Database Connection Pooling: Performance and Best Practices

Introduction

Opening a database connection is expensive. A typical PostgreSQL connection takes 50-100ms to establish — time spent on TCP handshake, authentication, session initialization, and memory allocation. For a web application handling 1,000 requests per second, creating a new connection for each request would consume 50-100 seconds of CPU time per second, which is physically impossible.

Connection pooling solves this by maintaining a pool of open, reusable connections. When your application needs a database connection, it borrows one from the pool. When done, it returns the connection to the pool instead of closing it. This reduces connection overhead from 50-100ms to less than 1ms.

Yet most teams misconfigure connection pools, setting arbitrary limits that cause either connection exhaustion (requests waiting for connections) or resource waste (idle connections consuming memory). This guide covers the math, configuration patterns, and common pitfalls.

Why Connection Pooling Matters

The Cost of Database Connections

Each database connection consumes resources on both the application and database server:

Application Side:

Thread blocked waiting for connection establishment
Memory for connection object and buffers
Network socket

Database Side (PostgreSQL example):

Dedicated backend process (10-20MB memory)
Shared memory for connection state
CPU for context switching between processes

# Check PostgreSQL connection memory usage
ps aux | grep postgres | awk '{sum+=$6} END {print "Total memory: " sum/1024 " MB"}'

# With 100 connections, expect 1-2GB memory usage

Performance Impact

import time
import psycopg2

# Without pooling: create connection per request
def query_without_pool():
    start = time.time()
    conn = psycopg2.connect(
        host="localhost",
        database="mydb",
        user="user",
        password="pass"
    )
    cursor = conn.cursor()
    cursor.execute("SELECT 1")
    cursor.fetchone()
    conn.close()
    return time.time() - start

# With pooling: reuse connection
from psycopg2 import pool

connection_pool = pool.SimpleConnectionPool(
    minconn=5,
    maxconn=20,
    host="localhost",
    database="mydb",
    user="user",
    password="pass"
)

def query_with_pool():
    start = time.time()
    conn = connection_pool.getconn()
    cursor = conn.cursor()
    cursor.execute("SELECT 1")
    cursor.fetchone()
    connection_pool.putconn(conn)
    return time.time() - start

# Results:
# Without pool: 50-100ms per query
# With pool: 1-5ms per query

Connection Pool Architecture

flowchart TB
    A[Application Thread 1] -->|borrow| B[Connection Pool]
    C[Application Thread 2] -->|borrow| B
    D[Application Thread 3] -->|borrow| B
    E[Application Thread 4] -->|wait| B
    
    B -->|connection 1| F[Database]
    B -->|connection 2| F
    B -->|connection 3| F
    
    style E fill:#ff9999
    style B fill:#99ccff

Key Components:

Pool Manager: Tracks available and in-use connections
Connection Validation: Tests connections before lending them
Idle Connection Eviction: Closes connections idle too long
Connection Leak Detection: Warns when connections aren’t returned
Wait Queue: Holds threads waiting for connections

Connection Pool Sizing

The most common mistake is setting pool size too high. More connections don’t mean better performance — they mean more context switching and memory usage.

The Formula

For a single application instance:

pool_size = ((core_count * 2) + effective_spindle_count)

Example: 4-core server with SSD (spindle count = 1)

pool_size = (4 * 2) + 1 = 9 connections

For PostgreSQL specifically, a good starting point is:

pool_size = (number_of_cpu_cores * 2) + 1

Why Not More?

PostgreSQL uses a process-per-connection model. With 100 active connections on a 4-core server, the kernel spends more time context switching than executing queries.

-- Check active connections
SELECT count(*) FROM pg_stat_activity WHERE state = 'active';

-- If this number consistently exceeds (cores * 2), you have too many connections

Multi-Instance Scaling

With multiple application instances, the total connections to the database is:

total_connections = instances * pool_size_per_instance

Example: 10 application instances, 10 connections each = 100 total database connections

If your database can’t handle this, use a connection pooler like PgBouncer between applications and database.

Pool Size Recommendations by Workload

Web Application (typical): 10-20 connections for moderate traffic, 50-100 for high traffic. Use a connection pooler (PgBouncer, ProxySQL) for additional efficiency.

Batch Processing: Fewer connections with longer queries. Use 5-10 connections and process in chunks.

Real-time Applications: Many short queries require 50-100+ connections. Consider read replicas.

