[FEATURE]: Integration with External Analysis Platforms

[helabenkhalfallah]

✨ Description

This feature expands Vitality's capabilities by integrating with popular external analysis platforms: SonarQube, Content Square Speed Analysis, Sentry, and Datadog. This integration will provide developers with a centralized view of project data and insights from various sources, enabling a more comprehensive understanding of application quality, performance, and user experience.

🚀 Motivation

Consolidate data and insights from different analysis tools into a single platform, providing a holistic view of project health.

📝 Proposed Solution

• Establish API connections with SonarQube, Content Square Speed Analysis, Sentry, and Datadog to retrieve relevant data and metrics.
• Develop mechanisms to synchronize data between Vitality and the external platforms, ensuring data consistency and accuracy.
• Transform and map data from the external platforms to align with Vitality's data models and schemas.
• Process reports.
• Identify recommendations and keywords.
• Store reports.
• Update BFF schemas and resolvers.
• Frontend Visualisation.

🔗 Relevant Links (if any)

• SonarQube: https://www.sonarqube.org/
• Content Square: https://contentsquare.com/
• Sentry: https://sentry.io/welcome/
• Datadog: https://www.datadoghq.com/

[maelaubert56]

SonarQube

• Type: Code quality and security analysis.
• Purpose: Detects bugs, vulnerabilities, and code smells.
• Use case: Improve maintainability and enforce coding standards.

Content Square Speed Analysis

• Type: Web performance optimization.
• Purpose: Measures page load speed and user interactions.
• Use case: Enhance user experience and increase conversions.

Sentry

• Type: Error and performance monitoring.
• Purpose: Tracks errors and performance issues in applications.
• Use case: Debug and resolve user-impacting problems.

Datadog

• Type: Infrastructure and application monitoring.
• Purpose: Monitors metrics, logs, and traces in real-time.
• Use case: Ensure system reliability and detect anomalies.

[maelaubert56]

SonarQube API Overview

1. Key Features of the SonarQube API

• Project Management: Create, update, or delete projects.
• Metrics Retrieval: Access quality metrics like code coverage, bugs, or code smells.
• User Management: Manage users and permissions programmatically.
• Analysis Triggering: Automate analysis tasks from CI/CD pipelines.
• Integration with Repositories: Link repositories (e.g., GitLab) for automated analysis.

---

2. General Steps to Use the SonarQube API

1. Base URL: Access the API via your SonarQube server's base URL:

   http://<sonarqube-server>/api
   

2. Authentication: Use a personal access token (PAT) for secure communication. Generate a token under My Account > Security in the SonarQube UI. Pass it as part of the request headers or via Basic Authentication (token:).

3. Sending Requests: Use tools like curl, Postman, or integrate API calls in your CI/CD pipeline scripts.

---

3. Key Endpoints

a. Create a New Project

• Endpoint: /api/projects/create
• Method: POST
• Parameters:
  • name: The name of the project.
  • project: A unique key for the project.
• Example Workflow:
  • Use this endpoint to register a new project in SonarQube before running an analysis.

b. Configure GitLab Integration for a Project

• Endpoint: /api/alm_settings/set_gitlab_binding
• Method: POST
• Parameters:
  • project: The unique key of the SonarQube project.
  • almSetting: The GitLab ALM (Application Lifecycle Management) configuration ID.
  • repository: The GitLab repository path (e.g., group-name/project-name).
• Workflow:
  • Link the SonarQube project with the corresponding GitLab repository.
  • Ensure that a GitLab ALM integration is already configured in SonarQube (Administration > ALM Integrations).

c. Trigger Analysis in CI/CD

• Tool: Use the sonar-scanner CLI or the GitLab CI/CD integration.
• Steps:
  1. Add sonar-scanner to your pipeline configuration (e.g., .gitlab-ci.yml).
  2. Pass the project key and authentication token.
  3. After code push, the analysis is triggered, and results are uploaded to SonarQube.

d. Retrieve Project Metrics

• Endpoint: /api/measures/component
• Method: GET
• Parameters:
  • component: The project key.
  • metricKeys: Comma-separated list of metrics (e.g., bugs,coverage,code_smells).
• Workflow:
  • Fetch key quality metrics for reporting or gating builds in CI/CD.

