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Sakshi Nasha is a Software Engineer with a passion for building software and driving diversity in tech. An open-source enthusiast and OpenSearch Ambassador, she actively contributes to FOSS communities and speaks internationally on topics including GO, APIs, Security, PostgreSQL and... Read More →
David Nalley is Director of Developer Experience at Amazon Web Services (AWS), where he leads efforts to improve how developers interact with AWS services and technologies. He brings over two decades of experience in technology to his role. Nalley previously served as President of... Read More →
Blinkit is India's leading quick commerce platform, heavily powered by OpenSearch across core app experiences - product search, filtering, sorting, and aggregations - all delivered at very low latency.
This session walks through the re-architecture of Blinkit's OpenSearch cluster, addressing the limitations of our old setup that surfaced during peak festive traffic as the platform continued to scale rapidly.
We'll cover how our multi-cluster architecture is designed for low latency, high reliability, and availability - and the key decisions that got us there.
Harshit is a Software Engineer on Blinkit's Search Engineering team, working primarily on OpenSearch. A past speaker at OpenSearchCon 2024, he actively engages with the open-source community and brings those learnings to build search infrastructure that powers the magic of quick commerce... Read More →
Bharti is a Software Engineer on Blinkit's Search Engineering team, working on OpenSearch - indexing pipelines and multi-cluster architecture. Building search that matches Blinkit's pace.
Sakshi Nasha is a Software Engineer with a passion for building software and driving diversity in tech. An open-source enthusiast and OpenSearch Ambassador, she actively contributes to FOSS communities and speaks internationally on topics including GO, APIs, Security, PostgreSQL and... Read More →
Kubernetes platforms generate massive volumes of logs from microservices, infrastructure, and platform services. At scale, OpenSearch observability pipelines often struggle with shard explosion, indexing bottlenecks, JVM pressure, and slow queries.
This session presents a practical architecture for operating an OpenSearch observability platform handling 100M+ logs per day from Kubernetes environments. We will walk through the end-to-end pipeline—from log collection to ingestion and indexing—and share performance engineering techniques used to maintain cluster stability under heavy workloads.
Topics include shard and index design, JVM and thread-pool tuning, optimizing indexing throughput, and using lifecycle policies and hot-warm architectures to scale efficiently. Attendees will gain actionable strategies for building resilient OpenSearch observability platforms for cloud-native systems.
I’m a technology enthusiast specializing in Performance Engineering, DevSecOps, and Cloud-Native solutions, focused on building high-performing, resilient systems. I’m passionate about Kubernetes, continuous optimization, and secure-by-design practices. As a CNCF Hyderabad volunteer... Read More →
We migrated ~1,500 OpenSearch nodes across 7 clusters in 5 regions from Intel to AWS Graviton 4, upgrading from OpenSearch 2.13 to 3.4 and JVM 17 to 21 along the way. We saw 2x indexing throughput and ~30% cost savings. We want to share the benchmarking data, compare migration strategies (inline upgrade, snapshot-restore, and why CCS didn't work), and push more teams toward Graviton.
A practical guide to evaluating and executing a Graviton migration at scale — covering instance type benchmarking (i4i/i4g/i7i/i8g), migration strategy trade-offs (inline upgrade is fast but risky, snapshot-restore is safe but needs parallel infrastructure, CCS doesn't work across major versions), production validation steps, and the real performance and cost numbers from 1,500 nodes in production.
Large-scale Graviton migration data for OpenSearch is scarce. By sharing production numbers from 7 clusters across 5 regions — including what didn't work — we want to de-risk Graviton adoption for the community. The 2x throughput and 30% cost reduction we're seeing should motivate more teams to make the move, and our migration playbook gives them a concrete path to follow.
Staff Engineer at Freshworks with 8 years of experience designing high-scale observability platforms for logs, metrics, and traces. Focused on cost-efficient, petabyte-scale data management. Skilled in Python, AWS, Elasticsearch, OpenSearch, InfluxDB, Cortex, and ClickHouse. Passionate... Read More →
Principal Engineer at Freshworks with 14 years of experience in distributed systems, observability, and platform engineering. Leads the Observability team and built Freshworks’ Distributed Tracing Platform. Early contributor to OpenTelemetry SDKs. Previously worked on Search Platform... Read More →
Staff Engineer at Freshworks with around 10 years of experience, focused on building a high-scale observability platform for logs, metrics, and traces, managing petabytes of data across billions of points with an emphasis on cost-effectiveness. Skilled in Java, Infrastructure, AWS... Read More →
Monday June 15, 2026 10:50am - 11:30am IST 204 (Level 2)
Large Language Model agents frequently rely on retrieval systems to make decisions, yet most pipelines still depend on a naive top-k retrieval strategy. In agentic workflows this can lead to a dangerous failure mode: agents confidently acting on low-relevance results, amplifying hallucinations through iterative tool use.
