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Overview for Lyft

Handling Risky Business: Cluster Upgrades - Puneet Pruthi, Lyft

This talk is about how Lyft has solved the complexity of automating cluster upgrades and how that is incorporated into the design for - k8srotator - a Kubernetes custom controller.


Service Mesh in Kubernetes: It’s Not That Easy - Lita Cho & Tom Wanielista, Lyft

As Lyft migrated its applications to Kubernetes, assumptions baked into the networking layer were tested. This talk discusses how Lyft used Envoy’s xDS protocol to design their own flexible service mesh and handle new challenges from a multi-cluster architecture such as:
- Routing across multiple Kubernetes clusters
- Handling Deployments
- Rapid scale-in and scale-out
- Service Discovery
- Active/Passive Health Checking
- Readiness in the service mesh

This talk will also go over changes that were made in the Envoy codebase to make this work.


Evolution of Envoy as a Dynamic Redis Proxy - Nicolas Flacco & Henry Yang, Lyft | Mitch Sulaski, Workday

This talk highlights the the evolution of Redis support in Envoy.  Initially Envoy redis proxy only supported sharding to clusters of independent Redis nodes. Recent developments have enabled support for the open source Redis Cluster protocol as well as some unique features such as multicluster routing, flexible load balancing options, and traffic shadowing.

As the usage of Redis expanded different usage patterns emerged, requiring different availability, durability and consistency trade-offs. Henry Yang from Lyft and Mitch Sulaski from Workday discuss how the Envoy redis proxy was extended to support these new requirements in large scale environment(10+ Millions rps) at Lyft and Workday.


Envoy Mobile in Depth: From Server to Multi-platform Library

Learn how Lyft built, and deployed Envoy Mobile ( in their Swift/Kotlin apps and the motivation behind deploying a single, consistent Envoy-based network stack across every platform.


Apache Beam meetup 7 at Datatonic: Beam at Lyft + datalake using Beam + schemas

See how Lyft and Datatonic are using Apache Flink, Apache beam and python in stream processing, machine learning and analytics.


Python Streaming Pipelines With Beam On Flink

Thomas Weise a Software Engineer, Streaming Platform at Lyft shares with you vital information on gains attained using Beam technology. You will learn how Lyft has contributed towards the realization of the cross-language support and enabled processing pipelines written in Python to run on Apache Flink.