SQLWRITER
Role: Database Engineer
1. Problem: Performance Bottleneck in Alerts API
Business Context
The system tracks developer activity in real-time through heartbeats - signals sent from IDE extensions every few seconds during coding. In an environment with dozens or hundreds of active developers, this generates thousands of database write operations per hour.
Problem Identification
The initial architecture assumed that the Alerts API (main web application) would handle both:
✅ Business logic (alerts, pulses, analytics)
❌ Heavy heartbeat write operations to MySQL
Consequences of Synchronous Writes:
┌─────────────────────────────────────────────────────────────┐
│ BEFORE: Synchronous Model (Alerts API) │
└─────────────────────────────────────────────────────────────┘
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│
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┌─────────────────────────┐
│ Alerts API │
│ (main thread) │
│ │
│ 1. Receive heartbeat │ ◄─── Blocks thread
│ 2. Validation │ ◄─── Blocks thread
│ 3. INSERT to MySQL │ ◄─── SLOW I/O OPERATION (50-200ms)
│ 4. Process alerts │ ◄─── Blocks thread
│ 5. Return response │
└─────────────────────────┘
⚠️ ISSUES:
- Each heartbeat blocks the thread for 50-200ms
- 100 developers × 10 heartbeats/min = 1000 req/min
- Average response time: 150ms × 1000 = 2.5 minutes CPU time/min
- Risk of timeouts under heavy load
- Alerts API cannot scale horizontally (DB bottleneck)2. Solution: Asynchronous Consumer Pattern
Event-Driven Architecture
HB SQL Writer implements the Consumer pattern (Message Queue Pattern) which:
┌─────────────────────────────────────────────────────────────┐
│ AFTER: Asynchronous Model (Event-Driven) │
└─────────────────────────────────────────────────────────────┘
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│
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┌─────────────────────────┐
│ Google Pub/Sub │ ◄─── Message Queue (buffer)
│ (Topic: heartbeats) │ - Guaranteed delivery
└────────┬────────────────┘ - Retry mechanism
│ - Backpressure handling
│ (asynchronous triggers)
│
▼
┌─────────────────────────┐
│ HB SQL Writer │ ◄─── DEDICATED CONSUMER
│ (Cloud Run) │
│ │
│ Specialization: │
│ Receive from Pub/Sub │ (instant, non-blocking)
│ Data validation │ (< 5ms)
│ INSERT to MySQL │ (50-200ms, but doesn't block API)
│ Webhook to Alerts │ (async, fire-and-forget)
└─────────────────────────┘
│
│ (after write)
▼
┌─────────────────────────┐
│ Alerts API │ ◄─── LIGHTWEIGHT WEBHOOK
│ /api/alerts/ │ - Only alert logic
│ process-heartbeat │ - Doesn't wait for DB write
│ │ - Can scale freely
│ Process alerts │
│ Update pulses │
└─────────────────────────┘3. Performance Benefits
3.1 Main API Offloading
API Response Time
150-200ms
5-10ms
95% faster
API Throughput
~100 req/s
~1000 req/s
10x more
CPU Usage (API)
80-90%
20-30%
70% less
Timeout Risk
High
Minimal
Scalability
DB Limited
Horizontal
3.2 Preventing Thread Blocking
// BEFORE: Synchronous write (blocks thread)
app.post('/api/heartbeats', async (req, res) => {
const heartbeat = req.body;
// Blocks thread for 50-200ms
await db.query('INSERT INTO heartbeats ...', heartbeat);
// Subsequent operations wait
await processAlerts(heartbeat);
res.send('OK');
});
// AFTER: Asynchronous consumer (doesn't block API)
// HB SQL Writer - dedicated process
app.post('/ingest', async (req, res) => {
const heartbeat = decodePubSubMessage(req.body);
// Dedicated process, doesn't affect API
await db.query('INSERT INTO heartbeats ...', heartbeat);
// Fire-and-forget webhook (async)
triggerAlertProcessing(heartbeat).catch(err => {
console.error('Webhook failed, but heartbeat saved');
});
res.send('OK'); // Immediate response
});3.3 Backpressure Handling
Google Pub/Sub automatically manages load:
Scenario: 1000 heartbeats/second
┌─────────────────────────┐
│ Pub/Sub Topic │
│ (message buffer) │
│ │
│ Queues messages │
│ Retries on errors │
│ Dead Letter Queue │
└────────┬────────────────┘
