Exactly-once stream processing in Kafka - Step-by-Step Execution

Process Flow - Exactly-once stream processing

Start Stream Processing

↓

Read Input Record

↓

Process Record

↓

Begin Transaction

↓

Write Output Record

↓

Acknowledge Input

↓

Commit Transaction

↓

Next Record or End

The flow reads each record, processes it, writes output within a transaction, acknowledges input by sending offsets to the transaction, commits to ensure exactly-once, then moves on.

Execution Sample

Kafka

producer.initTransactions();
producer.beginTransaction();
producer.send(outputRecord);
producer.sendOffsetsToTransaction(consumer.assignment(), groupMetadata);
producer.commitTransaction();

This code snippet shows how a Kafka producer uses transactions, including output records and input offsets, to ensure exactly-once stream processing.

Process Table

Step	Action	Transaction State	Output Produced	Input Acknowledged
1	Initialize transactions	Initialized	None	No
2	Begin transaction	Active	None	No
3	Send output record	Active	Output record buffered	No
4	Send offsets to transaction	Active	Output record buffered	No
5	Commit transaction	Committed	Output record committed	Yes
6	Process next record	None	None	No

💡 Processing stops when no more input records are available.

Status Tracker

Variable	Start	After Step 2	After Step 4	After Step 5	Final
transactionState	None	Active	Active	Committed	None
outputBuffer	Empty	Empty	Buffered output record	Committed output record	Empty
inputAcknowledged	No	No	No	Yes	No

Key Moments - 3 Insights

Why do we need to begin and commit a transaction around sending output?

Because the transaction groups the output and input acknowledgment so they happen exactly once together, as shown in steps 2 to 5 in the execution table.

What happens if the transaction is not committed?

The output record is not saved permanently and the input is not acknowledged, so the record will be reprocessed, ensuring no data loss or duplicates.

Why send offsets to the transaction before committing?

Sending offsets before commit ensures that the output and input acknowledgment are stored atomically, preventing data loss or duplicates (see step 5).

Visual Quiz - 3 Questions

Look at the execution table, what is the transaction state after step 3?

A Initialized

B Committed

C Active

D None

💡 Hint

Check the 'Transaction State' column at step 3 in the execution table.

At which step is the input record acknowledged?

A Step 2

B Step 5

C Step 4

D Step 6

💡 Hint

Look at the 'Input Acknowledged' column in the execution table.

If the transaction is not committed, what happens to the output record?

A It is buffered but not saved

B It is permanently saved

C It is deleted immediately

D It is acknowledged as sent

💡 Hint

Refer to the 'Output Produced' column before and after commit in the execution table.

Concept Snapshot

Exactly-once stream processing in Kafka uses transactions.
Start a transaction before sending output.
Send output records inside the transaction.
Send input offsets to the transaction.
Commit the transaction to save output and offsets atomically.
This ensures each input is processed exactly once.

Full Transcript

Exactly-once stream processing means each input record is processed one time only, no duplicates or losses. Kafka achieves this by using transactions. The process starts by initializing transactions. For each input record, a transaction begins. The output record is sent inside this transaction but not visible outside yet. Input offsets are sent to the transaction. When the transaction commits, the output and offsets are saved atomically. If a failure happens before commit, the output is discarded and input is not acknowledged, so the record will be retried. This flow guarantees exactly-once processing by linking output and input acknowledgment in one atomic transaction.