Most Systems Get Consistency Wrong

DynamoDB Shows How to Do It at Scale

In the wild, most systems get consistency wrong. They build a backend that works great until two components try to write to the same record at the same time.

Then everything breaks.

You’ve seen it:

• Double charges in payment systems.

• Lost edits in collaborative tools.

• Inventory systems that sell stock that’s no longer there.

These aren’t edge cases. They’re the reality of modern software and will break your business if you ignore them.

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What’s at Stake?

If you don't handle consistency properly:

1. You lose data,

2. You break trust,

3. And you create a system that’s impossible to debug.

But consistency is hard, especially at scale. You're working with multiple nodes and services, often across regions. Distributed writes mean several systems may try to update the same data at the same time. Throw in unreliable networks, retries, and partial failures, and suddenly, maintaining a single source of truth becomes a serious challenge.

Despite the complexity, there are three ways to get consistency right:

1. Write-Ahead Logging (WAL)

Used by: PostgreSQL, MySQL, etc.

How it works:

If the system crashes in the middle, you replay the log. That gets you back to a consistent state.

✅ Durable and safe.
❌ Doesn’t handle concurrent write conflicts.

2. Locking (Pessimistic Concurrency)

Used by: Traditional RDBMS

How it works:

✅ Prevents conflicting updates.

❌ Doesn’t scale. Blocks fast paths. Poor fit for distributed systems.

3. Data Versioning (Optimistic Concurrency)

Used by: DynamoDB, Cassandra, Event Sourcing

How it works:

No locks. Just version checks.

✅ Scales well. High throughput.
❌ You must handle retries and merge conflicts.

Case Study: How DynamoDB Gets Consistency Right

Let’s say you're building a diagram collaborative platform where teams co-edit architecture diagrams in real time.

Two users, A and B, open the same diagram. User A moves a component and writes it to the database (version 1). At the same time, User B renames that component and also tries to write (still thinking it's version 1).

Without coordination, whichever writer hits the database last will silently overwrite the other, resulting in either the position change or the rename being lost.

DynamoDB avoids this with Conditional Writes:

**ConditionExpression** — a clause in DynamoDB that ensures an update only occurs if a specified condition (like a matching version) is true: "version = :expectedVersion"

Key DynamoDB Features Used

1. ConditionExpression
   Enforces that writes only succeed if the item’s current version matches the client’s expected version.

2. Atomic Counters
   The version attribute acts as an atomic counter, ensuring sequential updates.

3. Optimistic Locking
   Clients assume no conflicts by default but handle them gracefully when they occur.

Why This Works

• Prevents Silent Overwrites: Only one writer succeeds per version, eliminating "last write wins" chaos.

• Merge Flexibility: Clients can implement domain-specific merge logic (e.g., Operational Transform for diagrams).

• Scalability: No centralized locking, making it suitable for globally distributed teams.

Trade-offs in Practice

Tools & Frameworks

• Write-Ahead Logging: Native in PostgreSQL, MySQL, and SQLite

• Locking: Handled via transaction isolation levels (e.g., SERIALIZABLE) in most RDBMS

• Versioning:

  • AWS SDKs for DynamoDB support Conditional Writes out of the box

  • Event Sourcing frameworks like Axon (Java), Eventuate (Java), or Marten (C#) enable version-based control

Wrapping up

• There are only 3 ways to ensure consistency: WAL, Locking, and Versioning.

• DynamoDB scales by choosing versioning and pushing retry logic to clients.

• Versioning is ideal for distributed, high-throughput systems, but comes with trade-offs.

• If you're building a system that involves concurrent writes, don't rely on duct tape and blind hope. Rely on versions.

• Consistency isn’t magic; it’s engineering.

Most teams overengineer consistency or overlook it until it breaks. DynamoDB shows that with the right model, you can keep both scale and safety.

Your Move: How does your system manage consistency today? Locking? Logging? Or versioning?

Shoot me a reply or share your approach; I’m curious.

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