Philipp Krenn – Centralized Logging Patterns

Centralized logging patterns, best practices and solutions for complex systems, including ELK stack, Grok, Logstash, Elasticsearch, Beats, filebeat, Java SLF4J, Docker, Gradle, and more.

Key takeaways

Log centralized logging is important, especially for complex systems.
JSON logging is a good approach, as it provides structure and can be easily parsed.
The ELK stack (Elasticsearch, Logstash, Kibana) is a popular solution for centralized logging.
Grok is a powerful tool for parsing log lines, allowing for complex patterns and regular expressions.
Logstash can be used for parsing and enriching logs, but can be complex and heavy.
Elasticsearch can be used for storing and searching logs, with Kibana providing a UI for visualization.
Beats (filebeat, metricbeat, etc.) are lightweight agents that can be used for shipping logs to Logstash or Elasticsearch.
Filebeat can be used for collecting logs from files, and can handle multiline logs and parsing.
Docker provides a way to run applications in isolation, but can also be used for logging, with filebeat and Elasticsearch providing a centralized logging solution.
Java logging can be done using the SLF4J API, which provides a logging facade for different logging frameworks.
Gradle can be used for building and dependency management in Java projects.
The elastic stack provides a scalable and flexible solution for centralized logging, with support for many programming languages and frameworks.
Enrichment of logs with metadata such as container labels and host information is important for effective logging.
Kibana provides a powerful visualization and discovery tool for logs, with filtering, grouping, and aggregating capabilities.
Elasticsearch provides a scalable and efficient way to store and search logs, with support for many data types and features such as faceting and filtering.

Philipp Krenn – Centralized Logging Patterns

More talks