![]() ![]() Certainly, better than a bunch of log files, isn’t it? Setting up the Elasticsearch This means that data about your cluster will always be visible in an up-to-date, understandable manner. LogStash will transform and stash data into Elasticsearch, which will then serve this data to Kibana. The best part about the ELK stack is that it is built to run continuously. But the important takeaway here is that it integrates beautifully into the ELK stack and provides a lot of customizable visualisations. Kibana is a huge application and deserves its own course. You can also export and share data easily from within Kibana, and create alerts to notify you of certain trigger events. This helps improve policy compliance as well as usability. For example, people in management roles would want to see different dashboards from those working in system security. The dashboards can also be bound to specific roles. ![]() You can also use Kibana’s built-in query language to perform queries on the Elasticsearch datastore and have this data represented in a dashboard. This could be in the form of charts, graphs, time-series data, and much more. Kibana (similar to Grafana or other visualisation techniques), lays out all the data provided by Elasticsearch into an easy-to-read format. That’s Kibana.Īt this point, the data still isn’t very human-friendly. In a situation where there is a huge influx of data, LogStash can act as a buffer to prevent overloading the data store. Another great thing about LogStash is that it is scalable, which means that it can scale out to cater for increased demand. LogStash can identify this information before it gets stored and automatically anonymize/exclude it. This would be a huge breach in compliance if we consider regulations such as GDPR. Take, for instance, a situation where the raw data has personal information that should be anonymized. What does transform here mean? Well, it means doing things such as deriving information from the data (such as a structure that the data has), parsing the data, or filtering it. Now I did say that LogStash transforms the data. I needed, you could even have LogStash output data to multiple sources at a time. It could also dump the data in a DB such as MongoDB, a large-scale file system like Hadoop, or a different S3 bucket. However, there is no hard limitation saying that LogStash only works with Elasticsearch. Note that at this stage, the data would have already been transformed. ![]() LogStash feeds data directly into Elasticsearch, which handles the long-term storing of data. ![]() You must have guessed where the “somewhere” is. LogStash is responsible for accepting data, transforming it, and stashing all that data somewhere. This could be anything from a log file to Kafka to an S3 bucket. This is what actually takes in that data. We already know what the “E” stands for, so let’s skip ahead to the “L” LogStash For us to start making sense of this data, we should take the ELK stack into consideration. Instead of having heaps of data in a text file, you would now have heaps of data in a data store. This means that it isn’t of much use alone. ELK StackĮlasticsearch, despite how powerful it is, is only a datastore. So basically, Elasticsearch isn’t a brand new concept, and you can try to match it up with what you already know about databases to understand the concepts. Similar to NoSql databases, rows are replaced with documents, and columns are replaced with fields. Instead of DBs, you have indexes, and tables are replaced with patterns or types. Now, you might notice right away that the architecture here is rather different from either RDMBS or NoSql databases, but it really isn’t that far off. If you have used something like CouchDB before, this concept should be familiar to you. Secondly, you interact with the DB using REST API calls. This allows it to collect data from various sources such as application trace files, metrics, and plain text logs. We have brought forward Elasticsearch as an alternative to simply writing logs to a file, and in the same way, an ordinary file is unstructured, this datastore also needs to have the same properties. The reason for the unstructuredness is due to the nature of the problem Elasticsearch aims to solve. First of all, this is a JSON-based datastore that is very unstructured. This is similar to a database, with some key differences. What is Elasticsearch | kubelabs Star Fork Watch Follow What is Elasticsearch Get Started with Kubernetes View on GitHub Join Slack Kubectl Cheatsheet Kubernetes Tools Follow us on Twitter What is ElasticsearchĮlasticsearch is simply a data store. ![]()
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