Uploaded August 2026 | Updated September 2026, 2 weeks ago
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Running OpenSearch at Scale in High-Traffic Gaming Systems - Siddharth Vijay, Baazi Games
Running OpenSearch in a poker platform means no margin for error. Fraud detection can't wait. Player stats APIs can't lag. And the cluster doesn't care about your traffic spikes.
We operate OpenSearch under mixed workloads: high-volume writes from hand histories and gameplay events, aggregation-heavy fraud queries, and latency-sensitive reads serving live player statistics. Standard deployment advice doesn't survive this combination.
This talk is purely operational. We cover JVM heap sizing under write pressure, threadpool tuning for bulk ingestion and search isolation, shard sizing, rollover strategies, and capacity planning for uneven traffic — with the specific mitigations we applied after hitting queue saturation, GC pauses, and hot shards in production.
We also cover index lifecycle management, retention tradeoffs, and monitoring signals that matter when OpenSearch sits in your request path.
You'll leave with concrete operational guidance well beyond basic deployment patterns.
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
Running OpenSearch at Scale in High-Traffic Gaming Systems - Siddharth Vijay, Baazi Games
Running OpenSearch in a poker platform means no margin for error. Fraud detection can't wait. Player stats APIs can't lag. And the cluster doesn't care about your traffic spikes.
We operate OpenSearch under mixed workloads: high-volume writes from hand histories and gameplay events, aggregation-heavy fraud queries, and latency-sensitive reads serving live player statistics. Standard deployment advice doesn't survive this combination.
This talk is purely operational. We cover JVM heap sizing under write pressure, threadpool tuning for bulk ingestion and search isolation, shard sizing, rollover strategies, and capacity planning for uneven traffic — with the specific mitigations we applied after hitting queue saturation, GC pauses, and hot shards in production.
We also cover index lifecycle management, retention tradeoffs, and monitoring signals that matter when OpenSearch sits in your request path.
You'll leave with concrete operational guidance well beyond basic deployment patterns.
