Last updated on March 16, 2026
Google Cloud Spanner Cheat Sheet
- A fully managed, horizontally scalable relational database service with strong consistency, global availability, and multi-model support.
Features
- SLA availability up to 99.999% for multi-regional instances with 10x less downtime than four nines.
- Provides transparent, synchronous replication across region and multi-region configurations.
- Optimizes performance by automatically sharding the data based on request load and size of data so you can spend less time thinking about scaling your database and more time scaling your business.
- You can run instances on a regional scope or multi-regional where your database is able to survive regional failure.Â
- All tables must have a declared primary key (PK), which can be composed of multiple table columns.
- Can make schema changes like adding a column or adding an index while serving live traffic with zero downtime.
- Multi-model database: Supports relational, graph, key-value, and vector search workloads on a single database.
- Spanner Graph: Query complex relationships and connections in your data using graph processing.
- Built-in full-text search and vector search for semantic search and AI applications.
- TrueTime: Global distributed clock that guarantees strong, external consistency across regions.
- ZeroETL integration with Vertex AI for building AI-enabled applications directly on operational data.
- Data Boost: On-demand, isolated compute resources for running analytical queries without impacting transactional workloads.
- Set up an Autoscaler infrastructure in the same project where the Cloud Spanner is deployed to automatically scale the Cloud Spanner resources according to its CPU metric.
- Set up an alerting policy on Cloud Monitoring that sends an email alert to on-call Site Reliability Engineers (SRE) when the Cloud Spanner CPU metric exceeds the desired threshold. The SREs shall scale the resources up or down appropriately.
- Set up an alerting policy on Cloud Monitoring that sends an alert to a webhook when the Cloud Spanner CPU metric is over or under your desired threshold. Create a Cloud Function that listens to this HTTP webhook and resizes Spanner resources appropriately.
- Set up an alerting policy on Cloud Monitoring that sends an email alert to Google Cloud Support email when the Cloud Spanner CPU metric exceeds the desired threshold. The Google Support team shall scale the resources up or down appropriately.
, performance, and compliance for mission-critical workloads</li>
<li>Database Center: Centralized fleet management with performance and security recommendations.</li>
<li>Point-in-time recovery (PITR) for operational peace of mind.</li>
<li>Migration tools for sharded MySQL and Cassandra workloads with minimal downtime.</li>
</ul>
<p> </p>
<h2>Editions</h2>
<ul>
<li>Cloud Spanner Standard:
<ul>
<li>Regional (single-region) configurations</li>
<li>Core database capabilities</li>
</ul>
</li>
<li>Cloud Spanner Enterprise:
<ul>
<li>Multi-model capabilities (graph, search, vector)</li>
<li>Enhanced operational simplicity</li>
<li>Regional, dual-region, and multi-region configurations</li>
</ul>
</li>
<li>Cloud Spanner Enterprise Plus:
<ul>
<li>Highest availability: 99.999% SLA</li>
<li>Highest performance and compliance standards</li>
<li>Ideal for mission-critical, global workloads</li>
<li>Advanced governance and security features</li>
</ul>
</li>
</ul>
<p>Compute capacity is provisioned in processing units or nodes (1 node = 1000 processing units). Capacity is billed per replica.</p>
<h2>Â </h2>
<h2>Spanner Graph & Search</h2>
<ul>
<li>Spanner Graph: Build knowledge graphs that capture complex relationships between entities (nodes) and their connections (edges). Ideal for recommendation engines, fraud detection, and knowledge base systems.</li>
<li>Vector search: Perform semantic similarity search on embeddings for AI and machine learning applications.</li>
<li>Full-text search: Integrated keyword-based search capabilities.</li>
<li>Combined queries: Blend semantic understanding, keyword retrieval, and graph traversal for comprehensive results.</li>
</ul>
<p> </p>
<h2><strong>Pricing</strong></h2>
<p>Cloud Spanner pricing is based on compute capacity, database storage, backup storage, replication, and network usage. Committed use discounts reduce compute costs.</p>
<ul>
<li>Compute capacity:
<ul>
<li>Provisioned in processing units or nodes (1 node = 1000 processing units)</li>
<li>Billed per hour per replica (each replica in a configuration incurs compute charges)</li>
<li>Three editions available with different price points:</li>
<li>Standard edition</li>
<li>Enterprise edition</li>
<li>Enterprise Plus edition</li>
<li>Data Boost: On-demand, isolated compute for analytical workloads (billed per serverless processing unit per hour)</li>
</ul>
</li>
<li>Database storage:
<ul>
<li>SSD storage: For low-latency operational workloads (billed per GB per month per replica)</li>
<li>HDD storage: For less frequently accessed data with higher latency tolerance (billed per GB per month per replica)</li>
<li>Storage tiering policies can automatically move data from SSD to HDD after a specified time</li>
</ul>
</li>
<li>Backup storage:
<ul>
<li>Regional configurations: Billed per GB per month (includes storage in all replicas)</li>
<li>Dual-region and multi-region configurations: Billed per GB per month (includes storage in all replicas)</li>
</ul>
</li>
<li>Replication:
<ul>
<li>Intra-region replication: Free</li>
<li>Inter-region replication: Charged per GB</li>
</ul>
</li>
<li>Network:
<ul>
<li>Ingress: Free</li>
<li>Intra-region egress: Free</li>
<li>Inter-region egress: Charged per GB</li>
</ul>
</li>
</ul>
<p>For current pricing details, refer to the official Google Cloud Spanner pricing page.</p>
<h3 data-pm-slice=)
Validate Your Knowledge
Question 1
A company has an application that uses Cloud Spanner as its backend database. After a few months of monitoring your Cloud Spanner resource, you noticed that the incoming traffic of the application has a predictable pattern. You need to set up automatic scaling that will scale up or scale down your Spanner nodes based on the incoming traffic. You don’t want to use an open-source tool as much as possible.
