Customizing Retry Predicates in Google Cloud Python Libraries

Google Cloud's Python libraries are designed for resilience. They add strong retry mechanisms to handle transient errors effectively. However, there may be situations where the default retry behavior isn't suitable. For example, you might encounter certain errors that should not trigger a retry, or you may require more control over the retry logic.

This blog post explores how the Google Cloud's Python libraries interacts with custom retry predicates, allowing you to customize the retry behavior to better meet your specific requirements.

In this blog post, I want to highlight a specific example related to using service account impersonation within Google Cloud libraries. In an architecture I designed and am currently working on, we isolate user environments into separate Google Cloud projects. We noticed that some of our services were experiencing degraded performance in certain user flows. After investigating, we traced the issue back to the default retry behavior of the libraries mentioned earlier.

The Default Retry Mechanism

Before we go into customization, it's important to understand the default retry behavior of Google Cloud Python libraries. These libraries typically have an exponential backoff strategy with added jitter for retries. This means that when a transient error occurs, the library will retry the operation after a brief delay, with the delay increasing exponentially after each subsequent attempt. The inclusion of jitter introduces randomness to the delay, which helps prevent synchronization of retries across multiple clients.

While this strategy is effective in many situations, it may not be ideal for every scenario. For example, if you're using service account impersonation and encounter an authentication error, attempting to retry the operation may not be helpful. In such cases, the underlying authentication issue likely needs to be resolved before a retry can succeed.

Enter Custom Retry Predicates

In Google Cloud libraries, custom retry predicates enable you to specify the precise conditions under which a retry attempt should be made. You can create a function that accepts an exception as input and returns True if the operation should be retried, and False if it should not.

For example, here’s a custom retry predicate that prevents retries for certain authentication errors that occur during service account impersonation:

from google.api_core.exceptions import GoogleAPICallError
from google.api_core.retry import Retry, if_transient_error

def custom_retry_predicate(exception: Exception) -> bool:
    if if_transient_error(exception): # exceptions which should be retried
        if isinstance(exception, GoogleAPICallError):
            if "Unable to acquire impersonated credentials" in exception.message: # look for specific impersonation error
                return False
        return True
    return False

This predicate checks if the exception is a GoogleAPICallError and specifically looks for the message "Unable to acquire impersonated credentials". If this condition is met, it returns False, preventing a retry.

Using Custom Predicates with Google Cloud Libraries

Firestore:

from google.cloud import firestore

# ... your Firestore setup ...

retry = Retry(predicate=custom_retry_predicate, timeout=10)

# example of an arbitrary firestore api call, works with all
stream = collection.stream(retry=retry)

BigQuery:

from google.cloud import bigquery

# ... your BigQuery setup ...

retry = Retry(predicate=custom_retry_predicate, timeout=10)

# example of an arbitrary bigquery api call, works with all
bq_query_job = client.get_job(job_id, retry=retry)

In both examples, we create a Retry object with our custom predicate and a timeout value. This Retry object is then passed as an argument to the respective API calls.

Benefits of Custom Retry Predicates

• Fine-grained control: Define retry conditions based on specific exceptions or error messages with precision.
• Improved efficiency: Avoid unnecessary retries for non-transient errors, thus saving resources and time.
• Enhanced application stability: Handle specific errors gracefully to prevent cascading failures.

Conclusion

Custom retry predicates offer an effective way to enhance the resilience of your Google Cloud applications. By customizing the retry behavior to suit your specific requirements, you can ensure that your applications are robust, efficient, and scalable. Take charge of your error handling and master the retry process!