DoorDash's Game-Changing Strategy: 70% Hit Ratio in Cache Optimization!

Decentralization and Empowering Efficiency

TLDR;

Problem: Managing large and costly Redis clusters for the feature store, which are expensive to maintain and require rapid online prediction requests, necessitates exploring an in-process caching layer to enhance performance and scalability, especially for replica requests.

Solution: In order to maximize request efficiency and reduce dependency on Redis clusters, the solution proposes incorporating an in-process caching layer within microservices, hence improving system speed and scalability.

Flow

Doordash SPS(Feature request service)

Based on the features, the DoorDash Prediction Service (SPS) forecasts the outcomes. SPS requests a feature from the feature store if it is not offered by the upstream service.

• The feature store, mainly housed in Redis, incurs substantial compute costs due to high request volumes for ML features.

• DoorDash explores caching and alternative storage solutions to address scalability and cost efficiency concerns.

• Implementation of caching is expected to boost prediction performance, reliability, and scalability.

• Despite potential immediate gains, caching promises to reduce compute costs and enhance platform efficiency over time.