Imagine a backend made of dozens of services that need to talk to each other constantly. Data flows in all directions—some messages are tiny, others carry entire documents or user sessions. Some calls finish in milliseconds. Others stay open for minutes. In this world, every wasted byte and connection matters.
That’s where gRPC comes in. It’s a high-performance RPC framework developed by Google and now maintained by the Cloud Native Computing Foundation. Instead of sending JSON over HTTP/1.1 like traditional REST APIs, gRPC uses Protocol Buffers over HTTP/2. This lets systems exchange structured data in a compact binary format, using long-lived connections that support streaming in both directions.
Performance engineers need to understand what makes gRPC different—not just from a development standpoint, but because it changes how systems behave under load. This article breaks down what gRPC is, how it works, and how to think about it when testing for speed, reliability, and scale.
What is gRPC?
gRPC is a framework for building remote procedure call (RPC) APIs. It lets a client call a method on a remote server as if it were a local function. The contract between client and server is defined using a .proto file—a schema written in Protocol Buffers that describes available methods and their input/output messages.
Once that schema is in place, gRPC tools generate client stubs and server code in multiple programming languages. This automatic code generation ensures that client and server share a consistent view of the API. Developers work with typed data structures, not loosely defined JSON payloads.
gRPC runs on HTTP/2, which enables features like connection multiplexing, header compression, and full-duplex streaming. It’s designed for efficient communication in distributed systems, where service-to-service traffic can easily become the performance bottleneck.
What makes gRPC relevant for performance testers
Many systems adopt gRPC to reduce latency and improve throughput. But those same features—binary encoding, persistent connections, streaming—also affect how systems behave under load. Performance testers need to be aware of these differences so they can model user behavior accurately.
In REST, each request typically opens and closes a connection. With gRPC, a client might open a single connection and send hundreds of requests over it. Streaming RPCs mean a single user can keep a connection open for an extended period, receiving updates or sending telemetry data. These usage patterns can shift pressure from the HTTP layer to memory, garbage collection, and network I/O.
gRPC also adds flow control at the protocol level. Misconfigured window sizes can throttle throughput even when the server isn’t under heavy load. Testing tools need to simulate not just request rates, but also realistic message sizes, stream durations, and cancellation behavior.
What changes for performance testers
Load model: not just RPS anymore
In gRPC, streams replace the idea of discrete requests. A single connection can stay open for a long time, transmitting many messages. That means your virtual users aren't just issuing calls—they're holding open long-lived sessions. A client might keep a bidirectional stream open for 30 seconds or more.
This shift impacts how you think about concurrency, throughput, and connection scaling. Traditional RPS metrics only capture part of the picture. Flow control, backpressure, and session lifecycle events matter more than raw request counts.
New things to monitor
Testing gRPC involves new dimensions of telemetry:
- Measure message sizes, both compressed and uncompressed
- Track the number of active streams and their duration
- Monitor gRPC-specific status codes, not just HTTP-level responses
- Observe streaming throughput, jitter, and variation across time
These metrics expose bottlenecks in how your grpc service handles load, especially when multiple grpc client instances are connected simultaneously.
Real-world testing advice
When building test plans for a grpc server, include a variety of RPC types. Mix unary calls with server and client streaming. Always simulate timeouts and cancelled streams—these patterns happen in production and can surface resource leaks.
Also, use protocol-aware tools. General-purpose HTTP clients won’t help here. You need something that understands .proto contracts, supports stream lifecycles, and can drive gRPC load at scale. Gatling does this natively.
Common pitfalls in gRPC performance
Performance issues in gRPC systems often stem from implementation choices, not just code defects. Spotting these early requires a test model that reflects real-world traffic, including edge cases like dropped connections and flaky network links:
- Flow control settings too tight or too loose → uneven throughput
- Creating a new channel for every call → connection overload
- Overusing bidirectional streaming → state management complexity
- Forgetting to set deadlines → orphaned calls and memory waste
Why scaling gRPC services can be challenging
Load balancing
gRPC runs over HTTP/2, which uses long-lived connections. Many load balancers are built for HTTP/1.1 and may not manage HTTP/2 traffic efficiently, leading to uneven traffic distribution.
Resource usage
gRPC services can consume significant CPU and memory, especially under load. Scaling requires careful monitoring and tuning to avoid performance degradation as usage grows.
Backpressure and service dependencies
In microservice environments, services often rely on one another. If one becomes overloaded, it can trigger backpressure or cascading failures across the system.
The business impact of neglecting load testing
Skipping load testing for gRPC services introduces real risks:
- Poor user experience: Sluggish responses frustrate users and increase churn.
- Lost revenue: Outages or slowdowns during peak traffic can directly affect sales and brand trust.
- Higher infrastructure costs: Without visibility into performance bottlenecks, teams often overcompensate with extra compute—and extra cost.
How Gatling helps you test gRPC like a pro
Gatling includes first-class support for gRPC and Protocol Buffers. You define a .proto file, set up your gRPC request scenarios, and let the engine simulate complex traffic patterns—including long-lived streams and concurrent client interactions.
It provides real-time dashboards for stream duration, response times, and throughput. You can compare runs, observe regressions, and export data for reports. Also, since Gatling tests live as code, you get version control, repeatability, and easy integration with CI/CD.
With Gatling's gRPC plugin, you get
• Native gRPC Support: Gatling’s plugin allows you to craft detailed load testing scenarios that accurately reflect real-world gRPC communications.
• Protocol buffers handling: Seamlessly manage Protocol Buffers within your tests, eliminating the complexity of manual serialization.
• Bidirectional streaming simulation: Accurately replicate client-server interactions, including complex streaming scenarios, to ensure your services perform under varied conditions.
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Watch our async session: Intro to gRPC protocol
Watch our async session: Intro to gRPC protocol
](https://gatling.io/sessions/grpc-testing-gatling)
Conclusion: should you switch to gRPC?
gRPC uses typed contracts, binary encoding, and streaming over HTTP/2 to enable efficient communication in modern systems. It supports a wide range of RPC patterns across multiple programming languages and has become a common choice for internal APIs and microservices.
But be advised, this is also a testing challenge. If you adopt gRPC, you also adopt a new set of assumptions around concurrency, messaging, and network behavior.
Performance testers who recognize these shifts—and use tools that embrace the full gRPC framework—will be better prepared to ship fast, reliable systems.
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