│
│ (controlled rate)
▼
┌─────────────────────────┐
│ HB SQL Writer │
│ (autoscaling) │
│ │
│ Instances: 1-10 │ ◄─── Auto-scales
│ Processes: 100/s/inst │
└─────────────────────────┘
Results:
- No data loss (Pub/Sub guarantees delivery)
- Automatic scaling under load
- Graceful degradation (slower but stable)4. Technical Implementation
4.1 Technology Stack
// package.json
{
"name": "hb-sql-writer",
"dependencies": {
"express": "^4.18.2", // Lightweight HTTP server
"mysql2": "^3.9.4", // MySQL driver (promise-based)
"dotenv": "^16.3.1" // Environment configuration
}
}4.2 Key Components
A. Endpoint /ingest - Pub/Sub Consumer
/ingest - Pub/Sub Consumerapp.post('/ingest', async (req, res) => {
try {
// 1. Decode Pub/Sub message (base64)
const dataBase64 = req.body?.message?.data;
const jsonStr = Buffer.from(dataBase64, 'base64').toString('utf-8');
const hb = JSON.parse(jsonStr);
// 2. Data validation and normalization
const values = [
hb.user_id,
convertTimestamp(hb.ts),
hb.project ?? null,
hb.entity ?? null,
validateBranch(hb.branch),
validateUrl(hb.repository_url),
// ... additional fields
];
// 3. Database INSERT (connection pool)
const sql = `INSERT INTO heartbeats (...) VALUES (...)`;
await pool.query(sql, values);
// 4. Asynchronous webhook (fire-and-forget)
triggerAlertProcessing(hb).catch(err => {
console.error('Webhook failed, but heartbeat saved');
});
res.status(200).send('Heartbeat saved');
} catch (err) {
console.error('Error:', err);
res.status(500).send('Error');
}
});B. Connection Pool - DB Optimization
const pool = mysql.createPool({
host: process.env.DB_HOST,
user: process.env.DB_USER,
password: process.env.DB_PASS,
database: process.env.DB_NAME,
// Optimizations (mysql2 default values):
// connectionLimit: 10 // Max connections in pool
// queueLimit: 0 // Unlimited queue
// waitForConnections: true // Wait for free connection
// enableKeepAlive: true // Keep-alive for long connections
});
// Benefits:
// Connection reuse (no TCP handshake overhead)
// Automatic connection management
// Graceful handling under loadC. Data Validation - Preventing DB Errors
The system implements robust data validation including:
String length validation to prevent database errors
Timestamp normalization for consistent storage format
URL validation with reasonable length limits
Null handling for optional fields
This ensures data integrity and prevents common database insertion errors.
4.3 Database Optimization
The system uses strategic database indexing for optimal performance:
Branch indexing for fast filtering by development branch
URL indexing with prefix optimization for text fields
Composite indexes for common query patterns
Benefits:
Fast queries filtering by branch or repository
Efficient joins with related tables
Optimized GROUP BY operations
5. Data Flow (End-to-End)
Webhook → Main API
4b. Webhook (async)
POST https://main-app.../api/alerts/process-heartbeat
Fire-and-forget (doesn't block write)
Timeout: 5s
Errors logged, don't fail main process
Main API
Selects team (smart team selection)
Updates task activity
Updates pulse status
Triggers relevant alerts and notifications
Alerts generated
6. Monitoring and Observability
6.1 Key Metrics
// Logs in HB SQL Writer
console.log('Connected to database');
console.log('Parsed heartbeat:', hb);
console.log('Executing SQL INSERT with values:', values);
console.log('SQL insert result:', result);
console.log('Triggering alert processing');
console.log('Alert processing triggered successfully');
// Error logs
console.error('Error saving heartbeat:', err);
console.error('Alert webhook error:', error.message);6.2 Metrics to Monitor
INSERT latency
< 200ms
> 500ms
Message queue age
< 10s
> 60s
Error rate
< 1%
> 5%
Webhook success rate
> 95%
< 90%
DB connection pool
< 80%
> 90%
Container instances
1-5
> 10 (check load)
6.3 Diagnostic Commands
# Check service logs
cloud-service logs read sql-writer-service \
--region=your-region \
--limit=50
