When transitioning from file-based logging to database logging with MySQL, developers often miss the convenience of tail -f for real-time monitoring. Our Java application using logback now stores logs in MySQL, but we need a way to stream new entries as they're inserted.

Here are several approaches to implement tail-like functionality for MySQL tables:

1. Using MySQL Triggers and External Scripts

Create a trigger that notifies an external process when new rows are inserted:

DELIMITER //
CREATE TRIGGER log_alert 
AFTER INSERT ON application_logs
FOR EACH ROW 
BEGIN
    -- This could write to a file that another process monitors
    SELECT NEW.id, NEW.message INTO OUTFILE '/tmp/log_updates' 
    FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n';
END //
DELIMITER ;

2. Polling with Last ID Tracking

A simple Java solution using JDBC:

public class LogTail {
    private static long lastId = getMaxId();
    
    public static void main(String[] args) throws SQLException, InterruptedException {
        String query = "SELECT * FROM application_logs WHERE id > ? ORDER BY id ASC";
        
        try (Connection conn = DriverManager.getConnection(DB_URL);
             PreparedStatement stmt = conn.prepareStatement(query)) {
             
            while (true) {
                stmt.setLong(1, lastId);
                ResultSet rs = stmt.executeQuery();
                
                while (rs.next()) {
                    lastId = rs.getLong("id");
                    System.out.println(rs.getString("message"));
                }
                
                Thread.sleep(1000); // Poll every second
            }
        }
    }
    
    private static long getMaxId() {
        // Implementation to get current max ID
    }
}

3. Using MySQL Binlog

For more advanced solutions, consider parsing the MySQL binary log:

mysqlbinlog --read-from-remote-server --host=localhost --user=root --password \
--start-position=4 --stop-never mysql-bin.000001

When implementing database tailing:

• Add proper indexing on timestamp/ID columns
• Consider read replicas for production monitoring
• Batch processing may be more efficient than single-row polling
• Monitor the impact on database performance

For production environments, consider these specialized tools:

• Maxwell's Daemon (reads MySQL binlog)
• Debezium (change data capture platform)
• Kafka Connect with JDBC Source Connector

When transitioning from file-based logging to database logging (particularly with MySQL), many developers miss the convenience of tail -f for monitoring logs in real-time. While MySQL doesn't have a native tail -f equivalent, we can implement similar functionality through several approaches.

For applications using MySQL's binary logging, you can monitor changes with:

mysqlbinlog --read-from-remote-server --host=localhost --user=root --password \
--raw --stop-never mysql-bin.000001

A simple Java implementation using timestamp polling:

public void tailLogTable(Connection conn) throws SQLException {
    Timestamp lastSeen = new Timestamp(System.currentTimeMillis() - 60000);
    String query = "SELECT * FROM app_logs WHERE created_at > ? ORDER BY created_at";
    
    try (PreparedStatement stmt = conn.prepareStatement(query)) {
        while (true) {
            stmt.setTimestamp(1, lastSeen);
            ResultSet rs = stmt.executeQuery();
            
            while (rs.next()) {
                System.out.println(rs.getString("log_message"));
                lastSeen = rs.getTimestamp("created_at");
            }
            
            Thread.sleep(1000);
        }
    }
}

Create a trigger that writes to a secondary table, then monitor that table:

CREATE TRIGGER log_trigger AFTER INSERT ON app_logs
FOR EACH ROW BEGIN
    INSERT INTO log_notifications (log_id, created_at)
    VALUES (NEW.id, NOW());
END;

For MySQL 8.0+, you can use the JSON binary log format with a Kafka connector:

# Debezium configuration example
{
  "name": "log-connector",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "database.hostname": "localhost",
    "database.port": "3306",
    "database.user": "debezium",
    "database.password": "password",
    "database.server.id": "184054",
    "database.server.name": "logserver",
    "database.include.list": "logging_db",
    "table.include.list": "logging_db.app_logs",
    "database.history.kafka.bootstrap.servers": "kafka:9092",
    "database.history.kafka.topic": "schema-changes.logging_db"
  }
}

When implementing database log tailing:

• Add proper indexing on timestamp columns
• Consider read replicas for production monitoring
• Implement connection pooling
• Batch log reads when possible