dataops-observability

DataOps Observability

<p style="text-align: center;">DataOps Observability is part of DataKitchen’s Open Source Data Observability. DataOps Observability monitors every data journey from data source to customer value, from any team development environment into production, across every tool, team, environment, and customer so that problems are detected, localized, and understood immediately.</p>

Developer Setup

This repository requires Python 3.10 at minimum – prefer the latest 3.10.X.

A local Kubernetes cluster requires

• docker
• helm
• minikube

Installation

Prefer using a virtual Python environment when installing. Tools such as virtualenv can be used to set up the environment using a specific Python version. pyenv can be used to install the desired Python version if your choice of OS does not provide it for you.

Example install

python -m virtualenv -p /usr/bin/python3.10 venv
source venv/bin/activate
# Install platform and developer extra packages
pip install --editable '.[dev]'

Testing

pytest is used to run test.

cd /to/observability
pytest # runs both unit and integration tests

Invoke Testing

While tests can be run with pytest <OPTIONS> there is an invoke handler to run tests using common patterns. The tests are run in parallel by default (which can help determine if there are any unexpected dependencies between tests) so that running the tests locally takes less time.

NOTE: Requires pytest-xdist package to be installed. This is specified as a dev dependency when you perform initial environment setup. If you set up your local environment before the invoke commands were added, you may need to install this package.

Commands

Command	Purpose
invoke test.all	Run all tests
invoke test.unit	Run all tests marked as unittests
invoke test.integration	Run all tests marked as integration tests

Arguments

All of the invoke text.<CMD> commands have a few common arguments you may pass.

• --level=<VALUE> [str] (DEBUG, INFO, WARNING, ERROR, CRITICAL) Set logging output level. DEFAULT: DEBUG
• --maxfail=<VALUE> [int] Maximum number of tests allowed to fail before aborting test run. DEFAULT: 10
• --processes=<VALUE> [int] Number of test processes to run in parallel. DEFAULT: 5

Example:

$ invoke test.all --processes=2 --level="INFO" --maxfail=50

Running the App

After installing the required tools, run invoke deploy.local for an initial local installation of the Observability backend. It creates a minikube node in a docker instance (i.e. in a separate logical machine) running the required infrastructure along with the Observability services. Destroy the installation with invoke deploy.nuke.

More invoke info.

Useful commands

| Command | Purpose | |———|———| | minikube ssh | SSH into minikube machine | | minikube service list | List all services and the endpoints to reach them | | minikube image build <docker build params> | Build docker image inside minikube machine | | minikube image load <image> | Push docker image from host to minikube machine |

Developer Experience

Pre-commit + Linting

We enforce the use of certain linting tools. To not get caught by the build-system’s checks, you should use pre-commit to scan your commits before they go upstream.

The following hooks are enabled in pre-commit:

• black: The black formatter is enforced on the project. We use a basic configuration. Ideally this should solve any and all formatting questions we might encounter.
• isort: the isort import-sorter is enforced on the project. We use it with the black profile.

To enable pre-commit from within your virtual environment, simply run:

pip install pre-commit
pre-commit install

Additional tools

These tools should be used by the developer because the build-system will enforce that the code complies with them. These tools are pinned in the dev extra-requirements of pyproject.toml, so you can acquire them with

# within environment
pip install .[dev]

We use the following additional tools:

• pytest: This tool is used to run the test e.g. pytest .
• mypy: This is a static and dynamic type-checking tool. This also checks for unreachable and non-returning code. See pyproject.toml for its settings. This tool is not included in pre-commit because doing so would require installing this repo’s package and additional stubs into the pre-commit environment, which is inadvised by pre-commit, and poorly supported.
• invoke (shorthand inv): This is a make replacement.
  • Run invoke --list to see available commands and e.g. invoke deploy.restart --help for additional info on command restart.
  • Shell tab completion

FAQ: mypy errors

I’ve encountered ‘Unused “type: ignore” comment’

Good news, this means that mypy has found symbols for the thing which you are ignoring. That means its time to enable type-checking on these code-paths.

To resolve this error, do two things:

1. Remove the ignore and fix any type errors.
2. run mypy . --install-types and add any newly installed types-* packages installed to our dev dependencies.

Community

Getting Started Guide

We recommend you start by going through the Data Observability Overview Demo.

Support

For support requests, join the Data Observability Slack and ask post on #support channel.

Connect

Talk and Learn with other data practitioners who are building with DataKitchen. Share knowledge, get help, and contribute to our open-source project.

Join our community here:

• 🌟 Star us on GitHub

• 🐦 Follow us on Twitter

• 🕴️ Follow us on LinkedIn

• 📺 Get Free DataOps Fundamentals Certification

• 📚 Read our blog posts

• 👋 Join us on Slack

• 🗃 Sign The DataOps Manifesto

• 🗃 Sign The Data Journey Manifesto

Contributing

For details on contributing or running the project for development, check out our contributing guide (coming soon!).

License

DataKitchen DataOps Observability is Apache 2.0 licensed.

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