This session presents a practical framework for implementing search-confidence guardrails using OpenSearch. Instead of treating retrieval scores as opaque signals, we demonstrate how to convert heterogeneous ranking outputs into a normalized trust metric that agents can reason about.
We will explore techniques such as score normalization, Reciprocal Rank Fusion (RRF), and hybrid retrieval (BM25 + vector search) to build a deterministic confidence layer on top of OpenSearch queries. Using this signal, agents can dynamically decide whether to answer, re-query, or request clarification, preventing cascading hallucinations.
The talk includes a reference architecture for agent-search interaction, evaluation workflows using the OpenSearch comparison tooling.
Data & AI Architect with 18+ years of experience building large-scale search, Data and AI platforms for global enterprises. Specializes in semantic search, RAG architectures, and agentic AI systems powered by OpenSearch. Architected enterprise search platforms indexing 8M+ documents... Read More →
With over 6 years of experience in Data Analytics and AI/ML, partners with global enterprise customers to navigate the complexities of cloud migration and architectural re-engineering. An expert in high-performance Data Lakes and Warehousing solutions, leveraging AWS Glue, Redshift... Read More →
This session is about the recent productivity study we ran in our open source lab (OSL). I’ll walk through the end-to-end architecture: extracting device telemetry, transforming it into task sessions, and running association rule mining on top of OpenSearch to compute support and confidence for goal-relevant activity patterns. The entire setup runs locally, therefore no risk of activity data theft.
ActivityWatch is cross platform open-source time tracker that collects telemetry on how we spend time on devices. It comes with watchers that can do all the data collection from AFK to browser windows. In our setup, ActivityWatch runs on each device, and OpenSearch is self-hosted on our lab’s local LAN (I'm academic), and then we ingest logs into it every 10 seconds using API-based ingestion. While ActivityWatch runs, users tag their intended task (e.g., #learn, #java). We align tags with telemetry windows, sessionize events into transactions (items[]=apps/domains, duration), and mine rules with support/confidence/lift per tag. If the current window drifts from the active tag with high confidence for a short period, we send a nudge reminding they are distracted from the original goal.
Lecturer/ Researcher, Informatics Institute of Technology, Sri Lanka
After working in industry as a lead platform engineer, I decided to pursue an academic path. I find greater joy in OSS development and research. I’m also a recipient of the Most Valuable Professional(MVP) Award from Microsoft for cloud native contributions.
In this talk, we will share our experience building a highly available, multi-tenant, multi-AZ ListView platform on OpenSearch that powers large-scale list and filter experiences for near real-time dashboards.
The session will cover how we designed a fault-tolerant and cost-efficient architecture capable of delivering low-latency queries and high-performance filtering at scale. Using real-world examples from operating OpenSearch at Freshworks, we will walk through practical lessons from building an internal DSL to simplify OpenSearch adoption across teams, designing scalable ETL-driven indexing pipelines, handling concurrent updates & deletes, enabling tenant-level horizontal scaling, and managing dynamic field growth in multi-tenant environments.
This talk will highlight architectural patterns, trade-offs, and operational insights from running OpenSearch in a production SaaS environment. Attendees will gain practical guidance on building reliable, scalable, and performant search systems for large-scale filtering and analytics workloads.
I'm Karthikeya Surabhi, a Senior Engineering Manager at Freshworks, leading the Search and Foundational Services team. I own the search infrastructure and core platform services behind Freshworks products, operating at billions of reads per month across petabyte-scale data. My focus... Read More →
Software engineer specializing in search infrastructure, distributed systems, and large-scale data platforms, SAAS. Extensive experience designing and operating OpenSearch-based systems for enterprise-scale workloads. Expertise in building scalable, fault-tolerant, multi-tenant architectures... Read More →
Monday June 15, 2026 11:40am - 12:00pm IST 205 (Level 2)
Large-scale ingestion can significantly impact OpenSearch cluster stability, query latency, and operational efficiency. Traditional bulk APIs and Spark/Hadoop connectors rely on online indexing, where clusters must handle indexing and queries simultaneously—creating resource contention and long ingestion windows.
