feat: Major directory reorganization and cleanup
Reorganized project structure for better maintainability and reduced disk usage by 95.9% (11 GB -> 451 MB). Directory Reorganization (85% reduction in root files): - Created docs/ with subdirectories (deployment, testing, database, etc.) - Created infrastructure/vpn-configs/ for VPN scripts - Moved 90+ files from root to organized locations - Archived obsolete documentation (context system, offline mode, zombie debugging) - Moved all test files to tests/ directory - Root directory: 119 files -> 18 files Disk Cleanup (10.55 GB recovered): - Deleted Rust build artifacts: 9.6 GB (target/ directories) - Deleted Python virtual environments: 161 MB (venv/ directories) - Deleted Python cache: 50 KB (__pycache__/) New Structure: - docs/ - All documentation organized by category - docs/archives/ - Obsolete but preserved documentation - infrastructure/ - VPN configs and SSH setup - tests/ - All test files consolidated - logs/ - Ready for future logs Benefits: - Cleaner root directory (18 vs 119 files) - Logical organization of documentation - 95.9% disk space reduction - Faster navigation and discovery - Better portability (build artifacts excluded) Build artifacts can be regenerated: - Rust: cargo build --release (5-15 min per project) - Python: pip install -r requirements.txt (2-3 min) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
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docs/database/DATABASE_PERFORMANCE_ANALYSIS.md
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# Database Performance Analysis & Optimization
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**Database:** MariaDB 10.6.22 @ 172.16.3.30:3306
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**Table:** `conversation_contexts`
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**Current Records:** 710+
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**Date:** 2026-01-18
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---
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## Current Schema Analysis
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### Existing Indexes ✅
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```sql
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-- Primary key index (automatic)
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PRIMARY KEY (id)
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-- Foreign key indexes
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idx_conversation_contexts_session (session_id)
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idx_conversation_contexts_project (project_id)
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idx_conversation_contexts_machine (machine_id)
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-- Query optimization indexes
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idx_conversation_contexts_type (context_type)
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idx_conversation_contexts_relevance (relevance_score)
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-- Timestamp indexes (from TimestampMixin)
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created_at
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updated_at
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```
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**Performance:** GOOD
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- Foreign key lookups: Fast (indexed)
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- Type filtering: Fast (indexed)
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- Relevance sorting: Fast (indexed)
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---
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## Missing Optimizations ⚠️
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### 1. Full-Text Search Index
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**Current State:**
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- `dense_summary` field is TEXT (searchable but slow)
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- No full-text index
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- Search uses LIKE queries (table scan)
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**Problem:**
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```sql
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SELECT * FROM conversation_contexts
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WHERE dense_summary LIKE '%dataforth%'
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-- Result: FULL TABLE SCAN (slow on 710+ records)
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```
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**Solution:**
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```sql
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-- Add full-text index
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ALTER TABLE conversation_contexts
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ADD FULLTEXT INDEX idx_fulltext_summary (dense_summary);
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-- Use full-text search
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SELECT * FROM conversation_contexts
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WHERE MATCH(dense_summary) AGAINST('dataforth' IN BOOLEAN MODE);
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-- Result: INDEX SCAN (fast)
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```
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**Expected Improvement:** 10-100x faster searches
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### 2. Tag Search Optimization
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**Current State:**
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- `tags` stored as JSON string: `"[\"tag1\", \"tag2\"]"`
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- No JSON index (MariaDB 10.6 supports JSON)
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- Tag search requires JSON parsing
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**Problem:**
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```sql
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SELECT * FROM conversation_contexts
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WHERE JSON_CONTAINS(tags, '"dataforth"')
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-- Result: Function call on every row (slow)
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```
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**Solutions:**
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**Option A: Virtual Column + Index**
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```sql
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-- Create virtual column for first 5 tags
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ALTER TABLE conversation_contexts
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ADD COLUMN tags_text VARCHAR(500) AS (
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SUBSTRING_INDEX(SUBSTRING_INDEX(tags, ',', 5), '[', -1)
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) VIRTUAL;
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-- Add index
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CREATE INDEX idx_tags_text ON conversation_contexts(tags_text);
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```
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**Option B: Separate Tags Table (Best)**
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```sql
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-- New table structure
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CREATE TABLE context_tags (
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id VARCHAR(36) PRIMARY KEY,
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context_id VARCHAR(36) NOT NULL,
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tag VARCHAR(100) NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (context_id) REFERENCES conversation_contexts(id) ON DELETE CASCADE,
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INDEX idx_context_tags_tag (tag),
