lake

README

DevLake

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What is DevLake?

DevLake brings your DevOps data into one practical, customized, extensible view. Ingest, analyze, and visualize data from an ever-growing list of developer tools, with our open source product.

DevLake is designed for developer teams looking to make better sense of their development process and to bring a more data-driven approach to their own practices. You can ask DevLake many questions regarding your development process. Just connect and query.

Get started with just a few clicks

Run Locally

See the Guide

User Flow

Why DevLake?

1. Comprehensive understanding of software development lifecycle, digging workflow bottlenecks
2. Timely review of team iteration performance, rapid feedback, agile adjustment
3. Quickly build scenario-based data dashboards and drill down to analyze the root cause of problems

What can be accomplished with DevLake?

1. Collect DevOps performance data for the whole process
2. Share domain layer with similar tools to output standardized engineering data
3. Built-in 20+ engineering metrics and drill-down analysis capability
4. Support custom SQL analysis and drag and drop to build scenario-based data views
5. Flexible architecture and plug-in design to support fast access to new data sources

See Demo

Click here to see demo. The demo is based on data from this repo.
Username/Password: test/test

User setup

• If you only plan to run the product locally, this is the ONLY section you should need.
• If you want to run in a cloud environment, click here.
• Commands written like this are to be run in your terminal.

Required Packages to Install

• Docker
• docker-compose

NOTE: After installing docker, you may need to run the docker application and restart your terminal

Commands to run in your terminal

IMPORTANT: DevLake doesn't support Database Schema Migration yet, upgrading an existing instance is likely to break, we recommend that you deploy a new instance instead.

1. Download docker-compose.yml and env.example from latest release page into a folder

2. Rename env.example to .env

3. Start Docker on your machine, then run docker-compose up -d to start the services.

4. Visit localhost:4000 to setup configuration files.

   • Navigate to desired plugins pages on the Integrations page
   • You will need to enter the required information for the plugins you intend to use.
   • Please reference the following for more details on how to configure each one: -> Jira -> GitLab -> Jenkins -> GitHub

   • Submit the form to update the values by clicking on the Save Connection button on each form page

   • devlake takes a while to fully boot up. if config-ui complaining about api being unreachable, please wait a few seconds and try refreshing the page.
   • To collect this repo for a quick preview, please provide a Github personal token on Data Integrations / Github page.

5. Visit localhost:4000/create-pipeline to RUN a Pipeline and trigger data collection.

   Pipelines Runs can be initiated by the new "Create Run" Interface. Simply enable the Data Source Providers you wish to run collection for, and specify the data you want to collect, for instance, Project ID for Gitlab and Repository Name for GitHub.

   Once a valid pipeline configuration has been created, press Create Run to start/run the pipeline. After the pipeline starts, you will be automatically redirected to the Pipeline Activity screen to monitor collection activity.

   Pipelines is accessible from the main menu of the config-ui for easy access.

   • Manage All Pipelines http://localhost:4000/pipelines
   • Create Pipeline RUN http://localhost:4000/create-pipeline
   • Track Pipeline Activity http://localhost:4000/pipelines/activity/[RUN_ID]

   For advanced use cases and complex pipelines, please use the Raw JSON API to manually initiate a run using cURL or graphical API tool such as Postman. POST the following request to the DevLake API Endpoint.

   [
       [
           {
               "plugin": "github",
               "options": {
                   "repo": "lake",
                   "owner": "merico-dev"
               }
           }
       ]
   ]
   

   Please refer to this wiki How to trigger data collection.

6. Click View Dashboards button in the top left when done, or visit localhost:3002 (username: admin, password: admin).

   We use Grafana as a visualization tool to build charts for the data stored in our database. Using SQL queries, we can add panels to build, save, and edit customized dashboards.

   All the details on provisioning and customizing a dashboard can be found in the Grafana Doc.

Setup cron job

To synchronize data periodically, we provide lake-cli for easily sending data collection requests along with a cron job to periodically trigger the cli tool.

Developer Setup

Requirements

• Docker
• Golang v1.17+
• Make
  • Mac (Already installed)
  • Windows: Download
  • Ubuntu: sudo apt-get install build-essential

How to setup dev environment

1. Navigate to where you would like to install this project and clone the repository:

   git clone https://github.com/merico-dev/lake.git
   cd lake
   

2. Install dependencies for plugins:

   • RefDiff

3. Install Go packages

   go get
   

4. Copy the sample config file to new local file:

   cp .env.example .env
   

5. Update the following variables in the file .env:

   • DB_URL: Replace mysql:3306 with 127.0.0.1:3306

6. Start the MySQL and Grafana containers:

   Make sure the Docker daemon is running before this step.

   docker-compose up -d mysql grafana
   

7. Run lake and config UI in dev mode in two seperate terminals:

   # run lake
   make dev
   # run config UI
   make configure-dev
   

8. Visit config UI at localhost:4000 to configure data sources.

   • Navigate to desired plugins pages on the Integrations page
   • You will need to enter the required information for the plugins you intend to use.
   • Please reference the following for more details on how to configure each one: -> Jira -> GitLab, -> Jenkins -> GitHub

   • Submit the form to update the values by clicking on the Save Connection button on each form page

9. Visit localhost:4000/create-pipeline to RUN a Pipeline and trigger data collection.

   Pipelines Runs can be initiated by the new "Create Run" Interface. Simply enable the Data Source Providers you wish to run collection for, and specify the data you want to collect, for instance, Project ID for Gitlab and Repository Name for GitHub.

   Once a valid pipeline configuration has been created, press Create Run to start/run the pipeline. After the pipeline starts, you will be automatically redirected to the Pipeline Activity screen to monitor collection activity.

   Pipelines is accessible from the main menu of the config-ui for easy access.

   • Manage All Pipelines http://localhost:4000/pipelines
   • Create Pipeline RUN http://localhost:4000/create-pipeline
   • Track Pipeline Activity http://localhost:4000/pipelines/activity/[RUN_ID]

   For advanced use cases and complex pipelines, please use the Raw JSON API to manually initiate a run using cURL or graphical API tool such as Postman. POST the following request to the DevLake API Endpoint.

   [
       [
           {
               "plugin": "github",
               "options": {
                   "repo": "lake",
                   "owner": "merico-dev"
               }
           }
       ]
   ]
   

   Please refer to this wiki How to trigger data collection.

10. Click View Dashboards button in the top left when done, or visit localhost:3002 (username: admin, password: admin).

    We use Grafana as a visualization tool to build charts for the data stored in our database. Using SQL queries, we can add panels to build, save, and edit customized dashboards.

    All the details on provisioning and customizing a dashboard can be found in the Grafana Doc.

11. (Optional) To run the tests:

    make test
    

Project Roadmap

• Roadmap 2022: Detailed project roadmaps for 2022.
• DevLake already supported following data sources:
  • Jira(Cloud)
  • Git
  • GitHub
  • GitLab(Cloud)
  • Jenkins
• Supported engineering metrics: provide rich perspectives to observe and analyze SDLC.

Make Contribution

This section lists all the documents to help you contribute to the repo.

• Architecture: Architecture of DevLake
• Data Model: Domain Layer Schema
• Add a Plugin: Guide to add a plugin
• Add metrics: Guide to add metrics in a plugin
• Contribution guidelines: Start from here if you want to make contribution

Community

• Discord: Message us on Discord
• FAQ: Frequently Asked Questions

License

This project is licensed under Apache License 2.0 - see the LICENSE file for details.