This session introduces an Offline Batch Ingestion framework that builds OpenSearch-compatible Lucene segments outside the cluster using distributed Spark executors. The system leverages OpenSearch’s native indexing engine and snapshot/restore APIs to deploy indexes atomically with zero production impact.
We’ll cover the architecture (distributed indexing, shard merge, snapshot generation), technical implementation using OpenSearch Engine APIs, production learnings, scalability characteristics, and future extensions such as vector indexing. This approach decouples indexing compute from serving clusters, enabling faster ingestion, safer reindexing, and improved operational resilience.
Tarun Kishore is a Software Engineer II in Search Platform working in Uber He has led the design of offline indexing frameworks and production-safe deployment pipelines. He actively contributes to OpenSearch, including enhancements to Index State Management and documentation impr... Read More →
Monika Agarwal is a Staff Software Engineer specializing in large-scale distributed systems and search infrastructure. Her work spans indexing pipelines, batch as well as real-time ingestion systems, with a strong focus on scalability, reliability, and performance. She has led initiatives... Read More →
Search performance directly impacts user experience and system resilience. OpenSearch Query Insights provides unprecedented visibility into query execution with minimal performance overhead.
This presentation explores Query Insights capabilities and demonstrates how to identify performance bottlenecks, diagnose issues in real-time, and optimize your cluster. Through live demos, you'll see how to pinpoint the top queries consuming resources, understand query patterns for structural improvements, and leverage dashboards for data-driven optimization. Whether you're managing a small cluster or large-scale search platform, you'll gain the visibility needed to keep OpenSearch running at peak efficiency.
Key Features to Demo: - Real-time query monitoring and live execution tracking - Resource consumption rankings (CPU, memory, latency) - Advanced query grouping and pattern detection - Interactive Query Insights Dashboard with filtering and drill-down - Query metadata and node/shard-level insights - Historical query analysis and trend tracking - Performance metrics export and integration capabilities
OpenSearch contributor and plugin maintainer passionate about distributed search systems and large-scale observability. Skilled in full-stack development, containerization (Docker, Kubernetes), and data visualization. Currently exploring the intersection of LLMs, autonomous agents... Read More →
David is a Software Engineer on the OpenSearch Search team, where he focuses on enhancing search performance and observability. He has contributed to key initiatives such as coordinator node search latency tracking, distributed tracing, and the development of Query Insights. David... Read More →
Monday June 15, 2026 12:10pm - 12:30pm IST 204 (Level 2)
Running OpenSearch in production at scale is very different from what tutorials or books show. When you manage many clusters, you start seeing issues you didn’t know existed, like shard and disk imbalance, uneven traffic distribution, and unstable cluster states. These problems can degrade search performance and even cause incidents.
In this session, we will share real challenges we faced while operating large OpenSearch clusters and the practical solutions we used to stabilize them. We will explore how shard distribution can silently create problems, why some nodes end up using much more disk than others, and how clusters behave under heavy indexing and query load.
This talk focuses on the operational side of running OpenSearch in production. We’ll discuss strategies for better shard allocation, preventing disk imbalance, controlling indexing pressure, and keeping clusters stable under load.
Attendees will leave with practical techniques they can apply to run OpenSearch clusters reliably at large scale and improve stability and performance in real-world environments.
Dhruvan Tanna is a DevOps engineer with 8 years of experience building and operating large-scale cloud infrastructure. He specializes in Kubernetes, OpenSearch operations, and automated CI/CD systems across AWS and GCP. Passionate about reliability and performance, he focuses on solving... Read More →
Aditya Krishnakumar is a Senior Site Reliability Engineer at SentinelOne and has 8+ years of experience with Cloud, DevOps and Kubernetes infrastructure. His current focus is on Platform Reliability and Data Infrastructure within SentinelOne and has been a technical reviewer for books... Read More →
Most search systems are designed primarily for English. But what happens when we want search to truly understand regional languages like Tamil?
Tamil is one of the world’s oldest living languages with a rich literary tradition, yet building effective search for it comes with unique challenges such as complex morphology, script handling, tokenization, and language-specific relevance tuning.