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INDEX idx_context_tags_context (context_id)
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);
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-- Query becomes fast
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SELECT cc.* FROM conversation_contexts cc
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JOIN context_tags ct ON ct.context_id = cc.id
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WHERE ct.tag = 'dataforth';
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-- Result: INDEX SCAN (very fast)
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```
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**Recommended:** Option B (separate table)
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**Rationale:** Enables multi-tag queries, tag autocomplete, tag statistics
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### 3. Title Search Index
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**Current State:**
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- `title` is VARCHAR(200)
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- No text index for prefix search
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**Problem:**
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```sql
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SELECT * FROM conversation_contexts
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WHERE title LIKE '%Dataforth%'
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-- Result: FULL TABLE SCAN
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```
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**Solution:**
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```sql
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-- Add prefix index for LIKE queries
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CREATE INDEX idx_title_prefix ON conversation_contexts(title(50));
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-- For full-text search
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ALTER TABLE conversation_contexts
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ADD FULLTEXT INDEX idx_fulltext_title (title);
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```
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**Expected Improvement:** 50x faster title searches
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### 4. Composite Indexes for Common Queries
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**Common Query Patterns:**
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```sql
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-- Pattern 1: Project + Type + Relevance
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SELECT * FROM conversation_contexts
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WHERE project_id = 'uuid'
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AND context_type = 'checkpoint'
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ORDER BY relevance_score DESC;
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-- Needs composite index
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CREATE INDEX idx_project_type_relevance
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ON conversation_contexts(project_id, context_type, relevance_score DESC);
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-- Pattern 2: Type + Relevance + Created
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SELECT * FROM conversation_contexts
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WHERE context_type = 'session_summary'
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ORDER BY relevance_score DESC, created_at DESC
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LIMIT 10;
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-- Needs composite index
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CREATE INDEX idx_type_relevance_created
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ON conversation_contexts(context_type, relevance_score DESC, created_at DESC);
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```
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---
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## Recommended Schema Changes
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### Phase 1: Quick Wins (10 minutes)
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```sql
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-- 1. Add full-text search indexes
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ALTER TABLE conversation_contexts
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ADD FULLTEXT INDEX idx_fulltext_summary (dense_summary);
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ALTER TABLE conversation_contexts
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ADD FULLTEXT INDEX idx_fulltext_title (title);
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-- 2. Add composite indexes for common queries
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CREATE INDEX idx_project_type_relevance
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ON conversation_contexts(project_id, context_type, relevance_score DESC);
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CREATE INDEX idx_type_relevance_created
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ON conversation_contexts(context_type, relevance_score DESC, created_at DESC);
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-- 3. Add prefix index for title
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CREATE INDEX idx_title_prefix ON conversation_contexts(title(50));
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```
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**Expected Improvement:** 10-50x faster queries
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### Phase 2: Tag Normalization (1 hour)
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```sql
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-- 1. Create tags table
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CREATE TABLE context_tags (
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id VARCHAR(36) PRIMARY KEY DEFAULT (UUID()),
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context_id VARCHAR(36) NOT NULL,
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tag VARCHAR(100) NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (context_id) REFERENCES conversation_contexts(id) ON DELETE CASCADE,
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INDEX idx_context_tags_tag (tag),
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INDEX idx_context_tags_context (context_id),
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UNIQUE KEY unique_context_tag (context_id, tag)
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) ENGINE=InnoDB;
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-- 2. Migrate existing tags (Python script needed)
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-- Extract tags from JSON strings and insert into context_tags
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-- 3. Optionally remove tags column from conversation_contexts
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-- (Keep for backwards compatibility initially)
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```
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**Expected Improvement:** 100x faster tag queries, enables tag analytics
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### Phase 3: Search Optimization (2 hours)
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```sql
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-- 1. Create materialized search view
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CREATE TABLE conversation_contexts_search AS
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SELECT
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id,
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title,
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dense_summary,
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context_type,
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relevance_score,
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created_at,
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CONCAT_WS(' ', title, dense_summary, tags) AS search_text
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FROM conversation_contexts;
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-- 2. Add full-text index on combined text
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ALTER TABLE conversation_contexts_search
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ADD FULLTEXT INDEX idx_fulltext_search (search_text);
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-- 3. Keep synchronized with triggers (or rebuild periodically)
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```
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**Expected Improvement:** Single query for all text search
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---
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## Query Optimization Examples
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### Before Optimization
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```sql
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-- Slow query (table scan)
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SELECT * FROM conversation_contexts
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WHERE dense_summary LIKE '%dataforth%'
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OR title LIKE '%dataforth%'
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OR tags LIKE '%dataforth%'
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ORDER BY relevance_score DESC
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LIMIT 10;
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-- Execution time: ~500ms on 710 records
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-- Problem: 3 LIKE queries, no indexes
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```
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### After Optimization
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```sql
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-- Fast query (index scan)
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SELECT cc.* FROM conversation_contexts cc
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LEFT JOIN context_tags ct ON ct.context_id = cc.id
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WHERE (
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MATCH(cc.title, cc.dense_summary) AGAINST('dataforth' IN BOOLEAN MODE)
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OR ct.tag = 'dataforth'
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)
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GROUP BY cc.id
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ORDER BY cc.relevance_score DESC
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LIMIT 10;
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-- Execution time: ~5ms on 710 records
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-- Improvement: 100x faster
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```
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---
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## Storage Efficiency
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### Current Storage
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```sql
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-- Check current table size
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SELECT
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table_name AS 'Table',
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ROUND(((data_length + index_length) / 1024 / 1024), 2) AS 'Size (MB)'
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FROM information_schema.TABLES
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WHERE table_schema = 'claudetools'
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AND table_name = 'conversation_contexts';
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```
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**Estimated:** ~50MB for 710 contexts (avg ~70KB per context)
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### Compression Opportunities
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**1. Text Compression**
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- `dense_summary` contains compressed summaries but not binary compressed
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- Consider COMPRESS() function for large summaries
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```sql
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-- Store compressed
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UPDATE conversation_contexts
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SET dense_summary = COMPRESS(dense_summary)
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WHERE LENGTH(dense_summary) > 5000;
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-- Retrieve decompressed
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SELECT UNCOMPRESS(dense_summary) FROM conversation_contexts;
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```
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**Savings:** 50-70% on large summaries
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**2. JSON Optimization**
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- Current: `tags` as JSON string (overhead)
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- Alternative: Normalized tags table (more efficient)
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**Savings:** 30-40% on tags storage
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---
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## Partitioning Strategy (Future)
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For databases with >10,000 contexts:
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```sql
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-- Partition by creation date (monthly)
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ALTER TABLE conversation_contexts
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PARTITION BY RANGE (UNIX_TIMESTAMP(created_at)) (
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PARTITION p202601 VALUES LESS THAN (UNIX_TIMESTAMP('2026-02-01')),
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PARTITION p202602 VALUES LESS THAN (UNIX_TIMESTAMP('2026-03-01')),
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PARTITION p202603 VALUES LESS THAN (UNIX_TIMESTAMP('2026-04-01')),
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-- Add partitions as needed
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PARTITION pmax VALUES LESS THAN MAXVALUE
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);
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```
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|
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**Benefits:**
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- Faster queries on recent data
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- Easier archival of old data
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- Better maintenance (optimize specific partitions)
|
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|
||||
---
|
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|
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## API Endpoint Optimization
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|
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### Current Recall Endpoint Issues
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**Problem:** `/api/conversation-contexts/recall` returns empty or errors
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|
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**Investigation Needed:**
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|
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1. **Check API Implementation**
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```python
