KStream and KTable concepts in Kafka - Step-by-Step Execution

Process Flow - KStream and KTable concepts

Start: Input Data

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Process each event

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Output transformed

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Downstream apps

Data flows into Kafka as events. KStream processes each event individually as a stream. KTable keeps the latest state per key, updating as new events arrive.

Execution Sample

Kafka

KStream<String, String> stream = builder.stream("input-topic");
KTable<String, String> table = builder.table("input-topic");

stream.foreach((k,v) -> System.out.println("Stream event: " + k + ":" + v));
table.toStream().foreach((k,v) -> System.out.println("Table state: " + k + ":" + v));

This code reads the same Kafka topic as a stream and as a table, printing each event and the latest state per key.

Process Table

Step	Input Event	KStream Action	KTable Action	Output
1	key1: value1	Process event key1:value1	Update key1 state to value1	Stream event: key1:value1 Table state: key1:value1
2	key2: value2	Process event key2:value2	Update key2 state to value2	Stream event: key2:value2 Table state: key2:value2
3	key1: value3	Process event key1:value3	Update key1 state to value3 (overwrite)	Stream event: key1:value3 Table state: key1:value3
4	key3: value4	Process event key3:value4	Update key3 state to value4	Stream event: key3:value4 Table state: key3:value4
5	No more events	Stop processing	Stop updating	End of stream and table output

💡 No more input events; processing stops.

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
KStream current event	null	key1:value1	key2:value2	key1:value3	key3:value4	null
KTable state key1	null	value1	value1	value3	value3	value3
KTable state key2	null	null	value2	value2	value2	value2
KTable state key3	null	null	null	null	value4	value4

Key Moments - 2 Insights

Why does KTable overwrite the value for key1 at step 3?

Because KTable represents the latest state per key, it updates the stored value for key1 from value1 to value3 when a new event with key1 arrives, as shown in execution_table row 3.

Does KStream keep previous events after processing?

No, KStream processes each event as it arrives and does not store state. Each event is handled independently, as seen in the 'KStream Action' column of the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table at step 2. What is the KTable state for key2?

A null

B value2

C value1

D value3

💡 Hint

Check the 'KTable Action' and 'Output' columns at step 2 in the execution_table.

At which step does the KTable update the state for key1 to value3?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'KTable Action' column for key1 updates in the execution_table.

If a new event with key2:value5 arrives after step 4, what will happen to the KTable state for key2?

A It updates to value5

B It stays as value2

C It deletes key2

D It duplicates value2

💡 Hint

KTable always keeps the latest state per key, updating on new events as shown in variable_tracker.

Concept Snapshot

KStream: Processes each event as a stream, no state kept.
KTable: Represents latest state per key, updates on new events.
Both read from Kafka topics but differ in state handling.
Use KStream for event processing, KTable for stateful views.
KTable updates overwrite previous values for the same key.

Full Transcript

This visual execution shows how Kafka Streams processes data as KStream and KTable. KStream handles each event individually, printing each as it arrives. KTable keeps the latest value per key, updating state when new events with the same key come in. The execution table traces each input event, showing KStream processing and KTable state updates. Variable tracking shows how KTable state changes per key over time. Key moments clarify why KTable overwrites values and how KStream differs by not storing state. The quiz tests understanding of state updates and event processing steps. This helps beginners see the difference between streaming events and maintaining state in Kafka Streams.