Serverless (AWS Lambda): Each function instance needs connections. Use RDS Proxy or similar managed solutions. Keep connections outside function code.

Application-Level Pooling

HikariCP (Java)

HikariCP is the fastest JDBC connection pool, used by default in Spring Boot 2.0+.

import com.zaxxer.hikari.HikariConfig;
import com.zaxxer.hikari.HikariDataSource;

public class DatabaseConfig {
    public static HikariDataSource createDataSource() {
        HikariConfig config = new HikariConfig();
        
        // Connection settings
        config.setJdbcUrl("jdbc:postgresql://localhost:5432/mydb");
        config.setUsername("user");
        config.setPassword("password");
        
        // Pool sizing
        config.setMaximumPoolSize(10);              // Max connections
        config.setMinimumIdle(5);                   // Min idle connections
        
        // Connection timeout
        config.setConnectionTimeout(30000);         // 30s wait for connection
        config.setIdleTimeout(600000);              // 10min idle before eviction
        config.setMaxLifetime(1800000);             // 30min max connection lifetime
        
        // Connection testing
        config.setConnectionTestQuery("SELECT 1");
        config.setValidationTimeout(5000);          // 5s validation timeout
        
        // Leak detection
        config.setLeakDetectionThreshold(60000);    // Warn if connection held >60s
        
        // Performance tuning
        config.addDataSourceProperty("cachePrepStmts", "true");
        config.addDataSourceProperty("prepStmtCacheSize", "250");
        config.addDataSourceProperty("prepStmtCacheSqlLimit", "2048");
        
        return new HikariDataSource(config);
    }
}

// Usage
public class UserRepository {
    private final HikariDataSource dataSource;
    
    public User findById(Long id) throws SQLException {
        // Borrow connection from pool
        try (Connection conn = dataSource.getConnection();
             PreparedStatement stmt = conn.prepareStatement(
                 "SELECT * FROM users WHERE id = ?")) {
            
            stmt.setLong(1, id);
            ResultSet rs = stmt.executeQuery();
            
            if (rs.next()) {
                return new User(
                    rs.getLong("id"),
                    rs.getString("name"),
                    rs.getString("email")
                );
            }
            return null;
        }
        // Connection automatically returned to pool
    }
}

Spring Boot Configuration

# application.yml
spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/mydb
    username: user
    password: password
    hikari:
      maximum-pool-size: 10
      minimum-idle: 5
      connection-timeout: 30000
      idle-timeout: 600000
      max-lifetime: 1800000
      connection-test-query: SELECT 1
      leak-detection-threshold: 60000
      
      # PostgreSQL-specific optimizations
      data-source-properties:
        cachePrepStmts: true
        prepStmtCacheSize: 250
        prepStmtCacheSqlLimit: 2048
        useServerPrepStmts: true

Python with psycopg2

from psycopg2 import pool
from contextlib import contextmanager
import logging

class DatabasePool:
    def __init__(self, minconn=5, maxconn=20):
        self.pool = pool.ThreadedConnectionPool(
            minconn=minconn,
            maxconn=maxconn,
            host="localhost",
            database="mydb",
            user="user",
            password="password",
            # Connection options
            connect_timeout=10,
            options="-c statement_timeout=30000"  # 30s query timeout
        )
        self.logger = logging.getLogger(__name__)
    
    @contextmanager
    def get_connection(self):
        """Context manager for safe connection handling"""
        conn = None
        try:
            conn = self.pool.getconn()
            yield conn
        except pool.PoolError as e:
            self.logger.error(f"Pool exhausted: {e}")
            raise
        finally:
            if conn:
                self.pool.putconn(conn)
    
    def close_all(self):
        """Close all connections in pool"""
        self.pool.closeall()

# Usage
db_pool = DatabasePool(minconn=5, maxconn=20)

def get_user(user_id):
    with db_pool.get_connection() as conn:
        with conn.cursor() as cursor:
            cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))
            return cursor.fetchone()

# Cleanup on shutdown
import atexit
atexit.register(db_pool.close_all)

Python with SQLAlchemy

from sqlalchemy import create_engine, event
from sqlalchemy.pool import QueuePool, NullPool
import logging

