logging.yml` file. ### Package your project @@ -269,7 +261,7 @@ A run will take roughly six to seven minutes. When the status of your run is `Succeeded`, your job has successfully finished executing. You can now view the logs that your project produced while it was running. To retrieve the log file from the [location in DBFS where it was saved](#plan-your-projects-output), use the following `databricks CLI` command at your local command line with the `iris-databrick` conda environment activated: ```bash -databricks fs cp dbfs:/FileStore/iris-databricks/logs/info.log . +databricks fs cp dbfs:/FileStore/iris-databricks/info.log . ``` This will download the log file from DBFS to your local environment. 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qOQokK@~`*czf<~udzC&O6F5b8dhB#p+x!9ik(X9^SS0oM#s2~d@vqSU From e88612b0b0f0200999d2fba882ba07cde002bf9b Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Tue, 30 May 2023 12:09:22 +0100 Subject: [PATCH 50/55] Add full stops in list. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Juan Luis Cano Rodríguez --- .../deployment/databricks/databricks_deployment_workflow.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index 2b0850bb67..8a98b0f4e6 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -280,8 +280,8 @@ By following these steps, you packaged your Kedro project and manually ran it as Up to this point, this page has described a manual workflow for deploying and running a project on Databricks. The process can be automated in two ways: -- [Use the Databricks API.](#how-to-use-the-databricks-api-to-automate-deployment-of-a-kedro-project) -- [Use the Databricks CLI.](#how-to-use-the-databricks-cli-to-automate-deployment-of-a-kedro-project) +- [Use the Databricks API](#how-to-use-the-databricks-api-to-automate-deployment-of-a-kedro-project). +- [Use the Databricks CLI](#how-to-use-the-databricks-cli-to-automate-deployment-of-a-kedro-project). Both of these methods enable you to store information about your job declaratively in the same version control system as the rest of your project. For each method, the information stored declaratively is the same as what is entered manually in the [above section on creating and running a job in Databricks](#deploy-and-run-your-kedro-project-using-the-workspace-ui). From 085e9e5b7518ab9c43016021688876f02ae47ef8 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Tue, 30 May 2023 12:12:07 +0100 Subject: [PATCH 51/55] Fix conda environment name. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Juan Luis Cano Rodríguez --- .../deployment/databricks/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index 8a98b0f4e6..7ac9dd6796 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -258,7 +258,7 @@ The following things happen when you run your job: A run will take roughly six to seven minutes. -When the status of your run is `Succeeded`, your job has successfully finished executing. You can now view the logs that your project produced while it was running. To retrieve the log file from the [location in DBFS where it was saved](#plan-your-projects-output), use the following `databricks CLI` command at your local command line with the `iris-databrick` conda environment activated: +When the status of your run is `Succeeded`, your job has successfully finished executing. You can now view the logs that your project produced while it was running. To retrieve the log file from the [location in DBFS where it was saved](#plan-your-projects-output), use the following `databricks CLI` command at your local command line with the `iris-databricks` conda environment activated: ```bash databricks fs cp dbfs:/FileStore/iris-databricks/info.log . From d8bd5273074ad892263f4f361a3aa77de4dad370 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Tue, 30 May 2023 14:35:14 +0100 Subject: [PATCH 52/55] Modify wording and image for creating a new job cluster Signed-off-by: Jannic Holzer --- .../databricks_deployment_workflow.md | 4 ++++ .../images/databricks_create_job_cluster.png | Bin 63745 -> 230678 bytes 2 files changed, 4 insertions(+) diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index 2b0850bb67..c131dca232 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -210,8 +210,12 @@ To run your packaged project on Databricks, login to your Databricks account and 2. **Create a new job cluster**: Create a dedicated [job cluster](https://docs.databricks.com/clusters/index.html) to run your job by clicking on the drop-down menu in the `Cluster` field: +**Do not use the default `Job_cluster`, it has not been configured to run this job.