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Java Application Instrumentation with Log4J

Learn how to instrument your Java applications using log4j and regular expressions. It's as simple as mining your logs for application and performance metrics—in real-time.




Building the Right Environment to Support AI, Machine Learning and Deep Learning

pplication metrics, such as performance metrics, are key to understanding and improving application efficiency. Profiling and monitoring tools yield valuable information on CPU and resource usage, including OS and network metrics, JVM metrics, component- or method-level metrics, application server metrics, and HTTP (Web Server) request metrics. Such metrics can be used to detect bottlenecks and forewarn of production issues.

Find out how J2EE profiling and monitoring tools really work, and then create your own using log interception and log4j. No expensive tools or vendor-specific solutions are needed, just lightweight statistic derivation over your most basic application-level information source—your logs.

Approaches to Metric Collection
You can extract application-centric metrics, including performance metrics, in many ways. However, no single approach provides a total end-to-end solution that covers all the aspects of an enterprise application. J2EE-centric tools typically leverage one or more of the following approaches:

  1. Log/trace scrapping
    • Web server logs that conform to well known formats such as the Common
    • Log Format (CLF) can be processed for information on HTTP operations.
    • Verbose GC output from the JVM can be analyzed for GC bottlenecks.
  2. JVM hooks
    • Profilers plug into the JVM itself using the Profiler Interface (JVMPI) (deprecated in 1.5) and Tool Interface (JVMTI) introduced in Java 1.5.
    • Java Management Extensions (JMX) to publish statistics and notify of state changes and error conditions
    • JDBC call interception to measure the efficiency and performance of SQL operations
  3. External services
    • Operating system services such as ps, top, netstat, vmstat, and sar for resource information, and telnet, rsh, and ftp for executing commands to extract such information remotely
    • SNMP
  4. Homegrown
    • Code interleaved with homegrown statistic collection and derivation functions, and typically reported via logs

The code instrumentation approach is commonly used to insert code that measures memory, threads, and timing at a very fine-grained level, usually the method level. Code instrumentation is done by either post-processing class files or substituting code on the fly inside the JVM using JVMPI/JVMTI. Once instrumented, metrics can be logged, cached, persisted, or in an agent architecture, forwarded onto to a separate server to manage. The advantage of an agent architecture is that metrics can be captured and collated across multiple sources or tiers.

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