Multiple Publishers and Subscribers in Pub/Sub

If you're preparing for the Professional Data Engineer certification exam, understanding how messaging patterns work in Google Cloud is essential. One of the foundational concepts you'll encounter is how multiple publishers and subscribers can interact with a single Pub/Sub topic. This pattern forms the backbone of many data engineering architectures, from streaming analytics pipelines to event-driven microservices. Knowing how multiple publishers and subscribers work with Pub/Sub topics helps you design scalable, decoupled systems that can handle complex data flows across diverse applications.

Google Cloud Pub/Sub is a messaging service that enables asynchronous communication between independent applications. The ability to support multiple publishers sending messages to a single topic, along with multiple subscribers receiving those messages independently, makes it a powerful tool for building distributed systems. This many-to-many relationship distinguishes Pub/Sub from simpler point-to-point messaging patterns and enables the flexible architectures that modern data platforms require.

What Multiple Publishers and Subscribers Means in Pub/Sub

A Pub/Sub topic serves as a central message hub where publishers send messages and subscribers receive them. When we talk about multiple publishers and subscribers working with a single topic, we're describing an architecture where any number of applications can publish messages to the same topic, and any number of separate applications can subscribe to receive those messages through their own subscriptions.

The key concept here is decoupling. Publishers don't need to know anything about subscribers, and subscribers don't need to know about publishers. A publisher simply sends a message to a topic. That message becomes available to all subscriptions associated with that topic. Each subscription operates independently, maintaining its own acknowledgment state and delivery guarantees.

Think of a topic as a broadcast channel. When a mobile game studio publishes player achievement events to a topic, one subscription might feed those events to a real-time leaderboard service, another might send them to a data warehouse for analytics, and a third might trigger push notifications to friends. Each subscriber processes the same achievement events independently, at its own pace, without affecting the others.

How the Architecture Works

The architecture of multiple publishers and subscribers with a single GCP Pub/Sub topic follows a hub-and-spoke pattern. The topic sits at the center, receiving messages from any number of publishers. Each subscription creates an independent message queue that receives copies of messages published to the topic.

When a publisher sends a message to a topic, Pub/Sub stores that message temporarily. For each subscription attached to the topic, Pub/Sub maintains a separate pointer tracking which messages that subscription has acknowledged. This means the same message can be in different states for different subscriptions. One subscriber might have already processed and acknowledged a message while another subscriber is still working on it.

Consider a freight logistics company that tracks truck locations. Multiple trucks (publishers) send GPS coordinates to a single "truck-locations" topic every 30 seconds. Three different subscriptions consume these messages. A route optimization service processes location data to adjust delivery schedules. A customer notification service sends estimated arrival times. A compliance logging service archives all location data for regulatory requirements.

Each truck publishes independently without knowing about these downstream systems. Each subscription processes the location messages at its own rate, with its own acknowledgment deadlines and retry policies.

Message Delivery and Independence

Each subscription receives its own copy of every message published to the topic after that subscription was created. Subscriptions don't share message queues or compete for messages. If you have five subscriptions on a topic and publish one message, that single published message results in five independent message deliveries, one per subscription.

This independence extends to message acknowledgment. When a subscriber pulls a message and processes it, that subscriber must explicitly acknowledge the message. The acknowledgment only affects that specific subscription. Other subscriptions maintain their own delivery state for the same message. If one subscriber fails to process a message and never acknowledges it, Pub/Sub will redeliver that message to that subscriber according to its retry policy. However, other subscriptions are completely unaffected by this failure.

A hospital network might publish patient vital sign readings to a central topic. One subscription feeds a real-time monitoring dashboard that acknowledges messages immediately after displaying them. Another subscription performs complex anomaly detection analysis that takes several seconds per message. A third subscription writes all readings to BigQuery for historical analysis. Each operates on its own schedule, and if the anomaly detection service experiences a temporary outage, the dashboard and data warehouse continue functioning normally.

Publisher Independence and Scalability

Just as subscribers operate independently, so do publishers. Any application with the appropriate permissions can publish messages to a topic. Publishers don't coordinate with each other or share any state. This makes it easy to add new data sources to your system without modifying existing components.

From a GCP perspective, this means you can have vastly different types of applications publishing to the same topic. A Python service running on Google Kubernetes Engine might publish order events to a topic. Simultaneously, a JavaScript application running in Cloud Functions might publish the same types of events triggered by a different workflow. A legacy system running on-premises might publish via the REST API. All these messages flow into the same topic and become available to all subscriptions.

An online learning platform might aggregate student interaction events from multiple sources into a single "student-activity" topic. Web application servers publish page view events, mobile apps publish video watch events, quiz engines publish assessment completion events, and discussion forum services publish comment events. A subscription feeding a real-time engagement dashboard receives all these event types, while another subscription filters for specific event patterns to trigger personalized recommendations.

Creating Topics and Subscriptions

Setting up multiple publishers and subscribers starts with creating a topic and then adding subscriptions to it. Using the gcloud command line tool, you can create a topic with:

gcloud pubsub topics create student-activity

Once the topic exists, any application with the Pub/Sub Publisher role can send messages to it. To create subscriptions that will receive these messages:

gcloud pubsub subscriptions create dashboard-feed \
  --topic=student-activity

gcloud pubsub subscriptions create analytics-feed \
  --topic=student-activity

gcloud pubsub subscriptions create recommendation-feed \
  --topic=student-activity

Each subscription now receives all messages published to the student-activity topic. Publishers send messages using client libraries or the API:

from google.cloud import pubsub_v1

publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path('your-project-id', 'student-activity')

message_data = b'{"user_id": "12345", "action": "video_complete"}'
future = publisher.publish(topic_path, message_data)
print(f'Published message ID: {future.result()}')

This code can run in any service, anywhere. Multiple services can use identical code to publish to the same topic, and Google Cloud handles routing messages to all subscriptions.