** + ![Create Databricks job cluster](../../meta/images/databricks_create_job_cluster.png) +Once you click `Add new job cluster`, the configuration page for this cluster appears. + Configure the job cluster with the following settings: - In the `name` field enter `kedro_deployment_demo`. diff --git a/docs/source/meta/images/databricks_create_job_cluster.png b/docs/source/meta/images/databricks_create_job_cluster.png index 0cd866ed8dce81fa40a75eef387a6a0ce1d260a1..d429dabba34a923fa26db213dfdd87f45d0a1e5e 100644 GIT binary patch literal 230678 zcmeEvXIN9)wl-Z66_u?>6WvIYDoF3T0U z7eY%yOF~E}`NH1k+$zaKt6m?h7gYt1p{c;E4kF&FqwM@#k61?CH6WMr4r z9{-_BMn+pmMn=tXo`&=ti4Y5U(g(SRuIfXwiXqkw(m!5XKUcHS)Fit{x;{@vO@57x z@^lF44;eW#8TFsnWMt~(EdO<_OV0C;F%)EEk@jR%{}^LL`aHc$(#Gw>_R8M7Ucq19>&7RU@Fl0Gnv;D=Z^d;P zwtM`(74w!TSM(blIR(}EE4LLx$bMb?a>B1j_=SRBDENhfUnuy6f?p{3g@Ruw_=SRB 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zl!Q$0tfZ4A@dGNZ?IO|4j=WeeZ(532{_JR-@wBqAZG?swJgYZ>s#L_0rbWK21qK_* z?G8pV^#GbRvK+I<<$oje3d2RQMcyU8ed`{$rfOtRzH@gqzyYhcG~U>VQ{WhS1k$D} z^!=V(qx^}qTF8D?Z*oKXt^VLo_WcdQsBJ<+)d{RDE2HVd+M2>tW5iI0=NNtSDsNOO zY)m5y8S{}}wesZ9kG!xe;oxq17+0k6EeZIxMFyegxn#fzXC0q9qyxZdM5Ckrotnbx zOSqJPG_8sos+oPaienf53z^<$rb{|5#m&1zkLQA8@{}FS5ca;i_Akck{o=<994F|> zun7)uz4gK2N}wa_DR(C}F#zHkFzZIEW8K?K?k|o0`{sY|XcbR%HQF7rJmkF(_4rTw z-LH>4vZ5hD?P`O)9k06C-Ry6v{s~Lv!`=&qY+J8>gu(^>??E3-O@a>6Y5!-APaClI z0_jv0O<+VQq6b!tD>`K;4lMK?&X0_j-^-l%|ND9h1D?s*bR_owbgCblIdlbNAq%JV z(>n?RF&O?wJuZTM+v%#D@&HHzxD{8&|J7k%^Tnf3Q9nG}{d4{Ne77uZiuA1x_3OMJ zdiK5)BQaU;_mme-o3y>L?qw@}Zyj<25#hB1AHMHh2J zktc(%mt+eb$<}V7O2>~w@>|$$=rX)LxA+2RlwF+Uzx-%BN^&!1Ec=wr-Ql}u-`##^ zTN?lF&xe*kGwYDbSJ`eop0%P2r%8C$gPJ&)i_X10H^N&{8n#d`JsxjCM;2=Ry~^DB z@N1tH2Va~PIEm`2j2m2a4yKA9KHbhQ$f|}2e;Gfdk# Date: Tue, 30 May 2023 14:37:17 +0100 Subject: [PATCH 53/55] Modify wording in guide to create new job cluster Signed-off-by: Jannic Holzer --- .../deployment/databricks/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index c131dca232..7c6f74b63e 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -208,7 +208,7 @@ To run your packaged project on Databricks, login to your Databricks account and ![Create Databricks job](../../meta/images/databricks_create_new_job.png) -2. **Create a new job cluster**: Create a dedicated [job cluster](https://docs.databricks.com/clusters/index.html) to run your job by clicking on the drop-down menu in the `Cluster` field: +2. **Create a new job cluster specific to your job**: Create a dedicated [job cluster](https://docs.databricks.com/clusters/index.html) to run your job by clicking on the drop-down menu in the `Cluster` field and then clicking `Add new job cluster`: **Do not use the default `Job_cluster`, it has not been configured to run this job.** From 1af24e4c72d933a797a7110cc30d1e1fb9412032 Mon Sep 17 00:00:00 2001 From: Jannic <37243923+jmholzer@users.noreply.github.com> Date: Tue, 30 May 2023 14:46:49 +0100 Subject: [PATCH 54/55] Remove --upgrade option MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Juan Luis Cano Rodríguez --- .../deployment/databricks/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index 167d56de36..e274c76e65 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -59,7 +59,7 @@ conda activate iris-databricks With your Conda environment activated, install Kedro and the Databricks CLI: ```bash -pip install kedro databricks-cli --upgrade +pip install kedro databricks-cli ``` ### Authenticate the Databricks CLI From 7be816659732b233ca4e9b46da7efa3caf977a8c Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Tue, 30 May 2023 16:43:58 +0100 Subject: [PATCH 55/55] Add both ways of creating a new job Signed-off-by: Jannic Holzer --- .../deployment/databricks/databricks_deployment_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/deployment/databricks/databricks_deployment_workflow.md b/docs/source/deployment/databricks/databricks_deployment_workflow.md index 167d56de36..724f3701e9 100644 --- a/docs/source/deployment/databricks/databricks_deployment_workflow.md +++ b/docs/source/deployment/databricks/databricks_deployment_workflow.md @@ -204,7 +204,7 @@ You should see the contents of the project's `data/` directory printed to your t To run your packaged project on Databricks, login to your Databricks account and perform the following steps in the workspace: -1. **Create a new job**: In the Databricks workspace, navigate to the `Worfklows` tab and click `Create Job`: +1. **Create a new job**: In the Databricks workspace, navigate to the `Worfklows` tab and click `Create Job` **or** click the `New` button, then `Job`: ![Create Databricks job](../../meta/images/databricks_create_new_job.png)

