To design a basic SQL queue list, it is necessary to include the id, payload, status, created_at, locked_until fields, and establish a combined index for status and locked_until; 1. Use UPDATE ... RETURNING to implement atomic operation to extract and lock tasks; 2. Set reasonable locking time, failed retry and complete status updates; 3. Improve performance by batch processing, regular cleaning, optimistic locking, retry mechanisms and asynchronous polling strategies; 4. Applicable to lightweight scenarios, such as timed mail, log processing, etc., but not suitable for systems with high throughput, low latency or strong consistency requirements.

When dealing with high concurrency tasks, it is indeed a common practice to implement queue mechanisms using SQL. Although the database is not a special queue system, in lightweight scenarios, the SQL queue solution is practical enough, and it is low in implementation and easy to maintain.

The following is based on several actual needs and talk about how to do queue management in SQL.

How to design a basic SQL team list?

The core fields of the queue list usually include:

• id : unique identifier
• payload : task content (such as JSON string)
• status : current status (such as pending, processing, done)
• created_at : Entry time
• locked_until : used to avoid multiple consumers processing the same task at the same time

It is recommended to add indexes, especially the combined index of status and locked_until , so that querying unprocessed tasks is more efficient.

For example, a simple table creation statement might look like this:

 CREATE TABLE job_queue (
    id SERIAL PRIMARY KEY,
    payload TEXT NOT NULL,
    status VARCHAR(20) DEFAULT 'pending',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    locked_until TIMESTAMP DEFAULT NULL
);

How to safely "removing and locking" a task?

The key is atomic operations to ensure that multiple consumers do not get the same data at the same time.

Using UPDATE ... RETURNING is a common way. For example:

 UPDATE job_queue
SET status = 'processing', locked_until = NOW() INTERVAL '30 seconds'
WHERE id = (
    SELECT id FROM job_queue
    WHERE status = &#39;pending&#39; AND locked_until <= NOW()
    ORDER BY created_at ASC
    LIMIT 1
)
RETURNING *;

This statement will find the earliest available task, mark it as being processed, and return this record to the consumer. Since it is an atomic operation, even if multiple consumers perform at the same time, there will be no conflict.

A few points to note:

• Don't lock the time too short to prevent the task from being released before it is processed.
• If the task fails, you can reset status back to pending and update locked_until
• After the processing is completed, remember to set the status to done

How to improve queue performance and reliability?

Although SQL queues are simple and easy to use, they also have performance bottlenecks. Here are some optimization directions:

• Batch processing : take out multiple tasks at once to reduce the number of database interactions
• Regularly clean up completed tasks : you can set up a timed task to archive or delete done state data
• Use optimistic locking mechanism : Check version number or timestamp when updating task status to avoid conflict overwrite
• Introduce a retry mechanism : the number of retry times will be automatically increased after the task fails, and the maximum limit will be exceeded and the failure queue will be entered.
• Consider asynchronous polling strategies : do not poll the database frequently, you can appropriately extend the interval or use notification mechanisms (such as LISTEN/NOTIFY of PostgreSQL)

Which scenarios are suitable for? What are the restrictions?

SQL queues are suitable for scenarios with small tasks and low latency requirements, such as:

• Send emails or text messages regularly
• Log processing
• Internal state synchronization

But it doesn't fit:

• Real-time messaging system with high throughput and low latency
• Financial transactions with extremely high requirements for message persistence
• Scenarios where strong consistency guarantee is needed in distributed environments

This type of requirement is more suitable for using professional queue middleware, such as RabbitMQ, Kafka or Redis Streams.

Basically that's it. SQL queues are not complex but are easy to ignore details, especially when it comes to concurrency control and error handling.

The above is the detailed content of Implementing SQL Queueing Solutions. For more information, please follow other related articles on the PHP Chinese website!