ACIDとBASEの原則の比較

Imagine a bank transfer. You send $100. Does it actually leave your account and arrive in the recipient's? Data consistency ensures that all parts of your system agree on the state of data.

In microservices, where data is spread across many services, maintaining consistency becomes a significant challenge.

For decades, traditional relational databases have relied on ACID properties to guarantee reliable transactions. ACID is an acronym for:

• Atomicity
• Consistency
• Isolation
• Durability

These properties ensure that database transactions are processed reliably, critical for sensitive operations.

• Atomicity (A): A transaction is an indivisible unit of work. It either completes entirely (commits) or fails entirely (rolls back). There's no partial completion.
• Consistency (C): A transaction brings the database from one valid state to another. It must adhere to all predefined rules, constraints, and triggers.

Think of a money transfer: either both debit and credit succeed, or neither does, maintaining balance rules.

• Isolation (I): Concurrent transactions execute without interfering with each other. It's like each transaction is running alone, even if many are happening simultaneously.
• Durability (D): Once a transaction is committed, its changes are permanent and survive any subsequent system failures (like power outages).

Your bank transfer, once confirmed, won't disappear if the bank's server crashes.

ACID properties provide strong guarantees for data integrity and reliability, essential for sensitive operations like financial transactions.

However, achieving strict ACID across multiple independent microservices in a distributed system is incredibly difficult and often comes with significant performance and availability trade-offs. This led to the emergence of other models.

In distributed systems, prioritizing high availability and partition tolerance often means relaxing strict consistency. This is where BASE properties come into play. BASE is an acronym for:

• Basically Available
• Soft State
• Eventual consistency

BASE offers a different approach, embracing the realities of distributed environments.

Basically Available (BA): The system guarantees availability for read/write operations. Even if some parts of the system fail, the remaining parts continue to function and respond to requests.

This means users can always access the service, though the data they see might not be the very latest version from all parts of the system.

• Soft State (S): The state of the system can change over time, even without any external input. Data might not be consistent across all replicas at any given instant.
• Eventual Consistency (E): If no new updates are made to a given data item, eventually all accesses to that item will return the last updated value.

Data will eventually become consistent, but there's a delay. Think of social media 'likes' – they might not show up instantly everywhere, but they will eventually sync.

BASE principles prioritize availability and partition tolerance over immediate consistency. This makes systems highly scalable and resilient to network partitions and node failures.

The trade-off is that you accept a period of inconsistency, where different parts of the system might see slightly different versions of the data.

• ACID: Focuses on strong consistency, reliability, and data integrity. Ideal for single-node databases and transactions requiring immediate, strict data correctness.
• BASE: Prioritizes availability and partition tolerance. Accepts eventual consistency. Ideal for highly scalable, distributed systems where some data staleness is acceptable.

Choosing between them depends on your application's specific requirements.

We explored two fundamental approaches to data consistency:

• ACID: Atomicity, Consistency, Isolation, Durability. Guarantees strong consistency, crucial for traditional databases and critical transactions.
• BASE: Basically Available, Soft State, Eventual Consistency. Prioritizes availability and scalability, allowing temporary inconsistencies, common in distributed microservices.

Understanding these principles helps you choose the right data consistency model for your system.