Practical Guide to Using ELK: Log Collection, Analysis, and Query with Logstash and Kibana

Backend developers often need to locate logs, query request details, and generate daily statistics; the ELK (Elasticsearch + Logstash + Kibana) platform provides a user‑friendly solution for these tasks.

Logstash configuration : The tutorial presents a complete Logstash pipeline with input (file, Kafka, RabbitMQ), filter (mutate, date, grok, kv, json, geoip, urldecode), and output (stdout for debugging, Elasticsearch indices, daily file output). Sample configuration files for collecting Nginx access logs are included, along with a custom Grok pattern SNS_NGINX_ACCESS that parses timestamps, request details, and custom fields.

Elasticsearch RESTful APIs : Common API categories (Document, Search, Indices, Cat, Cluster) are introduced. Example queries demonstrate retrieving cluster info, searching logs, filtering by status, limiting fields, counting documents, and using scroll for large result sets. Aggregation examples show daily request counts, term aggregations for top interfaces, and nested date‑histogram aggregations for hourly breakdowns.

Kibana usage : The guide explains basic filter queries, keyword vs. analyzed fields, multi‑value filters, and logical operators. Advanced Lucene/Kuery queries cover full‑text search, field search, wildcards, regex, fuzzy, proximity, range, boosting, and boolean logic. Visualization steps illustrate creating Data Tables, line charts, and area charts, configuring metrics and buckets, adding secondary axes, and exporting results as CSV.

Additional tips : Adding index patterns in Kibana, using Script Fields for type conversion, and customizing visualizations (smoothing, area charts) are covered. The tutorial concludes that mastering ELK enables engineers to efficiently collect, parse, query, and visualize log data, improving daily operational workflows.