The official Go client for Elasticsearch is one of the latest additions to the family of clients developed, maintained, and supported by Elastic. The initial version was published early in 2019 and has matured over the past year, gaining features such as retrying requests, discovering cluster nodes, and various helper components. We also provide comprehensive examples to facilitate using the client.
We recently released our new training Quick Start guides for the products in the Elastic Enterprise Search solution: Elastic Workplace Search and Elastic App Search. Each product is built on the Elastic Stack, so you can enjoy its speed, scale, and relevance without the heavy development and maintenance requirements of building your own search solution. Each 15-minute video tutorial provides everything you need to start creating powerful search experiences for your workplace, websites, and apps.
In Elasticsearch 7.9, we’ll be introducing a new “wildcard” field type optimised for quickly finding patterns inside string values. This new field type addresses best practices for efficiently indexing and searching within logs and security data by taking a whole new approach to how we index string data. Depending on your existing field usage, wildcards can provide: The most exciting feature of this new data type is its simplification of partial matches.
The ELK stack is an industry-recognized solution for centralizing logging, analyzing logs, and monitoring your system use and output. However, the challenges of maintaining your own stack overcoming common Elasticsearch problems need to be considered. The popularity of the ELK Stack can be boiled down to two, key tenets. First, the management and analysis of logs is an incessant issue for developers, SMBs, and enterprises alike.
The core responsibility of the UK's Driver and Vehicle Licensing Agency (DVLA) is to maintain more than 48 million driver records, more than 40 million vehicle records, and to collect approximately £6 billion ($7.75 billion) a year in Vehicle Excise Duty. The agency is at the forefront of public digital services, and has made significant progress in transforming its IT systems into new cloud-based platforms.
Welcome to another blog in our series on cost management and optimisation in Elasticsearch Service. In previous installments, we looked at hot-warm architecture and index lifecycle management as ways of managing the costs associated with data retention and at managing replicas as a means of optimising the structure of your Elasticsearch Service deployment. Be sure to check out the other blogs in the series for additional tips to help you as you build out your deployment.
Elasticsearch is a complex piece of software by itself, but complexity is further increased when you spin up multiple instances to form a cluster. This complexity comes with the risk of things going wrong. In this lesson, we’re going to explore some common Elasticsearch problems that you’re likely to encounter on your Elasticsearch journey.
This post is a brief summary of a presentation I gave recently where I deploy Elastic App Search, show off the ease of setup, data indexing, and relevance tuning, and take look at a few of the many refined APIs. It’s also written up in a codelab with step-by-step instructions for building a movies search engine app using Python Flask. The app will work on desktop or mobile and is a fast, simple, and reliable way to query the information.
This is part of our series on cost management and optimization in Elasticsearch Service. If you’re new to the cloud, be sure to think about these topics as you build out your deployment. If you are yet to start, you can test out the content here by signing up to a 14-day free trial of Elasticsearch Service on Elastic Cloud.
