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Practical Elasticsearch Query Language
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 LearningsWrite 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 ContentRunning ES|QL QueriesRetrieve Desired DataCompute and Transform ValuesSummarize Data with AggregationsClean and Reshape Messy DataCorrelate Data across IndicesSearch by RelevanceBuild AI-Powered SearchVisualize and Build DashboardsIntegrate ES|QL into Applications
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 LearningsWrite 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 ContentRunning ES|QL QueriesRetrieve Desired DataCompute and Transform ValuesSummarize Data with AggregationsClean and Reshape Messy DataCorrelate Data across IndicesSearch by RelevanceBuild AI-Powered SearchVisualize and Build DashboardsIntegrate ES|QL into Applications
