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Practical Elasticsearch Query Language
Query, Analyze, Filter, Aggregate, Join, and Search across Billions of Records with ES|QL
This book is 100% completeLast updated on 2026-07-08
ES|QL is a fresh, piped language with its own compute engine, built for the way people actually think. You start with a source, then chain small, clear steps with a single pipe, just like you would at a command line. Filter, transform, aggregate, search, enrich, and rank, all in one easy-to-read line that goes from top to bottom like a sentence.
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About
About the Book
Have you ever thought how great it would be if you could filter, transform, aggregate, search and enrich billions of Elasticsearch records in a single, readable line?
Well, that's the promise of ES|QL. It's the modern piped query language that's at the heart of this book. To get started, you just need to pick a source, chain each step with a pipe, and read your query from top to bottom like a sentence. The amount of JSON needed for this kind of work is much less than it used to be. This book is for data analysts, security professionals, and developers who work with large datasets, and it takes you from your very first query to production-ready integration. You'll be shaping and summarising data, cleaning messy logs, joining across indices, ranking results by relevance, and building semantic and hybrid AI-powered search.
Plus, you'll be visualising your findings in Kibana dashboards and running ES|QL directly from Python, JavaScript, and automation.This book is all about getting you hands-on with real-life examples in an online retail setting, so you can put each concept into practice and get writing ES|QL straight away.
Key Learnings
- Write piped ES|QL queries that can filter, transform, and aggregate in one readable flow.
- Retrieve rows and columns with precise filtering and shaping.
- Compute new columns using math, string, date, and conditional functions inside queries.
- Summarise millions of rows into totals, averages, and trends using STATS.
- Clean multi-value fields and parse unstructured logs into typed, query-ready columns.
- Correlate data across indices using ENRICH policies and LOOKUP JOIN.
- Rank results with full-text MATCH and relevance scoring.
- Build semantic, hybrid, and AI-assisted search with vectors and inference.
- Turn queries into Kibana charts and interactive dashboards.
- Run ES|QL from Python, JavaScript, with further automation too.
Table of Content
- Running ES|QL Queries
- Retrieve Desired Data
- Compute and Transform Values
- Summarize Data with Aggregations
- Clean and Reshape Messy Data
- Correlate Data across Indices
- Search by Relevance
- Build AI-Powered Search
- Visualize and Build Dashboards
- Integrate ES|QL into Applications
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About the Author
GitforGits | Asian Publishing House
We are the engineer’s publisher, the coder’s mentor, and the content alchemist—meticulously turning dense tech into practical gold. With a growing library of 100+ titles, we don’t just develop technical books, rather we build roadmaps for professionals across Python, MySQL, DevOps, Rust, AI, Kotlin, Arduino, Golang and everything around the massive IT ecosystem. Every chapter, every script, every project is a tool in the hands of developers who want to get things done.
Where others summarize, we construct step-by-step learning blueprints, cutting through clutter, banning the fluff, and ensuring every paragraph delivers hands-on value. Our audience isn’t learning from scratch—they’re leveling up with purpose, and we stand by them with code-first content, consistent project workflows, and a zero-redundancy approach.
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