---

4. Pricing : (SaaS or Self-Hosted)

Option	Cost	Infrastructure	Best Use Case
SonarCloud (SaaS)	Free (public) / Paid (private, starts at €10/month for 100k LOC)	None	Small teams
SonarQube Community (Self-Hosted)	Free	Requires server and database	Teams
SonarQube Commercial (Self-Hosted)	Paid (€150/year for Developer Edition; starts at €20k/year for Enterprise)	Dedicated performant server	Enterprises

[maelaubert56]

Content Square Speed Analysis API Overview

1. Key Features of the API

• Performance Metrics: Retrieve metrics like page load time, first contentful paint, and time to interactive.
• User Interaction Tracking: Understand how performance impacts user behaviors.
• Custom Reporting: Generate performance reports for pages or groups of pages.
• Event-Based Data: Collect insights on specific events (e.g., clicks, scrolls).
• API Integration: Seamless integration with CI/CD pipelines for automated performance testing.

---

2. General Steps to Use the API

1. Base URL: Access the API through the Content Square platform. The base URL is typically provided during account setup, e.g.:

   https://api.contentsquare.net/speed-analysis
   

2. Authentication:

   • Use API keys or tokens for secure access. These are usually generated via the Content Square dashboard under the "API Keys" section.
   • Include the token in the Authorization header of your requests.

3. Sending Requests:

   • Use REST tools like curl or integrate the API into your automation scripts.

---

3. Key Endpoints

a. Analyze Page Performance

• Endpoint: /analyze
• Method: POST
• Parameters:
  • url: The URL of the webpage to analyze.
  • settings: Optional configurations, such as device type or network conditions.
• Workflow:
  • Submit a page URL for performance analysis and receive a report on metrics like load time and interactivity.

b. Retrieve Performance Metrics

• Endpoint: /metrics
• Method: GET
• Parameters:
  • page_id: Identifier for the analyzed page.
• Workflow:
  • Fetch detailed metrics for a previously analyzed page, such as first paint, speed index, and interaction timings.

c. Grouped Page Reports

• Endpoint: /reports/grouped
• Method: GET
• Parameters:
  • group: A set of related pages (e.g., all pages in a section of your site).
• Workflow:
  • Generate aggregated reports to compare performance across multiple pages.

d. User Event Analysis

• Endpoint: /events
• Method: GET
• Parameters:
  • page_id: Identifier for the analyzed page.
  • event_type: The type of user event (e.g., click, scroll).
• Workflow:
  • Understand how performance affects user interactions.

---

4. Pricing

Plan	Cost	Features	Best Use Case
Free	0€	Up to 5k sessions/month, session replay, unlimited heatmaps, basic filters, integrations (e.g., Google Analytics)	Small projects
Growth	€49/month	Free plan features + 7k+ sessions, funnels, frustration scores, advanced filtering, 15+ integrations, impact quantification	Small-to-medium businesses focusing on engagement.
Pro	Custom pricing	Growth plan features + 1M+ sessions, journey analysis, zone-based heatmaps, revenue impact prioritization, 115+ integrations, retroactive precision filtering	Businesses requiring deep insights and large data
Enterprise	Custom pricing	Pro plan features + real-time error alerts, speed/error analysis, digital experience monitoring, session replay with network detail, unlimited projects in a region.	Enterprises seeking comprehensive optimization.

[maelaubert56]

Sentry API Overview

1. Key Features of the Sentry API

• Error Tracking: Automatically report and manage exceptions in real-time.
• Performance Monitoring: Analyze transaction traces and identify bottlenecks.
• Project Management: Automate project creation and configuration.
• GitLab Integration: Link repositories to track commits, releases, and associate errors with code changes.
• Alerts and Workflows: Automate notifications and integrate with external tools like Slack or PagerDuty.

