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Then there is **Access Frequency**. Check how often your objects are being accessed. Infrequent access might be a signal that you should move those objects to a cheaper storage tier. **_Object Size_** matters. Be aware of the size of your objects. Large objects may require more bandwidth to access, which can increase your costs. You will also look at **Request Count**. Monitor the number of requests made to your GCS buckets. A high number of requests could indicate a performance issue or a security concern. **Error Rate**. Pay attention to error rates. A high error rate could indicate a problem with your application or a misconfiguration of your GCS buckets. We can also see **Latency**. Measure the time it takes to access your objects. High latency can lead to a poor user experience. Also, it's important to keep an eye on the **Storage Costs**. Track your storage costs over time to ensure that your storage usage is optimized. You might even want to monitor the **Data Lifecycle Management**. If you're using data lifecycle management policies, monitor how they're affecting your storage costs and object access patterns. These are the crucial ones, but there might be other things to consider, depending on what you're doing with GCS. However, by keeping an eye on these metrics, you can ensure your GCS buckets are running smoothly, optimize your costs, and catch any potential issues before they become major problems.
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