autoscale cloud function based on no. of messages in pubsub

[akasranjan]

is there a way to autoscale cloud function based on number of messages in pubsub. My understanding of scaling in cloud functions is it scales basis the usage of cpu/memory. my requirement is to parallely process very lightweight data which are huge in number.

Basically the application takes the payload from the queue and inserts to bigquery. but this needs to happen very rapidly. how can i achieve this?

[ms4446]

Yes, Cloud Functions can effectively handle the rapid processing of lightweight data triggered by messages in a Pub/Sub subscription. However, it's important to understand the nuances of Cloud Function scaling and how it can be optimized for your BigQuery use case.

Understanding Cloud Function Scaling with Pub/Sub:

• Event-Driven Scaling: Cloud Functions scale automatically based on the rate of incoming Pub/Sub events. This scaling is managed by Google Cloud and is not directly user-configurable based on specific Pub/Sub metrics like num_undelivered_messages.
• Concurrency Handling: Each Cloud Function instance processes one message at a time. Google Cloud automatically manages the creation of multiple instances to handle large volumes of messages concurrently.

Setting Up Your Cloud Function:

1. Create a Cloud Function: Triggered by your Pub/Sub topic, this function should efficiently process messages and insert data into BigQuery.
2. Optimize Processing: Ensure your function is optimized for quick execution. Since you're dealing with lightweight data, focus on streamlining the BigQuery insertion process.

Optimizing for BigQuery Insertion:

• Batching: Aggregate messages into batches before inserting them into BigQuery. This approach is more efficient than processing messages individually.
• Streaming Inserts: Utilize BigQuery's streaming insert API for low-latency, real-time data ingestion.
• Function Efficiency: While parallel processing within a single Cloud Function instance is limited, ensure your code is efficient and can process each message as quickly as possible.

Important Considerations:

• Cost Management: Monitor the usage and costs associated with Cloud Functions and BigQuery operations. Adjust your implementation as needed to balance performance and cost.
• Instance Limits: Be aware of the Cloud Functions' limits and quotas. If necessary, request a quota increase to handle higher volumes of messages.
• Error Handling: Implement robust error handling and retry mechanisms within your Cloud Function to manage potential issues with BigQuery insertions and message redelivery.

While Google Cloud Functions do not offer direct user-configurable scaling based on Pub/Sub metrics, their automatic, event-driven scaling model is well-suited for handling high volumes of lightweight, rapid processing tasks. By optimizing your Cloud Function for efficient processing and BigQuery insertion, you can achieve the rapid data handling you require.