Docs/grpc #417
Docs/grpc #417
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| Bruno provides comprehensive support for gRPC requests, enabling you to test and interact with gRPC services directly from the interface. With Bruno's gRPC support, you can: | ||
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| - **Add Proto files** for enhanced IntelliSense and method discovery |
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@ganesh-bruno can talk about serve reflection which is the alternative for protofiles
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| ### Step 4: Send the Request |
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@ganesh-bruno can add details on how refresh methods work.
| You have two options to add message schema: | ||
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| **Option A: Auto-Generate Messages** | ||
| - Bruno can auto-generate message templates based on server reflection (Click the **Auto Fill** 🔄 button) |
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- Bruno can auto-generate message templates based on server reflection or protofile
| ## Understanding gRPC Streaming | ||
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| gRPC streaming allows you to send and receive multiple messages over a single connection, making it ideal for: | ||
| - **Real-time applications** (chat, notifications, live updates) |
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Some of these real world use cases are still not well adopted among web world! for example, it is really cumbersome to integrate gRPC to a client website, Is it best used within server to server communication, file uploads is still debatable
Microservices Communication
gRPC is widely used in large-scale systems (Google, Netflix, Square) to connect microservices efficiently. Its HTTP/2 foundation and Protocol Buffers enable fast, strongly-typed, and language-agnostic APIs.
Real-Time Streaming
Applications like chat systems, live dashboards, and online games use gRPC’s bidirectional streaming for low-latency communication. This makes it ideal for continuous data exchange, like Slack’s internal messaging or stock trading updates.
IoT & Edge Computing
IoT devices and sensors leverage gRPC to stream telemetry data to servers. Its lightweight, binary protocol ensures efficiency over limited bandwidth and unreliable networks.
Service Mesh & API Gateways
Platforms like Istio and Envoy use gRPC for service-to-service and control-plane communication. It supports load balancing, retries, and discovery—key for managing distributed, polyglot environments.
Machine Learning & Data Systems
ML model servers (e.g., TensorFlow Serving) expose inference APIs via gRPC for fast and scalable predictions. It handles large payloads well and works seamlessly across languages used in training and serving pipelines.
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| ### Step 5: End Stream and View Timeline | ||
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| 1. **End Message Stream**: Click the ✓ icon (end stream button) to stop sending messages |
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End Message Stream and Complete Request i guess these terminologies can be a bit confusing
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| ### Step 2: Auto-Generate or Add Proto Files |
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auto generation and proto files are independent, we can auto generate from reflection as well as protofiles
| - **Continuous data processing** (sensor data, analytics) | ||
| - **Bidirectional communication** (collaborative editing, gaming) | ||
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| ## Streaming Workflow |
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I guess we need to explain 4 types of methods available in gRPC like unary, client streaming, server streaming, bi directional streaming etc. rather than
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| 1. Open your gRPC request in Bruno | ||
| 2. In the gRPC interface, locate the **Using Reflection** section | ||
| 3. Click on the **Select Proto File** button or toggle the proto file option |
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@ganesh-bruno i guess we have Browse for proto file option
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@ganesh-bruno we also support collection level protofiles, we can be setup from collection settings/grpc, we should mention the same within docs to
This PR includes the following update: