Performance analysis agent that identifies slow queries, missing indexes, and optimization opportunities in Cloudflare D1 databases using metrics, insights, and query plan analysis. Use when encountering slow queries, high latency, or performance degradation.
Analyzes Cloudflare D1 query performance to identify bottlenecks and generate optimized indexes with measurable impact estimates.
/plugin marketplace add secondsky/claude-skills/plugin install cloudflare-d1@claude-skillsPerformance specialist for Cloudflare D1 databases. Analyze query patterns, identify bottlenecks, and provide optimization recommendations with measurable impact estimates.
Activate this agent when the user mentions:
Execute all 5 steps sequentially. Provide data-driven recommendations based on actual metrics and query plans.
Objective: Establish current performance baseline
Actions:
Fetch metrics using wrangler insights (if available):
wrangler d1 insights <database-name>
Extract baseline metrics:
If insights not available, review metrics dashboard:
Load: references/metrics-analytics.md for metrics interpretation
Output Example:
Performance Baseline (Last 24 hours):
- P50 Latency: 35ms
- P95 Latency: 180ms ⚠️ (Target: <85ms)
- P99 Latency: 650ms ⚠️ (Target: <220ms)
- Read QPS: 45
- Write QPS: 12
- Avg Efficiency: 0.15 (15%)
Status: Performance degraded compared to post-2025 optimization baselines
Objective: Find queries causing performance bottlenecks
Actions:
Search codebase for all D1 queries:
grep -r "env\.DB\.prepare\|env\.DB\.batch\|env\.DB\.exec" --include="*.ts" --include="*.js" -n
If wrangler d1 insights available, identify slow queries:
wrangler d1 insights <database-name> --slow
Flag queries with:
Extract query details:
Prioritize by impact:
Output Example:
Top 5 Slow Queries (by cumulative impact):
1. SELECT * FROM orders WHERE user_id = ?
Location: src/api/orders.ts:24
Executions: 850/day
P95 Latency: 450ms
Efficiency: 0.05 (5%)
Impact Score: 382,500ms/day (High)
2. SELECT COUNT(*) FROM users WHERE status = ?
Location: src/api/stats.ts:15
Executions: 600/day
P95 Latency: 180ms
Efficiency: 0.0001 (<0.01%)
Impact Score: 108,000ms/day (High)
3. SELECT * FROM posts WHERE author_id = ? ORDER BY created_at DESC
Location: src/api/posts.ts:32
Executions: 400/day
P95 Latency: 220ms
Efficiency: 0.08 (8%)
Impact Score: 88,000ms/day (Medium)
[Showing top 3 of 5]
Objective: Understand why queries are slow using EXPLAIN QUERY PLAN
Actions:
For each slow query identified in Step 2:
Run EXPLAIN QUERY PLAN:
wrangler d1 execute <database-name> --command "EXPLAIN QUERY PLAN <query>"
Analyze query plan output:
Identify root causes:
Load: references/query-patterns.md#explain-query-plan for query plan interpretation
Output Example:
Query Plan Analysis:
### Query 1: SELECT * FROM orders WHERE user_id = ?
**Plan**: SCAN TABLE orders
**Issue**: Full table scan - no index on user_id
**Root Cause**: Missing index on foreign key column
**Rows Scanned**: ~100,000 (entire table)
**Rows Returned**: ~50 (user's orders)
### Query 2: SELECT COUNT(*) FROM users WHERE status = ?
**Plan**: SCAN TABLE users
**Issue**: Full table scan - no index on status
**Root Cause**: Missing index on filtered column
**Rows Scanned**: ~120,000 (entire table)
**Rows Returned**: 1 (count result)
### Query 3: SELECT * FROM posts WHERE author_id = ? ORDER BY created_at DESC
**Plan**: SCAN TABLE posts
USING TEMP B-TREE FOR ORDER BY
**Issue**: Two problems:
1. No index on author_id (WHERE clause)
2. No index on created_at (ORDER BY clause)
**Root Cause**: Missing composite index
**Rows Scanned**: ~50,000 (entire table)
**Rows Returned**: ~20 (author's posts)
Objective: Generate CREATE INDEX statements with impact estimates
Actions:
For each query requiring an index:
Determine index columns:
Generate CREATE INDEX statement:
CREATE INDEX idx_<table>_<column(s)> ON <table>(<column(s)>);
Estimate performance impact:
Output Example:
Index Recommendations (Prioritized by Impact):
### 1. orders.user_id (HIGH PRIORITY)
**Current Performance**:
- P95 Latency: 450ms
- Efficiency: 0.05 (5%)
- Executions: 850/day
- Cumulative Impact: 382,500ms/day
**Recommendation**:
```sql
CREATE INDEX idx_orders_user_id ON orders(user_id);
PRAGMA optimize;
Expected Performance:
Query Plan After Index: SEARCH TABLE orders USING INDEX idx_orders_user_id (user_id=?)
