Columnar Storage and VectorDB Indexing

8/4/2026

Introduced Columnar Mode as an opt-in indexing option for Elasticsearch, storing fields in a column store without an inverted index by default to reduce storage footprints and improve indexing and analytical query performance. Also introduced VectorDB index mode, a simplified configuration for vector search that applies optimized defaults for quantization, merge policy, and cache loading, making vector search easier to set up and performant out of the box.

7/23/2026

This post details the engineering behind Elastic's new metrics capabilities, focusing on the columnar metrics engine that improves uptime and cost efficiency for public sector IT. It highlights the re-engineering of Elasticsearch's time series data storage and querying, resulting in a 6.6x reduction in storage footprint for metrics and up to 160x faster time series queries. The post also covers native Prometheus and PromQL support, agentic investigation workflows, flexible deployment options (Cloud, on-prem, air-gapped), and a data volume-based pricing model. A streamlined migration process using the Observability Migration Platform is also introduced.

7/15/2026

This post details Elastic's recognition as a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms. It elaborates on the technical reasons for this recognition, focusing on: 1. High cost-efficiency for logs, metrics, and traces achieved through specialized storage engines (columnar for metrics, full-text for logs/traces) that offer significant compression compared to standard indexing. 2. Elastic Streams for automatically extracting structure and operational context from raw logs, enabling proactive AI-driven investigations. 3. AI-driven investigations leveraging complete telemetry context, semantic retrieval, and context engineering for data enrichment at ingest. 4. Support for open standards like OpenTelemetry and Prometheus, allowing native ingestion and querying without schema translation.

7/25/2023

This post details the implementation of Retrieval Augmented Generation (RAG) using Elasticsearch as a vector database and Amazon SageMaker JumpStart for hosting Large Language Models (LLMs). It outlines the architecture, data ingestion process (including converting text to vectors and storing them in Elasticsearch), and the real-time query flow where hybrid search (BM25 and kNN) is used to retrieve relevant documents. The LLM then uses these documents to generate domain-specific responses. The post also provides step-by-step instructions for setting up an Elasticsearch cluster, loading embedding models, configuring web crawling for data ingestion, and deploying LLMs via SageMaker.