Cloud Run Pubsub Pull
You run a cloud run pubsub pull workflow by using a background worker or a job. Standard web services pause their CPU when no web requests arrive, which stops pull loops. Teams often fix this by keeping at least one instance awake with always-on CPU. During setup, developers often attach broad service roles to get the queue working fast. Overly broad roles let a worker read or delete messages from every queue in the project. If a bad message exploits the app, the attacker gains wide project perm
Pull Request Security Checks: the complete guide
Architecture patterns, implementation strategies, and security best practices. Delivered to your inbox.
You run a cloud run pubsub pull workflow by using a background worker or a job. Standard web services pause their CPU when no web requests arrive, which stops pull loops. Teams often fix this by keeping at least one instance awake with always-on CPU. During setup, developers often attach broad service roles to get the queue working fast. Overly broad roles let a worker read or delete messages from every queue in the project. If a bad message exploits the app, the attacker gains wide project permissions.
How a cloud run pubsub pull setup works
Standard serverless apps wait for incoming web traffic before they start working. A push queue fits this model because the queue sends a web request to the app. A pull queue works the other way around. Your app must reach out to the queue, ask for messages, and send back a receipt. If the platform freezes the CPU between web requests, the pull loop stops mid-task.
Teams solve this timing mismatch in two main ways. The first way uses a long-running service with CPU kept on at all times. You set a minimum instance count so at least one worker stays awake. That worker runs a steady loop that pulls messages, processes them, and sends back receipts. This approach works well when messages arrive at a steady pace all day.
The second way uses scheduled jobs for batch work. A timer starts a job every few minutes. The job wakes up, pulls a batch of waiting messages, finishes the work, and shuts down. You must also attach a service role to the worker or job. This role needs permission to pull from the specific queue and update message status.
Managing background loops and scaling limits
Running a pull loop inside a web container takes careful tuning. The platform scales web services by counting incoming web requests, not by checking queue depth. If ten thousand messages land in your queue, a single idle web container will not scale up alone. Teams must set up custom scaling rules based on queue metrics so more workers start when backlogs grow.
Message receipts also need close tracking during long tasks. When a worker pulls a message, the queue hides that message from other workers for a short window. If your app takes too long to finish the work, the timer runs out. The queue then sends the same message to another worker, causing duplicate work. Your pull loop must ask for more time while it works on big tasks, and your shutdown handler must finish open tasks before a container stops.
Pull Request Security Checks: architecture patterns and best practices
Continue reading? Get the full guide.
Free. No spam. Unsubscribe anytime.
What native queue roles leave open
Native cloud roles decide which service identity can talk to a queue or a database. Once the worker starts and passes that first check, the cloud role steps back. The role does not check what individual queries or commands the worker runs next. A worker that processes queue data often needs to write results into a production database.
This creates a blind spot during both daily operations and incident fixes. If a developer logs into the same database to debug a failed message, cloud roles grant full table permissions. Native roles cannot block a bad drop statement or rewrite emails, phone numbers, or card numbers in query results. Teams need controls that inspect live commands and mask sensitive fields in flight.
Govern statements and responses across databases and APIs
Good config and scoped roles are needed setup. They do not check single queries in flight or mask sensitive fields in returned rows. Across Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes APIs, SSH, and internal HTTP endpoints, hoop.dev runs beside each supported service as an open-source proxy, a sidecar for your database or API.
First, hoop.dev guardrails block banned queries before they run. Then hoop.dev data masking rewrites matched sensitive values in responses in memory. The audit trail records every statement and verdict per resource. Furthermore, hoop.dev holds SOC 2 Type II and is open source on GitHub.
Frequently asked questions
Why did my pull worker stop reading messages after deployment?
Your service likely pauses its CPU when no web requests are active. Set the CPU to stay on at all times and keep at least one instance running so the background loop never freezes.
When should I use pull instead of push on serverless containers?
Use pull when you process huge batches, need strict rate control, or run jobs on a schedule. Push works better for small, single events that trigger quick web responses.
How do I stop duplicate message processing in a pull loop?
Make sure your worker extends the message deadline while it runs long tasks. Always design your database writes to handle repeat runs safely in case a worker crashes before sending a receipt.