Abstract
Register
$2,995, Conference (3 days). Current pricing ends November 10th. All pass options.
76%
senior dev or higher
1:11
speaker ratio
60+
practitioners
QCon San Francisco 2026 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Slides & video
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From the same track
Tuesday 17 November
10:35 Ballroom BC Session Data Platforms Orderly Keys, Wild Values: Adaptive Compression for Distributed Key-Value Storage Joseph Lynch, Ayushi Singh At Netflix scale - billions of requests per day and petabytes of key-value data - even small inefficiencies in storage and network paths become expensive. 11:45 Ballroom BC Session Databases How to Build Online Systems with Object Storage Almog Gavra Co-Founder @Responsive.dev - Building Object-Native Databases, Previously @Confluent and @LinkedIn “Diskless” systems that delegate durability to object storage are everywhere, and for three good reasons: 13:35 Ballroom BC Session Autoscaling 800 Valkey Clusters: Distributed Systems Lessons from Scaling Stateful Fleets Kishor Yadav Kommanaboina Staff Software Engineer @Snapchat - Leading Caching and Storage Infrastructure Autoscaling stateless services is a solved problem. In a sharded datastore, a resize is not a capacity change — it's moving ownership of key ranges between nodes while the cluster serves live traffic, and rebalancing takes long enough that reacting to a traffic peak is already too late. 14:45 Seacliff D Unconference Unconference: Distributed Systems in Production 15:55 Ballroom BC Session Distributed Systems When Your Users Are Agents: Lessons from a Distributed Postgres Platform Gwen Shapira Co-Founder and CPO @Nile, Previously Engineering Leader @Confluent, PMC Member @Kafka, & Committer Apache Sqoop Distributed systems are built around assumptions about workload behavior: connections have reasonable lifetimes, retries eventually stop, traffic spikes have recognizable causes, and application code produces somewhat predictable query patterns. 17:05 Ballroom BC Session Adaptive Systems in Production: What Recommendation Systems Can Teach Us About Agents Mallika Rao Senior Engineering Manager @Zocdoc, Previously @Netflix, @Twitter and @Walmart As organizations race to build AI agents, many teams are encountering challenges that feel new: evaluation uncertainty, feedback loops, behavioral drift, exploration versus exploitation, and maintaining user trust in systems that continuously adapt. But these challenges are not new.