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z7s2+wY~z0py#M$w$9}+ULTSrU{`tB8e!Bnb`5Oyx(K>%EIK=zUv+SSC^k-rIpIz>M z9+k!OwE{jM$?LzIEg@iYoNXPF|M}DZ@0SDo&JrHrrm)_#LI0Pt6@Fe!Z~x?ft?%E* z#(#Qfd=6t-NUah6cHIB(524SC34_A%&#mdWg)%s@xPoc&U0XiOl(i^ zpV#`IV>^EB`Sdxh(c`I^Wonr1znn7H3&_=W-z!7Z|6lLn5XSWwGP|L(@_)sW|M_EA z#y=gWI!U`HusX^Amy Date: Mon, 22 May 2023 09:49:47 +0100 Subject: [PATCH 05/55] Remove use of 'allows', add summary Signed-off-by: Jannic Holzer --- .../integrations/databricks_deployment_workflow.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index db5f15873d..3081f3228b 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -8,7 +8,7 @@ Running your packaged project as a Databricks job is very different to running i - **Machine learning pipeline with MLflow**: your Kedro project runs an ML model and metrics about your experiments are tracked in MLflow. - **Data engineering pipeline**: the output of your Kedro project is a file or set of files containing cleaned and processed data. -- **Automated, scheduled runs**: your Kedro project should be run on Databricks on an automated schedule. +- **Automated, scheduled runs**: your Kedro project should be run on Databricks on an [automated schedule](https://docs.databricks.com/workflows/jobs/schedule-jobs.html#add-a-job-schedule). - **CI/CD integration**: you have a CI/CD pipeline set up that produces a packaged Kedro project. By the end of this guide, you'll have a clear understanding of the steps involved in packaging your Kedro project and running it as a job on Databricks. @@ -101,7 +101,7 @@ Remember to replace with the correct package name for your projec This process adds an entry point to your project which can be used to run it on Databricks. ```{note} -Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it allows. Instead, the `databricks_run` entry point in PySpark-Iris contains a simple implementation of the following options: +Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in PySpark-Iris contains a simple implementation of the following options: - `--package`: the name - `--env`: specifies a configuration environment to load for your run. - `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. @@ -245,7 +245,7 @@ This page describes a manual workflow for deploying and running project on Datab - [Use the Databricks API.](#databricks-api) - [Use the Databricks CLI.](#databricks-cli) -Both of these methods allows you to store information about your job declaratively in the same version control system as the rest of your project. For each method, the information stored declaratively is the same as what is entered manually in the [above section on creating and running a job in Databricks](#deploy-and-run-your-kedro-project-using-the-workspace-ui). +Both of these methods enable you to store information about your job declaratively in the same version control system as the rest of your project. For each method, the information stored declaratively is the same as what is entered manually in the [above section on creating and running a job in Databricks](#deploy-and-run-your-kedro-project-using-the-workspace-ui). These methods can be integrated into a CI/CD pipeline to automatically deploy a packaged Kedro project to Databricks as a job. @@ -267,3 +267,7 @@ The Databricks Command Line Interface (CLI) is another way to automate deploymen 3. Create a JSON file containing your job's configuration. 4. Use the [`jobs create` command](https://docs.databricks.com/dev-tools/cli/jobs-cli.html#create-a-job) to create a new job. 5. Use the [`jobs run-now` command](https://docs.databricks.com/dev-tools/cli/jobs-cli.html#run-a-job) to run your newly created job. + +## Summary + +This guide demonstrated how to deploy a packaged Kedro project on Databricks. This is a structured and reproducible way to run your Kedro projects on Databricks that can be automated and integrated into CI/CD pipelines. From 0f20bb750a0d7679262ce44d3413229908fcd0f1 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Mon, 22 May 2023 10:27:20 +0100 Subject: [PATCH 06/55] Remove link to missing image Signed-off-by: Jannic Holzer --- .../integrations/databricks_deployment_workflow.