To further reduce the number of unplanned rollbacks of erroneous production deployments, introducing a green-blue deployment model and fragmenting the monolithic platform into microservices are effective strategies. A green-blue deployment model allows for a gradual and controlled release of new versions, reducing the risk of deployment failures affecting users. Fragmenting the monolithic platform into microservices isolates different components, minimizing the impact of errors in one part of the system and making deployments more manageable.
To run a development environment in Google Cloud and provide cost visibility, the following steps should be taken: First, use persistent disks with the --no-auto-delete flag. This ensures that the state persists even when VMs are stopped and avoids data loss by not automatically deleting the disks when the instance is stopped. Second, use Google BigQuery billing export and labels to associate costs to groups. This allows the finance department to have visibility into cost allocation and analysis by associating resource consumption with specific labels, enabling better cost management and accountability.
Given the scenario where there are 1000 meeting rooms equipped with motion sensors reporting their status every second, the most appropriate database type would be one that can handle massive volumes of rapidly changing, potentially unstructured data. A NoSQL database fits this requirement well. NoSQL databases are designed to manage large amounts of data and can be scaled horizontally across many servers, which is crucial for handling data from multiple sources in real-time. Additionally, NoSQL databases are flexible, allowing the storage of various types of data without a fixed schema, making it easier to adapt to different kinds of sensor data. Other options, such as flat files, relational databases, and blobstores, do not offer the same level of scalability, flexibility, and ease of handling such large, rapidly evolving datasets.
If virtual machine instances are being terminated and re-launched every minute, it suggests that the instances are failing their health checks. Ensuring that a firewall rule exists to allow load balancer health checks to reach the instances in the instance group would address this issue, as it would allow the load balancer to communicate with the instances to verify their health. When these health checks fail, the instances are often terminated and recreated by the autoscaling group to maintain a healthy service. Therefore, configuring the firewall to permit these health checks is crucial for the stable operation of the instances.
To fix the script, create a new service account with BigQuery access and execute your script using that user. Service accounts are used for server-to-server interactions, such as those between a virtual machine and BigQuery, and provide a way to authenticate applications to Google Cloud services. By assigning the appropriate BigQuery access role to the service account and using that account to run your script, the script will be able to connect to BigQuery and access the data it needs.