Why Distributed Transactions Are Hard

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Distributed systems have become the default architectural choice for modern applications. Microservices promise flexibility, scalability, and independent deployments—but they also introduce a complexity tax. And nowhere is that tax more visible than in distributed transactions.

Coordinating state changes across services, databases, and networks sounds simple until you try it. Then you quickly discover that the problem isn’t just technical—it’s mathematical. The moment multiple systems must agree on a shared outcome, you’re fighting physics, latency, partial failures, and the infamous constraints of the CAP theorem.

In this issue, we unpack why distributed transactions are hard and explore the frameworks engineers use to tame that complexity: Two-Phase Commit, Sagas, and Idempotency.

1. The Root of the Problem: Consensus Across Boundaries

A single-node transaction is easy because everything happens inside one ACID-compliant database engine.

Distributed transactions require multiple services—often running on different machines, networks, or even geographic regions—to agree on a single commit.

That means every transaction must survive:

• Machine failures

• Network partitions

• Slow or flaky nodes

• Communication delays

• Service restarts

Any service can fail at any moment, leaving data in inconsistent or ambiguous states.

At scale, failure is not an edge case—it’s the baseline. Distributed transactions are, in essence, attempts to negotiate consistency in a world where failure is guaranteed.

2. Two-Phase Commit (2PC): The Classic, but Imperfect Solution

Two-phase commit is the earliest formal attempt to coordinate transactions across distributed systems. It works in two steps:

Phase 1 – Prepare

The coordinator asks all participants,
“Can you commit this?”
Each participant locks resources and replies either yes or no.

Phase 2 – Commit / Abort

If everyone votes yes, the coordinator tells all nodes to commit.
If any vote no, it tells everyone to roll back.

Why 2PC is hard in practice

• Blocking: Participants must hold locks while waiting.

• Coordinator failure: If the coordinator dies at the wrong time, systems can get stuck.

• Slow recovery: Nodes may wait indefinitely for a final decision.

• Scalability constraints: Locking and coordination add latency and reduce throughput.

2PC delivers atomicity but imposes huge operational constraints in real-world networks. This makes it unsuitable for high-throughput microservices operating at Internet scale.

3. Sagas: The Microservices-Friendly Alternative

Sagas break a large transaction into a sequence of local transactions executed by individual services.

Instead of locking resources, each step completes independently—and if something fails, the system executes compensating actions to undo previous changes.

Two saga patterns are common:

a) Orchestration

A central controller executes each step and triggers compensations when necessary.

b) Choreography

Each service reacts to events from others, forming a chain of autonomous interactions.

Why sagas help

• No long-held locks

• Better scalability

• Better failure isolation

• Fits naturally into event-driven systems

But sagas aren’t magic

• Compensations can be complicated or imperfect

• Data may temporarily appear inconsistent

• Rollbacks are harder than commits

• Race conditions can emerge if events arrive out of order

Sagas trade strict atomicity for eventual consistency—a compromise many distributed architectures are forced to accept.

4. Idempotency: The Safety Valve for Unreliable Networks

In a distributed environment, retries are inevitable. A message might be:

• Delivered twice

• Delayed

• Processed twice due to a timeout

• Replayed from logs after a crash

Without idempotency, retries create corruption.
With idempotency, a repeated operation produces the same effect as one execution.

Common strategies:

• Unique request IDs

• Upsert semantics

• Write-once logs

• Version checking (optimistic concurrency)

• Stateless handlers

Idempotency is essential because distributed transactions rely on retries—there is no reliable distributed system without it.

5. Microservices Make Everything Harder

Microservices multiply the complexity because each service has:

• Its own database

• Its own API

• Independent deployments

• Independent scaling behaviors

• Independent failure modes

That means consistency problems now span:

• Multiple data stores

• Multiple message queues

• Multiple network segments

• Multiple teams shipping code independently

A transaction that was once local becomes a multi-hop workflow with unpredictable latency and partial failures.

Distributed systems don’t fail cleanly—they fail weirdly.
That’s why correctness requires deep design discipline, not just good intentions.

So… Why Are Distributed Transactions Hard?

Because they require systems to agree in an environment where agreement itself is fragile.

Because failures aren’t rare—they’re constant.

Because microservices fragment state and force engineers to think about coordination, ordering, compensation, and consistency in a way that monoliths rarely do.

And because the techniques that ensure safety—2PC, sagas, idempotency—each bring trade-offs that must be weighed against performance, correctness, and operational overhead.

Distributed transactions aren’t impossible. They’re just engineering reality at scale.
Understanding these tools is the difference between building a system that degrades gracefully and one that falls apart under pressure.

More Interesting Reads…

Until next time,

— Team Nullpointer Club