Elastic Looks to Simplify Queries with New Piped Language

Elasticsearch Query Language (ES|QL), Elastic’s latest offering that introduces a piped query syntax and supports concurrent processing, is being touted by the company as a major leap forward in data querying technology, claiming to boost productivity and streamline data operations for a diverse array of users including IT professionals and data analysts.

ES|QL, not to be confused with IBM’s Extended Structured Query Language (ESQL), is being presented by Elastic as a game-changer for professionals who must handle increasingly complex data tasks. The language’s design, focusing on concurrent processing, is intended to expedite data handling, allowing for quicker and more responsive analysis.

Elastic says that with ES|QL from a single query data professionals will be able to conduct complex data transformations, enrich their datasets, and simplify the data investigation process. This unified query approach is aimed to not only streamline the workflow but also significantly reduce the time and effort needed to glean insights from vast and varied data sources.

One of the key features Elastic promotes is ES|QL’s piped syntax, which allows the chaining of multiple commands into a single, cohesive command. Elastic says this makes the querying process more intuitive and efficient, allowing complex sequences of operations without necessitating multiple, separate queries, and potentially reducing complexity and errors.

Elastic also highlights ES|QL’s ability to manage concurrent processing, a feature crucial for large-scale data operations. Concurrent processing enables the ES|QL engine to process multiple data streams simultaneously, significantly speeding up response times. This is especially beneficial when working with large, complex datasets, as it ensures that queries are executed more quickly and efficiently, even when handling voluminous and intricate data.

Elastic says that the combination of piped syntax and concurrent processing in ES|QL not only enhances data querying but also bolsters overall data analysis capabilities. The logic being that this dual feature set allows for more efficient data handling and more complex analytical tasks to be performed in less time. And the assumption being that your system can handle the additional load without hiccuping.

When writing ES|QL queries, users will receive visual representations powered by the Lens suggestion engine. The query’s nature determines the type of visualization. Credit: Elastic.

Amreth Chandrasehar, director of ML Engineering, Observability and Site Reliability Engineering at Informatica sees ES|QL as a game changer for his company. “Once released, it will be our primary query expression language,” he said in a recent press release.

Elastic also recently announced a new two-year agreement with Amazon Web Services (AWS) to integrate Amazon Bedrock with the Elastic AI Assistant. Elastic says the first Bedrock integration with the Elastic AI Assistant will be for security use cases, with observability to follow thereafter.

The technical preview of ES|QL is out now for users to test with the complete version set for a 2024 release.

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