What is Pub/Sub?

Pub/Sub is an asynchronous and scalable messaging service that decouples services producing messages from services processing those messages.

Pub/Sub allows services to communicate asynchronously, with latencies typically on the order of 100 milliseconds.

Pub/Sub is used for streaming analytics and data integration pipelines to load and distribute data. It's equally effective as a messaging-oriented middleware for service integration or as a queue to parallelize tasks.

Pub/Sub lets you create systems of event producers and consumers, called publishers and subscribers. Publishers communicate with subscribers asynchronously by broadcasting events, rather than by synchronous remote procedure calls (RPCs).

Publishers send events to the Pub/Sub service, without regard to how or when these events are to be processed. Pub/Sub then delivers events to all the services that react to them. In systems communicating through RPCs, publishers must wait for subscribers to receive the data. However, the asynchronous integration in Pub/Sub increases the flexibility and robustness of the overall system.

To get started with Pub/Sub, check out the Quickstart using Google Cloud console. For a more comprehensive introduction, see Building a Pub/Sub messaging system.

Common use cases

  • Ingesting user interaction and server events. To use user interaction events from end-user apps or server events from your system, you might forward them to Pub/Sub. You can then use a stream processing tool to deliver the events to databases. Pub/Sub lets you gather events from many clients simultaneously.
  • Real-time event distribution. Events, raw or processed, may be made available to multiple applications across your team and organization for real- time processing. Pub/Sub supports an "enterprise event bus" and event-driven application design patterns. Pub/Sub lets you integrate with many systems that export events to Pub/Sub.
  • Replicating data among databases. Pub/Sub is commonly used to distribute change events from databases. These events can be used to construct a view of the database state and state history in BigQuery and other data storage systems.
  • Parallel processing and workflows. You can efficiently distribute many tasks among multiple workers by using Pub/Sub messages to communicate with the workers. Examples of such tasks are compressing text files, sending email notifications, evaluating AI models, and reformatting images.
  • Enterprise event bus. You can create an enterprise-wide real-time data sharing bus, distributing business events, database updates, and analytics events across your organization.
  • Data streaming from applications, services, or IoT devices. For example, a SaaS application can publish a real-time feed of events. Or, a residential sensor can stream data to Pub/Sub for use in other Google Cloud products through a data-processing pipeline.
  • Refreshing distributed caches. For example, an application can publish invalidation events to update the IDs of objects that have changed.
  • Load balancing for reliability. For example, instances of a service may be deployed on Compute Engine in multiple zones but subscribe to a common topic. When the service fails in any zone, the others can pick up the load automatically.

Comparing Pub/Sub to other messaging technologies

Pub/Sub combines the horizontal scalability of Apache Kafka and Pulsar with features found in messaging middleware such as Apache ActiveMQ and RabbitMQ. Examples of such features are dead-letter queues and filtering.

Another feature that Pub/Sub adopts from messaging middleware is per-message parallelism, rather than partition-based messaging. Pub/Sub "leases" individual messages to subscriber clients, then tracks whether a given message is successfully processed.

By contrast, other horizontally scalable messaging systems use partitions for horizontal scaling. This forces subscribers to process messages in each partition in order and limits the number of concurrent clients to the number of partitions. Per-message processing maximizes the parallelism of subscriber applications, and helps ensure publisher and subscriber independence.

Next steps