When moving to a Kotlin gRPC framework for backend services, handling image data can be challenging.
Category Archives: Backend
Using Display Modules to Enable Rapid Experimentation on DoorDash’s Homepage
Online companies need flexible platforms to quickly test different product features and experiences.
Integrating a Search Ranking Model into a Prediction Service
As companies utilize data to improve their user experiences and operations, it becomes increasingly important that the infrastructure supporting the creation and maintenance of machine learning models is scalable and will enable high productivity.
Eliminating Task Processing Outages by Replacing RabbitMQ with Apache Kafka Without Downtime
Scaling backend infrastructure to handle hyper-growth is one of the many exciting challenges of working at DoorDash.
Building Reliable Workflows: Cadence as a Fallback for Event-Driven Processing
Amid the hypergrowth of DoorDash’s business, we found the need to reengineer our platform, extracting business lines from a Python-based monolith to a microservices-based architecture in order to meet our scalability and reliability needs.
Scaling Splunk Securely by Building a Custom Terraform Provider
Terraform lets DoorDash programmatically manage certain accesses across our infrastructure.
Enabling Efficient Machine Learning Model Serving by Minimizing Network Overheads with gRPC
The challenge of building machine learning (ML)-powered applications is running inferences on large volumes of data and returning a prediction over the network within milliseconds, which can’t be done without minimizing network overheads.
Next-Generation Optimization for Dasher Dispatch at DoorDash
At DoorDash, our logistics team focuses on efficiently fulfilling high quality deliveries.
Writing Delightful HTTP Middleware in Go
While writing complex services in go, one typical topic that you will encounter is middleware.
Speeding Up Redis with Compression
One of challenges we face almost everyday is to keep our API latency low.