Index tuning

docs/PRDs/210-index-tuning.md から始め、そこに記載されている既存のデータベーススキーマ、インデックス、重要なクエリを確認してください。インデックスの変更を提案する前に、EXPLAIN と ANALYZE を使用してベースラインを確立してください。クエリパフォーマンスを 20% 向上させる目標を、書き込みに大きな性能低下を生じさせずに実証し、変更を文書化し、段階的なロールアウトの基準を完了すれば完了です。

📋 Product Requirements Document

PRD: Index tuning

Issue: #210
Milestone: Phase 10: Polish & Launch
Labels: performance-optimization, hacktoberfest

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PRD: Index Tuning for MeshHook Performance Optimization

Overview

The objective of this Product Requirements Document (PRD) is to provide a comprehensive plan for optimizing the Postgres database indexes used by MeshHook, a webhook-first, deterministic, Postgres-native workflow engine. This optimization aims to significantly improve the efficiency and speed of database queries, which is critical for sustaining the high performance of MeshHook's features, including webhook triggers, visual DAG builders, and durable, replayable runs. The task is identified under issue #210 and is a part of the Phase 10: Polish & Launch milestone.

Purpose

The purpose of the index tuning task is to enhance MeshHook's database query performance by identifying and optimizing inefficient indexes. This will ensure MeshHook can handle increased loads without compromising on response times, thereby maintaining a smooth and efficient user experience.

Alignment with Project Goals

This task directly supports MeshHook's core objectives by:

• Enhancing the performance and scalability of the system, particularly in processing webhooks and executing workflows.
• Preserving the integrity and efficiency of durable, replayable runs.
• Ensuring the robustness of the multi-tenant RLS security model through optimized data access paths.

Functional Requirements

1. Performance Analysis: Use tools like EXPLAIN and ANALYZE to identify slow-performing queries that could benefit from index optimization.
2. Index Optimization Plan: Develop a comprehensive plan for index creation and modification to improve query performance while minimizing the impact on write operations.
3. Implementation and Evaluation: Apply index changes in a controlled environment and conduct thorough testing to evaluate the improvements in performance and any potential negative impacts.
4. Documentation and Deployment: Fully document the index optimization process, including the logic behind each change, and outline a strategy for a safe, incremental rollout of these changes to the production environment.

Non-Functional Requirements

• Performance: Demonstrate a measurable improvement in the performance of database queries, with a target of reducing query times by at least 20% without significantly impacting write operations.
• Reliability: Ensure that the index optimizations do not negatively affect the reliability of the system or the integrity of the data.
• Security: Maintain MeshHook's security standards, ensuring that index optimizations do not compromise data isolation or the effectiveness of row-level security (RLS) policies.
• Maintainability: Ensure that the database schema, including the new and modified indexes, remains manageable, well-documented, and easily understandable.

Technical Specifications

Architecture Context

MeshHook leverages a Postgres database hosted on Supabase, utilizing features such as Realtime subscriptions and RLS. Index tuning must be approached with an understanding of this architecture and the specific workload patterns of MeshHook, focusing on areas like webhook processing, workflow execution tracking, and log streaming.

Implementation Approach

1. Pre-Analysis Phase: Audit existing queries, especially those critical to core functionalities like webhook processing, using EXPLAIN and ANALYZE to identify performance bottlenecks.
2. Index Planning: Based on the analysis, identify columns and tables where new indexes or modifications could yield performance improvements. Prioritize changes that offer the most significant impact with the least overhead.
3. Testing in Development Environment: Implement the proposed index changes in a development environment. Use a mix of synthetic and real-world data to benchmark performance improvements.
4. Phased Rollout: Gradually deploy the optimized indexes to the production environment, beginning with a limited rollout to monitor impact before proceeding to full deployment.

Data Model

The index tuning process is not expected to alter the underlying data model but will involve adjustments to the database schema related to index definitions. These changes will be thoroughly documented.

API Endpoints

N/A

Acceptance Criteria

• Benchmark tests demonstrate at least a 20% improvement in query performance for identified bottlenecks.
• No significant degradation in write performance is observed as a result of the new or modified indexes.
• Documentation accurately reflects all index changes, including the reasoning and expected impact on performance.
• The phased rollout of index changes is completed without critical issues or regressions in system behavior.
• Final approval is obtained from the MeshHook development team.

Dependencies and Prerequisites

• Access to a testing environment that closely mimics the production setup for accurate benchmarking.
• A comprehensive list of critical queries impacting MeshHook's performance for targeted optimization.
• Existing documentation on the database schema and indexes to serve as a reference.

Implementation Notes

Development Guidelines

• Follow Postgres best practices for index creation, considering the appropriate index type (e.g., B-tree, GIN, GiST) for each use case.
• Document the performance benchmarks and any observed impacts on database resources for each index modification.
• Manage all changes through version control, ensuring a clear history of modifications and an easy path to rollback if necessary.

Testing Strategy

• Conduct detailed performance testing, including both micro-benchmarks for individual queries and macro-benchmarks to assess overall system performance.
• Watch for potential regressions in write performance or system responsiveness.

Security Considerations

• Assess the potential impact of index changes on RLS policies and data access patterns, ensuring no compromise to security standards.
• Ensure that the optimization process adheres to MeshHook's security guidelines, maintaining data isolation and integrity.

Monitoring & Observability

• Enhance monitoring tools to track the performance of optimized queries, establishing baselines and alerting for significant deviations.
• Monitor system performance closely following the rollout of index changes to detect and address any unforeseen issues promptly.

Related Documentation

• Database Schema Documentation: For reference and comparison before and after index optimizations.
• MeshHook Performance Benchmarks: To establish performance baselines and quantify improvements.
• Security Guidelines: To ensure all changes comply with established security practices.

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #210
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 210-index-tuning.md
• 🎨 PlantUML Diagram: 210-index-tuning.puml
• 🖼️ Diagram Image: 210-index-tuning.png

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This issue body was auto-generated from the PRD. Original issue content is preserved in the PRD document.
Last updated: 2025-10-10