Practical Use Cases and Patterns

The multiple publishers and subscribers pattern works well in scenarios requiring fan-out distribution or event aggregation. Fan-out means taking a single stream of events and distributing them to multiple independent processors. Event aggregation means collecting events from diverse sources into a unified stream.

A payment processor might use both patterns simultaneously. Multiple merchant websites (publishers) send transaction events to a central "transactions" topic. Multiple backend services (subscribers) process these events: a fraud detection service analyzes patterns, an accounting service records revenue, a notification service sends receipts to customers, and a data pipeline loads transaction data into BigQuery for reporting. Adding a new merchant requires no changes to downstream services. Adding a new processing service requires no coordination with existing subscribers or publishers.

In scientific research, a climate modeling organization might collect sensor readings from weather stations worldwide. Each station publishes temperature, humidity, and pressure readings to a shared topic. Research teams run different subscriptions for different purposes: one team studies temperature trends, another analyzes pressure systems, and a third validates sensor accuracy by comparing readings from nearby stations. Each team processes data independently without affecting others.

A video streaming service uses this pattern to handle user viewing events. Smart TV apps, mobile apps, and web players all publish viewing events (play, pause, stop, seek) to a "viewing-events" topic. Separate subscriptions handle recommendations, billing calculations, content popularity analytics, and quality-of-service monitoring. Each subscriber can scale independently based on its processing requirements.

When to Use This Pattern

Multiple publishers and subscribers with a single Pub/Sub topic works well when you need to decouple event producers from consumers. If you have multiple data sources that generate similar types of events, aggregating them into a single topic simplifies downstream processing. If you need multiple systems to react to the same events independently, creating multiple subscriptions on one topic is more efficient than having publishers send messages to multiple destinations.

This pattern is particularly valuable when different subscribers have different processing speeds or reliability requirements. Because each subscription maintains independent state, a slow subscriber doesn't impact faster ones. If you're building a system where you expect to add new event consumers over time without modifying producers, this pattern provides the flexibility you need.

However, this pattern may not be ideal when messages need to be processed by exactly one consumer in a competing consumer pattern. In those cases, you would create a single subscription that multiple worker instances pull from, rather than multiple subscriptions. Additionally, if your publishers send fundamentally different types of messages that require completely different processing, you might benefit from separate topics rather than combining everything into one.

Configuration Considerations

When implementing multiple publishers and subscribers in GCP, several configuration choices affect behavior. Subscription acknowledgment deadlines determine how long Pub/Sub waits before redelivering unacknowledged messages. Different subscriptions on the same topic can have different acknowledgment deadlines based on their processing needs.

Message retention duration on the topic determines how long Pub/Sub stores messages. This affects how far back new subscriptions can reach when they're first created. By default, topics retain messages for seven days. Subscriptions created during that window can receive messages published before the subscription existed, up to the retention limit.

Filtering can be applied at the subscription level, allowing different subscribers to receive only specific messages from a topic. A solar farm monitoring system might publish all panel telemetry to one topic, but create filtered subscriptions so that maintenance alerts go to one team while performance metrics go to another.

Access control through IAM roles determines which services can publish to a topic and which can create or consume from subscriptions. In a multi-team environment, you might grant different teams the ability to create their own subscriptions on shared topics while restricting who can publish.

Integration with the Google Cloud Ecosystem

Pub/Sub topics with multiple publishers and subscribers integrate naturally with other GCP services. Cloud Functions can be triggered by Pub/Sub messages, allowing you to create lightweight subscribers that execute code in response to events without managing servers. Each function subscription operates independently.

Dataflow jobs commonly consume from Pub/Sub subscriptions, transforming streaming data before writing to destinations like BigQuery or Cloud Storage. Multiple Dataflow pipelines can subscribe to the same topic, each performing different transformations or aggregations on the same source data.

BigQuery subscriptions provide a direct path from Pub/Sub to data warehousing. You can create a subscription that automatically writes messages to a BigQuery table, while other subscriptions on the same topic feed real-time processing systems. This allows you to satisfy both operational and analytical requirements from a single message stream.

Cloud Logging and Cloud Monitoring integrate with Pub/Sub, allowing you to publish logs or metrics to topics. Multiple teams can subscribe to these operational data streams for their own monitoring dashboards or alerting systems without interfering with each other.

Key Takeaways

Understanding how multiple publishers and subscribers work with a single Pub/Sub topic is fundamental to building decoupled, scalable systems on Google Cloud. Publishers send messages to a topic without knowing about subscribers. Each subscription receives its own copy of messages and maintains independent acknowledgment state. This architecture enables fan-out patterns where many services process the same events independently, and aggregation patterns where diverse sources feed into unified streams.

The pattern provides flexibility to add new data sources and new consumers without modifying existing components. It supports different processing speeds and reliability requirements across subscribers while ensuring that issues in one subscriber don't cascade to others. Whether you're building event-driven microservices, streaming analytics pipelines, or complex data integration workflows, this foundational pattern enables the loose coupling and independent scalability that modern distributed systems require.

For those preparing for the Professional Data Engineer certification exam, this concept frequently appears in questions about system design, data pipeline architecture, and service integration. If you're looking for comprehensive exam preparation that covers Pub/Sub patterns and the full range of Google Cloud data engineering topics, check out the Professional Data Engineer course.