The article walks through EasySearch's Rules plugin, showing how its high‑performance C++ rule engine can automatically match and tag documents during the ingest pipeline, enabling zero‑latency content classification for scenarios like regional, sentiment, and entity tagging.
Elasticsearch does not store a built‑in write timestamp, so to trace when a document was indexed you must add the field during ingest, using either an Ingest Pipeline, Logstash/Beats configuration, or application‑side code, with guidance on advantages, caveats, and handling historical data.
When legacy or sensitive fields bloat an Elasticsearch index, rebuilding the index with reindex can be costly, so this article examines why fields cannot be removed directly and presents four practical, non‑reindex approaches—_source filtering, index templates, ingest pipelines, and alias‑based gradual migration—detailing their trade‑offs and implementation steps.
The article analyzes the semantic gap in e‑commerce search, compares traditional keyword matching with vector‑based retrieval, and provides a step‑by‑step implementation using Elasticsearch/Easysearch pipelines, embedding models, and a hybrid search strategy to improve user intent understanding.
This article walks through creating a custom Elasticsearch 8.x query that first groups documents into time buckets (e.g., within 3 days, 4‑7 days, older) using a pre‑computed time_bucket field, then sorts each bucket by the _score relevance of the content field, covering pipeline definition, mapping, sample data, DSL query, and practical considerations.
The article explains how to sort image filenames containing numbers in Elasticsearch by either using a painless _script to extract the numeric part at query time or, more efficiently, by preprocessing filenames with an ingest pipeline that creates a numeric field for fast sorting, recommending the pipeline for performance‑critical use cases.
The article explains how Elasticsearch stored scripts can be created and invoked within ingestion pipelines to decode Base64 and hexadecimal data, showing reusable Painless scripts, pipeline configuration, and the advantages of modular, maintainable transformations without inline code duplication.
This guide walks you through the full Elasticsearch data‑modeling workflow, covering business‑driven design, handling large data volumes, optimal settings, mapping choices, and complex parent‑child relationships, while providing practical code examples and visual diagrams for immediate application.
This article demonstrates how to use Elasticsearch, its ingest‑attachment plugin, and the IK analyzer to upload various file types, preprocess them, store them in an index, and perform accurate keyword searches with highlighting, providing complete Java code examples and configuration steps.
This tutorial explains how to leverage Elasticsearch to support file upload and download, preprocess PDF/Word/TXT files via ingest pipelines and the attachment processor, configure index mappings with Chinese IK analyzers, and perform accurate keyword searches with highlighting, all demonstrated with Java code examples.
This guide explains how to upload Word, PDF, and TXT files, preprocess them with Elasticsearch ingest pipelines, index their content using appropriate analyzers, and perform accurate keyword searches with highlighting, providing complete Java code examples and configuration steps.
This guide walks through creating a file upload service that indexes Word, PDF, and TXT files in Elasticsearch, uses an ingest‑attachment pipeline to extract text, configures Chinese IK analyzers for precise keyword search, and demonstrates Java code for indexing, querying, and highlighting results.
This article explains three practical methods for assigning default values in Elasticsearch—using ingest pipelines, update‑by‑query scripts, and pipeline scripts—while also addressing how to maintain create_time and update_time fields in a way similar to relational databases.
