OpenSearchCon Europe 2026

In this talk, we present the integration of Workload Management (WLM) and Query Insights (QI) in OpenSearch to provide workload-aware query performance insights. By treating WLM group information as a first-class attribute in Query Insights records, operators can filter, group, and analyze top queries in the context of workload isolation.

We’ll demonstrate how this integration enables clearer performance attribution, reduces manual data correlation, and supports more targeted optimization and troubleshooting workflows. Attendees will gain a practical understanding of how unifying WLM and Query Insights improves observability and operational efficiency in shared OpenSearch clusters.

Search systems are hitting a wall. Static sharding causes hotspots, slow queries, and costly overprovisioning. But what if your search infrastructure could evolve in real time - splitting, scaling, and optimizing itself on the fly?

We present a cutting-edge dynamic shard topology in OpenSearch that redefines how scalability and search efficiency coexist. Inspired by distributed systems principles and real-world cloud constraints, this architecture uses a root-child shard model with mutually exclusive hash ranges to enable conflict-free, high-throughput ingestion and ultra-fast targeted search.

But we didn’t stop there. We embedded a playoff-style lookup algorithm - a smarter alternative to brute-force fan-out—ensuring queries scale logarithmically, not linearly.

The result?
🔹 True infinite horizontal scalability without reindexing.
🔹 Near-zero latency lookups, even during shard splits.
🔹 Elastic, self-healing architecture balancing cost, performance, and reliability.

This isn't a prototype. It's cloud-native and production-ready. If you're building systems at scale - or aiming to - this session will change how you think about search.

Join us to explore the latest performance improvements in OpenSearch, highlighting the integration of Lucene’s bulk collection API and enhancements to the gRPC Search API. This session will dive into how bulk collection optimizes aggregation execution, delivering measurable gains across analytical workloads by batching operations and reducing computational overhead. We will also cover the expanded features of the gRPC Search API in OpenSearch 3.4, which now supports new query types, improved bulk requests, and multiple document formats. To make these concepts clear and practical, we will showcase live demos illustrating performance boosts in aggregation workloads and expanded query handling through gRPC. Attendees will gain a clear understanding of how these advancements significantly enhance efficiency and broaden query capabilities in real‑world use cases. Key takeaways include understanding Lucene’s bulk collection benefits, learning about new gRPC query types, and discovering improvements in bulk request handling and document format support.

Open source powers most of today’s technology, yet many people still judge a project only by visible activity. The problem is that activity does not always reflect real health. A project can look busy while struggling behind the scenes. It can also look quiet while staying stable and strong.

This talk explores the gap between what we see on the surface and what a community is actually experiencing. It draws from my work as a contributor and member of the Linux Foundation’s CHAOSS project, where researchers and maintainers built shared ways to study open-source health in a practical and human way.

The session explains two core ideas. Sustainability covers contributor experience, documentation, onboarding, clarity, and the ability to move forward without burnout. Survivability covers risk, key person dependence, governance strength, and the ability to handle sudden change.

Attendees will learn how to ask better questions, spot early signals, avoid misreading data, and support communities with a clearer understanding of what health truly looks like. This talk is designed for community managers, contributors, OSPO teams, and anyone who works inside open source every day.

Logs are the foundation of most observability platforms, but they are also where OpenSearch clusters fail first. Shard explosion, uncontrolled mappings, slow queries, and rising storage costs are common outcomes of poorly designed log workloads.

This session focuses on a single, concrete problem: how to design and operate OpenSearch for high-throughput log observability in production. Drawing from real-world experience, it goes deep into the architectural and operational decisions that matter most when handling large log volumes.

The talk covers log ingestion pipelines, index and shard design, lifecycle management, and query performance tuning specifically for observability use cases. Instead of default setups and theory, attendees will learn what actually works, what breaks at scale, and how to fix it.

By the end of the session, participants will have a practical blueprint for running OpenSearch as a reliable, cost-effective log observability platform.

AI agents rely on LLM reasoning or vector similarity to decide and choose tools. But these approaches lack deterministic control, explainability and low-latency guarantees. In real production systems, agents need a fast and reliable way to decide which tool to invoke based on structured rules and historical context.

With this, Agentic observability has emerged as a critical discipline in 2026, shifting from simple logging to in-depth tracing of non-deterministic, multi-step workflows. Building a reliable GenAI app monitoring strategy for tracing the reasoning chain, now becomes more important from an observability pov.

In this talk, we will demonstrate how we can understand AI Agent decisioning for tool calling and further reasoning with OpenSearch. We will showcase how OpenSearch indexes agent logs, tool call metadata, execution constraints and past outcomes.

We will showcase a live hands-on demo of OpenSearch ML, indexing the AI Agent logs-metrics and further use them to comprehend the logs to understand the functioning and reasoning of AI agents.

Confused about how AI Agents are making decisions ? Then this talk is for you !

Search has evolved from matching tokens to understanding intent, but the new challenge is doing it at scale, with high precision and low latency.

In this session, we define the "State of the Art" for OpenSearch 3.x by diving into its most advanced retrieval and optimization capabilities.

We will move beyond basic k-NN to showcase specific ranking techniques, including Radial Search for precise vector scoping and Maximal Marginal Relevance (MMR) to solve the "diversity problem" in AI-generated results.

Crucially, we will look under the hood at Memory Optimized Search to demonstrate how to reduce infrastructure footprint without sacrificing accuracy. We will explain how techniques like Asymmetric Distance Computation and Random Rotation drive the latest quantization engines, allowing you to run billion-scale vector workloads efficiently.

Finally, we will show how to operationalize these complex architectures using the new OpenSearch Workflow Builder.
Attendees will see how to chain these advanced processors into reusable AI search pipelines using a visual interface, proving that next-generation semantic search can be both powerful and accessible.