Community Advocate, Elastic
Alexander is an Elasticsearch developer interested in all things search and scale. He enjoys writing code, giving talks and trainings, as well as introducing people to all parts of the Elastic Stack. When offline, he goes hiking, watches basketball, and tries to get online again.
Today we will dive into the two available Terraform providers from Elastic. Terraform allows you to define your infrastructure as a code and keep that in repositories to easily make changes.
In this article we will go from nothing to a fully running Spring Boot application querying Elastic App Search, which has crawled the contents of a website. We will start up the cluster and configure the application step by step.
The new point-in-time functionality in Elasticsearch allows you to execute consistent search requests by retrieving data from a given point in time, even as data changes. Learn why we recommend using PIT instead of the scroll API.
Learn how Elasticsearch leverages various caches to ensure you are retrieving data as fast as possible. We'll take a deep dive into page, shard, and query caching to see how each can be used to boost query speeds.
Every new release of the Elastic Stack is packed full of new features. Learn about the new aggregations that have been added to Elasticsearch since 7.0, like rare_terms, top_metrics, auto_date_histogram, and a bunch more.
Learn how the Elastic Stack uses seccomp to prevent the execution of certain system calls, and how you can monitor seccomp violations using the Elastic Stack.
Find out how our engineers refactored the Elasticsearch code base to support nanosecond timestamps for logging events at a higher resolution than milliseconds.
A look at upcoming changes in Elasticsearch 6.0 related to the disk allocation decider.
Spin up a fully loaded deployment on the cloud provider you choose. As the company behind Elasticsearch, we bring our features and support to your Elastic clusters in the cloud.