PostgreSQL: FATAL Error – Dealing with ‘Too Many Clients’

The “Too Many Clients” issue in PostgreSQL occurs when the number of concurrent client connections exceeds the configured maximum limit. When this happens, the PostgreSQL server is unable to accept any new connections, leading to the org.postgresql.util.PSQLException: FATAL error. This error message indicates that the database server has reached its maximum capacity for handling client connections.

Resolving the “Too Many Clients” issue is crucial for ensuring the stability, performance, and availability of your PostgreSQL database. If left unaddressed, this issue can lead to severe performance degradation, application failures, and even downtime. By understanding and implementing appropriate strategies to handle excessive client connections, you can improve the scalability, reliability, and overall user experience of your application.

By the end of this tutorial, you will have a comprehensive understanding of the “Too Many Clients” issue in PostgreSQL and a range of practical methods to resolve it. You will be equipped with the knowledge and tools necessary to optimize connection management, enhance application code, and ensure the smooth operation of your PostgreSQL database.

Understanding the “Too Many Clients” Issue

When encountering the “Too Many Clients” issue in PostgreSQL, it typically manifests as the org.postgresql.util.PSQLException with a FATAL error message. This error occurs when the number of concurrent connections to the PostgreSQL database exceeds the maximum allowed limit.

The implications of this error can be severe, as it hampers the ability of your application to establish new connections and interact with the database. Consequently, your application may experience degraded performance, increased response times, or even complete failure in extreme cases.

Several factors can contribute to the occurrence of the “Too Many Clients” issue. Understanding these factors is crucial for effectively addressing the problem. Let’s explore some common causes:

  1. Limited Database Connections: PostgreSQL, like many databases, enforces a maximum limit on the number of concurrent connections that can be established. This limit is defined by the max_connections configuration parameter. When the number of active connections surpasses this limit, the “Too Many Clients” error occurs. This limitation can stem from inadequate configuration or resource constraints on the database server.
  2. Inefficient Connection Management: Inefficient handling of database connections within your application code can also lead to the “Too Many Clients” issue. For example:
    // Inefficient connection management example
    public void performDatabaseOperation() {
        Connection connection = null;
        try {
            // Obtain a new connection for each operation
            connection = DriverManager.getConnection("jdbc:postgresql://localhost:5432/mydatabase", "username", "password");
            // Perform database operations
        } catch (SQLException e) {
            // Handle exceptions
        } finally {
            // Close the connection (if not using connection pooling)
            if (connection != null) {
                try {
                } catch (SQLException e) {
                    // Handle exceptions

    In the above example, a new connection is created for each database operation, but connections are not efficiently released. Over time, this can result in an accumulation of open connections, exceeding the database’s capacity.

To mitigate the “Too Many Clients” issue, we need to address these factors by implementing appropriate solutions. The subsequent sections of this tutorial will delve into various methods to resolve this issue, including optimizing connection pooling, adjusting database connection settings, enhancing application code, implementing load balancing and scaling techniques, as well as monitoring and troubleshooting strategies.

Method 1: Optimizing Connection Pooling

Connection pooling is a technique that involves creating a pool of pre-established database connections that can be reused by multiple clients. This approach helps mitigate the “Too Many Clients” issue in PostgreSQL by efficiently managing and reusing connections instead of creating a new connection for each client request.

Utilizing connection pool libraries simplifies the implementation of connection pooling in your Java application. Two popular connection pool libraries for PostgreSQL are HikariCP and Apache Commons DBCP. These libraries provide robust features, excellent performance, and seamless integration with the PostgreSQL database.

HikariCP example (Maven dependency):


Apache Commons DBCP example (Maven dependency):


To configure and manage connection pooling in PostgreSQL, follow these steps:

  1. Instantiate the connection pool: Depending on the connection pool library used, you’ll need to configure the pool parameters such as maximum pool size, minimum idle connections, and connection timeout. Here’s an example using HikariCP:
    HikariConfig config = new HikariConfig();
    HikariDataSource dataSource = new HikariDataSource(config);
  2. Obtain connections from the pool: Once the connection pool is set up, you can obtain connections from it as needed in your application. Here’s an example:
    try (Connection connection = dataSource.getConnection()) {
        // Use the connection for database operations
    } catch (SQLException e) {
        // Handle connection acquisition or usage errors
  3. Release connections back to the pool: After using a connection, it’s important to release it back to the pool so that it can be reused by other clients. This is typically done by closing the connection object:
  4. Properly shut down the connection pool: When your application terminates, it’s crucial to shut down the connection pool gracefully to release any held resources. Here’s an example:

By optimizing connection pooling in PostgreSQL using libraries like HikariCP or Apache Commons DBCP, you can effectively manage database connections, improve performance, and mitigate the “Too Many Clients” issue.