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 3081f3228b..29c55cfa9d 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -204,9 +204,11 @@ The final configuration for your job should look the same as the following: ![Configure Databricks job](../meta/images/databricks_configure_new_job.png) -4. **Run the job**: Click "Run Now" to start a run of the job. The job's status will be displayed in the `Runs` tab. Navigate to the `Runs` tab and view the logs and results by clicking on the job run in the list: +Click `Save task` to save the configuration. -![Databricks run job](../meta/images/databricks_run_job.png) +4. **Run the job**: Click `Run now` to start a run of the job. The job's status will be displayed in the `Runs` tab. Navigate to the `Runs` tab of the `Workflow` panel and view the logs and results by clicking on the job run in the list: + +![Databricks job status](../meta/images/databricks_job_status.png) After you run the job: @@ -214,10 +216,6 @@ After you run the job: - The packaged Kedro project and all its dependencies will be installed. - The packaged Kedro project will be run from the specified `databricks_run` entry point. -You can view the status of the job run by clicking on the `Runs` tab of the `Workflow` panel: - -![Databricks job status](../meta/images/databricks_job_status.png) - A run will take roughly six to seven minutes. When the status of your run is `Succeeded`, your job has successfully finished executing. You can now proceed to view the logs that your project produced while it was running. To retrieve the logs file from the [location in DBFS where it was saved](#plan-your-projects-output), use the following `databricks CLI` command at your local command line with the `iris-databrick` conda environment activated: From 1baa5e260f13a8264815eb0b4a3b1a7ba5993bbf Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Mon, 22 May 2023 10:39:36 +0100 Subject: [PATCH 07/55] Add deployment workflow to toctree Signed-off-by: Jannic Holzer --- docs/source/integrations/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/integrations/index.md b/docs/source/integrations/index.md index fbd37440f9..7618eb81dd 100644 --- a/docs/source/integrations/index.md +++ b/docs/source/integrations/index.md @@ -8,6 +8,7 @@ databricks_workspace.md databricks_development_workflow.md +databricks_deployment_workflow.md databricks_visualisation.md ``` From ed73514a2683d67fde9b717cc3fdb79b4d5bdea9 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Mon, 22 May 2023 10:51:30 +0100 Subject: [PATCH 08/55] Lint and fix missing link Signed-off-by: Jannic Holzer --- docs/source/integrations/databricks_deployment_workflow.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index 29c55cfa9d..ea8b0d82ca 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -85,7 +85,7 @@ The default entry point of a Kedro project uses a Click command line interface ( The PySpark Iris starter has this entry point pre-built, so there is no extra work to do in this guide. **Generally you must create it manually for your own projects using the following steps**: -1. **Create an entry point script**: Create a new file in `/src` named `databricks_run.py`. Copy the contents of the `/src/databricks_run.py` file defined in the [PySpark Iris starter]() to this new file. +1. **Create an entry point script**: Create a new file in `/src` named `databricks_run.py`. Copy the contents of the `/src/databricks_run.py` file defined in the PySpark Iris starter to this new file. 2. **Define a new entry point**: Open `/src/setup.py` in a text editor or IDE and add a new line in the definition of the `entry_point` tuple, so that it becomes: @@ -102,7 +102,7 @@ This process adds an entry point to your project which can be used to run it on ```{note} Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in PySpark-Iris contains a simple implementation of the following options: -- `--package`: the name +- `--package`: the name - `--env`: specifies a configuration environment to load for your run. - `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. ``` From 6057994770d384662f049599b4756cd156ba7bbf Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Mon, 22 May 2023 20:11:06 +0100 Subject: [PATCH 09/55] Minor style, syntax and grammar improvements Signed-off-by: Jannic Holzer --- .../databricks_deployment_workflow.md | 43 ++++++++++++++++--- 1 file changed, 37 insertions(+), 