{"id":89038,"date":"2020-07-17T19:42:30","date_gmt":"2020-07-17T17:42:30","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/logirovanie-v-kubernetes-efk-protiv-plg"},"modified":"2020-07-17T19:42:30","modified_gmt":"2020-07-17T17:42:30","slug":"logirovanie-v-kubernetes-efk-protiv-plg","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hu\/blog\/administrirovanie\/logirovanie-v-kubernetes-efk-protiv-plg","title":{"rendered":"Bejelentkez\u00e9s a Kubernetesbe: EFK vs. PLG","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Bejelentkez\u00e9s<\/p>\n

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A monitoring system has become a crucial component of growing cloud solutions due to the increasing complexity of distributed systems. It is essential for understanding their behavior. Scalable tools are needed to collect data from all services and provide specialists with a unified interface for performance analysis, error demonstration, availability, and logs. <\/p>\n

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These tools must also be efficient and productive. In this article, we will examine two popular technology stacks: EFK (Elasticsearch) and PLG (Loki), and explore their architectures and differences.<\/p>\n

<\/a><\/noindex><\/p>\n

EFK Stack<\/h2>\n

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You may have already heard of the very popular ELK or EFK. The stack consists of several separate components: Elasticsearch (object storage), Logstash or FluentD (log collection and aggregation), and Kibana for visualization.<\/p>\n

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A typical operational scheme looks like this:<\/p>\n

<\/p>\n

\"Bejelentkez\u00e9s<\/p>\n

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Elasticsearch<\/strong> \u2014 a distributed object storage with real-time search and analytics. An excellent solution for semi-structured data, such as logs. Information is stored as JSON documents, indexed in real-time, and distributed across cluster nodes. An inverted index is applied, containing all unique words and the documents associated with them for full-text search, which is based on the Apache Lucene search engine.<\/p>\n

<\/p>\n

FluentD<\/strong> \u2014 is a data collector that performs data unification during collection and consumption. It strives to organize data in JSON as much as possible. Its architecture is extensible, with over hundreds of different extensions<\/a><\/noindex>, supported by the community, for all use cases.<\/p>\n

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Kibana<\/strong> \u2014 a data visualization tool for Elasticsearch with various additional capabilities, such as time series analysis, graphs, machine learning, and more.<\/p>\n

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Elasticsearch Architecture<\/h3>\n

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Data in the Elasticsearch cluster is stored distributed across all its nodes. The cluster consists of multiple nodes to improve availability and resilience. Any node can perform all roles in the cluster, but in large scalable deployments, nodes are usually assigned specific tasks.<\/p>\n

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Types of cluster nodes:<\/p>\n

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