![]() Kibana lets you visualize the logs data to generate insights. Elasticsearch indexes all data in every field. Both the key of the JSON object and the contents of the key are indexed. Once the log data is collected, it is stored as unstructured JSON objects. FluentD and Filebeat are two popular log collectors used in the pipeline. There are other log-collecting tools too that can be used for collecting logs. The ELK stack comprises of following independent components: For log analytics, Elasticsearch is combined with Logstash or FluentD and Kibana. ![]() What is Elasticsearch? Įlasticsearch is a search engine built on Apache Lucene. This can make Loki very slow as it requires building a huge index. For example, if you create a label for the user's IP address, you will have thousands of log streams, as every user will have a unique IP. But Loki does not support high cardinality efficiently. Labels act as an index to Loki's log data and keep the complexity low. The above config will let you query the log stream with. Labels are any key-value pairs that can be used to describe a log stream. It is inspired by Prometheus and is designed to be cost-effective and easy to operate. ![]() Loki is a open source log aggregation tool developed by Grafana labs. That’s where Log analytics tools like Loki and Elasticsearch come into the picture.īefore we look at the differences between these two tools, let us have a brief overview of both tools. Collecting log data from these systems and deriving timely insights from them can be complex. Most modern applications are now based on distributed components based on container technologies. Log data helps application owners debug their applications while also playing a critical role in cyber security. In this article, we will do a detailed comparison between these two tools for log analytics. Grafana leads the development of Loki, while Elastic is the company behind Elasticsearch. The Loki project was started at Grafana Labs in 2018. Logstash has a broader approval, being mentioned in 561 company stacks & 278 developers stacks compared to Prometheus, which is listed in 243 company stacks and 85 developer stacks.Elasticsearch, or the ELK stack, is a popular log analytics solution. It seems that Prometheus with 25K GitHub stars and 3.55K forks on GitHub has more adoption than Logstash with 10.3K GitHub stars and 2.78K GitHub forks.Īirbnb, reddit, and Typeform are some of the popular companies that use Logstash, whereas Prometheus is used by Uber Technologies, Slack, and DigitalOcean. Logstash and Prometheus are both open source tools. "Free" is the top reason why over 60 developers like Logstash, while over 32 developers mention "Powerful easy to use monitoring" as the leading cause for choosing Prometheus.
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