6 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index ea8b0d82ca..a065b2092e 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -4,11 +4,11 @@ In this guide, you will explore the process of packaging a Kedro project and run By packaging your Kedro project and running it on Databricks, you can execute your pipeline without the need for a notebook. This approach is particularly well-suited for production, as it provides a structured and reproducible way to run your code. However, it is not ideal for development, where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_development_workflow.md). -Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does not **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line. Here are some example use cases for running your packaged Kedro project as a Databricks job: +Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does not **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. Here are some example use cases for running a packaged Kedro project as a Databricks job: -- **Machine learning pipeline with MLflow**: your Kedro project runs an ML model and metrics about your experiments are tracked in MLflow. - **Data engineering pipeline**: the output of your Kedro project is a file or set of files containing cleaned and processed data. -- **Automated, scheduled runs**: your Kedro project should be run on Databricks on an [automated schedule](https://docs.databricks.com/workflows/jobs/schedule-jobs.html#add-a-job-schedule). +- **Machine learning with MLflow**: your Kedro project runs an ML model and metrics about your experiments are automatically tracked in MLflow via custom integrations. +- **Automated and scheduled runs**: your Kedro project should be run on Databricks on an [automated schedule](https://docs.databricks.com/workflows/jobs/schedule-jobs.html#add-a-job-schedule). - **CI/CD integration**: you have a CI/CD pipeline set up that produces a packaged Kedro project. By the end of this guide, you'll have a clear understanding of the steps involved in packaging your Kedro project and running it as a job on Databricks. @@ -40,6 +40,8 @@ Your databricks host must include the protocol (`https://`). ### Install Kedro and the databricks CLI in a new virtual environment +The following commands will create a new Conda environment, activate it, and then install Kedro and the Databricks CLI. + In your local development environment, create a virtual environment for this tutorial using Conda: ```bash @@ -52,7 +54,7 @@ Once it is created, activate it: conda activate iris-databricks ``` -With your Conda environment activated, install Kedro and the Databricks CLIs: +With your Conda environment activated, install Kedro and the Databricks CLI: ```bash pip install kedro databricks-cli --upgrade @@ -85,7 +87,36 @@ The default entry point of a Kedro project uses a Click command line interface ( The PySpark Iris starter has this entry point pre-built, so there is no extra work to do in this guide. **Generally you must create it manually for your own projects using the following steps**: -1. **Create an entry point script**: Create a new file in `/src` named `databricks_run.py`. Copy the contents of the `/src/databricks_run.py` file defined in the PySpark Iris starter to this new file. +1. **Create an entry point script**: Create a new file in `/src/iris_databricks` named `databricks_run.py`. Copy the following code to this file: + +```python +import argparse +import logging +from kedro.framework.project import configure_project +from kedro.framework.session import KedroSession + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--env", dest="env", type=str) + parser.add_argument("--conf-source", dest="conf_source", type=str) + + args = parser.parse_args() + env = args.env + conf_source = args.conf_source + + # https://kb.databricks.com/notebooks/cmd-c-on-object-id-p0.html + logging.getLogger("py4j.java_gateway").setLevel(logging.ERROR) + logging.getLogger("py4j.py4j.clientserver").setLevel(logging.ERROR) + + configure_project(package_name) + with KedroSession.create(env=env, conf_source=conf_source) as session: + session.run() + + +if __name__ == "__main__": + main() +``` 2. **Define a new entry point**: Open `/src/setup.py` in a text editor or IDE and add a new line in the definition of the `entry_point` tuple, so that it becomes: @@ -101,7 +132,7 @@ Remember to replace with the correct package name for your projec This process adds