In this talk, we share how we built an intelligent Tamil search system using OpenSearch. Using Tirukkural, the classic Tamil text of 1,330 couplets, as a practical dataset, we explore how OpenSearch can be adapted to understand Tamil queries and return meaningful results.
We’ll walk through practical techniques such as custom analyzers, tokenization strategies, stemming approaches, and relevance tuning for Tamil search. We’ll also demonstrate how users can search Kurals using natural Tamil phrases and still discover the most relevant verses.
Beyond Tamil, these techniques can help developers build better search experiences for many regional Indian languages bridging ancient knowledge with modern search technology.
Tamil vanan is a cloud native Tech lead. He is passionate about finding solutions to problems in the cloud native environment.
He works with cloud-native technologies like Kubernetes, multi-cloud and networking. He is a passionate supporter of open source and CNCF and actively participates in it... Read More →
AI agents are moving from demos to production, but observability hasn't kept up. When an agent takes a wrong path, hallucinates mid-task, or silently degrades, how do you investigate? Traditional APM treats agent execution as a black box. We need purpose-built, OpenTelemetry-native observability for agentic AI.
We introduce the Agent Traces and Agent Health for OpenSearch: a native UI for exploring agent execution traces. OTel SDKs with GenAI semantic conventions (gen_ai.* attributes) instrument your agents, Data Prepper ingests the spans, and Agent Traces show you hierarchical trace views, detail agent maps, and aggregate metrics like token usage and latency percentiles - all queryable via PPL.
We demonstrate root-cause investigation: expanding execution trees to inspect each LLM call and tool invocation, querying spans to answer "which tool call caused the agent to diverge?" We then go deeper with Agent Health's golden path comparison that evaluates trajectories against expected behavior. Whether you're building agents for customer support, code generation, or data pipelines, you'll leave with a practical playbook for agent observability.
A DevOps engineer with a great passion for building communities around DevOps. Organiser of Google Cloud Gandhinagar, CNCF Gandhinagar, Hashicorp User Group Gandhinagar and Open Source Weekend. Have mentored 15+ hackathons and open source programs. I have given more than 15 talks... Read More →
Monday June 15, 2026 12:40pm - 1:20pm IST 205 (Level 2)
Traditional keyword search is no longer sufficient for modern SRE workflows because infrastructure error messages are often too generic to be actionable. This session explores a technical shift in observability: moving from standard indexing to high-dimensional vector embeddings for terminal traces and post-mortems using OpenSearch.
We will deep-dive into a privacy-first AI architecture, demonstrating how to integrate OpenSearch’s vector engine with local, private models (such as Llama) to ensure sensitive production logs never leave your VPC.
You will learn the mechanics of building a Retrieval-Augmented Generation (RAG) pipeline designed specifically for infrastructure telemetry. The session includes a live demo where a local agent "recalls" the exact historical fix for a messy, real-time stack trace, providing a direct link to the relevant PR or configuration change from the past.
I am a Senior Site Reliability Engineer at KodeKloud . I am an Open source contributor, evaluating and contributed in various open source tools and projects, such as, Microsoft's Open source libraries, OpenCV, SUSE, etc. I was also a Google Summer of Code contributor 2022 and a GitHub... Read More →
Vector search in OpenSearch offers a rich set of configuration options, but choosing the right combination of engine, algorithm, quantization, and storage tier can be daunting — especially as your workload grows or shifts from semantic search to agentic AI. This session covers both workload types at small, medium, and high scale. You'll learn how to apply the cost-recall-latency curve to choose between Faiss and Lucene, HNSW and IVF, and the right quantization technique for your budget and recall requirements. You'll explore tiered storage from in-memory to disk-based and memory-optimized, and production tuning techniques including bulk indexing strategies, GPU-accelerated builds, and automated parameter optimization. You'll leave with best practices to apply whether you're running thousands of vectors on a single node or billions across a fleet.
