![]() On dashboards, it is possible to refine the set of data presented by using additional search parameters introduced via a search box (another Elasticsearch query). The main area of the Kibana user interface includes a search box where you can try any Elasticsearch queries, visualize the results, and save the queries that produce the results you are looking for to dashboards. Source: Įvery time the dashboard needs to update, the query runs and produces the most recent counts for the different HTTP statuses. In Kibana, create a time series view that looks for the items that have your desired HTTP statuses.Ī full breakdown of HTTP requests by status, country, OS and other factors in Kibana.On the machine that produces the example logs above, set up Logstash to process the logs and write them to Elasticsearch.If you want to present the amount of successful HTTP queries vs those that didn't return valid results, you do the following: For example, if the log lines contain information on HTTP requests: method=post api=books result=201 With Kibana, you query log lines to produce metrics that you are looking for. However, Kibana offers more functionality for the Elasticseach source, like exploring available data and performing a full-text search on the logs. Kibana focuses on Elasticsearch and doesn't support any data sources besides Elasticsearch. MySQL, PostgreSQL, Microsoft SQL Server.Comparison Data sourcesīoth Grafana and Kibana support Elasticsearch as a data source.Īpart from Elasticsearch, Grafana supports sourcing metrics from: Kibana focuses on the exploration of available data and the flexibility of extracting metrics from raw log lines. What's the difference between the two use cases? Grafana focuses on efficiently displaying a defined set of metrics in real time. The three tools allow you to query and parse relevant information out of the collected logs and display it in different ways. It was created to facilitate log analysis in combination with the popular Elasticsearch and Logstash. The data sources it supports are those most commonly used for storing application metrics and Grafana produces alerts in real time. Grafana is a monitoring tool, and its functionality is optimized for monitoring tasks and time series data. ![]() Use casesĪt their core, Grafana and Kibana cover two different use cases and sets of functionality. In this Stackup we look at one tool from each of the two sides: Grafana, a monitoring solution, and Kibana, a log analysis solution that is part of the Elasticsearch, Logstash, and Kibana stack, or ELK. Due to the decreasing latency in log processing over the past years, you can now accomplish log analysis in near-real-time. Log analysis is a post-event inquiry into the log entries, and therefore past events, that a running application produced. Logs are information about the specific events that took place at a certain moment in time. That instance can be a database instance, a web server, or any other part of the web service Monitoring systems are generally focused on real-time metrics. Metrics are usually submitted directly to the monitoring system by the running instance of an application. The monitoring of applications is usually performed by analyzing the changes in discrete data points describing the state of the system at a given moment, called metrics. Two approaches for creating observable applications are monitoring and log analysis.
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