Abstract
Register
$2,835, Conference (3 days). Current pricing ends September 8th. 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.
From the same track
Tuesday 17 November
10:35 Ballroom BC Session 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 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. 13:35 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. 14:45 Ballroom BC Session 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: 15:55 Seacliff D Unconference Unconference: Distributed Systems in Production 17:05 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.