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Filebeat's Harvester uses a persisted offset registry to resume tailing after restarts without duplicates. This post explains the mechanism, shows how to verify it with a minimal test, and covers the flush-interval trade-off and horizontal scaling caveats.
Filebeat's close_older and close_inactive settings bound file handle usage by closing stale or idle log files. This guide explains the difference, shows a production-ready configuration, and covers verification steps plus common pitfalls like incomplete tailing and truncation data loss.
Learn how Elastic Beats autodiscover dynamically starts and stops log harvesters in Kubernetes based on pod labels, with a concrete Filebeat nginx example and practical limitations.
A practical diagnostic guide for Beats Connection Refused and publishing timeouts to Elasticsearch. Covers network checks, beats.yml validation, cluster health verification, and safe fixes with escalation criteria.
Filebeat’s Autodiscover can silently drop new logs when templates fail to apply. This guide lists symptoms, a cause‑diagnostic matrix, ordered checks, fixes tied to findings, and escalation criteria to quickly restore log ingestion.
Learn when to use Filebeat with a persistent queue shipping directly to Elasticsearch versus adding a Logstash tier, including trust boundaries and failure modes.
Filebeat Autodiscover lets Beats automatically detect and ship logs from new containers, eliminating manual config. Learn how to set up Docker providers, write matching templates, test, and avoid common pitfalls.
Learn how to design a minimal, secure Filebeat deployment that leverages modules for rapid log ingestion, meets compliance, and includes operational checks and failure‑mode mitigation.
Filebeat’s multiline feature lets you aggregate multi‑line logs into single events, cutting noise and storage costs. Follow this practical guide to configure, test, and validate the feature safely.
beats
elasticsearch
logstash
docker