Columnar Storage and VectorDB Indexing

6 posts · Jul 2023 – Aug 2026

What this trail covers

Elasticsearch 9.5 introduces Columnar Mode for reduced storage and faster analytics, and VectorDB index mode for out-of-the-box vector search optimization. These features enhance data storage efficiency and simplify the implementation of AI and RAG applications. The new columnar metrics engine in Elastic Observability enables public sector IT teams to combine logging, metrics, and traces in one platform, improving uptime and cost efficiency. It offers native Prometheus and PromQL support, agenti

All Elastic trails

Aug 2026Elastic 9.5: Columnar, VectorDB index mode & auto-calibration, and AI-driven alert triageElasticsearch 9.5 introduces Columnar Mode, an opt-in indexing mode that stores fields in a column store for reduced storage and faster analytical queries. It also introduces Columnar Logs, a specialized profile for log data that keeps a single inverted index on the message field for fast full-text search while storing everything else columnarly. VectorDB index mode is introduced for high-performance vector search with a single setting, automatically tuning quantization, merge policy, and cache loading.
Jul 2026Elastic’s new metrics capabilities will dramatically improve uptime for public sector ITThis 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.
Nov 2024Crafting a hybrid geospatial RAG application with Elastic and Amazon BedrockThis post details the creation of a hybrid geospatial RAG application using Elasticsearch and Amazon Bedrock. It highlights Elasticsearch's capabilities in combining lexical, geospatial, and vector search within a single query. The application leverages Elasticsearch for storing and querying geospatial and vector data, Amazon Bedrock for LLM integration and entity extraction, and AWS Location Service for geocoding. This demonstrates a practical application of advanced search techniques for generative AI.
Apr 2024Understanding the approximate nearest neighbor (ANN) algorithmThis post explains the Approximate Nearest Neighbor (ANN) algorithm, its trade-offs with exact Nearest Neighbor (NN), and its importance in vector search. It details how ANN works through dimensionality reduction and indexing, when to use it (large/high-dimensional data, real-time applications), and introduces specific ANN algorithms like KD-trees, Locality-Sensitive Hashing (LSH), Annoy, and linear scan, highlighting their respective strengths and weaknesses. The post emphasizes ANN's role in enabling fast similarity search and scalability for vector databases.
Apr 2024Vector database vs. graph database: Understanding the differencesThis post compares vector databases and graph databases, detailing their data structures, querying mechanisms, and use cases in areas like fraud detection, scientific research, e-commerce, and media. It highlights how Elasticsearch can support both vector search (for similarity) and graph-like relationship analysis, suggesting a hybrid approach for advanced applications. The post provides a framework for choosing between the two based on data characteristics, use cases, and performance needs.
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Jul 2023Generative AI (GAI) using Elastic & Amazon SageMaker JumpStart - Elasticsearch LabsThis post details the implementation of a Retrieval Augmented Generation (RAG) system using Elasticsearch as a vector database and Amazon SageMaker JumpStart for hosting open-source LLMs. It outlines the architecture, offline data ingestion process (document ingestion, vectorization, storage in Elasticsearch), and real-time query flow (hybrid search in Elasticsearch, context passing to LLM, response generation). Specific setup steps for an Elasticsearch cluster, loading an embedding model, creating an index, and configuring a web crawler are provided. It also covers choosing and deploying an LLM from SageMaker JumpStart and integrating it with Elasticsearch for RAG.