UPDATED 15:53 EST / DECEMBER 14 2023

Ken Exner, Supercloud 5, November 17 AI

Elastic aims for faster performance for queries with launch of dedicated query language

Observability practitioners may be familiar with using one query language for logs, another for metrics and another for traces and application performance monitoring. That can sometimes pose challenges when it comes to pulling data together from different datasets and doing all of it in one query language.

Elastic N.V. hopes to solve that with the launch of Elasticsearch Query Language. It’s a piped query language, according to Ken Exner (pictured), chief product officer of Elasticsearch Inc.

“[It] allows developers and practitioners to query various data sets, pull the data together all in one query language and be able to construct fields on the fly, to be able to do math and operations, to do joins on different data sets,” Exner said. “It’s a really powerful new query language that makes it possible for people to do things in one query language that used to be only possible in different query languages.”

Exner spoke with theCUBE industry analyst Rebecca Knight at the “Supercloud 5: The Battle for AI Supremacy” event live on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the problems Elasticsearch Query Language aims to solve and how the new language relates to artificial intelligence.

Pulling data together

Observability practitioners, such as SRE practitioners, have always had a hard time pulling data together from different datasets and doing it in one query language, according to Exner. The goal of Elasticsearch Query Language is to enable customers to have one query language that allows them to work across different datasets, pull things together and do things they couldn’t before.

“Because it’s a new query engine that’s built directly in Elasticsearch, it is native in the engine, and it is super fast. Customers have always loved the speed of Elasticsearch for search,” he said. “Now, with this new query API and new query engine, we have blazing fast performance for queries as well.”

This being 2023, all the buzz is around generative AI. But the thing about gen AI is that it’s only as good as the foundation it’s built on, according to Exner.

“When you’re using code generation, for example, you need to build on top of APIs, on top of libraries, on top of frameworks. If you’re using a code generation tool and say you’re trying to build a thumbnail service on top of an object storage system, like something that creates thumbnail images, it’s not going to build the computer first,” he said. “It’s not going to build S3 first. It’s going to start from some foundational primitives and then build up from that.”

Elastic AI Assistant

When considering building a thumbnail service, it’s going to build on top of S3 libraries that build on top of S3, according to Exner. If a customer wants to do some analytics on different data sets, they want to build it on top of some foundational primitives.

“That’s what ESQL gives them. It gives them a really robust foundational language for how to pull data from different data sets, whether it’s structured or unstructured data and be able to do different types of aggregations, set alerts based on whatever kind of setting you want to come up with in your language, create detection rules that run in real-time,” he said. “All these things can happen on top of the data that you’re bringing in.”

If customers want to build a natural language interface on top of that, the company can do that too, Exner added. That’s where the company’s Elastic AI Assistant comes in.

“The AI Assistant allows you to use natural language to map into the Elastic query language. So, you can just think of how you want to construct a query,” he said.

One might want to pull data from a data source and look at all the hosts with CPU above 40% that have had a particular type of latency, for instance, greater than two seconds. They might want to look at where there have been errors of more than 50% over the last hour.

“Constructing a query like that across different data sets and doing it in natural language is possible now because of the foundational capabilities that we have with ESQL,” Exner said.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the “Supercloud 5: The Battle for AI Supremacy” event:

(* Disclosure: Elasticsearch Inc. sponsored this segment of theCUBE. Neither Elasticsearch nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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