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Discover why standard Kubernetes StatefulSets might not be sufficient for your database workloads and how custom operators can provide better solutions for stateful applications.
Andrew Charlton, Staff Software Engineer at Timescale, explains how they replaced Kubernetes StatefulSets with a custom operator called Popper for their PostgreSQL Cloud Platform. He details the technical limitations they encountered with StatefulSets and how their custom approach provides more intelligent management of database clusters.
You will learn:
Why StatefulSets fall short for managing high-availability PostgreSQL clusters, particularly around pod ordering and volume management
How Timescale's instance matching approach solves complex reconciliation challenges when managing heterogeneous database workloads
The benefits of implementing discrete, idempotent actions rather than workflows in Kubernetes operators
Real-world examples of operations that became possible with their custom operator, including volume downsizing and availability zone consolidation
Sponsor
This episode is brought to you by mirrord — run local code like in your Kubernetes cluster without deploying first.
More info
Find all the links and info for this episode here: https://ku.bz/fhZ_pNXM3
Interested in sponsoring an episode? Learn more.
5
22 ratings
Discover why standard Kubernetes StatefulSets might not be sufficient for your database workloads and how custom operators can provide better solutions for stateful applications.
Andrew Charlton, Staff Software Engineer at Timescale, explains how they replaced Kubernetes StatefulSets with a custom operator called Popper for their PostgreSQL Cloud Platform. He details the technical limitations they encountered with StatefulSets and how their custom approach provides more intelligent management of database clusters.
You will learn:
Why StatefulSets fall short for managing high-availability PostgreSQL clusters, particularly around pod ordering and volume management
How Timescale's instance matching approach solves complex reconciliation challenges when managing heterogeneous database workloads
The benefits of implementing discrete, idempotent actions rather than workflows in Kubernetes operators
Real-world examples of operations that became possible with their custom operator, including volume downsizing and availability zone consolidation
Sponsor
This episode is brought to you by mirrord — run local code like in your Kubernetes cluster without deploying first.
More info
Find all the links and info for this episode here: https://ku.bz/fhZ_pNXM3
Interested in sponsoring an episode? Learn more.
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