Jon Handler is a Senior Principal Solutions Architect at Amazon Web Services based in Palo Alto, CA. Jon works with OpenSearch and Amazon OpenSearch Service, helping customers who have vector, search, and log analytics workloads that they want to move to the AWS Cloud. Prior to AWS... Read More →
Monday June 15, 2026 12:40pm - 1:20pm IST 204 (Level 2)
This session explores building and maintaining highly available OpenSearch clusters with robust disaster recovery strategies. We'll examine critical components of resilient OpenSearch infrastructure, focusing on multi-node cluster design across availability zones and effective backup solutions. The presentation covers essential aspects of shard allocation, replica configuration, and cross-cluster replication for continuous operation during failures. Learn practical approaches to automated failover, snapshot management, and monitoring strategies for optimal cluster health. Through real-world examples and lessons learned from large-scale implementations, we'll discuss common pitfalls and best practices for both cloud and on-premises deployments. This talk is designed for OpenSearch administrators, DevOps engineers, and architects managing mission-critical search infrastructure.
Key Takeaways:
-Understanding HA architecture patterns and DR strategies -Implementing effective backup and recovery solutions -Monitoring and maintaining cluster health -Real-world examples and practical demonstrations -Best practices for different deployment scenarios
As a Security Engineer at Amazon, I specialize in developing and enhancing security features for distributed systems. I'm an active contributor to the OpenSearch Security project, focusing on authentication mechanisms and access controls. My contributions include implementing critical... Read More →
Security isn't just about firewalls and alerts, it’s about curiosity, context, and catching the weird stuff before it gets weird. But let’s be real: most teams don’t have the budget (or patience) for a million-dollar SIEM.
In this talk, I’ll walk you through how we turned OpenSearch into a scrappy, surprisingly powerful threat-hunting platform. Using native tools, open-source plugins, and a healthy dose of creativity, we built real-time alerting and investigation flows without blowing up costs. We'll cover:
• Designing log schemas that highlight anomalies. • Building threat-detection pipelines using ingest processors and OpenSearch Dashboards. • Real-life incident where OpenSearch helped us catch something our cloud provider missed.
If you’ve ever felt like security tools are either overkill or underwhelming, this session is for you. You’ll walk away with practical patterns and open-source recipes for turning OpenSearch into your security command center — no license key required.
Prerit is a Cloud-Native Platform Leader with extensive experience designing and scaling secure, resilient cloud infrastructures. As the former CTO of KubeCloud, he built no-code solutions bridging Cloud, DevOps, and SRE, leading the company to a successful acquisition. Currently... Read More →
Ever wondered what really happens after you hit “Search”? This talk takes you on a fast, funny, and surprisingly eye‑opening journey through the secret life of a query inside OpenSearch. We’ll follow it as it squeezes through analyzers, hops across shards, meets vectors, dodges caches, and races toward the perfect answer, all in milliseconds. Along the way, you’ll discover why some queries feel instant, why others take a coffee break, and how tiny architectural choices can totally change the search experience. Whether you're new to OpenSearch or a seasoned engineer, you’ll walk away with a delightful mental model of how search really works and how to make it faster, smarter, and a whole lot more magical.
Shubhi Khanna is a senior engineer and technical storyteller who specializes in making complicated systems feel simple. Her background spans firmware, data flows, and AI‑driven workloads, and she has presented at global conferences. She’s passionate about helping developers understand... Read More →
Databases are the backbone of every application, yet monitoring them effectively remains a challenge. In this session, we walk through monitoring production databases powering an e-commerce platform Valkey for caching and PostgreSQL for the catalog deployed on Kubernetes & Docker, using OpenSearch and Prometheus as the observability backbone.
We demonstrate a complete OpenTelemetry-based pipeline: instrumenting both databases with OTel SDKs, routing telemetry through Data Prepper to store time-series metrics in Prometheus and traces/logs in OpenSearch. With AI-assisted investigation using text-to-PPL and text-to-PromQL, engineers query live telemetry in natural language asking "show me slow queries in the catalog service in the last hour" without writing complex syntax.
We also show how to use AI to generate RCA visualizations and surface performance insights across your database fleet. Whether you manage a handful of databases or hundreds, you'll leave with practical patterns for building an OpenTelemetry-native database observability stack.
