Google Cloud Pub/Sub - Messaging Service

Scalable event streaming and messaging platform for asynchronous communication and real-time data processing.

What is Google Cloud Pub/Sub?

Google Cloud Pub/Sub is a fully managed messaging service based on the publish-subscribe pattern. The system decouples senders (publishers) and receivers (subscribers) of messages, enabling highly scalable, resilient event-driven architectures. Publishers send messages to topics, while subscribers consume these messages independently through subscriptions. This architecture allows multiple services to process the same events without needing to know about each other.

A core feature is the exactly-once delivery guarantee, ensuring that each message is processed exactly once, even during system failures or retries. This is achieved through a combination of unique message IDs, server-side deduplication, and intelligent acknowledgment mechanisms. For use cases with high data volumes and predictable load patterns, Pub/Sub Lite offers a cost-optimized alternative with manual capacity planning and zonal availability.

Pub/Sub integrates seamlessly with other Google Cloud services: BigQuery subscriptions enable direct streaming to data warehouses without additional ETL code, Dataflow can transform messages in real-time, and Cloud Functions can be triggered by events. Schema validation with Avro and Protocol Buffers ensures data quality, while message ordering with ordering keys guarantees correct sequence for event sourcing and change data capture. The global infrastructure automatically scales from a few messages per second to millions, without manual intervention.

Common Use Cases

Event-driven Microservices

Pub/Sub serves as a central event bus for microservices architectures. Services publish domain events (e.g., OrderCreated, PaymentProcessed) to topics, while other services asynchronously consume these events. This decouples services, enables independent scaling, and simplifies implementation of CQRS and event sourcing patterns. Example: An e-commerce system uses Pub/Sub for order events, processed in parallel by inventory, shipping, and analytics services.

Streaming Analytics with Dataflow

Combination of Pub/Sub and Dataflow for real-time data analysis. Pub/Sub collects events from various sources (web, mobile, IoT), while Dataflow aggregates, transforms, and analyzes them in real-time. Results are written to BigQuery, Cloud Storage, or other systems. Example: A financial services provider analyzes transaction streams in real-time for fraud detection, processing millions of events per second.

IoT Data Ingestion

Pub/Sub automatically scales for millions of IoT devices sending telemetry data. The global infrastructure ensures low latency, while message retention (up to 31 days) buffers temporary failures of downstream systems. Example: A smart city project collects data from sensors for traffic, air quality, and energy consumption, processes it with Dataflow, and visualizes insights in dashboards.

Real-time Notifications

Push subscriptions enable real-time notifications to webhooks or Cloud Functions. This is suitable for user notifications, alerts, or workflow triggering. Pub/Sub guarantees reliable delivery with automatic retries and dead letter topics for failed messages. Example: A SaaS platform sends real-time notifications to users on system events, while internal services process audit logs and analytics in parallel.

Log Aggregation

Centralized log collection from distributed systems. Applications and services send logs to Pub/Sub topics, then processed by different consumers: long-term storage in Cloud Storage, real-time monitoring in Cloud Logging, SIEM integration, or compliance archiving. Message filtering enables selective processing by log level or source.

ETL Pipelines with BigQuery Subscriptions

BigQuery subscriptions write Pub/Sub messages directly into BigQuery tables without additional code. Schema validation ensures data quality, while automatic partitioning and clustering optimize query performance. Example: A marketing team collects clickstream data via Pub/Sub, automatically landing in BigQuery and available for SQL-based analysis.

Asynchronous Task Queues

Pub/Sub as a robust task queue for time-intensive operations. Web requests trigger quick responses, while expensive tasks (image processing, report generation, batch jobs) are processed asynchronously via Pub/Sub. Pull subscriptions with manual acknowledgment enable controlled processing with backpressure handling.

Best Practices

Message Ordering with Ordering Keys

Use ordering keys for use cases requiring guaranteed order. Messages with the same key are delivered sequentially, while different keys are processed in parallel. Important: Ordering keys reduce throughput per key, so use a sufficient number of different keys for optimal performance. Ideal for user-specific events or entity updates.

Dead Letter Topics for Failed Messages

Configure dead letter topics for each subscription to isolate messages that cannot be processed after multiple attempts. Set max_delivery_attempts to a reasonable value (e.g., 5-10) and actively monitor dead letter topics. This prevents individual faulty messages from blocking entire processing and enables later error analysis.

Enable Exactly-Once Delivery

Enable exactly-once delivery for critical workloads where duplicates lead to inconsistencies (e.g., payment processing, inventory management). Note that this causes higher latency and costs. For non-critical logs or metrics, at-least-once with idempotent consumers suffices. Test the impact on throughput before production deployment.

Schema Evolution with Avro or Protocol Buffers

Define schemas for all topics and use schema validation. Plan schema evolution from the start: use optional fields, avoid breaking changes, and version schemas for major changes. This prevents runtime errors from incompatible messages and documents the data structure for all teams.

Choose Push vs. Pull Subscriptions Correctly

Use push subscriptions for event-triggered Cloud Functions or webhooks with low to medium load. Pull subscriptions are suitable for batch processing, controlled parallelization, and backpressure handling. Pull enables more flexible error handling and is better suited for on-premises integration. Combine both types depending on the use case.

Optimize Message Retention and Storage Costs

Standard retention is 7 days but can be increased to 31 days. Longer retention causes storage costs for unacknowledged messages. Ensure subscriptions actively consume and acknowledge messages. Use Pub/Sub Lite for high data volumes with predictable load patterns to reduce costs.

Monitoring with Cloud Monitoring and Alerting

Monitor key metrics: unacknowledged messages (backlog), oldest unacknowledged message age, subscription throughput, and dead letter topic size. Set alerts for abnormal values. Use Cloud Logging for detailed message traces and error analysis. Dashboards should combine publisher and subscriber metrics for end-to-end visibility.

Google Cloud Pub/Sub Comparison

vs. AWS SNS/SQS: Pub/Sub unifies pub/sub and queue semantics in one service, while AWS uses separate services (SNS for fanout, SQS for queuing). Pub/Sub offers native ordering and exactly-once delivery, while AWS achieves this only with additional complexity (FIFO queues). AWS has more integration possibilities in the AWS ecosystem.

vs. Azure Service Bus: Service Bus offers similar features (topics, queues, message sessions) but runs primarily in Azure regions. Pub/Sub has better global availability and automatic scaling. Service Bus offers more configuration options for message TTL and scheduling. Both meet enterprise requirements; choice depends on cloud provider.

vs. Apache Kafka: Kafka offers higher throughput and more control over partitioning and consumer groups, but requires self-operated clusters (or managed services like Confluent Cloud). Pub/Sub is fully serverless without cluster management, but is less suitable for log compaction or long message retention (Kafka retains logs historically). Choose Kafka for on-premises or hybrid, Pub/Sub for cloud-native architectures.

Integration with innFactory

As a Google Cloud partner, innFactory supports you in implementing event-driven architectures with Pub/Sub: from architecture consulting to migration of existing messaging systems to operations and cost optimization. We help with schema design, subscription configuration, monitoring setup, and integration with Dataflow, BigQuery, and other Google Cloud services.

Contact us for consulting on Google Cloud Pub/Sub and event streaming architectures.