You may have noticed how on sites like Google you get suggestions as you type. With every letter you add, the suggestions are improved, predicting the query that you want to search for. Achieving Elasticsearch autocomplete functionality is facilitated by the search_as_you_type field datatype. This datatype makes what was previously a very challenging effort remarkably easy.

Elastic 7.6 introduced the inference processor for performing inference on documents as they are ingested through an ingest pipeline. Ingest pipelines are incredibly powerful and flexible but they are designed to work at ingest. So what happens if your data is already ingested? Introducing the new Elasticsearch inference pipeline aggregation, which lets you apply new inference models on data that's already been indexed.

Chances are you already spent a big part of your day looking for a document, an email, or an answer that lies deep within a Google Slides presentation. Thankfully, you landed in the right place. With Workplace Search, finding the right information across all your cloud and on-premises data platforms is now easier than ever, and it’s a few clicks closer than you expect.

In our previous two blogs, we provided an overview of the architecture and design of the Elasticsearch Go client and explored how to configure and customize the client. In doing so, we pointed to a number of examples available in the GitHub repository. The goal of these examples is to provide executable "scripts" for common operations, so it's a good idea to look there whenever you're trying to solve a specific problem with the client.

As work from home has ballooned in 2020, virtual methods for communicating with colleagues have become more critical than ever. Same goes for all the useful productivity and collaboration tools at our disposal. The emerging downside is the difficulty of finding needed information among so many tools. Compounding the problem is the tendency for info to get siloed off by department.

We recently announced the general availability of our Elasticsearch Service API. APIs help to automate tasks such as creating and scaling deployments, integrating with existing workflows, and testing. The Elasticsearch Service API supports the Open API Specification, which allows you to use tools like Swagger to generate software development kits (SDKs) in any programming language. You can import the API spec onto Postman and create a Postman Collection to create a test suite.

In a previous blog, we saw that the seemingly simple job of an Elasticsearch client — moving data between the calling code and the cluster — is actually quite complicated under the hood. Naturally, as much as we try to make the default behaviour of the client optimal for the majority of scenarios, there are situations where you want to configure, customize, or enable/disable certain features.

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.