zabbix/zabbix-graphql-api
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zabbix-graphql-api/roadmap.md
Andreas Hilbig 97a0f70fd6 feat(query-optimization): implement GraphQL query optimization and enhance regression suite 
- **Optimization**: Implemented automatic Zabbix parameter optimization by analyzing GraphQL selection sets.

- **ZabbixRequest**: Added optimizeZabbixParams with support for skippable parameters and implied field dependencies (e.g., state -> items).

- **Resolvers**: Updated allHosts, allDevices, allHostGroups, and templates to pass requested fields to data sources.

- **Data Sources**: Optimized ZabbixQueryHostsGenericRequest and ZabbixQueryTemplatesRequest to skip unnecessary Zabbix API calls.

- **Regression Tests**: Enhanced RegressionTestExecutor with new tests for optimization (REG-OPT, REG-OPT-NEG), state retrieval (REG-STATE), dependent items (REG-DEP), and empty results (REG-EMPTY).

- **Documentation**: Created query_optimization.md How-To guide and updated roadmap.md, README.md, and tests.md.

- **Bug Fixes**: Fixed deviceType tag assignment during host import and corrected ZabbixCreateHostRequest to support tags.
2026-02-02 06:23:35 +01:00

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🗺️ Roadmap

This document outlines the achieved milestones and planned enhancements for the Zabbix GraphQL API project.

✅ Achieved Milestones

  • 🎯 VCR Product Integration: Developed a specialized GraphQL API as part of the VCR Product to enable the use of Zabbix as a robust base for monitoring and controlling IoT devices.

    • First use case: Control of mobile traffic jam warning installations on German Autobahns.

  • 🔓 Open Source Extraction & AI Integration: Extracted the core functionality of the API to publish it as an Open Source project.

    • AI Integration: Enhanced with Model Context Protocol (MCP) and AI agent integration to enable workflow and agent-supported use cases.

📅 Planned Enhancements

  • ⚡ Query Optimization: Optimize GraphQL API queries to reduce the amount of data fetched from Zabbix depending on the fields really requested and improve performance.

  • 🏗️ Trade Fair Logistics Use Case: Extend the API to support trade fair logistics use cases by analyzing requirements from business stakeholders.

    • Analysis: Analysis of "Trade Fair Logistics" and derived requirements document.
    • Simulation:
      • Create mocked "real world sensor devices" relevant for the use case.
      • Create a sample device collecting relevant information from public APIs, e.g. weather conditions or traffic conditions at a given location.
      • Simulate the traffic conditions on the route by using the simulated sensor devices.
    • Configuration: Analyze a real-world transport and configure Zabbix by placing sensor devices at the right places of the route.

  • 📦 CI/CD & Package Publishing: Build and publish the API as an npm package as part of the .forgejo workflow to simplify distribution and updates.

  • 🧱 Flexible Usage: Transform the package into a versatile tool that can be used either standalone or as a core library (published to npmjs.com).

  • 🧩 Extension Project: Add support for problem and trigger-related queries through the zabbix-graphql-api-problems extension.

    • AI Integration: Leverage MCP + AI agents to automatically react to Zabbix problems within external workflows.