Jon Handler is a Senior Principal Solutions Architect at Amazon Web Services based in Palo Alto, CA. Jon works with OpenSearch and Amazon OpenSearch Service, helping customers who have vector, search, and log analytics workloads that they want to move to the AWS Cloud. Prior to AWS... Read More →
Monday June 15, 2026 3:00pm - 3:40pm IST 206 (Level 2)
Most teams have a dirty secret: they're running Elasticsearch versions they can't afford to upgrade and can't afford to stay on. The migration feels impossible due to data loss risk, query regression, extended downtime, and no rollback. So they wait. And wait. This talk ends the waiting. We walk through the OpenSearch Migration Assistant end-to-end: a fully open-source toolkit that handles metadata migration, historical backfill via Reindex-from-Snapshot, and live traffic capture + replay so your users never feel the move. We'll cover all three migration scenarios, the trickiest mapping transformation pitfalls (dense_vector → knn_vector, anyone?), and live comparative response diffing before the final cutover. Migrate smarter, not harder.
Sakshi Nasha is a Software Engineer with a passion for building software and driving diversity in tech. An open-source enthusiast and OpenSearch Ambassador, she actively contributes to FOSS communities and speaks internationally on topics including GO, APIs, Security, PostgreSQL and... Read More →
Monday June 15, 2026 3:00pm - 3:40pm IST 204 (Level 2)
Modern ecommerce search systems must do more than return results. They must understand customer intent, retrieve relevant products, and do all of this within extremely tight latency budgets. Even small delays can impact user experience and conversion.
In this talk, I will walk through the architecture of a product discovery system designed to deliver relevant product suggestions in under 200 milliseconds using OpenSearch and Lucene. The session will explore how search infrastructure can power real time product discovery across high traffic ecommerce platforms.
We will cover how queries are processed, how candidate products are retrieved efficiently, and how filtering and ranking strategies help surface high quality and buyable items. The talk will also discuss practical techniques for managing latency budgets, optimizing search queries, and designing indexes that balance speed and relevance.
Attendees will gain practical insights into building fast and scalable product discovery systems and learn how OpenSearch and Lucene can be used to power low latency search experiences in modern ecommerce applications.
Kartik is a software engineer at Uber with over six years of experience building and running large scale systems. He has previously worked at Amazon and Akamai, solving platform, reliability, and automation problems in production environments. He is a Google Summer of Code contributor... Read More →
Piped Processing Language (PPL) has quietly become one of OpenSearch's most powerful tools for observability data transformation. OpenSearch 3.5 added various powerful functions that unlock advanced use cases: join , Lookup, mvcombine, mvzip, mvfind, and mvmap for multivalue field operations, addtotals for instant summary tables, and streamstats for cumulative statistical calculations as events are processed. This session showcases these capabilities through real-world observability scenarios — correlating multivalue log fields across microservices, building running error rate dashboards with streamstats, and performing lightweight anomaly detection using just PPL with no ML model required. We'll also demonstrate cross-signal analysis by combining PPL log queries with Prometheus metric data using the new Discover experience for Prometheus data sources shipped in 3.5. Attendees will leave with ready-to-use PPL patterns for incident investigation that are more intuitive than equivalent SQL approaches.
Bharav Patel is a Specialist Solution Architect, Analytics at Amazon Web Services. He primarily works on Amazon OpenSearch Service and helps customers with key concepts and design principles of running OpenSearch workloads on the cloud. Bharav likes to explore new places and try out... Read More →
Search finds. Discovery reveals. At Shutterstock, serving one of the world's largest licensed creative content libraries at 2000+ requests per second, the difference is everything. Generative Discovery changes the contract entirely. It treats every interaction as a signal of intent, builds context across modalities, and applies cognition to surface what users did not know to ask for. This talk introduces a production architecture built around three principles: Intent: multimodal signals including text, image, and behavioral context unified into rich intent representations driving OpenSearch k-NN and hybrid retrieval. Context: session aware, editorially grounded RAG pipelines using OpenSearch as a dynamic retrieval backbone at scale. Cognition: generative agents that think, orchestrate multi step retrieval, and resolve ambiguous intent, treating OpenSearch as an intelligent reasoning substrate rather than a passive index. You will learn from real production decisions, evolving thinking, and lessons still being learned on the frontier of Generative Discovery.
Staff AI Engineer at Shutterstock | AI Platforms for Generative Discovery | Multimodal Systems, Shutterstock
Staff AI Engineer at Shutterstock designing and scaling multimodal generative discovery platforms, with 15+ years of experience building large-scale distributed systems and AI infrastructure. Currently leading the architecture of generative discovery systems operating at 2,000+ rps... Read More →
Monday June 15, 2026 3:50pm - 4:30pm IST 206 (Level 2)
Enterprise search is evolving from keyword-based retrieval into intelligent, context-aware systems powered by generative AI. While many RAG examples focus on simple vector lookups, building a production-grade, multi-modal RAG platform requires more—especially at enterprise scale.