# Check message queue metrics
cloud-service subscriptions describe writer-subscription \
--format="table(ackDeadlineSeconds, messageRetentionDuration)"
# Check DB connections
mysql> SHOW PROCESSLIST;
mysql> SHOW STATUS LIKE 'Threads_connected';7. Scalability and Performance
7.1 Autoscaling Configuration
The service is configured for automatic scaling:
Minimum instances: 1 (prevents cold starts)
Maximum instances: 10 (handles peak load)
Concurrency: 80 concurrent requests per instance
Estimated throughput: ~5,300 heartbeats/second at full scale
7.2 Database Optimizations
-- Partitioning the heartbeats table (for large volumes)
ALTER TABLE heartbeats
PARTITION BY RANGE (YEAR(ts)) (
PARTITION p2024 VALUES LESS THAN (2025),
PARTITION p2025 VALUES LESS THAN (2026),
PARTITION p2026 VALUES LESS THAN (2027)
);
-- Benefits:
-- ✅ Faster queries (query pruning)
-- ✅ Easier archiving of old data
-- ✅ Smaller indexes (per partition)7.3 Caching Strategy (Future Enhancement)
// Potential optimization: Batch INSERT
const batchBuffer = [];
const BATCH_SIZE = 100;
const BATCH_TIMEOUT = 5000; // 5s
function addToBatch(heartbeat) {
batchBuffer.push(heartbeat);
if (batchBuffer.length >= BATCH_SIZE) {
flushBatch();
}
}
async function flushBatch() {
if (batchBuffer.length === 0) return;
const sql = 'INSERT INTO heartbeats (...) VALUES ?';
await pool.query(sql, [batchBuffer.map(hb => [/* values */])]);
batchBuffer.length = 0;
}
// Benefits:
// ✅ 100 INSERTs → 1 batch INSERT (10x faster)
// ✅ Lower DB load
// ⚠️ Trade-off: Up to 5s delay (acceptable for heartbeats)8. Security and Reliability
8.1 Webhook Authentication
The system implements secure communication between the SQL Writer and the main API using:
Shared secret authentication via environment variables
Request timeout protection (5 second limit)
Header-based verification to ensure authorized requests
This ensures that only legitimate heartbeat processing requests are accepted by the main application.
8.2 Error Handling
The system implements robust error handling with graceful degradation:
Database failures: Automatic retry mechanism via message queue
Webhook failures: Non-blocking - heartbeat data is preserved even if alert processing fails
Connection issues: Connection pooling with automatic reconnection
Timeout protection: All external calls have defined timeout limits
This ensures high availability and data integrity even during partial system failures.
8.3 Dead Letter Queue
The system uses a Dead Letter Queue (DLQ) pattern for handling persistent failures:
Maximum retry attempts: 5 attempts before moving to DLQ
Failure isolation: Failed messages don't block processing of valid messages
Manual recovery: Failed messages can be analyzed and reprocessed manually
System stability: Prevents infinite retry loops that could overwhelm the system
This ensures that temporary issues don't cause permanent data loss while maintaining system performance.
9. Summary: Why HB SQL Writer Exists
Problem
The main API must be fast and responsive for users. Heavy heartbeat write operations (50-200ms each) were blocking the main thread, causing:
Slow API response times
Risk of timeouts under heavy load
Inability to scale horizontally
Solution
HB SQL Writer operates as an asynchronous Consumer that:
Retrieves raw heartbeats from message queue
Executes INSERT/UPDATE operations in a dedicated process
Triggers webhook to main API (fire-and-forget)
Benefits
✅ Offloads main API - response time from 150ms → 5ms (95% faster) ✅ Prevents thread blocking - DB operations in separate process ✅ Scales independently - autoscaling container instances (1-10) ✅ Guarantees delivery - message queue retry + Dead Letter Queue ✅ Graceful degradation - slower but stable under overload
Event-Driven Architecture
Client → Message Queue → HB SQL Writer → Database
↓
Webhook (async)
↓
Main API10. Future Optimizations (Roadmap)
Short-term
Long-term
Author: Database Engineer Date: 2025-11-30 Version: 1.0