an entry point to your project which can be used to run it on Databricks. ```{note} -Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in PySpark-Iris contains a simple implementation of the following options: +Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in the PySpark Iris starter contains a simple implementation of the following options: - `--package`: the name - `--env`: specifies a configuration environment to load for your run. - `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. From 3d22bd6e54f34af286bba6e9209d71080a70b316 Mon Sep 17 00:00:00 2001 From: Jannic Holzer Date: Tue, 23 May 2023 13:49:24 +0100 Subject: [PATCH 10/55] Fixes for correctness during validation Signed-off-by: Jannic Holzer --- .../databricks_deployment_workflow.md | 40 ++++++++++-------- .../images/databricks_configure_new_job.png | Bin 157496 -> 186022 bytes 2 files changed, 22 insertions(+), 18 deletions(-) diff --git a/docs/source/integrations/databricks_deployment_workflow.md b/docs/source/integrations/databricks_deployment_workflow.md index a065b2092e..6fe184090f 100644 --- a/docs/source/integrations/databricks_deployment_workflow.md +++ b/docs/source/integrations/databricks_deployment_workflow.md @@ -7,7 +7,7 @@ By packaging your Kedro project and running it on Databricks, you can execute yo Running your packaged project as a Databricks job is very different to running it from a notebook. In particular, it does not **not** allow you to see the log output of your project as you may be used to seeing it when running Kedro from a command line or notebook. Here are some example use cases for running a packaged Kedro project as a Databricks job: - **Data engineering pipeline**: the output of your Kedro project is a file or set of files containing cleaned and processed data. -- **Machine learning with MLflow**: your Kedro project runs an ML model and metrics about your experiments are automatically tracked in MLflow via custom integrations. +- **Machine learning with MLflow**: your Kedro project runs an ML model and metrics about your experiments are tracked in MLflow. - **Automated and scheduled runs**: your Kedro project should be run on Databricks on an [automated schedule](https://docs.databricks.com/workflows/jobs/schedule-jobs.html#add-a-job-schedule). - **CI/CD integration**: you have a CI/CD pipeline set up that produces a packaged Kedro project. @@ -100,10 +100,12 @@ def main(): parser = argparse.ArgumentParser() parser.add_argument("--env", dest="env", type=str) parser.add_argument("--conf-source", dest="conf_source", type=str) + parser.add_argument("--package-name", dest="package_name", type=str) args = parser.parse_args() env = args.env conf_source = args.conf_source + package_name = args.package_name # https://kb.databricks.com/notebooks/cmd-c-on-object-id-p0.html logging.getLogger("py4j.java_gateway").setLevel(logging.ERROR) @@ -122,8 +124,8 @@ if __name__ == "__main__": ```python entry_point = ( - ... - "databricks_run = .databricks_run" + ..., + "databricks_run = .databricks_run:main" ) ``` @@ -132,8 +134,7 @@ Remember to replace with the correct package name for your projec This process adds an entry point to your project which can be used to run it on Databricks. ```{note} -Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in the PySpark Iris starter contains a simple implementation of the following options: -- `--package`: the name +Because you are no longer using the default entry-point for Kedro, you will not be able to run your project with the options it usually provides. Instead, the `databricks_run` entry point in the above code and in the PySpark Iris starter contains a simple implementation of two options: - `--env`: specifies a configuration environment to load for your run. - `--conf-source`: specifies the location of the `conf/` directory to use with your Kedro project. ``` @@ -146,11 +147,11 @@ You will not be able to directly see your project's log output when it is run as - Upload metrics from a machine learning model to MLflow. - Save a dataset as a CSV file in DBFS. -In this guide, your project will save its logs to a location in DBFS. The `databricks` environment of the PySpark Iris starter is already configured to do this. The configuration used to achieve this can be found in the `conf/databricks/logging.yml` file. +In this guide, your project will save its logs to a location in DBFS. **The `databricks` environment of the PySpark Iris starter is already configured to do this.