Implementing streaming API for MSA: is it just WebSockets vs gRPC vs MQ?

Essay by Maria Tsvetkova(maantsvetkova@edu.hse.ru)

In a microservice architecture, the key role is played by the organization of interactions between services, ensuring scalability and fault tolerance. The choice of technologies for these communications depends on architectural features, application complexity, performance requirements, and specific scenarios. It is necessary to take into account the frequency of service interactions and the expected load volumes. Particular attention should be paid to streaming data transfer. Streaming data transfer is a data transfer method in which data is transferred in a continuous stream, rather than in portions or packets [1]. The choice of a specific implementation of such data transfer requires an analysis of the type and volume of data, latency requirements, and message delivery guarantees. To determine in which cases a WebSockets is better suited, in which cases it is good to use gRPC and in which MQ, you need to consider their features.

WebSockets provide a full-duplex communication channel over a single, long-running HTTP connection, allowing real-time, two-way communication between client and server [2-4]. This is especially useful for applications that require instant data exchange, such as chat applications or live notifications.

Advantages:

• Perfect for interactive web applications.
• Native Browser Support.
• Provides fast and efficient data transmission.

Disadvantages:

• Requires handling persistent connections and connection states.
• Offers less built-in functionality for error handling and message delivery guarantees compared to gRPC and MQs.
• Data structure is more flexible but less structured than gRPC with Protobuf.

When to choose WebSockets:

• Direct Browser Interaction: When you need low-latency, two-way communication directly with a web browser.
• Interactive User Experiences: When your application requires a highly interactive, real-time user interface.
• Continuous Data Exchange: When you need to push data from the server to the client continuously without explicit client requests.
• Persistent Connections: When you need long-lived, stateful connections for continuous data transfer.

Use Cases:

• Real-time chat applications.
• Online gaming.
• Financial trading platforms.
• Real-time dashboards.
• Collaboration tools.
• Push notifications.
• IoT applications.

gRPC (Google Remote Procedure Call) is a modern, high-performance framework for building distributed applications [5-7]. While gRPC's traditional unary RPCs are great for single request-response interactions, its streaming capabilities provide a powerful way to handle scenarios involving continuous or large amounts of data. gRPC's streaming APIs allow you to send a sequence of messages over a single connection, offering significant efficiency improvements for a variety of use cases.

Advantages:

• HTTP/2 and Protobuf for efficient serialization and transportation.
• Ensures type safety and fewer errors with Protobuf definitions.
• Powerful for sending large amounts of data in real-time.
• Provides structured error codes.
• Excellent cross-language support using code generation.

Disadvantages:

• Can be more complex to set up and develop with, especially with Protobuf definitions and asynchronous programming.
• Limited support in web browsers (requires gRPC-Web or proxy servers).
• Streaming can be stateful, requiring management of persistent connections and their resources.

When to choose gRPC:

• Strongly Typed Data: When you need a well-defined data structure with strong typing (using Protocol Buffers).
• High Performance: When you require low latency and high throughput.
• Cross-Language Interoperability: When you need different services written in different languages to communicate effectively.
• Streaming Requirements: When you need to stream data between client and server efficiently.
• Internal Communications: For backend communications that require structured APIs.

Use Cases:

• Large File Transfer.
• Collaborative Editing:.
• Game Development.
• Machine Learning.
• Background Processing.
• Microservices Communication.

Message Queues (MQs) are a fundamental building block in distributed systems, enabling asynchronous communication between different components or services [8]. Instead of direct, synchronous interactions, MQs act as intermediaries, storing messages temporarily until the recipient is ready to process them. This decoupling of components offers significant advantages in terms of scalability, reliability, and flexibility. Two popular message broker implementations:

1. RabbitMQ: A widely used, open-source message broker that supports various messaging protocols [9].
2. Apache Kafka: A distributed, highly scalable streaming platform designed for high-throughput and fault-tolerance [10].

Advantages:

• Asynchronous Communication: Decouples systems, increasing resilience and scalability.
• Scalability and Reliability: Can handle large volumes of messages and can be configured for high availability.
• Persistence: Offers message persistence, ensuring delivery even in case of failures.
• Load Balancing: Distributes workloads across multiple consumers.

Disadvantages:

• Added Complexity.
• Asynchronous nature may introduce latency compared to WebSockets or gRPC.
• Asynchronous flows can be more difficult to debug.

When to choose Message Queues:

• Asynchronous Communication: When you need to decouple producers and consumers and avoid direct synchronous interactions.
• Reliable Message Delivery: When you need to ensure messages are delivered even if a component is temporarily unavailable.
• Scalable Systems: When you need to handle a large number of messages and scale components independently.
• Event Processing: When you need to trigger actions based on events within your system.
• Resilient Architectures: When you need to build fault-tolerant systems where failures do not cascade.

Use Cases:

• Microservices Communication.
• Task Queues.
• Asynchronous Notifications.

Thus, while WebSockets, gRPC, and message queues are often considered when choosing streaming APIs in a microservice architecture, implementing them successfully requires a detailed understanding of the application needs, existing infrastructure, and operational challenges. Each technology has its own strengths and weaknesses, and combining them wisely will create a robust architecture that leverages the best qualities of each. WebSockets are best suited when your primary need is real-time, bidirectional communication in a web browser. gRPC is great when you need high-performance, strongly typed communication between backend services, especially if streaming data is involved. Message queues are ideal when you need asynchronous communication, decoupled components, and reliable message delivery, as is often the case with task and event-driven systems. A combination of these technologies is often the most effective approach, such as using WebSockets for dynamic UI updates, gRPC for communication between microservices, and message queues for background job processing.

1. “What is real-time data streaming?”: https://aws.amazon.com/ru/what-is/real-time-data-streaming/
2. “The WebSocket API (WebSockets)”: https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API
3. “WebSocket”: https://developer.mozilla.org/en-US/docs/Web/API/WebSocket
4. “WebSocket: How it works”: https://habr.com/ru/sandbox/171066/
5. “gRPC: synchronous and asynchronous Server streaming RPC”: https://pankaj02.medium.com/grpc-synchronous-and-asynchronous-server-streaming-rpc-3deb4a1e6cd3
6. “gRPC - Server Streaming RPC”: https://www.tutorialspoint.com/grpc/grpc_server_streaming_rpc.html
7. “Streaming data with gRPC”: https://programmingpercy.tech/blog/streaming-data-with-grpc/
8. “Streaming queues”: https://www.ibm.com/docs/en/ibm-mq/9.2?topic=scenarios-streaming-queues
9. “RabbitMQ”: https://www.rabbitmq.com/
10. “Kafka”: https://kafka.apache.org/