One Pager Cheat Sheet

• The article discusses how centralized logging with the ELK stack (comprising of Elasticsearch, Logstash, and Kibana) can overcome the limitations of traditional logging methods by offering a scalable, one-stop shop for logs, powerful search and filters, custom visualizations, and enhanced security.
• Elasticsearch, built on Apache Lucene, is a distributed, scalable architecture that stores and indexes documents as JSON documents, and its main components include Nodes, Shards, Replicas, and Index. It can be installed on multiple platforms, and provides REST APIs for key functions such as indexing, searching, updating, and deleting documents; it also supports scalability and various security features, and allows cluster management and monitoring through the cluster health API and tools like Cerebro.
• Adding plugins early on in the workflow doesn't scale an Elasticsearch cluster as it mainly extends functionality, not capacity; key scaling methods include distributing nodes and setting up replicas, while prematurely adding plugins may introduce unnecessary complexity.
• Logstash employs a pipeline-based architecture consisting of three stages: Inputs, where various data types are received; Filters, which transform raw data; and Outputs, which direct the processed data to their final destination. It features various plugins for each stage including File, Syslog, and Beats for inputs; Grok, Mutate, GeoIP, and Date for filters; and an output plugin for Elasticsearch. Through Logstash configuration, data processing logic is defined, transforming raw data into structured, query-ready logs.
• The article discusses the importance of structured logging and offers strategies and tools for mastering it, emphasizing the use of Logstash's Grok filter for parsing logs, best practices for log formatting, and various Beats (Filebeat, Metricbeat, Heartbeat) modules for log shipping and monitoring your system's health. Additionally, it mentions the use of Curator for archiving logs.
• Kibana is a tool for analyzing and visualizing log data in Elasticsearch, offering features such as discover, visualize, dashboard, timelion, alerting, and APM which aid in extracting insights from log data and creating operational dashboards, analyzing trends, and debugging issues in real-time.
• The best practices for optimizing the ELK stack include using log forwarding agents like Filebeat, enabling TLS for secure connections, strategically using indexes, monitoring various metrics, ensuring multi-node Elasticsearch clusters with replication for high availability, utilizing Curator for log index management, ingesting metrics into Elasticsearch for combined analytics and monitoring, and integrating with CI/CD pipelines and ITSM tools using the ELK APIs.
• Including timestamps in logs is vital as it facilitates tracing the sequence of events, determining when specific events took place, and allows tools like the ELK stack to index and search logs based on their timestamps and for visualizing log data over time.
• Graylog, Splunk, and Sumo Logic are viable alternatives to the ELK stack, offering varying advantages such as simplicity, premium features, and cloud-native stack support respectively.