Method 2: Adjusting Database Connection Settings

In PostgreSQL, the max_connections configuration parameter determines the maximum number of concurrent connections allowed to the database server. By default, PostgreSQL sets this value to 100. However, for scenarios with higher client loads, it may be necessary to increase this limit.

To adjust the max_connections parameter, follow these steps:

  1. Locate the PostgreSQL configuration file postgresql.conf. The exact location of this file varies depending on your operating system and installation method. Common locations include /etc/postgresql/{version}/main/postgresql.conf on Linux systems or C:\Program Files\PostgreSQL\{version}\data\postgresql.conf on Windows.
  2. Open the postgresql.conf file in a text editor.
  3. Search for the max_connections parameter. It is usually listed under the “Connections and Authentication” section.
  4. Modify the value of max_connections to the desired number of concurrent connections. For example, if you want to increase the limit to 200, change the line to:
    max_connections = 200
  5. Save the postgresql.conf file.
  6. Restart the PostgreSQL server for the changes to take effect. On Linux, you can typically restart the server using the command:
    sudo service postgresql restart

When adjusting connection settings, there are a few important considerations:

  1. Resource Usage: Increasing the max_connections value consumes more system resources, such as memory and CPU. Ensure that your server has sufficient resources to handle the increased connections.
  2. System Limitations: The maximum number of connections allowed is constrained by your hardware, operating system, and PostgreSQL version. It’s crucial to review these limitations and set an appropriate value for max_connections accordingly.
  3. Connection Pooling: Adjusting the max_connections parameter may not be sufficient to handle high client loads efficiently. Consider implementing connection pooling techniques, such as using a connection pool library, to optimize connection management and minimize resource usage.

By adjusting the database connection settings, specifically the max_connections parameter, you can effectively handle increased client loads in PostgreSQL. However, be mindful of the trade-offs and considerations mentioned above to ensure optimal performance and resource utilization.

Method 3: Enhancing Application Code

Implementing proper connection management practices can help alleviate the “Too Many Clients” issue. Consider the following recommendations:

  1. Closing Connections Promptly: Ensure that connections are closed promptly after use. Failing to close connections can lead to resource leaks and exhaust the available connection slots. Use try-with-resources or explicitly close connections in a finally block to guarantee closure.
    Connection connection = null;
    Statement statement = null;
    ResultSet resultSet = null;
    try {
        connection = DriverManager.getConnection("jdbc:postgresql://localhost:5432/mydatabase", "username", "password");
        statement = connection.createStatement();
        resultSet = statement.executeQuery("SELECT * FROM users");
        // Process the result set
    } catch (SQLException e) {
        // Handle exceptions
    } finally {
        // Close resources in reverse order
        if (resultSet != null) {
        if (statement != null) {
        if (connection != null) {
  2. Connection Pooling Integration: If you are using a connection pool library, ensure that you configure it properly and integrate it seamlessly with your application code. This enables efficient connection acquisition and release, reducing the chances of exceeding the maximum connection limit.

Code Examples for Efficient Connection Handling

Consider the following code examples that demonstrate efficient connection handling techniques:

  1. Connection Reuse Example:
    // Assuming the 'connection' object is obtained from the connection pool
    try (Statement statement = connection.createStatement();
         ResultSet resultSet = statement.executeQuery("SELECT * FROM customers")) {
        // Process the result set
    } catch (SQLException e) {
        // Handle exceptions
  2. Connection Pooling with HikariCP Example:
    // Assuming HikariCP is properly configured and initialized
    try (Connection connection = dataSource.getConnection();
         Statement statement = connection.createStatement();
         ResultSet resultSet = statement.executeQuery("SELECT * FROM products")) {
        // Process the result set
    } catch (SQLException e) {
        // Handle exceptions

By implementing these techniques and following best practices, you can optimize connection usage in your application code, mitigating the “Too Many Clients” issue and ensuring efficient handling of database connections.