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# api/routers/conversation_contexts.py
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# Verify recall() function uses proper SQL
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```
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2. **Enable Query Logging**
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```sql
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-- Enable general log to see actual queries
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SET GLOBAL general_log = 'ON';
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SET GLOBAL log_output = 'TABLE';
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|
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-- View queries
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SELECT * FROM mysql.general_log
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WHERE command_type = 'Query'
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AND argument LIKE '%conversation_contexts%'
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ORDER BY event_time DESC
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LIMIT 20;
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```
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3. **Check for SQL Errors**
|
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```sql
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-- View error log
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SELECT * FROM performance_schema.error_log
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WHERE error_code != 0
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ORDER BY logged DESC
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LIMIT 10;
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```
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|
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### Recommended Fix
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|
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```python
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# api/services/conversation_context_service.py
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|
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async def recall_context(
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search_term: Optional[str] = None,
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tags: Optional[List[str]] = None,
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project_id: Optional[str] = None,
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limit: int = 10
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):
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query = select(ConversationContext)
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# Use full-text search if available
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if search_term:
|
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query = query.where(
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or_(
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func.match(ConversationContext.title, ConversationContext.dense_summary)
|
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.against(search_term, mariadb.dialect().match_boolean_mode()),
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ConversationContext.title.like(f"%{search_term}%")
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)
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)
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# Tag filtering via join
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if tags:
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query = query.join(ContextTag).where(ContextTag.tag.in_(tags))
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|
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# Project filtering
|
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if project_id:
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query = query.where(ConversationContext.project_id == project_id)
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|
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# Order by relevance
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query = query.order_by(desc(ConversationContext.relevance_score))
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query = query.limit(limit)
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|
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return await session.execute(query)
|
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```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Priority
|
||||
|
||||
### Immediate (Do Now)
|
||||
|
||||
1. ✅ **Add full-text indexes** - 5 minutes, 10-100x improvement
|
||||
2. ✅ **Add composite indexes** - 5 minutes, 5-10x improvement
|
||||
3. ⚠️ **Fix recall API** - 30 minutes, enables search functionality
|
||||
|
||||
### Short Term (This Week)
|
||||
|
||||
4. **Create context_tags table** - 1 hour, 100x tag query improvement
|
||||
5. **Migrate existing tags** - 30 minutes, one-time data migration
|
||||
6. **Add prefix indexes** - 5 minutes, 50x title search improvement
|
||||
|
||||
### Long Term (This Month)
|
||||
|
||||
7. **Implement compression** - 2 hours, 50-70% storage savings
|
||||
8. **Create search view** - 2 hours, unified search interface
|
||||
9. **Add partitioning** - 4 hours, future-proofing for scale
|
||||
|
||||
---
|
||||
|
||||
## Monitoring & Metrics
|
||||
|
||||
### Queries to Monitor
|
||||
|
||||
```sql
|
||||
-- 1. Average query time
|
||||
SELECT
|
||||
ROUND(AVG(query_time), 4) AS avg_seconds,
|
||||
COUNT(*) AS query_count
|
||||
FROM mysql.slow_log
|
||||
WHERE sql_text LIKE '%conversation_contexts%'
|
||||
AND query_time > 0.1;
|
||||
|
||||
-- 2. Most expensive queries
|
||||
SELECT
|
||||
sql_text,
|
||||
query_time,
|
||||
rows_examined
|
||||
FROM mysql.slow_log
|
||||
WHERE sql_text LIKE '%conversation_contexts%'
|
||||
ORDER BY query_time DESC
|
||||
LIMIT 10;
|
||||
|
||||
-- 3. Index usage
|
||||
SELECT
|
||||
object_schema,
|
||||
object_name,
|
||||
index_name,
|
||||
count_read,
|
||||
count_fetch
|
||||
FROM performance_schema.table_io_waits_summary_by_index_usage
|
||||
WHERE object_schema = 'claudetools'
|
||||
AND object_name = 'conversation_contexts';
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Expected Results After Optimization
|
||||
|
||||
| Metric | Before | After | Improvement |
|
||||
|--------|--------|-------|-------------|
|
||||
| Text search time | 500ms | 5ms | 100x faster |
|
||||
| Tag search time | 300ms | 3ms | 100x faster |
|
||||
| Title search time | 200ms | 4ms | 50x faster |
|
||||
| Complex query time | 1000ms | 20ms | 50x faster |
|
||||
| Storage size | 50MB | 30MB | 40% reduction |
|
||||
| Index overhead | 10MB | 25MB | Acceptable |
|
||||
|
||||
---
|
||||
|
||||
## SQL Migration Script
|
||||
|
||||
```sql
|
||||
-- Run this script to apply Phase 1 optimizations
|
||||
|
||||
USE claudetools;
|
||||
|
||||
-- 1. Add full-text search indexes
|
||||
ALTER TABLE conversation_contexts
|
||||
ADD FULLTEXT INDEX idx_fulltext_summary (dense_summary),
|
||||
ADD FULLTEXT INDEX idx_fulltext_title (title);
|
||||
|
||||
-- 2. Add composite indexes
|
||||
CREATE INDEX idx_project_type_relevance
|
||||
ON conversation_contexts(project_id, context_type, relevance_score DESC);
|
||||
|
||||
CREATE INDEX idx_type_relevance_created
|
||||
ON conversation_contexts(context_type, relevance_score DESC, created_at DESC);
|
||||
|
||||
-- 3. Add title prefix index
|
||||
CREATE INDEX idx_title_prefix ON conversation_contexts(title(50));
|
||||
|
||||
-- 4. Analyze table to update statistics
|
||||
ANALYZE TABLE conversation_contexts;
|
||||
|
||||
-- Verify indexes
|
||||
SHOW INDEX FROM conversation_contexts;
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
**Generated:** 2026-01-18
|
||||
**Status:** READY FOR IMPLEMENTATION
|
||||
**Priority:** HIGH - Fixes slow search, enables full functionality
|
||||
**Estimated Time:** Phase 1: 10 minutes, Full: 4 hours
|
||||
Reference in New Issue
Block a user