![]() You can use Grafana to query and visualize the log data stored in Loki. Loki is developed by Grafana Labs, the company behind the popular Grafana dashboards. Promtail is an agent that needs to be deployed to every machine that has applications to be monitored. Configuring Logstash is a bit more complex than Promtail. Logstash uses input plugins to ingest data from different sources. It can collect data from different sources, process it, and ship it to a destination. Logstash can send data to a number of destinations, not just Elasticsearch. Logstash is used to collect and forward logs to Elasticsearch. It primarily does three things: discovers targets, attaches labels to the log stream, and pushes them to the Loki instance. It discovers log files stored on disk and forwards them to Loki. Promtail is specifically designed for Loki. Loki uses Promtail to discover log files. As KQL is much older, you can find more examples online to learn and implement for your use case. You can also choose to turn off Kibana Query Language and use Lucene Query syntax instead.īoth LogQL and KQL have a learning curve, and it depends on users how quickly they can become accustomed to it. ![]() If you are using Kibana to visualize log data from Elasticsearch, you can use the Kibana Query Language (KQL). The performance of query execution depends on how many labels are selected to filter down log streams. Each query consists of two parts: a log stream selector and a filter expression. Loki uses its own language for querying logs, called LogQL. But it loses the rich text search capabilities that Elasticsearch provides. Since indexes are low in Loki, it is more cost-effective and performant. While Loki is designed to keep indexing low, Elasticsearch indexes all data in every field, and each indexed field has a dedicated, optimized data structure. The biggest difference between Loki and Elasticsearch is how they index data. Elasticsearch makes documents fully searchable but at the cost of requiring more storage space. The data is stored on-disk as unstructured JSON objects. Both index and chunks can be stored on the object store from Loki 2.0.Įlasticsearch indexes the full contents of stored documents. Since only a set of labels is indexed for each log stream, the index created is small. The compressed log data is called a chunk. Once the indexing is done, the log data is compressed and stored in object stores like S3 and GCS. Loki only indexes the metadata(labels) of logs. So it’s important to understand how Loki and Elasticsearch store log data. Log data is often huge, with every application, host machine, and infrastructure component generating its own set of log streams. ![]() Key differences between Loki and Elasticsearch Storage Now that we have an overview of both tools let’s discuss the key differences between them. 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. Elasticsearch, or the ELK stack, is a popular log analytics solution. ![]()
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