# Basic connection pool configuration
engine = create_engine(
    "postgresql://user:password@localhost/mydb",
    poolclass=QueuePool,
    pool_size=10,              # Regular connections
    max_overflow=20,            # Additional connections under load
    pool_timeout=30,            # Wait up to 30 seconds
    pool_recycle=3600,         # Recycle after 1 hour
    pool_pre_ping=True,        # Test connection before use
    echo=False                 # Set True for SQL debugging
)

# For high-performance scenarios
high_perf_engine = create_engine(
    "postgresql://user:password@localhost/mydb",
    poolclass=QueuePool,
    pool_size=20,
    max_overflow=30,
    pool_timeout=10,
    pool_recycle=1800,
    pool_pre_ping=True,
    max_identifier_length=256
)

# For testing (no pooling)
test_engine = create_engine(
    "postgresql://user:password@localhost/mydb",
    poolclass=NullPool       # No connection pooling
)

# Using the engine
from sqlalchemy.orm import sessionmaker

Session = sessionmaker(bind=engine)

def get_user(user_id: int) -> dict:
    session = Session()
    try:
        result = session.query(User).filter(User.id == user_id).first()
        return {"id": result.id, "name": result.name} if result else None
    finally:
        session.close()  # Returns connection to pool

# Using context manager (recommended)
def get_user_safe(user_id: int) -> dict:
    with engine.connect() as connection:
        result = connection.execute(
            text("SELECT id, name FROM users WHERE id = :id"),
            {"id": user_id}
        )
        row = result.fetchone()
        return dict(row) if row else None

Node.js with pg

const { Pool } = require('pg');

const pool = new Pool({
  host: 'localhost',
  database: 'mydb',
  user: 'user',
  password: 'password',
  
  // Pool configuration
  max: 20,                    // Maximum connections
  min: 5,                     // Minimum idle connections
  idleTimeoutMillis: 600000,  // 10min idle timeout
  connectionTimeoutMillis: 30000,  // 30s connection timeout
  
  // Connection options
  statement_timeout: 30000,   // 30s query timeout
  query_timeout: 30000,
  
  // Error handling
  allowExitOnIdle: true
});

// Connection error handling
pool.on('error', (err, client) => {
  console.error('Unexpected error on idle client', err);
  process.exit(-1);
});

// Usage with async/await
async function getUser(userId) {
  const client = await pool.connect();
  try {
    const result = await client.query(
      'SELECT * FROM users WHERE id = $1',
      [userId]
    );
    return result.rows[0];
  } finally {
    client.release();  // Return to pool
  }
}

// Or use pool.query() for simple queries
async function getUserSimple(userId) {
  const result = await pool.query(
    'SELECT * FROM users WHERE id = $1',
    [userId]
  );
  return result.rows[0];
}

// Graceful shutdown
process.on('SIGTERM', async () => {
  await pool.end();
  process.exit(0);
});

Go with pgx

package main

import (
    "context"
    "fmt"
    "os"
    
    "github.com/jackc/pgx/v5"
    "github.com/jackc/pgx/v5/pgxpool"
)

func main() {
    // Create connection pool
    config, err := pgxpool.ParseConfig("")
    if err != nil {
        fmt.Fprintf(os.Stderr, "Unable to parse config: %v\n", err)
        os.Exit(1)
    }
    
    // Pool configuration
    config.MinConns = 5
    config.MaxConns = 50
    config.MaxConnLifetime = 3600  // 1 hour
    config.MaxConnIdleTime = 600   // 10 minutes
    config.HealthCheckPeriod = 60  // Check every minute
    
    // Create pool
    pool, err := pgxpool.NewWithConfig(context.Background(), config)
    if err != nil {
        fmt.Fprintf(os.Stderr, "Unable to create pool: %v\n", err)
        os.Exit(1)
    }
    defer pool.Close()
    
    // Test connection
    if err := pool.Ping(context.Background()); err != nil {
        fmt.Fprintf(os.Stderr, "Unable to ping: %v\n", err)
        os.Exit(1)
    }
    
    // Query using pool
    var name string
    err = pool.QueryRow(context.Background(), 
        "SELECT name FROM users WHERE id = $1", 1).Scan(&name)
    if err != nil {
        fmt.Fprintf(os.Stderr, "Query failed: %v\n", err)
    }
    fmt.Println("User:", name)
}

Server-Side Pooling

PgBouncer

PgBouncer is a lightweight connection pooler that sits between your application and PostgreSQL. It’s essential for microservices architectures where many application instances connect to one database.

