When planning database capacity, we follow a systematic approach:

1. Scenario Setup → 2. Monitoring Implementation → 
3. Traffic Generation → 4. Performance Analysis → 
5. Optimization → 6. Iterative Testing

Essential database performance indicators include:

• Query execution time (P99, P95, median)
• Transactions per second (TPS)
• Connection pool utilization
• Cache hit ratio
• Disk I/O throughput
• CPU and memory consumption

Popular tools for database stress testing:

1. Sysbench (MySQL/PostgreSQL)

sysbench oltp_read_write \
--db-driver=mysql \
--mysql-host=127.0.0.1 \
--mysql-port=3306 \
--mysql-user=sbtest \
--mysql-password=password \
--mysql-db=sbtest \
--tables=10 \
--table-size=100000 \
--threads=32 \
--time=300 \
--report-interval=10 \
run

2. JMeter JDBC Testing

// JMeter JDBC Connection Configuration
jdbc:mysql://localhost:3306/testdb
username=testuser
password=testpass
Validation Query: SELECT 1
Pool Configuration:
Max Connections: 50
Timeout: 30000 ms

Rule of Thumb Calculations

For relational databases:

Required Capacity = (Current Peak Usage) × 
(Growth Factor) × 
(Safety Margin)

Where:
Growth Factor = 1.5 (typical annual growth)
Safety Margin = 1.2 (20% buffer)

Cloud Database Scaling

AWS RDS Auto Scaling example:

aws rds modify-db-instance \
--db-instance-identifier mydbinstance \
--max-allocated-storage 2000 \
--apply-immediately

Common MySQL performance bottlenecks and fixes:

# Slow query optimization
EXPLAIN ANALYZE 
SELECT * FROM orders 
WHERE customer_id = 12345 
ORDER BY order_date DESC;

# Solution
CREATE INDEX idx_customer_order_date 
ON orders(customer_id, order_date DESC);

Prometheus configuration for MySQL monitoring:

# mysqld_exporter config
[client]
user=exporter_user
password=securepassword

# Prometheus scrape config
scrape_configs:
  - job_name: 'mysql'
    static_configs:
      - targets: ['mysql-exporter:9104']

Adopt a CI/CD approach for capacity planning:

while (performance_requirements_not_met) {
    run_load_test();
    analyze_bottlenecks();
    implement_optimizations();
    verify_improvements();
}

---

When scaling database systems, two critical questions emerge:

• How much load can my current database infrastructure handle?
• When will I need to scale up/out based on projected growth?

Popular open-source tools:

# Example using sysbench for MySQL
sysbench --db-driver=mysql --mysql-host=127.0.0.1 \
--mysql-port=3306 --mysql-user=root \
--mysql-password= --mysql-db=test \
--tables=10 --table-size=100000 \
oltp_read_write --threads=8 --time=300 \
--report-interval=10 run

Category	Specific Metrics
Performance	Query latency, Throughput (QPS), Connection wait time
Resource	CPU usage, Memory pressure, Disk I/O, Network throughput
Database-specific	Lock contention, Cache hit ratio, Replication lag

Three-phase approach:

1. Baseline Establishment: Measure current performance under normal load
2. Stress Testing: Push beyond expected peak loads (120-150% of projected maximum)
3. Failure Testing: Simulate node failures and network partitions

For cloud-native databases like AWS RDS or Azure SQL:

# AWS CloudWatch metrics example
aws cloudwatch get-metric-statistics \
--namespace AWS/RDS \
--metric-name CPUUtilization \
--dimensions Name=DBInstanceIdentifier,Value=my-db-instance \
--start-time 2023-01-01T00:00:00Z \
--end-time 2023-01-02T00:00:00Z \
--period 3600 \
--statistics Average

PostgreSQL performance tuning test:

-- Create test table
CREATE TABLE load_test (
    id SERIAL PRIMARY KEY,
    data JSONB,
    created_at TIMESTAMP DEFAULT NOW()
);

-- Generate test data
INSERT INTO load_test (data)
SELECT jsonb_build_object(
    'field1', md5(random()::text),
    'field2', floor(random() * 1000)
)
FROM generate_series(1, 1000000);

• Test with production-like data volumes and distributions
• Include representative query mixes (reads vs writes)
• Simulate connection churn and connection pooling effects
• Test failover scenarios and recovery times