---

2. General Steps to Use the API

1. Base URL: https://sentry.io/api/0
2. Authentication:
   • Generate an API token in Sentry and use it in the Authorization header:

     Authorization: Bearer <your-token>
     

3. Request Methods: Send requests using tools like curl, Postman, or CI/CD scripts.

---

3. Key Endpoints

a. Create a New Project

• Endpoint: /organizations/{organization_slug}/projects/
• Method: POST
• Purpose: Automate project creation to onboard new applications or services.

b. Capture Events (Log Errors)

• Endpoint: /projects/{organization_slug}/{project_slug}/events/
• Method: POST
• Purpose: Manually report exceptions or custom events.

c. Fetch Issues

• Endpoint: /projects/{organization_slug}/{project_slug}/issues/
• Method: GET
• Purpose: Retrieve unresolved or newly reported issues for debugging.

d. Link GitLab Repositories

• Endpoint: /organizations/{organization_slug}/repos/
• Method: POST
• Purpose: Automate the integration of GitLab repositories with Sentry for tracking commits and releases.
• Parameters:
  • provider: "integrations:gitlab"
  • name: "group-name/repo-name"
• Workflow:
  1. Use the GitLab API to fetch repositories (/projects endpoint).
  2. Automate repository addition using a loop to send POST requests for each repository.

---

4. Practical Applications

• Error Reporting: Capture exceptions and link them to specific commits.
• Release Tracking: Monitor the impact of new releases with GitLab commits and tags.
• Performance Monitoring: Identify slow transactions and optimize critical paths.
• Automated GitLab Integration: Automatically link new GitLab repositories to Sentry for comprehensive tracking, enabling a seamless workflow for developers.

[maelaubert56]

Datadog API Overview

1. Key Features of the Datadog API

• Metric Collection: Send custom metrics and monitor performance in real-time.
• Log Management: Index and search application logs efficiently.
• Dashboards & Alerts: Automate the creation of dashboards and alert conditions.
• Infrastructure Monitoring: Track servers, containers, and cloud environments.
• GitLab Integration: Automatically link GitLab repositories to enhance observability workflows.

---

2. General Steps to Use the API

1. Base URL: https://api.datadoghq.com/api/v1/
2. Authentication:
   • Use your API and Application keys in the headers:

     DD-API-KEY: <your-api-key>
     DD-APPLICATION-KEY: <your-app-key>
     

3. Request Methods: Use tools like curl, Postman, or integrate into CI/CD pipelines.

---

3. Key Endpoints

a. Send Custom Metrics

• Endpoint: /series
• Method: POST
• Purpose: Submit application metrics (e.g., response times, error counts).
• Payload Example:

  {
    "series": [
      {
        "metric": "custom.metric.name",
        "points": [[<timestamp>, <value>]],
        "tags": ["env:prod", "app:myapp"],
        "type": "gauge"
      }
    ]
  }

b. Create Dashboards

• Endpoint: /dashboard
• Method: POST
• Purpose: Automate the creation of dashboards for visualizing key metrics.

c. Set Up Monitors (Alerts)

• Endpoint: /monitor
• Method: POST
• Purpose: Create alerts for specific conditions like high CPU usage or error spikes.

d. Log Management

• Endpoint: /logs/events
• Method: POST
• Purpose: Submit application or system logs for indexing and analysis.

e. Automate GitLab Integration

• Workflow:
  • Use the GitLab API to list projects.
    • Endpoint: /projects
    • Method: GET
    • Include the GitLab API token in the PRIVATE-TOKEN header.
  • Configure Datadog monitoring for these repositories using the Datadog API.

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4. Automating GitLab Integration with Datadog

Step 1: Fetch GitLab Repositories

Use the GitLab API to retrieve a list of available repositories:

• Endpoint: /projects
• Method: GET
• Purpose: Retrieve project information like repository names and URLs.

Step 2: Automate Monitoring Setup in Datadog

For each repository:

1. Set Up Metrics Collection: Use the /series endpoint to submit metrics from CI/CD pipelines.
2. Configure Logs: Use /logs/events to send logs generated during pipeline execution.
3. Create Dashboards: Automatically create dashboards with repository-specific metrics using the /dashboard endpoint.

Example Use Case

• Automate monitoring for all new GitLab repositories by integrating with Datadog via a script that:
  1. Fetches GitLab repositories dynamically.
  2. Sets up dashboards and alerts for each repository’s pipelines or deployments.

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5. Practical Applications

• End-to-End Monitoring: Track application performance, log errors, and monitor infrastructure health.
• Custom Metrics: Analyze specific KPIs, such as build times or deployment success rates.
• GitLab CI/CD Integration: Automate Datadog setup for GitLab repositories to monitor pipeline execution, deployments, and related logs.
• Scalable Monitoring: Automatically onboard new repositories or environments into Datadog for consistent observability.