flowchart LR
    A[App Instance 1<br/>10 connections] --> B[PgBouncer<br/>100 client connections<br/>20 server connections]
    C[App Instance 2<br/>10 connections] --> B
    D[App Instance 3<br/>10 connections] --> B
    E[App Instance 4<br/>10 connections] --> B
    F[App Instance 5<br/>10 connections] --> B
    
    B --> G[PostgreSQL<br/>20 connections]

Configuration (pgbouncer.ini):

[databases]
mydb = host=localhost port=5432 dbname=mydb

[pgbouncer]
# Connection limits
max_client_conn = 1000        # Max client connections
default_pool_size = 20        # Connections per database
reserve_pool_size = 5         # Emergency reserve
reserve_pool_timeout = 3      # Wait 3s for reserve pool

# Pooling mode
pool_mode = transaction       # transaction | session | statement

# Connection lifetime
server_lifetime = 3600        # Close server connection after 1h
server_idle_timeout = 600     # Close idle server connection after 10min

# Authentication
auth_type = md5
auth_file = /etc/pgbouncer/userlist.txt

# Logging
log_connections = 1
log_disconnections = 1
log_pooler_errors = 1

# Performance
max_prepared_statements = 100

Pooling Modes:

Session Mode: Client gets same server connection for entire session
- Use for: Applications that use session-level features (temp tables, prepared statements)
- Pooling efficiency: Low
Transaction Mode: Server connection returned after each transaction
- Use for: Most web applications
- Pooling efficiency: High
- Limitation: Can’t use session-level features
Statement Mode: Server connection returned after each statement
- Use for: Simple read-only queries
- Pooling efficiency: Highest
- Limitation: No transactions

Deployment:

# Install PgBouncer
sudo apt-get install pgbouncer

# Start PgBouncer
pgbouncer -d /etc/pgbouncer/pgbouncer.ini

# Connect through PgBouncer (port 6432 by default)
psql -h localhost -p 6432 -U user mydb

# Monitor PgBouncer
psql -h localhost -p 6432 -U pgbouncer pgbouncer
SHOW POOLS;
SHOW STATS;
SHOW SERVERS;

pgpool-II

pgpool-II provides connection pooling plus load balancing, replication, and query caching.

# pgpool.conf
listen_addresses = '*'
port = 5432

# Connection pooling
num_init_children = 32              # Max concurrent connections
max_pool = 4                        # Connections per child process
child_life_time = 300               # Child process lifetime (5min)
connection_life_time = 0            # Connection lifetime (0 = unlimited)
client_idle_limit = 0               # Client idle timeout (0 = unlimited)

# Load balancing
load_balance_mode = on
backend_hostname0 = 'db1.example.com'
backend_port0 = 5432
backend_weight0 = 1
backend_hostname1 = 'db2.example.com'
backend_port1 = 5432
backend_weight1 = 1

# Health checking
health_check_period = 10            # Check every 10s
health_check_timeout = 20           # Timeout after 20s
health_check_max_retries = 3        # Retry 3 times

ProxySQL (MySQL)

ProxySQL provides connection pooling, load balancing, and query routing for MySQL databases:

-- Add backend
INSERT INTO mysql_servers(hostgroup_id, hostname, port) 
VALUES (1, '127.0.0.1', 3306);

-- Add user
INSERT INTO mysql_users(username, password, default_hostgroup) 
VALUES ('app_user', 'password', 1);

-- Configure connection pool
UPDATE mysql_variables 
SET variable_value='50' 
WHERE variable_name='mysql-max_connections';

-- Set pool size
UPDATE mysql_servers 
SET max_connections=50 
WHERE hostname='127.0.0.1';

Best Practices

1. Size Pools Correctly

Don’t: Set pool size to 100 “just to be safe”

# Bad: Way too many connections
hikari:
  maximum-pool-size: 100

Do: Calculate based on cores and measure

# Good: Sized for 4-core server
hikari:
  maximum-pool-size: 10
  minimum-idle: 5

2. Set Connection Timeouts

Don’t: Wait forever for a connection

// Bad: No timeout, threads block indefinitely
config.setConnectionTimeout(0);

Do: Fail fast with reasonable timeout

// Good: Fail after 30s
config.setConnectionTimeout(30000);

// Even better: Fail fast and alert
config.setConnectionTimeout(5000);  // 5s timeout

