1 views collected around this technical thread.
This technical case study details how a high‑traffic Kafka logging cluster was optimized by adjusting Filebeat and Kafka parameters, increasing compression batch size, and tuning Kubernetes settings, resulting in significant reductions in request volume, network traffic, CPU usage, and overall resource consumption.
This article explains how to use Filebeat for log shipping, configure its YAML files, deploy Graylog with Docker and Elasticsearch, and integrate logging into Spring Boot applications, providing step‑by‑step commands, code examples, and best‑practice recommendations for centralized log management.
This guide walks through enabling Kubernetes auditing, configuring the API server and audit policy, collecting logs with Filebeat, forwarding them via Logstash to Elasticsearch, and visualizing the audit data in Kibana, providing a complete cloud‑native logging pipeline.
This guide walks through installing NFS, configuring a dynamic storage class via Helm, deploying the Elastic Cloud on Kubernetes (ECK) operator, setting up Elasticsearch and Kibana clusters, and installing Filebeat for log collection, including system tuning and Kubernetes manifests for a production‑ready environment.
This step‑by‑step guide shows how to use a Helm chart to deploy Filebeat in a Kubernetes cluster, automatically collect container logs, and forward them to a Kafka cluster for reliable, scalable observability.
This article explains how to use Filebeat as a lightweight log shipper together with Graylog for centralized log aggregation, storage, and analysis, covering installation, configuration files, Docker deployment, Spring Boot integration, and query techniques for efficient operations monitoring.
This comprehensive guide details a step‑by‑step deep‑inspection and monitoring strategy for an Elasticsearch‑Filebeat‑Logstash‑Kibana (EFLK) stack, covering cluster health, node and shard metrics, index status, query profiling, Filebeat, Logstash and Kibana validation, DSL query examples, and a Python script for automated metric collection.
This article explains how to use Filebeat as a lightweight log shipper together with Graylog, Elasticsearch, and MongoDB to collect, process, and visualize logs from multiple environments, including detailed configuration examples, deployment scripts, and integration with Docker and Spring Boot.
This article explains why centralized log collection is essential for multi‑environment services, introduces Graylog as a lightweight alternative to ELK, details Filebeat's role and workflow, provides configuration examples, shows how to deploy both Filebeat and Graylog via Docker or packages, and demonstrates integration with Spring Boot and log search techniques.
This article provides a comprehensive guide on building a unified log collection system using Filebeat and Graylog, covering the tools' concepts, configuration files, component functions, Docker deployment, and practical code examples for efficient log monitoring across multiple environments.
This article provides a comprehensive, hands‑on guide to installing and configuring the ELK logging stack—including Elasticsearch, Logstash, Kibana, and Filebeat—using Docker on an Ubuntu VM, covering architecture, command‑line setup, configuration files, troubleshooting tips, and future extension options.
This article introduces the core components of the ELK stack, compares three typical deployment architectures—including Logstash‑only, Filebeat‑assisted, and Kafka‑backed designs—and provides concrete configuration examples and troubleshooting tips for multiline merging, timestamp handling, and module‑level log filtering.
Facing data security and cost challenges in SaaS, the author evaluates ClickHouse versus Elasticsearch, highlighting ClickHouse’s superior write throughput, query speed, lower storage and CPU usage, and provides detailed deployment guides for Zookeeper, Kafka, FileBeat, and ClickHouse to build a cost‑effective private analytics platform.
This article introduces the ELK stack’s core components, compares four typical deployment architectures—including Logstash‑only, Filebeat‑based, and Kafka‑enhanced setups—discusses their trade‑offs, and provides practical configurations and solutions for multiline log merging, timestamp handling, and module‑specific filtering.
This article explains how to use Filebeat together with Graylog to collect, ship, store, and analyze logs from multiple environments, covering tool introductions, configuration files, Docker deployment, Spring Boot integration, and practical search syntax for effective log monitoring.
This article explains the ELK stack components, compares three typical deployment architectures—including Logstash‑only, Filebeat‑based, and a Kafka‑backed version—and provides detailed configuration examples for multiline log merging, timestamp replacement, and module‑based log filtering.
This article explains why centralized logging is essential for modern micro‑service systems, outlines an ELK‑based architecture with Filebeat, Kafka, Logstash, Elasticsearch and Kibana, and provides detailed configuration examples for both file‑based and Kubernetes‑based log collection, plus visualization techniques.
This guide explains the three main ELK deployment architectures, compares Logstash and Filebeat collectors, introduces a cache‑queue option for high‑volume logs, and provides practical solutions for multiline log merging, timestamp correction, and module‑level filtering in Kibana, helping operations teams build efficient log pipelines.
This article provides a detailed comparative overview of nine popular log management solutions—including Filebeat, Graylog, LogDNA, ELK, Grafana Loki, Datadog, Logstash, Fluentd, and Splunk—covering their core features, pricing models, strengths, and weaknesses to help readers choose the most suitable tool for their environment.
This article traces ZuanZuan's log‑collection journey from the bare‑metal era through containerization to a cloud‑native solution, detailing the challenges, custom developments like log‑pilot, ByteCompass, and fb‑advisor, and the performance gains achieved with each transition.