在 PHP 中编写批处理器时（如，它显然必须是 cron-ed），使用 Gearman 和简单地在 Redis 中存储要处理的数据之间有什么实际区别？
到目前为止，我的观察是，虽然 Gearman 能够实时推送工作，因为 PHP 代码只能间隔运行，但在 Redis 中使用定期调度的命令似乎或多或少是等效的。
此外，似乎使用 Gearman 通过将其绑定到 Gearman 库的调度生命周期给应用程序增加了不必要的复杂性。
综上所述，假设批处理器不会持续运行，假设 Gearman+PHP 与 Redis+PHP 相比没有任何优势是正确的吗？
Gearman is a distributed job server, Redis is a distributed store. So it is bit like comparing apples to oranges.
Now, it is possible to implement Gearman-like features with Redis (based on the list data type for instance), but it is a do-it-yourself approach. While the principle is simple, the devil is in the details.
The best Redis distributed queue implementations are for Ruby (Resque) and Python (Celery, RQ). There is a port of Resque for PHP:
https://github.com/chrisboulton/php-resque
There are important points to consider when comparing Gearman to a Redis-based implementation:
Gearman jobs notify their completion to the client, and can be synchronous or asynchronous. If you do not implement something specific, a Redis queue will only support asynchronous jobs without completion notification.
High-availability of the broker. Gearman proposes an off-the-shelf strategy. Redis does not. While you can configure master-slave replication, and use Redis Sentinel, Redis HA is not a simple problem.
Persistency. Gearman supports in-memory queues, but also some persistent backends (MySQL, Drizzle, sqlite, PostgreSQL). Redis proposes various persistency options, but none of them is as reliable as a transactional engine like MySQL or PostgreSQL.
Vertical scalability. While Redis is very efficient, it is a single-threaded process. Gearmand is a multi-threaded process, that can probably scale better (considering a single process).
Implementing a Redis-based distributed job system is fun and interesting, but if you need something working quickly, Gearman is your best bet.
除了 Didier 的回答之外，Gearman 还可以提供合并功能，例如，如果在工作人员完成工作之前，一堆客户端都发出了相同的请求，它可以将工作的响应发送回所有客户端。
来自维基百科：
Gearman 对客户发送的工作执行合并。如果两个或多个客户端要求在同一工作主体上完成工作，或者通过看到正在发送相同的块或通过使用客户端发送的唯一值，它将合并工作以便只有一个工人用过的。它专门这样做是为了避免缓存命中失败常见的群体问题
这在 Redis 中实现起来要复杂得多。