3. Validate Connections

Don’t: Assume connections are always valid

// Bad: No validation
config.setConnectionTestQuery(null);

Do: Test connections before use

// Good: Lightweight validation query
config.setConnectionTestQuery("SELECT 1");
config.setValidationTimeout(5000);

4. Detect Connection Leaks

Don’t: Let connections leak silently

// Bad: No leak detection
config.setLeakDetectionThreshold(0);

Do: Warn about long-held connections

// Good: Warn if connection held >60s
config.setLeakDetectionThreshold(60000);

// Log the stack trace of where connection was obtained
config.setRegisterMbeans(true);

5. Set Maximum Connection Lifetime

Don’t: Keep connections open forever

// Bad: Connections never recycled
config.setMaxLifetime(0);

Do: Recycle connections periodically

// Good: Recycle after 30min
config.setMaxLifetime(1800000);

// Should be less than database's connection timeout
// PostgreSQL default: 10 hours

6. Monitor Pool Metrics

import com.zaxxer.hikari.HikariPoolMXBean;
import javax.management.MBeanServer;
import javax.management.ObjectName;
import java.lang.management.ManagementFactory;

public class PoolMonitor {
    public void logPoolStats(HikariDataSource dataSource) {
        HikariPoolMXBean poolBean = dataSource.getHikariPoolMXBean();
        
        System.out.println("Active connections: " + poolBean.getActiveConnections());
        System.out.println("Idle connections: " + poolBean.getIdleConnections());
        System.out.println("Total connections: " + poolBean.getTotalConnections());
        System.out.println("Threads waiting: " + poolBean.getThreadsAwaitingConnection());
    }
}

Common Pitfalls

1. Pool Size Too Large

Problem: 50 application instances, each with 20 connections = 1,000 database connections

# Bad: Each instance has 20 connections
hikari:
  maximum-pool-size: 20

Solution: Use PgBouncer to multiplex connections

# Good: Each instance has 5 connections
hikari:
  maximum-pool-size: 5

# PgBouncer multiplexes to 50 database connections

2. Not Returning Connections

Problem: Forgetting to close connections causes pool exhaustion

// Bad: Connection never returned
public User getUser(Long id) throws SQLException {
    Connection conn = dataSource.getConnection();
    PreparedStatement stmt = conn.prepareStatement("SELECT * FROM users WHERE id = ?");
    stmt.setLong(1, id);
    ResultSet rs = stmt.executeQuery();
    // Connection leaked!
    return mapUser(rs);
}

Solution: Use try-with-resources

// Good: Connection automatically returned
public User getUser(Long id) throws SQLException {
    try (Connection conn = dataSource.getConnection();
         PreparedStatement stmt = conn.prepareStatement("SELECT * FROM users WHERE id = ?")) {
        stmt.setLong(1, id);
        ResultSet rs = stmt.executeQuery();
        return mapUser(rs);
    }
}

3. Mixing Pooling Layers

Problem: Application pool + PgBouncer + pgpool-II = confusion

App (20 connections) → PgBouncer (100 connections) → pgpool-II (50 connections) → PostgreSQL

Solution: Choose one pooling strategy

# Option 1: Application-level only (small scale)
App (10 connections) → PostgreSQL

# Option 2: Application + PgBouncer (microservices)
App (5 connections) → PgBouncer (20 connections) → PostgreSQL

# Option 3: PgBouncer only (legacy apps)
App (no pool) → PgBouncer (20 connections) → PostgreSQL

4. Ignoring Connection Validation

Problem: Stale connections cause query failures

# Bad: No validation
conn = pool.getconn()
cursor = conn.cursor()
cursor.execute("SELECT * FROM users")  # Fails if connection died

Solution: Validate before use or handle errors

# Good: Validate connection
conn = pool.getconn()
try:
    cursor = conn.cursor()
    cursor.execute("SELECT 1")  # Validate
    cursor.execute("SELECT * FROM users")
except psycopg2.OperationalError:
    # Connection dead, get new one
    pool.putconn(conn, close=True)
    conn = pool.getconn()
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM users")

5. Not Monitoring Pool Exhaustion

Problem: Pool exhaustion causes request timeouts, but you don’t know why

Solution: Export pool metrics to monitoring system

// Micrometer metrics for HikariCP
@Configuration
public class MetricsConfig {
    @Bean
    public MeterBinder hikariMetrics(HikariDataSource dataSource) {
        return new HikariDataSourceMetrics(dataSource, "hikari", Collections.emptyList());
    }
}