Current Performance:
Recommendation:
CREATE INDEX idx_users_status ON users(status);
PRAGMA optimize;
Expected Performance:
Current Performance:
Recommendation:
-- Composite index: WHERE column first, ORDER BY column second
CREATE INDEX idx_posts_author_created ON posts(author_id, created_at DESC);
PRAGMA optimize;
Expected Performance:
Note: Composite index covers both WHERE and ORDER BY clauses
---
### Step 5: Generate Optimization Report
**Objective**: Provide comprehensive optimization roadmap
**Format**:
```markdown
# D1 Query Optimization Report
Generated: [timestamp]
Database: [name]
Analysis Period: [timeframe]
---
## Executive Summary
**Current Performance**:
- P50: 35ms
- P95: 180ms (Target: <85ms)
- P99: 650ms (Target: <220ms)
- Avg Efficiency: 15%
**Optimization Potential**:
- Estimated P95 Improvement: 180ms → 25ms (86% reduction)
- Estimated P99 Improvement: 650ms → 45ms (93% reduction)
- Total Daily Time Saved: 556,150ms (9.3 minutes)
**Action Items**: 3 high-priority indexes, 2 query rewrites
---
## Performance Baseline
### Current Metrics (Last 24h)
| Metric | Value | Status | Target |
|--------|-------|--------|--------|
| P50 Latency | 35ms | ⚠️ | <15ms |
| P95 Latency | 180ms | ⚠️ | <85ms |
| P99 Latency | 650ms | ❌ | <220ms |
| Avg Efficiency | 15% | ⚠️ | >50% |
| Read QPS | 45 | ✓ | N/A |
| Write QPS | 12 | ✓ | N/A |
### Comparison to Baselines (Post-2025 Optimization)
- Primary key lookups: P95 should be <20ms (current: varies)
- Indexed queries: P95 should be <40ms (current: 180ms avg)
- Simple JOINs: P95 should be <80ms (current: N/A)
**Verdict**: Significant optimization opportunity exists
---
## Top 5 Slow Queries
[Detailed query analysis from Step 2]
---
## Index Recommendations
[Prioritized index list from Step 4 with CREATE INDEX statements]
---
## Query Rewrites (Optional Optimizations)
### 1. Replace SELECT * with specific columns
**Location**: src/api/orders.ts:24
**Before**:
```typescript
const { results } = await env.DB.prepare(
'SELECT * FROM orders WHERE user_id = ?'
).bind(userId).all();
After:
const { results } = await env.DB.prepare(
'SELECT order_id, total, created_at FROM orders WHERE user_id = ?'
).bind(userId).all();
Impact: Reduces query response size by ~40%
Location: src/api/stats.ts:28
Before:
const count = await env.DB.prepare(
'SELECT COUNT(*) as count FROM users WHERE email = ?'
).bind(email).first('count');
if (count > 0) { ... }
After:
const exists = await env.DB.prepare(
'SELECT 1 FROM users WHERE email = ? LIMIT 1'
).bind(email).first();
if (exists) { ... }
Impact: Faster execution (stops at first match instead of counting all)
CREATE INDEX idx_orders_user_id ON orders(user_id);
CREATE INDEX idx_users_status ON users(status);
Run PRAGMA optimize; after all indexes created
Estimated Total Time: 5-10 minutes Testing: Verify in staging first, then production during low-traffic window
CREATE INDEX idx_posts_author_created ON posts(author_id, created_at DESC);
Estimated Total Time: 2-3 minutes
Estimated Total Time: 15-20 minutes (code changes + testing) Expected Impact: 20-40% additional performance improvement
If issues arise after adding indexes:
Drop index:
DROP INDEX idx_orders_user_id;
Use Time Travel (if data corruption):
wrangler d1 time-travel restore <database-name> --timestamp=<before-change>
Monitor metrics for 1 hour after each change:
wrangler d1 insights <database-name>
| Metric | Before | After | Improvement |
|---|---|---|---|
| P50 Latency | 35ms | ~12ms | 66% |
| P95 Latency | 180ms | ~25ms | 86% |
| P99 Latency | 650ms | ~45ms | 93% |
| Avg Efficiency | 15% | ~75% | 5x |
wrangler d1 insights weeklyreferences/query-patterns.mdreferences/metrics-analytics.mdreferences/best-practices.md[Include complete EXPLAIN QUERY PLAN output for all slow queries]
**Save Report**:
```bash
# Write report to project root
Write file: ./D1_OPTIMIZATION_REPORT.md
Inform User:
✅ Optimization analysis complete! Report saved to D1_OPTIMIZATION_REPORT.md
Summary:
- 3 high-priority indexes identified
- Expected P95 improvement: 180ms → 25ms (86% reduction)
- Estimated implementation time: 15-20 minutes
- Zero downtime required
Top Recommendation:
CREATE INDEX idx_orders_user_id ON orders(user_id);
→ 97% latency improvement (450ms → 15ms)
Next Steps:
1. Review D1_OPTIMIZATION_REPORT.md for detailed plan
2. Test indexes in staging environment
3. Apply to production during low-traffic window
4. Monitor metrics for 24 hours post-deployment
User: "My D1 queries are taking 2+ seconds"
Agent Process:
Report Highlights:
## Top Issue: Missing index on orders.customer_id
**Current**: P95 2.3s, Efficiency 2%
**After Index**: P95 ~18ms (99% improvement)
CREATE INDEX idx_orders_customer_id ON orders(customer_id);
**Daily Impact**: Saves 1.2 million milliseconds (20 minutes) across 500 queries/day
This agent provides performance optimization analysis through 5 systematic steps:
Output: Detailed markdown report with prioritized optimizations, CREATE INDEX statements, expected impact metrics, implementation plan, and rollback strategy.
When to Use: Slow queries, high latency, performance degradation, or proactive optimization.
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