How to configure logback as json format

To configure Logback to output logs in JSON format, you can use the Logstash encoder, which formats log events as JSON objects. Here's how to configure Logback to output logs in JSON format:

1. Add Logback Dependencies: Ensure that you have the necessary dependencies in your project. Include logback-classic and logstash-logback-encoder in your Maven or Gradle build file, or download the JAR files manually.

For Maven, add these dependencies to your pom.xml:

   <dependencies>
     <!-- Logback Classic -->
     <dependency>
       <groupId>ch.qos.logback</groupId>
       <artifactId>logback-classic</artifactId>
       <version>2.0.0</version> <!-- Use the latest version -->
     </dependency>

     <!-- Logstash Logback Encoder -->
     <dependency>
       <groupId>net.logstash.logback</groupId>
       <artifactId>logstash-logback-encoder</artifactId>
       <version>6.6</version> <!-- Use the latest version -->
     </dependency>
   </dependencies>

1. Create a Logback Configuration File: Create a logback.xml configuration file in your project's classpath. This file will specify the log format and layout.

2. Configure Logback with JSON Encoder:

Inside your logback.xml, configure Logback to use the Logstash encoder. Here's a sample configuration:

   <?xml version="1.0" encoding="UTF-8"?>
   <configuration>
     <appender name="console" class="ch.qos.logback.core.ConsoleAppender">
       <encoder class="net.logstash.logback.encoder.LogstashEncoder">
         <fieldNames>
           <timestamp>timestamp</timestamp>
           <version>version</version>
           <message>message</message>
         </fieldNames>
       </encoder>
     </appender>

     <root level="INFO">
       <appender-ref ref="console"/>
     </root>
   </configuration>

In this configuration:

• We define an appender named "console" that logs to the console.
• We use the LogstashEncoder to format logs as JSON objects.
• Inside the fieldNames section, you can specify how you want log fields to be named in the JSON output. The example above maps timestamp, version, and message fields.

1. Log Messages in Your Code:

In your Java code, use a logging framework like SLF4J with Logback bindings to log messages. For example:

   import org.slf4j.Logger;
   import org.slf4j.LoggerFactory;

   public class MyService {
     private static final Logger logger = LoggerFactory.getLogger(MyService.class);

     public void doSomething() {
       // Log a message
       logger.info("This is an info message.");
     }
   }

1. Run Your Application: When you run your application, Logback will format log events as JSON and send them to the specified output (in this case, the console).

With this configuration, your application will produce logs in JSON format, which can be easily ingested by log aggregation tools, including Elasticsearch, Logstash, and Kibana (ELK stack), for further analysis and visualization.

Here's an example of what log messages would look like when using the Logback configuration I provided earlier, along with the Java code:

Assuming you have a class MyService as described earlier:

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class MyService {
   private static final Logger logger = LoggerFactory.getLogger(MyService.class);

   public void doSomething() {
      // Log an info message
      logger.info("This is an info message.");

      // Log an error message
      logger.error("This is an error message.");
   }
}

When you call the doSomething() method, Logback will format log events as JSON. Here's what the log messages might look like in JSON format:

{"@timestamp":"2023-09-20T12:00:00.123Z","@version":"1","message":"This is an info message.","logger_name":"com.example.MyService","thread_name":"main","level":"INFO","level_value":20000}
{"@timestamp":"2023-09-20T12:00:01.456Z","@version":"1","message":"This is an error message.","logger_name":"com.example.MyService","thread_name":"main","level":"ERROR","level_value":40000}

In the JSON log messages:

• @timestamp represents the timestamp when the log event occurred.
• @version indicates the log format version (usually "1").
• message contains the log message itself.
• logger_name shows the name of the logger, which is typically the class name.
• thread_name displays the name of the thread that generated the log event.
• level specifies the log level (e.g., INFO or ERROR).
• level_value is the numeric value associated with the log level (e.g., 20000 for INFO and 40000 for ERROR).

These log messages in JSON format are structured and suitable for ingestion into log aggregation and analysis tools, such as Elasticsearch and Kibana. You can easily search, filter, and visualize these logs in such tools to monitor and troubleshoot your application.

Comments

[tonybui1812]

Why they use level value instead of level code?

[tonybui1812]

Logging frameworks often include both a textual representation of the log level (e.g., "INFO," "ERROR") and a numerical representation called the "level value" (e.g., 20000 for INFO, 40000 for ERROR). There are a few reasons why the level value is used alongside the level:

1. Efficiency: Internally, it's more efficient for logging frameworks to compare and evaluate numerical values than text strings. When determining whether a log message should be logged based on its log level, comparing integers is faster than comparing strings.

2. Consistency: Using numerical values ensures consistency in log level comparisons. Different programming languages and libraries may have variations in the textual representation of log levels (e.g., "INFO" vs. "Info" vs. "info"). Numerical values provide a standardized way to represent log levels across different systems.

3. Custom Log Levels: Some logging frameworks allow users to define custom log levels with custom level values. Using numerical values allows for the flexibility of adding custom log levels without relying on predefined textual representations.

4. Reduced Memory Consumption: Storing log levels as numerical values consumes less memory compared to storing them as strings, which can be important in systems with a high volume of log messages.

While log level values are primarily used internally by the logging framework, the textual log level representation (e.g., "INFO") remains essential for developers and operators to understand log messages easily. Typically, log aggregation and visualization tools, like Kibana, provide user-friendly representations of log levels based on the textual log level, making it easier for humans to interpret log data.

In summary, the inclusion of both level values and textual log levels in log messages is a design choice that balances efficiency and consistency in log level handling while maintaining readability for developers and operators.