What should you do?
Correct Answer: 3
When you create a Cloud Spanner instance, you choose the number of nodes that provide compute resources for the instance. As the instance’s workload changes, Cloud Spanner does not automatically adjust the number of nodes in the instance. As a result, you need to set up several alerts or use an Autoscaler tool to ensure that the instance stays within the recommended maximums for CPU utilization and the recommended limit for storage per node.
You can invoke Cloud Functions with an HTTP request using the POST, PUT, GET, DELETE, and OPTIONS HTTP methods. To create an HTTP endpoint for your function, specify –trigger-http as the trigger type when deploying your function. From the caller’s perspective, HTTP invocations are synchronous, meaning the result of the function execution will be returned in the response to the HTTP request.
Cloud Spanner (Autoscaler)
Autoscaler tool for Cloud Spanner (Autoscaler), an open-source tool that you can use as a companion tool to Cloud Spanner. This tool lets you automatically increase or reduce the number of nodes or processing units in one or more Spanner instances based on how their capacity is being used.
Autoscaler monitors your instances and automatically adds or removes nodes or processing units to help ensure that they stay within the following parameters:
– The recommended maximums for CPU utilization.
– The recommended limit for storage per node, plus or minus a configurable margin.
To deploy Autoscaler, decide which of the following topologies is best to fulfill your technical and operational needs:
Per-project topology: The Autoscaler infrastructure is deployed in the same project as Cloud Spanner that needs to be autoscaled.
Centralized topology: Autoscaler is deployed in one project and manages one or more Cloud Spanner instances in different projects.
Distributed topology:: Most of the Autoscaler infrastructure is deployed in one project, but some infrastructure components are deployed with the Cloud Spanner instances being autoscaled in different projects.
In the scenario, you have to find a method where you can automatically scale your Cloud Spanner resources based on a traffic pattern. As much as possible, you also don’t want to use an open-source tool. Since Cloud Spanner does not scale automatically, you have to check for CPU usage of your Spanner instances and find a way to trigger your Cloud Spanner database to scale its resources accordingly. Moreover, you have to ensure that these steps are done automatically.
Hence the correct answer is: Set up an alerting policy on Cloud Monitoring that sends an alert to a webhook when the Cloud Spanner CPU metric is over or under your desired threshold. Create a Cloud Function that listens to this HTTP webhook and resizes Spanner resources appropriately.
The option that says: Set up an Autoscaler infrastructure in the same project where the Cloud Spanner is deployed to automatically scale the Cloud Spanner resources according to its CPU metric is incorrect because the Autoscaler tool for Cloud Spanner (Autoscaler) is an open-source tool.
The option that says: Set up an alerting policy on Cloud Monitoring that sends an email alert to on-call Site Reliability Engineers (SRE) when the Cloud Spanner CPU metric exceeds the desired threshold. The SREs shall scale the resources up or down appropriately is incorrect because this method requires an on-call SRE every time there is an alert which means that the scaling will be done manually rather than automatically.
The option that says: Set up an alerting policy on Cloud Monitoring that sends an email alert to Google Cloud Support email when the Cloud Spanner CPU metric exceeds the desired threshold. The Google Support team shall scale the resources up or down appropriately is incorrect because this does not satisfy the requirement to scale the Cloud Spanner resources automatically. In this method, you will delegate the task of scaling the Spanner resources to a Technical Account Manager every time an alert is triggered.
References:
https://cloud.google.com/spanner/docs/monitoring-cloud
https://cloud.google.com/functions/docs/writing/http
https://cloud.google.com/architecture/autoscaling-cloud-spanner
https://github.com/cloudspannerecosystem/autoscaler
Note: This question was extracted from our Google Certified Associate Cloud Engineer Practice Exams.
For more Google Cloud practice exam questions with detailed explanations, check out the Tutorials Dojo Portal:
Google Cloud Spanner Cheat Sheet Reference:
https://cloud.google.com/spanner
https://docs.cloud.google.com/spanner/docs/getting-started/set-up
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Written by: Jon Bonso
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