This talk presents a reference architecture for agent-assisted, multi-modal RAG systems with OpenSearch as the core retrieval and indexing layer. OpenSearch combines lexical relevance, vector similarity, and metadata filtering across text, images, audio, and video, while preserving deterministic control over retrieval in AI-driven workflows.
The session covers ingestion pipelines, chunking strategies, hybrid and multi-vector indexing, and retrieval orchestration, and explains how OpenSearch acts as the retrieval intelligence boundary between probabilistic agent reasoning and enterprise data. Key production trade-offs around relevance tuning, multi-tenant isolation, performance scaling, and cost control are also discussed, providing practical guidance for building reliable, enterprise-ready AI search platforms.
Hands-on architect specializing in resilient distributed systems, microservices, event-driven architecture, and cloud-native modernization. AWS User Group Nagpur Leader and 6+ year AWS Community Builder, active in speaking, mentoring, and community building. My recent focus is Generative... Read More →
With over 14 years of experience in technology, I have built expertise across solution architecture, cloud platforms, DevOps, Kubernetes, AI-powered platforms, and modern software development practices.Currently, I work as an Architect at Persistent Systems, where I help design and... Read More →
Debugging production incidents in distributed systems often means manually searching through thousands of log lines to understand what went wrong. During high-impact outages, engineers must quickly correlate signals across services, identify error patterns, and reconstruct the sequence of events.
In this talk, we explore how AI investigation agents can assist engineers by using OpenSearch as the investigation backbone for incident analysis. Instead of simply retrieving logs, an agent can iteratively query OpenSearch, identify dominant error patterns, correlate events across services, and build a timeline of failures before producing a concise root-cause explanation.
We will demonstrate a lightweight investigation agent diagnosing a simulated microservices failure using logs stored in OpenSearch. Attendees will see how engineers can ask questions like “Why did the ingestion service fail?” and watch the agent autonomously investigate and explain the incident.
Participants will leave with a practical architecture for building AI-assisted debugging workflows on top of OpenSearch.
Aman Kimothi is a Senior Software Engineer at Oracle Cloud Infrastructure, working on OCI OpenSearch and large-scale distributed systems. His work spans AI agents, observability, cloud security, and performance engineering, with a focus on solving real-world production challenges... Read More →
Senior Development Manager, OCI (Oracle Cloud Infrastructure)
Ashish Gupta is a Senior Development Manager at OCI, leading the engineering for the OCI OpenSearch Managed Service in India. A cloud leader with 18+ years of industry experience—including 15+ years spent earlier at major cloud providers, Ashish has been involved in OCI OpenSearch... Read More →
Jai Mashalkar is a Software Engineer with over 10 years of experience in the software industry, specializing in large-scale distributed applications and databases. A graduate of IIT Bombay, she has worked with numerous customers to optimize OpenSearch clusters for different workloads... Read More →
As data volumes and AI-driven workloads grow, operating large-scale search and observability platforms comes with rising infrastructure costs and energy consumption. GreenOps, an approach that combines operational efficiency, cost management, and environmental responsibility, is emerging as a practical way for engineering teams to run cloud systems more sustainably.
This session explores how GreenOps principles apply when operating OpenSearch for search and observability workloads. We will examine strategies to optimise cluster sizing, storage, and indexing patterns to reduce unnecessary compute usage while maintaining performance and reliability. The talk will also cover ways teams can use observability data to identify inefficient workloads, control infrastructure costs, and reduce the overall carbon footprint of their systems.
Attendees will gain practical insights into running more efficient OpenSearch deployments while aligning operational decisions with broader sustainability and cost-optimization goals.
I am a Software Engineer II at Kantata, building scalable systems and AI-powered product features using React, Django, and Gen AI. A long-time hackathon enthusiast since college, I’ve won and placed in global competitions, including Smart India Hackathon, Digital Gov hack & the... Read More →
Within the open source community, supply chain security of software is a critical topic. What does this mean for open source software that you consume?
In this talk I intend to walk users through some security basics for open source software, using OpenSearch as an example. Some of the information will include scanning the repository, container images, dependency management, and deploying attestations, among others.
