Pass Associate-Cloud-Engineer Exam Guide

Comprehensive and Detailed In Depth Explanation:

Let's evaluate each database option against the requirements: automatic scaling, multi-region support, and low latency for a growing social media platform:

A. Bigtable: Bigtable is a highly scalable NoSQL database designed for large analytical and operational workloads with low latency. It offers excellent horizontal scalability and can be deployed across multiple regions for high availability and lower latency for a global user base. However, it's a NoSQL database and might require significant changes to your existing PostgreSQL data model and application code.

B. BigQuery: BigQuery is a fully managed, serverless data warehouse optimized for analytical queries on large datasets. It's not designed for low-latency transactional workloads that a social media platformwould require for real-time user interactions. While it's globally available, its primary use case is not operational database needs.

C. Spanner: Spanner is a globally distributed, horizontally scalable relational database service with strong consistency. It offers automatic scaling, built-in multi-region and multi-continental configurations for high availability and low latency across a global user base, and supports standard SQL (with some extensions). This makes it a strong candidate for a rapidly growing platform needing scalability, global presence, and low latency. While it's not directly PostgreSQL, it offers a relational model and tools to aid migration.

D. Cloud SQL for PostgreSQL: Cloud SQL offers managed PostgreSQL instances with automatic scaling capabilities. It supports high availability within a region and cross-region read replicas for disaster recovery and read scaling. However, its multi-region capabilities for write operations and automatic scaling across regions are more limited compared to Spanner. For a rapidly growing platform with a primarily North American user base but future global expansion in mind and a need for low latency, Spanner's architecture is better suited for true multi-region write capabilities and consistent low latency globally.

Considering the requirements for automatic scaling, multi-region support for both reads and writes with low latency for a growing user base, Spanner is the most appropriate choice.

Google Cloud Documentation References:

Cloud Spanner Overview: - This document highlights Spanner 's global scalability, strong consistency, and multi-region capabilities.

Cloud Bigtable Overview: - While scalable and low-latency, it 's a NoSQL database, which might require significant application changes.

BigQuery Overview: - Focuses on analytics, not low-latency transactional workloads.

Cloud SQL for PostgreSQL Overview: - While it offers scaling and regional HA, its multi-region write capabilities are not as robust as Spanner 's.

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As to mexblood1's point, CPU utilization is a recommended proxy for traffic when it comes to Cloud Spanner. See: Alerts for high CPU utilization The following table specifies our recommendations for maximum CPU usage for both single-region and multi-region instances. These numbers are to ensure that your instance has enough compute capacity to continue to serve your traffic in the event of the loss of an entire zone (for single-region instances) or an entire region (for multi-region instances).

To enforce a mandatory restriction across all resources deployed in the organization and its nested folders/projects, the policy should be set at the highest possible level, which is the Organization level.

The constraint constraints/gcp.resourceLocations (or similar resource location constraints) works by defining a list of allowed regions or zones. The most secure and explicit way to enforce that resources must be deployed in the EEA is to define the allowed EEA locations and apply the policy at the Organization level to ensure it covers all current and future projects and folders.

Google HTTP(S) Load Balancing has native support for the WebSocket protocol when you use HTTP or HTTPS, not HTTP/2, as the protocol to the backend.

So the next possible step is to Meet with the cloud enablement team to discuss load balancer options.

We dont need to convert WebSocket code to use HTTP streaming or Redesign the application, as WebSocket support is offered by Google HTTP(S) Load Balancing. Reviewing the encryption requirements is a good idea but it has nothing to do with WebSockets.