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Control Plane for Large Mesh in a Heterogeneous Environment - Fuyuan Bie & Zhimeng Shi, Pinterest

Building service mesh in a heterogeneous environment of a large number of clusters is challenging. At Pinterest,  they have a complicated mixture of thousands of clusters ranging from IaaS to dockerized services to kubernetes; They are developed with C++/Java/Python/Node/Go/Elixir.Using open source go control plane as the interface to Envoy, the data engineering team at Pinterest meshed Pinterest services with a control plane namely tower they developed. From edge to backends, 100% services are managed by Tower. They use actor model and event sourcing to make it performant, reliable, scalable and extensible.

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Governance on K8s: How to Solve Ownership, Metering & Capacity Planning - Micheal Benedict & Yongwen Xu, Pinterest

Pinterest is a cloud first visual discovery engine that serves over 250MM users. To support this scale, there are thousands of services running on tens of thousands of hosts, processing 300+PB of data. Pinterest operates large kubernetes clusters across several availability zones, across regions. The cluster is auto scaled with support for pod level auto-scaling. Finally,to effectively utilize resources within the clusters, Pinterest operates heterogeneous workloads on a kitchen sink of instance types. 

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Tinder's Move to Kubernetes - Chris O'Brien & Chris Thomas, Tinder

This discussion is about how Tinder moved it's platform to Kubernetes, challenges encountered along the way and how they were solved. 

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How and why we moved away from Kafka Mirror Maker to Brooklin- LinkedIn's story

See how Linkedin is using Brooklin Mirror Maker (BMM) to provide improved performance and stability at the same facilitating better management through finer control of data pipelines.

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