// Metrics available:
// - hikaricp.connections.active
// - hikaricp.connections.idle
// - hikaricp.connections.pending
// - hikaricp.connections.timeout

Testing Connection Pools

Load Testing

import concurrent.futures
import time
from database_pool import DatabasePool

def query_user(user_id):
    start = time.time()
    try:
        with db_pool.get_connection() as conn:
            with conn.cursor() as cursor:
                cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))
                cursor.fetchone()
        return time.time() - start, None
    except Exception as e:
        return time.time() - start, str(e)

# Simulate 100 concurrent requests
db_pool = DatabasePool(minconn=5, maxconn=20)

with concurrent.futures.ThreadPoolExecutor(max_workers=100) as executor:
    futures = [executor.submit(query_user, i % 1000) for i in range(100)]
    results = [f.result() for f in futures]

# Analyze results
times = [r[0] for r in results if r[1] is None]
errors = [r[1] for r in results if r[1] is not None]

print(f"Successful queries: {len(times)}")
print(f"Failed queries: {len(errors)}")
print(f"Average time: {sum(times) / len(times):.3f}s")
print(f"Max time: {max(times):.3f}s")

Serverless Considerations

AWS Lambda with RDS Proxy

import json
import os
import boto3
import pymysql

# RDS Proxy endpoint
RDS_PROXY_HOST = os.environ['RDS_PROXY_HOST']

def lambda_handler(event, context):
    try:
        connection = pymysql.connect(
            host=RDS_PROXY_HOST,
            user=os.environ['DB_USER'],
            password=os.environ['DB_PASSWORD'],
            database=os.environ['DB_NAME'],
            connect_timeout=5
        )
        
        try:
            with connection.cursor() as cursor:
                cursor.execute("SELECT * FROM users LIMIT 1")
                result = cursor.fetchone()
                return {"statusCode": 200, "body": json.dumps(result)}
        finally:
            connection.close()
            
    except Exception as e:
        return {"statusCode": 500, "body": str(e)}

Connection Management for Serverless

In serverless environments, each function invocation runs in an isolated container. Traditional connection pooling (holding connections open between invocations) doesn’t work the same way. Best practices include:

Use RDS Proxy or similar managed proxy services that maintain connection pools on your behalf
Initialize connections outside the handler to reuse across invocations within the same container
Set short timeouts to avoid hung connections during function cold starts
Monitor connection limits as serverless functions can scale rapidly

# BAD: Create new connection per invocation
def lambda_handler(event, context):
    conn = pymysql.connect(...)  # Expensive!
    # ... query ...
    conn.close()

# GOOD: Reuse connection outside handler
import pymysql

connection = None

def get_connection():
    global connection
    if connection is None or not connection.open:
        connection = pymysql.connect(...)
    return connection

Resources

HikariCP GitHub — Fast JDBC connection pool
PgBouncer Documentation — Lightweight PostgreSQL connection pooler
pgpool-II Documentation — PostgreSQL connection pooler with load balancing
PostgreSQL Connection Pooling — Official PostgreSQL connection settings
Azure: Connection Pooling Best Practices — Cloud-specific guidance

Introduction

Why Connection Pooling Matters

The Cost of Database Connections

Performance Impact

Connection Pool Architecture

Connection Pool Sizing

The Formula

Why Not More?

Multi-Instance Scaling

Pool Size Recommendations by Workload

Application-Level Pooling

HikariCP (Java)

Spring Boot Configuration

Python with psycopg2

Python with SQLAlchemy

Node.js with pg

Go with pgx

Server-Side Pooling

PgBouncer

pgpool-II

ProxySQL (MySQL)

Best Practices

1. Size Pools Correctly

2. Set Connection Timeouts

3. Validate Connections

4. Detect Connection Leaks

5. Set Maximum Connection Lifetime

6. Monitor Pool Metrics

Common Pitfalls

1. Pool Size Too Large

2. Not Returning Connections

3. Mixing Pooling Layers

4. Ignoring Connection Validation

5. Not Monitoring Pool Exhaustion

Testing Connection Pools

Load Testing

Serverless Considerations

AWS Lambda with RDS Proxy

Connection Management for Serverless

Resources

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