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Google Cloud Spanner

Master Google Cloud Spanner: globally distributed SQL database, ACID transactions, and horizontal scaling.

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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

200,000
Reads/sec
60,000
Writes/sec
15ms
Read Latency
$1187
Monthly Cost

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.

• GPS and atomic clock sync
• Bounded timestamp uncertainty
• Global transaction ordering
• External consistency guarantee
• Causal ordering preservation

Paxos Consensus

Distributed consensus for replication and leader election across regions.

• Multi-Paxos for replication
• Automatic leader election
• Byzantine fault tolerance
• Quorum-based decisions
• Network partition tolerance

Automatic Sharding

Dynamic data distribution based on access patterns and data size.

• Dynamic splits and merges
• 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:

Strong Consistency Example
-- 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:

Bounded Staleness Example
-- 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
-- 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
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