DB Transaction with Kafka and REST: Is It Possible?

• ⚠️ When transactions span databases, Kafka, and REST services, they often fail partially if not carefully coordinated.
• 🧠 Kafka's transaction APIs write data atomically, but these don't work for outside systems like databases or REST APIs.
• 💊 Transactional Outbox and Change Data Capture (CDC) are good ways to keep things consistent without the extra work of XA.
• 🧰 The Saga pattern helps fix failures in multi-step processes, especially when coordinating REST API transactions.
• 🚦 Using eventual consistency with retry logic and idempotent design makes distributed systems stronger.

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DB Transactions with Kafka and REST API: What Can You Do?

Connecting a database transaction with outside systems like Kafka and REST APIs is hard in today's distributed systems. Old single-piece systems could easily undo everything using ACID databases. But modern microservices have to handle things like events happening at different times, shaky networks, system crashes, and data that becomes correct over time. So, can you put a database transaction, Kafka, and a REST API transaction all into one atomic step? No, not completely. But there are proven methods that get you very close.

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Why Distributed Transactions Are Hard

ACID vs. How Distributed Systems Work

Relational databases use ACID properties—Atomicity, Consistency, Isolation, and Durability—to keep data accurate. These properties work well when all actions happen inside one system, like a local database. But in distributed systems using Kafka and REST APIs, these promises stop working.

• Kafka handles many messages fast, and data becomes consistent over time. Its producers and consumers work on their own. This means your messages might not show up for consumers right away. While Kafka allows transactional publishing, these promises only apply inside Kafka.
• REST APIs do not store information between requests. They also don't have built-in transaction features. A POST or DELETE usually cannot be undone unless someone writes special code for it.

When you make these technologies work together, you leave the old ways of consistency. You enter a situation with partial failure, retries, and ways to fix problems.

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XA and Two-Phase Commit: Why They Don't Work Well

Extended Architecture transactions (XA) and the Two-Phase Commit (2PC) protocol were made to manage transactions across many parts. They aim for distributed atomicity, where every system either saves or undoes changes all at once.

These protocols sound good in theory, but they:

• Need a lot of coordination, which slows things down when changes are saved.
• Hold up resources while waiting for changes to save, cutting down how much work can be done.
• Can get stuck or fail without warning if the network splits.
• Need parts to be tightly connected, which takes away from how flexible and strong microservices should be.

Martin Kleppmann says in "Designing Data-Intensive Applications" that trying to keep things very consistent across systems not built for it makes weak, hard-to-grow systems (Kleppmann, 2017).

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How DB, Kafka, and REST Transactions Work

DB Transaction

Normal relational databases offer clear transaction limits using commands such as:

BEGIN TRANSACTION;
-- Multiple operations here
COMMIT;

If anything goes wrong, a ROLLBACK makes sure no unfinished data is left. This gives developers good control and reliability. But this only works inside that one database.

Kafka Transaction

Since version 2.5.0, Kafka has Exactly-Once Semantics (EoS) for producers (Apache Kafka Documentation, n.d.). With Kafka's transaction APIs, you can:

• Send messages to many partitions all at once.
• Stop duplicate messages when retrying.
• Make sure consumers get all or none of a group of transaction messages.

But these promises only count for Kafka. They do not apply to outside systems like databases or REST API services.

REST API Transaction

REST APIs work one request at a time. This makes them stateless and not transactional by nature. A usual POST request to make something might return 201 Created. But it has no built-in way to undo itself, unless:

• The API has a DELETE or undo option.
• You use a custom Saga or a manual way to fix things.

Because there are no atomic undo tools, REST interactions often need idempotency, status checks, and error fixing methods to keep systems consistent.

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Why Simply Combining Them Fails

Here is an example:

1. Start a transaction in your database.
2. Add order details.
3. Send an “order.created” event to Kafka.
4. Call a REST API to hold inventory.

If everything works, all systems will show the right state. But think about just one failure:

• The database saves the order.
• The Kafka event does not send.
• The inventory REST call stops working due to time.