This topic is important specifically for end-user companies that are large, risk-averse, and depend on open source heavily. OSS projects, and the communities that surround them, are now compelled to adopt security best practices in order to position these projects as viable ones for commercial adoption.
OpenSearch is a perfect example of a popular open source project, backed by heavyweights, and in use by a large number of companies. Therefore, securing this project is of paramount importance for the community.
Ram Iyengar is an engineer by practice and an educator at heart. He was (cf) pushed into technology evangelism along his journey as a developer and hasn’t looked back since! He enjoys helping engineering teams around the world discover new and creative ways to work. He is a proponent... Read More →
Monday June 15, 2026 4:50pm - 5:10pm IST 204 (Level 2)
Agentic AI systems are moving from experiments to production—but most teams lack visibility into how agents think, coordinate, and evolve. This session explores using OpenSearch as the cognitive backbone for modern agent frameworks like Strands and LangGraph, positioning it as: a long-term memory store, reasoning trace index, multi- agent coordination layer, and observability platform.
Through a live architecture walkthrough, we'll build a multi-agent system where every planning step, tool invocation, state transition, and outcome is indexed in OpenSearch. We'll demonstrate how to debug hallucinations using reasoning traces, replay agent decisions across sessions, analyze performance with hybrid search, detect behavioral drift, and coordinate multiple agents through search-backed state.
Attendees will gain practical architectural patterns for running stateful, observable, production-grade AI agents using OpenSearch as core infrastructure—beyond basic retrieval.
Key Takeaways: • Design stateful agent memory architectures • Implement agent observability pipelines • Use OpenSearch for reasoning trace analysis • Coordinate multi-agent systems via indexed state
Hitesh Subnani is a Solutions Architect at AWS with specialized expertise in Search, Analytics, and Data Warehouse technologies. With a comprehensive background in data engineering and architecture, he brings a wealth of technical knowledge to complex cloud infrastructure challenges... Read More →
Monday June 15, 2026 5:20pm - 6:00pm IST 205 (Level 2)
This OpenSearchCon talk covers setting up and enabling agents for private environments, from initial configuration to solving common challenges. We faced numerous issues connecting OpenSearch Agents to our private LLM. Through trial and error, we succeeded by setting certificates in the JDK keystore, enabling private MCPs, and more. We identified gaps in OpenSearch and addressed them via pull requests, including creating a blueprint for Ollama and adding an option to disable certificate validation for LLM connectors.
In this session, we'll share our journey, discuss use cases, and provide a live demo using the latest OpenSearch features, including the brand new Agentic UI. You'll leave with practical insights to configure and optimize OpenSearch Agents for private LLMs, solutions to common integration issues, and the knowledge to deploy customized, fully private search experiences.
I am a passionate open-source developer actively contributing to OpenSearch, now mainly in to ml-commons. I am a Maintainer in ml-commons plugin. I contributed to other projects like OpenSearch core, Security and Cross Cluster Replication. I presented in OpenSearchCon Europe and India... Read More →
Rudraksh is a Forward Deployed AI Engineer (FDE) at Simplismart, where he builds solutions focused on high-performance AI inference. He previously worked as an AI Engineer at ZS Associates. He is a two-time Google Summer of Code participant with the openSUSE Project and is currently... Read More →
Monday June 15, 2026 5:20pm - 6:00pm IST 204 (Level 2)
Search engines are widely used but rarely understood at a deeper level. What actually happens between the moment a user submits a query and the results appear on the screen? This session provides a technical deep dive into the OpenSearch query execution pipeline, exploring how user queries are processed, distributed, and ranked across a cluster. The talk begins with how text is analyzed and indexed using Lucene, including tokenization, analyzers, and inverted indexes. It then walks through how queries are parsed, executed across shards, and scored using relevance algorithms such as BM25. Participants will also learn how OpenSearch coordinates distributed search, merges shard results, and optimizes performance through caching and query planning. Finally, we will examine debugging tools and profiling techniques that help developers understand and tune search relevance and performance. By the end of the session, attendees will have a deeper understanding of the internals of OpenSearch and how those internals influence search quality and system performance.
Samyuktha is a Software Developer at IBM India Software Labs who loves building things that actually work in production, from voice agents and multilingual multi-agent pipelines to self-healing infrastructure using MCP, LangGraph, Claude, and Qdrant. A 13x hackathon winner including... Read More →
