{"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\/sl\/blog\/administrirovanie\/logirovanie-v-kubernetes-efk-protiv-plg","title":{"rendered":"Logovanje u Kubernetes: EFK naspram PLG","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Logovanje<\/p>\n

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Monitoring has become a crucial component of growing cloud solutions with the increasing complexity of distributed systems. It is essential for understanding their behavior. Scalable tools are needed to collect data from all services \u2014 providing 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 high-performing. In this article, we will explore two popular technology stacks: EFK (Elasticsearch) and PLG (Loki) and examine their architectures and differences.<\/p>\n

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EFK stack<\/h2>\n

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You may have 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 workflow diagram looks like this:<\/p>\n

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\"Logovanje<\/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 the nodes of the cluster. An inverted index is used that contains all unique words and the related documents for full-text search, which in turn is based on the Apache Lucene search engine.<\/p>\n

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FluentD<\/strong> \u2014 is a data collector that performs data unification during collection and consumption. It tries to structure data into JSON as much as possible. Its architecture is extensible, with over hundreds of different extensions<\/a><\/noindex>, supported by the community, for all occasions.<\/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 across all its nodes. The cluster consists of multiple nodes to improve availability and resilience. Any node can take on all cluster roles, but in large scalable deployments, nodes are typically assigned separate tasks.<\/p>\n

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

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