Now your systems are not consistent: an order was made, but not reported or filled. These different transaction promises happen because databases, Kafka, and REST systems do not use the same transaction methods or ways to undo things.

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Good Ways to Make Things Work

Instead of trying to force unreliable undos across systems, we learn from experience to use methods based on events happening at different times, doing things more than once safely, and data becoming consistent over time. Here are the best ones.

Transactional Outbox Pattern

The most dependable way to connect a database transaction and Kafka is the Transactional Outbox pattern.

How it works

1. When you run your business logic (like creating an order), you write to both your main table (orders) and a special outbox_messages table in one database transaction.
2. A background service then looks for new rows in outbox_messages and sends them to Kafka.
3. Once sent, the message is marked as processed.

This method joins the atomic actions of a database transaction with Kafka's eventual consistency. It does this safely and without extra coordination work.

Example schema

CREATE TABLE outbox_messages (
    id UUID PRIMARY KEY,
    event_type VARCHAR(255),
    payload JSONB,
    status VARCHAR(50) DEFAULT 'PENDING',
    created_at TIMESTAMP DEFAULT NOW()
);

Change Data Capture (CDC)

Change Data Capture tools like Debezium read a database's WAL (Write-Ahead Log) to stream changes to Kafka right away.

Advantages

• No extra work for developers after setup.
• Quick and handles many changes fast.
• Gets changes safely from transactions—only saved database changes are sent.

Debezium is good when you want to separate your application code from sending events. And it still keeps data strictly consistent from the database to Kafka.

Saga Pattern

A Saga is a way to manage distributed transactions. It breaks a big transaction into many smaller, separate local transactions. After each step, there might be a step to undo it if needed.

Example flow

1. An order is placed in Service A.
2. Service A sends an event to Kafka.
3. Inventory Service sees the event and holds stock.
4. If holding inventory fails, it sends an order.revert event.
5. Order Service gets order.revert and logically cancels the first database transaction.

Sagas let you manage multi-step processes with events happening at different times. This makes them great for REST API transaction situations where there is no easy way to undo things (Fowler, n.d.).

Idempotent REST APIs + Retry Logic

When working with REST API transactions, their stateless nature makes actions hard. Good ways to handle this include:

• Make REST actions idempotent: doing the same request many times gives the same result.
• Use UUIDs to track requests to stop double actions.
• Use exponential backoff and retries when things fail.
• Check responses and poll for status for actions that take a long time.

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How to Use the Transactional Outbox

Main Steps

1. Improve your database setup: Add a dependable outbox_messages table.
2. Put messages with business logic: Write message rows as part of the same database transaction.
3. Create a sender module/service: This can use a cron job or Kafka Connect to check for, send, and mark messages.
4. Deal with failures: Use retries, backups, or dead-letter queues (DLQ) if a message fails to send many times.

Best Way to Send Messages

• When you send a message:
  • Add message IDs so messages are consumed safely even if sent more than once.
  • Use Kafka headers to keep extra info like correlation IDs.
  • Only mark status = 'processed' after Kafka confirms it got the message.

Good and Bad Points

Good Points:

• Makes sure your database transaction and Kafka integration save atomically.
• Separates your business logic from the messaging system.
• Easy to watch and retry for dependable work.

Bad Points:

• Slows down how fast outside messages are processed.
• Needs extra setup for checking and watching the outbox.
• Messages might be sent more than once if there is a mistake.

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Using Eventual Consistency

The CAP theorem shows us that we have to make choices between Consistency, Availability, and Partition Tolerance. In today's connected systems, partition tolerance is a must. So, we choose between availability and strong consistency.

Eventual consistency does not mean "it will eventually be right." It means that your system will reach a correct state over time, using retries, fixing things, or undoing mistakes.

Thinking About User Experience

To match what users see with how the system works:

• Show "Processing…" for transaction status instead of "Confirmed."
• Let users cancel or retry actions that failed.
• Use email, webhooks, or app messages to tell users about delays.

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Connecting REST Calls in Transaction Flows

Since REST APIs cannot be part of normal transactions, you can connect them by using different management methods:

Compensating Transactions

Make helper APIs that undo actions:

• Cancel shipments.
• Refund payments.
• Undo stock holds.

These should be well-designed, work the same way, and be safe to stop misuse.

Managers and Schedulers

Use tools such as:

• Temporal.io or Cadence: These are workflow engines for distributed systems that handle retries and timers reliably.
• Camunda or Zeebe: For building workflows using BPMN, with open-source options.

These engines can manage workflows that need many REST and Kafka steps to work together. They also handle retries and failures carefully.

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How Kafka Helps Keep Things Reliable

Kafka acts as the main support for data becoming consistent over time. Some settings and features help make it more reliable:

• Exactly-Once Semantics (EoS): Stops duplicate messages.
• Partitioning and Ordering: Keeps the right sequence in services that work with events.
• Offset Management: Lets you replay messages or skip them using consumer commits.
• Dead-letter Topics: Catches events that cannot be processed for someone to check or for later fixing.

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Dealing with Failures Well

What Can Go Wrong and How to Fix It

What happens	How to fix it
Kafka message does not send	Retry with pauses, warn if it happens too much
REST call fails after database save	Make a way to undo the call
Kafka message is duplicated	Use logic to remove duplicates on the consumer side
Event is read but not completely handled	Write down its state, use a retry system

Strength Checklist

• ✅ Use unique IDs for every action or message.
• ✅ Make sure producers and consumers can handle the same action more than once safely.
• ✅ Watch for delays, errors, and stuck outbox items.
• ✅ Make sure retry rules can be changed and can recover.

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Useful Tools and Libraries

Tool/Library	What it does
Debezium	CDC for fast Kafka connection
Kafka Connect	Ready-made tools for linking databases
Temporal.io	Strong management for actions
Axon Framework	Command, Event Sourcing, and Saga control
Spring Outbox	Automatic outbox connection for Spring apps

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Example: How an Order Service Works

Here is how a service might work using the Transactional Outbox pattern and Kafka:

1. 💬 A user uses the website to order something.
2. 🗃 The Order Service writes to orders and outbox_messages in one database transaction.
3. ⚙️ A background worker reads the outbox message and sends order.created to Kafka.
4. 📦 The Inventory Service gets order.created, holds stock, and replies using REST or Kafka with inventory.confirmed.
5. 🤖 If stock is not there, Inventory sends an order.revert. The Order Service gets this to cancel the order or tell the user.

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What Developers Should Do

• 🔁 Make services that can do operations safely more than once (idempotent by default).
• 🔄 Add retries for all outside communication.
• ⚠️ Watch queues, delays, and how healthy the system is right away.
• 🔐 Keep compensating transactions safe—check who uses them and record their use.

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When to Avoid Putting Everything in One Transaction

Sometimes, keeping things simple is best:

• You care more about speed and quick responses than making things exactly right all the time.
• Your microservices are built on their own and should not be tightly linked.
• You choose dependability over perfect consistency, and you accept small delays.

For these situations, it is better to use systems where parts are loosely connected and work with events, improved by patterns like Sagas and Outboxes.

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Putting a database transaction, Kafka, and a REST API transaction into one atomic step usually doesn't work in real systems. But, you can definitely connect them reliably. This is thanks to strong methods like Transactional Outbox, CDC, and Sagas. With the right plan, tools, and developer thinking, your system can be strong and grow big. It can also recover well from the unavoidable problems of distributed systems.

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If you work with microservices that use events and want more practical advice like this, stay with Devsolus for clear, developer-focused methods. Have questions or want to share your methods? Leave a comment—we'd like to hear what you do.

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Citations

• Apache Kafka Documentation. (n.d.). Exactly-Once Semantics. Retrieved from https://kafka.apache.org/documentation/#semantics_eos
• Debezium. (n.d.). Change Data Capture with Debezium. Retrieved from https://debezium.io/documentation/reference/stable/
• Fowler, M. (n.d.). Saga Pattern. Retrieved from https://martinfowler.com/articles/patterns-of-distributed-systems/saga.html
• Kleppmann, M. (2017). Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O'Reilly Media.