# Uber

Published articles for Uber.

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## Uber Separates Scaling Intent From Execution on Kubernetes Platform

DevFeed: [Uber Separates Scaling Intent From Execution on Kubernetes Platform](<https://devfeed.tech/articles/uber-separates-scaling-intent-from-execution-on-kubernetes-platform-60814.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/09/uber-kubernetes-scaling/>)

Author: Matt Saunders

Published: 2026-09-28T09:00:00Z

Content type: news

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [control-plane](<https://devfeed.tech/topics/control-plane.md>), [Cloud Architecture](<https://devfeed.tech/topics/cloud-architecture.md>)

Tags: [cloud](<https://devfeed.tech/tags/cloud.md>), [clusters](<https://devfeed.tech/tags/clusters.md>), [data-centres](<https://devfeed.tech/tags/data-centres.md>), [devops](<https://devfeed.tech/tags/devops.md>), [failover](<https://devfeed.tech/tags/failover.md>), [google](<https://devfeed.tech/tags/google.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [news](<https://devfeed.tech/tags/news.md>), [oracle](<https://devfeed.tech/tags/oracle.md>), [pod](<https://devfeed.tech/tags/pod.md>), [reuse](<https://devfeed.tech/tags/reuse.md>), [scaling](<https://devfeed.tech/tags/scaling.md>), [uber](<https://devfeed.tech/tags/uber.md>), [uber-kubernetes-scaling](<https://devfeed.tech/tags/uber-kubernetes-scaling.md>)

### AI overview

Uber's Service Scale Controller lets multiple orchestrators express scaling intent for the same Kubernetes workloads. The design supports regional failover by scaling down lower-tier workloads and using that capacity for higher-tier services, while keeping failover logic separate from the controller responsible for normal service operations.

### Source excerpt

Uber has published a detailed account of its new ServiceScale controller, which allows multiple orchestrators to safely manage the scaling of the same Kubernetes workloads. The blog post, written by senior software engineers Egor Grishechko and Srikar Paruchuru, describes how the company separated scaling intent from execution to support regional failover without carrying reserved idle capacity. By Matt Saunders

## Uber Redesigns M3DB Sharding with Subclusters to Limit Failure Impact

DevFeed: [Uber Redesigns M3DB Sharding with Subclusters to Limit Failure Impact](<https://devfeed.tech/articles/uber-redesigns-m3db-sharding-with-subclusters-to-limit-failure-impact-57620.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/09/uber-m3db-subcluster-sharding/>)

Author: Leela Kumili

Published: 2026-09-21T14:37:00Z

Content type: news

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [sharding](<https://devfeed.tech/topics/sharding.md>), [Database](<https://devfeed.tech/topics/database.md>), [Replication](<https://devfeed.tech/topics/replication.md>), [Availability](<https://devfeed.tech/topics/availability.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [architecture-design](<https://devfeed.tech/tags/architecture-design.md>), [availability](<https://devfeed.tech/tags/availability.md>), [cluster](<https://devfeed.tech/tags/cluster.md>), [clusters](<https://devfeed.tech/tags/clusters.md>), [data-movement](<https://devfeed.tech/tags/data-movement.md>), [database](<https://devfeed.tech/tags/database.md>), [database-replication](<https://devfeed.tech/tags/database-replication.md>), [development](<https://devfeed.tech/tags/development.md>), [devops](<https://devfeed.tech/tags/devops.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [fault-tolerance](<https://devfeed.tech/tags/fault-tolerance.md>), [load-balancing](<https://devfeed.tech/tags/load-balancing.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [migrations](<https://devfeed.tech/tags/migrations.md>), [news](<https://devfeed.tech/tags/news.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [replication](<https://devfeed.tech/tags/replication.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [scaling](<https://devfeed.tech/tags/scaling.md>), [sharding](<https://devfeed.tech/tags/sharding.md>), [time-series-data](<https://devfeed.tech/tags/time-series-data.md>), [uber](<https://devfeed.tech/tags/uber.md>), [uber-m3db-subcluster-sharding](<https://devfeed.tech/tags/uber-m3db-subcluster-sharding.md>)

### AI overview

Uber redesigned shard placement in its M3DB distributed time series database by introducing fixed-size subclusters. The design limits the effects of node failures, maintenance, and cluster scaling while preserving replica isolation. A greedy algorithm selects shard migrations to balance load and avoid unnecessary rebalancing and data movement.

### Source excerpt

Uber has redesigned shard placement in M3DB with fixed size subclusters to limit the impact of node failures, maintenance, and cluster scaling. The approach bounds shard dependencies, preserves replica isolation, and uses a greedy algorithm to select shard migrations while avoiding a separate rebalancing pass and unnecessary data movement. By Leela Kumili