A brief introduction to MQ

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In distributed systems, Message Queue (MQ), as an important communication method, can effectively decouple the dependencies between systems and improve the scalability and stability of the system. This article will give you an in-depth understanding of the basic concepts, types and usage scenarios of message queues, hoping to help you better use message queues to build efficient distributed systems.

1. Basic concepts of message queue

A message queue is an application that follows a specific communication protocol for asynchronous communication in a distributed system. It allows independent applications to communicate by reading and writing messages in and out of queues, thereby achieving the goals of decoupling, asynchronous and peak-shaving. In a message queue, the producer of the message is responsible for sending the message to the queue, while the consumer of the message obtains and processes the message from the queue.

Message queue has the following characteristics:

• Message persistence: Messages in the message queue are usually persisted to disk to ensure that messages are not lost even in the event of a system crash.
• Consumer subscription system: The message queue supports multiple consumers, and consumers can subscribe to specific topics (Topic) to obtain the messages they are interested in.
• Asynchronous communication: The message queue allows producers and consumers to communicate asynchronously, and consumers can obtain and process messages in the queue at any time.
• Sequence guarantee: The message queue usually guarantees the sequence of messages, that is, the messages sent by the producer will be obtained and processed by the consumer in the order in which they are sent.

2. Types of message queues

According to different classification standards, message queues can be divided into multiple types. The following are some common classifications:

Depending on the deployment method:

• Local message queue: stores messages in local memory, suitable for inter-process communication within a single machine.
• Remote message queue: stores messages on a remote server and is suitable for communication between distributed systems.

According to reliability:

• Reliable message queue: To ensure that messages are not lost, it is usually necessary to implement at least once delivery semantics.
• Unreliable message queue: Message loss may occur and is suitable for scenarios that do not require high data consistency.

According to usage scenario:

• Task Queue: used for task distribution, scheduling and execution.
• Publish-Subscribe System: Supports multiple topics and subscription relationships, and is suitable for large-scale real-time information push.
• Event-Driven Architecture: Events trigger the execution of business logic to achieve agile response and scalability of the system.

According to the message format:

• Text message queue: stores messages in text form and is suitable for general messaging.
• Binary message queue: stores messages in binary form and is suitable for the transmission of large amounts of data.
• Structured message queue: supports serializing messages into structured data to facilitate data analysis and processing.

Optimized for performance:

• Memory message queue: stores messages in memory to improve read and write speed and throughput.
• Segmented message queue: Split large messages into multiple small segments for transmission to reduce the impact of a single message on system performance.
• Parallel processing: Supports multiple threads/processes to read and write queues at the same time to improve processing efficiency.

Depending on whether it is open source or not:

• Open source message queues: such as RabbitMQ, Kafka, ActiveMQ, etc., have high community support and scalability.

• Commercial message queues: such as IBM MQ, Oracle BPM, etc., usually provide more comprehensive commercial services and technical support.

• Other classification standards: such as time series-based message queues (such as Apache Kafka), event-driven message queues (such as Amazon SQS), etc.

3. Historical background and significance of MQ

In the computer field, message queues were originally developed by IBM in the 1970s to solve the problem of inter-process communication in a mainframe environment. At that time, message queue was implemented as a shared, reliable, ordered messaging system to ensure data consistency and reliability in distributed systems. With the continuous development of distributed systems, message queues have gradually become an important communication method, especially in processing large-scale data and real-time applications.

4. Common methods of MQ

Common methods of message queue

(1) Point-to-point mode: The producer sends the message to a specific queue, and the consumer obtains and processes the message from the queue. This mode is suitable for one-to-one communication scenarios.
(2) Publish-subscribe model: Producers publish messages to one or more topics, and consumers subscribe to topics of interest and obtain corresponding messages. This mode is suitable for one-to-many communication scenarios.
(3) Request-response mode: The producer sends a request message to a specific queue, and the consumer obtains the request from the queue and responds. This mode is suitable for synchronous communication scenarios.

Main advantages and disadvantages of message queues

(1) Advantages: decoupling, asynchronous, peak shaving, valley filling, scalability, high reliability, persistence, etc.
(2) Disadvantages: delay, difficulty in ensuring order, and may become a bottleneck, etc.

5. The historical evolution of MQ

With the continuous advancement of technology, message queue has gradually developed into a huge ecosystem. In this ecosystem, many excellent message queue products have emerged, such as RabbitMQ, Kafka, ActiveMQ, etc. As these message queue products continue to develop and grow, they continue to promote the advancement of message queue technology.

• The development of message middleware

Message middleware is the predecessor of message queue. It mainly solves the problem of inter-process communication in distributed systems. Early message middleware products, such as IBM's MQSeries and BEA's MessageQ, laid the foundation for the development of message queues.

• The push for Enterprise Application Integration (EAI)

After entering the 21st century, enterprise application integration (EAI) has become one of the important directions for enterprise information construction. In this process, message queue, as an important integration technology, is widely used in the field of enterprise application integration. As enterprises continue to invest in informatization construction, the technical threshold and cost of message queues have gradually decreased, allowing more enterprises and developers to access and use this technology.

• The need for big data processing and real-time analysis

In recent years, with the continuous development of big data technology, enterprises and developers have increasingly higher demands for data processing and real-time analysis. As an efficient data processing and real-time transmission technology, message queue is widely used in the fields of big data and real-time analysis. For example, Apache
Kafka is a big data processing platform based on message queues. It can collect and process massive amounts of data in real time, providing strong support for enterprises' data analysis and decision-making.

MQ application cases and solutions With the popularity of distributed systems, the application scenarios of message queues are becoming more and more extensive.

• Decoupling and asynchronous processing

In a distributed system, the dependencies between components are often complex. If there is a hard coupling relationship, once a component has a problem, the entire system may be affected. By using message queues, the dependencies between various components can be decoupled, so that each component can run and process tasks independently. At the same time, through asynchronous processing, the system response time can be reduced and the system throughput and reliability can be improved. For example, trading systems and risk control systems in the financial field are suitable for using message queues for decoupling and asynchronous processing.

• Data stream processing and real-time analytics

In the era of big data, enterprises and developers need to process massive amounts of data and hope to analyze and process it in real time. By using message queues, data streams can be distributed and processed, and multiple consumers can process data streams at the same time. For example, risk control data processing in the financial field and user behavior analysis in the e-commerce field are suitable for using message queues for data flow processing and real-time analysis.