Method 4: Load Balancing and Scaling

Load balancing is a technique that distributes incoming client requests across multiple servers to achieve better performance, improved availability, and efficient resource utilization. By implementing load balancing, we can mitigate the “Too Many Clients” issue in PostgreSQL by distributing the client load across multiple database instances.

This allows each instance to handle a fraction of the total client connections, reducing the strain on individual servers and preventing connection saturation.

Different load balancing strategies

  1. Round-robin load balancing: In round-robin load balancing, client requests are distributed sequentially across a pool of PostgreSQL servers. Each new request is routed to the next server in line, cycling through the available servers in a circular manner. This strategy ensures that client connections are evenly distributed among the servers, promoting balanced resource utilization.
    Example code using Apache HTTP Server’s mod_proxy_balancer module for round-robin load balancing in an HTTP-based environment:

    <Proxy balancer://mycluster>
        BalancerMember http://postgres-server1:5432
        BalancerMember http://postgres-server2:5432
        BalancerMember http://postgres-server3:5432
        # Add more BalancerMember entries for additional servers
        ProxySet lbmethod=byrequests
    ProxyPass /myapp balancer://mycluster/myapp
    ProxyPassReverse /myapp balancer://mycluster/myapp
  2. Weighted load balancing: Weighted load balancing assigns a weight or priority to each server in the pool based on its capabilities or capacity. Servers with higher weights receive a larger proportion of client requests, enabling us to allocate more resources to robust servers. This strategy is particularly useful when servers have varying capacities or when specific servers need to handle heavier workloads.
    Example code using NGINX for weighted load balancing in a TCP-based environment:

    upstream mycluster {
        server postgres-server1:5432 weight=3;
        server postgres-server2:5432 weight=2;
        server postgres-server3:5432 weight=1;
        # Add more server entries with appropriate weights
    server {
        listen 5432;
        location / {
            proxy_pass http://mycluster;

Scaling approaches

  1. Vertical scaling: Vertical scaling, also known as scaling up, involves increasing the resources (e.g., CPU, memory, disk space) of an individual PostgreSQL server. This can be achieved by upgrading the hardware or modifying the server’s configuration. By vertically scaling the server, it can handle a larger number of client connections, thereby mitigating the “Too Many Clients” issue.
  2. Horizontal scaling: Horizontal scaling, also known as scaling out, involves adding more PostgreSQL servers to the infrastructure. This approach distributes the client load across multiple servers, allowing each server to handle a fraction of the total connections. Horizontal scaling not only helps alleviate the “Too Many Clients” issue but also improves overall system performance and provides fault tolerance.
    Example code for horizontal scaling using a database connection pool library like HikariCP:

    HikariConfig config = new HikariConfig();
    config.setMaximumPoolSize(20); // Adjust the maximum pool size based on the desired scalability
    HikariDataSource dataSource = new HikariDataSource(config);

By implementing load balancing strategies and employing vertical or horizontal scaling approaches, we can effectively address the “Too Many Clients” issue in PostgreSQL. These techniques help distribute the client load, optimize resource utilization, and ensure a more scalable and resilient database infrastructure.

Method 5: Monitoring and Troubleshooting

Monitoring PostgreSQL connections is crucial for identifying and resolving connection-related issues. By monitoring connections, you can gain insights into the overall health and performance of your PostgreSQL database.

Tools and Techniques for Monitoring Connection Usage and Identifying Bottlenecks

To effectively monitor PostgreSQL connections, consider utilizing the following tools and techniques:

  1. PostgreSQL’s Built-in System Views: PostgreSQL provides several system views that offer valuable information about connections. The two commonly used views are:
    • pg_stat_activity: This view displays information about the currently active connections, including the query being executed, the user, and the application name.
    • pg_stat_replication: If you’re using replication in your PostgreSQL setup, this view provides details about the replication connections, such as the replication lag and synchronization status.
  2. External Monitoring Tools: There are third-party monitoring tools available that provide more advanced features for monitoring PostgreSQL connections. Some popular options include:
    • pg_stat_monitor: This open-source extension offers detailed real-time monitoring of PostgreSQL connections and query performance. It provides useful metrics like connection count, transaction rates, and query execution times.
    • DataDog: A comprehensive monitoring platform that supports PostgreSQL monitoring. It offers features like real-time dashboards, alerts, and anomaly detection for monitoring connection usage and performance.
  3. Logging and Log Analysis: Enabling detailed logging in PostgreSQL can help you troubleshoot connection issues. By examining the log files, you can identify potential bottlenecks, errors, and warnings related to connections. Tools like pgBadger can parse and analyze PostgreSQL logs, providing insights into connection patterns, query performance, and potential issues.

Common Troubleshooting Steps for Resolving Connection-Related Issues

When encountering connection-related issues in PostgreSQL, consider the following troubleshooting steps:

  1. Check Connection Pool Configuration: Verify that the connection pool configuration, if you are using one, is properly set up. Ensure that the pool size is appropriate for handling the expected number of connections, and that connections are being released and returned to the pool correctly.
  2. Identify Resource Limitations: Review system resources such as CPU, memory, and disk I/O. Insufficient resources can cause connection issues. Monitor resource usage during peak times and consider scaling up the infrastructure if necessary.
  3. Investigate Long-Running Queries: Identify any long-running or inefficient queries that may be tying up connections. Use the system views mentioned earlier to identify queries with high execution times or excessive resource consumption. Optimize these queries or consider implementing query timeouts to prevent them from blocking connections.
  4. Analyze Connection Pool Metrics: If you’re using a connection pool, monitor its metrics to identify any abnormalities. Look for connection leaks, excessive connection acquisition times, or high connection churn rates. Adjust pool settings accordingly or consider switching to a different connection pool implementation if necessary.
  5. Review PostgreSQL Configuration Parameters: Examine PostgreSQL configuration parameters related to connection limits, such as max_connections. Ensure that these settings are properly configured to handle the expected workload. Adjust them cautiously, considering the available system resources and potential impact on performance.

Remember, monitoring and troubleshooting PostgreSQL connections is an ongoing process. Regularly monitor connection usage, review logs, and analyze performance metrics to proactively identify and address any connection-related issues.


In this tutorial, we explored various methods to tackle the “Too Many Clients” issue. We discussed optimizing connection pooling, adjusting database connection settings, enhancing application code, load balancing and scaling, as well as monitoring and troubleshooting techniques.

By implementing these methods, you can effectively manage PostgreSQL connections, mitigate bottlenecks, and ensure a smooth experience for your application users. Remember to regularly monitor connection usage, utilize appropriate tools, and proactively troubleshoot any connection-related issues.

Thank you for following this tutorial, and I hope it has provided you with valuable insights and practical solutions for dealing with the “Too Many Clients” issue in PostgreSQL. Make sure to explore the Troubleshooting JDBC Errors page for additional solutions to frequently encountered JDBC errors.

Frequently asked questions

  • How can I determine the maximum number of connections allowed in PostgreSQL?
    The maximum number of connections allowed in PostgreSQL is determined by the max_connections configuration parameter. You can check its value by executing the SQL command: SHOW max_connections;.
  • Can I adjust the maximum number of connections in PostgreSQL?
    Yes, you can adjust the maximum number of connections in PostgreSQL by modifying the max_connections configuration parameter in the PostgreSQL configuration file (postgresql.conf) or dynamically using the ALTER SYSTEM or ALTER DATABASE commands.
  • What should I do if I encounter connection leaks in my application?
    If you experience connection leaks in your application, ensure that you explicitly close connections after using them. Implement proper connection management techniques like connection pooling and make use of try-with-resources or finally blocks to ensure connections are released properly.
  • How can I determine the current number of active connections in PostgreSQL?
    You can query the pg_stat_activity view in PostgreSQL to retrieve information about the current active connections. The following SQL statement can be used:

    SELECT count(*) AS active_connections FROM pg_stat_activity;
  • Should I increase max_connections to a very high value to avoid “Too Many Clients” errors?
    Increasing max_connections excessively might solve the immediate issue, but it can lead to resource exhaustion and negatively impact performance. It’s essential to strike a balance between connection limits and available system resources.