** The configuration used to achieve this can be found in the `conf/databricks/logging.yml` file. ### Package your project -To package your Kedro project for deployment on Databricks, you must create a Wheel (`.whl`) file, which is a binary distribution of your project. In the root directory of your Kedro project, run the following command: +To package your Kedro project for deployment on Databricks, you must create a Wheel (`.whl`) file, which is a binary distribution of your project. In the root directory of your Kedro projec t, run the following command: ```bash kedro package @@ -196,7 +197,7 @@ A Kedro project's configuration and data do not get included when it is packaged ## Deploy and run your Kedro project using the workspace UI -To run your packaged project on Databricks using the workspace UI, perform the following steps: +To run your packaged project on Databricks, login to your Databricks account and, perform the following steps in the workspace: 1. **Create a new job**: In the Databricks workspace, navigate to the `Worfklows` tab and click `Create Job`: @@ -220,32 +221,35 @@ The final configuration for the job cluster should look the same as the followin 3. **Configure the job**: Configure the job with the following settings: - Enter `iris-databricks` in the `Name` field. -- In the dropdown menu for the `Type` field, select 'Python wheel' +- In the dropdown menu for the `Type` field, select `Python wheel`. - In the `Package name` field, enter `iris_databricks`. This is the name of your package as defined in your project's `src/setup.py` file. - In the `Entry Point` field, enter `databricks_run`. This is the name of the [entry point](#create-an-entry-point-for-databricks) to run your package from. -- Select your cluster in the dropdown menu for the `Cluster` field. -- In the `Dependent libraries` field, click `Add` and upload the `.whl` file that you [created of your project](#package-your-project). +- Ensure the job cluster you created in step two is selected in the dropdown menu for the `Cluster` field. +- In the `Dependent libraries` field, click `Add` and upload [your project's `.whl` file](#package-your-project), making sure that the radio buttons for `Upload` and `Python Whl` are selected for the `Library Source` and `Library Type` fields. - In the `Parameters` field, enter the following list of runtime options: ```bash -["--package","iris_databricks","--conf-source","/dbfs/FileStore/iris-databricks/conf","--env","databricks"] +["--conf-source", "/dbfs/FileStore/iris-databricks/conf", "--env", "databricks", "--package-name", "iris_databricks"] ``` The final configuration for your job should look the same as the following: ![Configure Databricks job](../meta/images/databricks_configure_new_job.png) -Click `Save task` to save the configuration. +Click `Create` and then `Confirm and create` in the following pop-up asking you to name the job. -4. **Run the job**: Click `Run now` to start a run of the job. The job's status will be displayed in the `Runs` tab. Navigate to the `Runs` tab of the `Workflow` panel and view the logs and results by clicking on the job run in the list: +4. **Run the job**: Click `Run now` in the top-right corner of your new job's page to start a run of the job. The status of your run can be viewed in the `Runs` tab of your job's page. Navigate to the `Runs` tab and track the progress of your run: ![Databricks job status](../meta/images/databricks_job_status.png) -After you run the job: +This page also shows an overview of all past runs of your job. As you only just started your job run, it's status will be `Pending`. A status of `Pending` indicates that the cluster is being started and your code is waiting to run. -- The job cluster will start. -- The packaged Kedro project and all its dependencies will be installed. -- The packaged Kedro project will be run from the specified `databricks_run` entry point. +The following things happen when you run your job: + +- The job cluster will be provisioned and started (job status: `Pending`). +- The packaged Kedro project and all its dependencies will be installed (job status: `Pending`) +- The packaged Kedro project will be run from the specified `databricks_run` entry point (job status: `In Progress`). +- The packaged code will finish executing and the job cluster will be stopped (job status: `Succeeded`). 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