Lessons learned from running a large gRPC mesh at Datadog

Datadog`, a company that provides monitoring and analytics tools, uses `gRPC` (a Remote Procedure Call framework) to enable efficient communication between its distributed systems. Implementing a networking solution for such large applications poses several challenges, including scalability, load balancing, fault tolerance, compatibility, and latency. Datadog started using gRPC due to its integration with Protocol Buffers (protobuf), which allows developers to easily create bindings for their services in various languages. As Datadog grew, they discovered that gRPC's built-in client-side load balancing features were key to scaling their backend. However, they also encountered challenges such as silent connection drops and IP recycling issues. To address these problems, they set the `round_robin` policy for load balancing on the client side, leveraged TLS to handle IP recycling, set `MAX_CONNECTION_AGE` to force gRPC to re-resolve from DNS, and configured the `keepalive` feature to mitigate silent connection drops. Proper monitoring of services is also crucial in identifying issues such as load imbalance and failed transmissions.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.