What makes Cloud Spanner different from a typical relational database?

Spanner combines standard relational features like SQL and ACID transactions with automatic horizontal scaling and global distribution, capabilities usually associated with NoSQL systems rather than relational databases.

TrueTime is Google's API for globally synchronized, bounded-uncertainty clocks that Spanner uses to guarantee externally consistent transaction ordering across distributed data centers.

Does Cloud Spanner require manual sharding?

No, Spanner automatically splits and rebalances data across nodes as it grows, without requiring developers to manually design a sharding scheme.

Database

Google Cloud Spanner

By Google Cloud

AdvancedService5.7K learners

Google Cloud Spanner is a fully managed, horizontally scalable relational database service that combines strong relational consistency (SQL schemas, ACID transactions) with the global scale typically associated with NoSQL systems.

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Definition

Google Cloud Spanner is a fully managed, horizontally scalable relational database service that combines strong relational consistency (SQL schemas, ACID transactions) with the global scale typically associated with NoSQL systems.

Overview

Spanner grew out of Google's internal infrastructure for globally distributing data with strong consistency, using synchronized atomic clocks and GPS receivers (Google's TrueTime API) to provide externally consistent transactions across data centers spread around the world — solving a problem most distributed databases avoid by relaxing consistency guarantees. Unlike typical Sharding approaches that require manual partitioning decisions, Spanner automatically splits and rebalances data across nodes while still supporting standard SQL, secondary indexes, and ACID Properties transactions spanning multiple rows and tables, blending the operational simplicity of a relational database with the horizontal scalability of distributed NoSQL systems. Spanner is used for workloads that need both global scale and strict transactional correctness — a combination that traditionally forced a choice between a scalable but eventually-consistent store and a strongly consistent but harder-to-scale relational database like Amazon Aurora.

Key Features

Horizontally scalable relational database with standard SQL
External consistency via Google's TrueTime API
Automatic data sharding and rebalancing
Multi-region and global deployment configurations
ACID transactions spanning multiple rows, tables, and regions
99.999% availability SLA for multi-region configurations

Use Cases

Global financial systems requiring strong consistency at scale

Gaming platforms with globally distributed player data

Supply chain and inventory systems spanning multiple regions

Applications outgrowing traditional sharded relational databases

Frequently Asked Questions

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