What is Google Cloud Spanner?
Google Cloud Spanner is the world's first fully managed, horizontally scalable, strongly consistent relational database service. It combines the benefits of relational database structure with non-relational horizontal scale, offering ACID transactions, SQL queries, and external consistency across globally distributed deployments.
Built on Google's TrueTime API and Paxos consensus algorithm, Spanner solves the previously impossible problem of achieving external consistency in a distributed system. It can scale to thousands of nodes across multiple continents while maintaining strong consistency and supporting petabytes of data with 99.999% availability.
Spanner Performance Calculator
Storage: 2500GB (includes backups)
TrueTime: ±7ms uncertainty
Write Latency: 23ms avg
Spanner Core Architecture
TrueTime API
Global clock synchronization with bounded uncertainty for external consistency.
• Bounded timestamp uncertainty
• Global transaction ordering
• External consistency guarantee
• Causal ordering preservation
Paxos Consensus
Distributed consensus for replication and leader election across regions.
• Automatic leader election
• Byzantine fault tolerance
• Quorum-based decisions
• Network partition tolerance
Automatic Sharding
Dynamic data distribution based on access patterns and data size.
• Load-based redistribution
• Directory-based routing
• Transparent to applications
• Optimal data placement
Spanner Consistency Models
Strong Consistency (External Consistency)
Linearizability across globally distributed transactions - the strongest consistency possible:
-- Read with strong consistency (default)
SELECT * FROM orders WHERE user_id = @user_id;
-- All reads see globally consistent state
-- Guaranteed to see all committed writes
-- External consistency across all regions
Bounded Staleness
Read data that is at most N seconds old for better performance:
-- Read with bounded staleness
SELECT * FROM inventory
WITH (max_staleness_seconds = 10)
WHERE product_id = @product_id;
-- Better performance with controlled staleness
-- Good for analytics and reporting queries
Read-Only Transactions
Lock-free read transactions with snapshot isolation:
-- Read-only transaction (no locks)
BEGIN TRANSACTION READ ONLY;
SELECT SUM(amount) FROM orders WHERE date >= '2024-01-01';
SELECT COUNT(*) FROM users WHERE created_at >= '2024-01-01';
COMMIT;
-- Perfect for analytics and reporting
Real-World Spanner Implementations
HSBC
Uses Spanner for global retail banking operations requiring strong consistency.
- • Global account balance consistency
- • Multi-region transaction processing
- • Real-time fraud detection across continents
- • 99.999% availability for critical banking operations
JDA Software
Leverages Spanner for global supply chain management and inventory optimization.
- • Global inventory consistency
- • Supply chain optimization algorithms
- • Real-time demand planning
- • Cross-region data analytics
Nintendo
Powers global gaming services with consistent user experiences worldwide.
- • Global user account management
- • Game progress synchronization
- • In-app purchase consistency
- • Real-time multiplayer game state
Go-Jek
Utilizes Spanner for ride-sharing and payments across Southeast Asia.
- • Driver and rider matching
- • Payment processing consistency
- • Real-time location tracking
- • Cross-country service expansion
Advanced Spanner Features
Query & Performance
- • SQL with extensions for distributed systems
- • Query optimizer for distributed execution
- • Parallel query processing across regions
- • Automatic index recommendations
- • Query insights and performance monitoring
- • Batch and streaming data processing
Operational Features
- • Zero-downtime schema changes
- • Automatic backups and point-in-time recovery
- • Import/export with Dataflow integration
- • IAM integration and fine-grained access control
- • VPC peering and private IP connectivity
- • Automatic scaling based on workload
Spanner Best Practices
✅ Do
- • Design primary keys to avoid hotspots
- • Use read-only transactions for analytics
- • Leverage bounded staleness for better performance
- • Implement retry logic with exponential backoff
- • Use batch operations for high-throughput writes
- • Monitor processing unit utilization closely
❌ Don't
- • Use sequential or timestamp-based primary keys
- • Create large transactions that span many rows
- • Ignore query performance and execution plans
- • Mix OLTP and OLAP workloads without planning
- • Underestimate the cost of cross-region writes
- • Skip capacity planning for processing units