State stores in Kafka - Step-by-Step Execution

Process Flow - State stores

Start Kafka Streams App

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Create State Store

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Process Incoming Records

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Update State Store

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Query State Store

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Emit Results or Continue Processing

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End

This flow shows how a Kafka Streams app creates a state store, updates it with incoming data, and allows querying or further processing.

Execution Sample

Kafka

store = builder.getStateStore("counts")
for record in input:
    current = store.get(record.key) or 0
    store.put(record.key, current + 1)
    output(record.key, current + 1)

Counts occurrences of keys from input records by updating a state store and outputting the count.

Process Table

Step	Input Record Key	Current Count from Store	Action	Updated Store Value	Output
1	apple	null	Get count (null), set to 0 + 1	1	apple: 1
2	banana	null	Get count (null), set to 0 + 1	1	banana: 1
3	apple	1	Get count (1), increment by 1	2	apple: 2
4	apple	2	Get count (2), increment by 1	3	apple: 3
5	banana	1	Get count (1), increment by 1	2	banana: 2
6	cherry	null	Get count (null), set to 0 + 1	1	cherry: 1
7	apple	3	Get count (3), increment by 1	4	apple: 4
Exit	-	-	No more records	-	-

💡 All input records processed, no more data to update state store.

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	After 6	After 7	Final
store['apple']	null	1	1	2	3	3	3	4	4
store['banana']	null	null	1	1	1	2	2	2	2
store['cherry']	null	null	null	null	null	null	1	1	1

Key Moments - 3 Insights

Why do we check if the current count is null before incrementing?

Because the state store may not have a value for a key yet (null means no count), so we start counting from zero. See execution_table step 1 where 'apple' count is null and set to 1.

Does the state store update immediately after each record?

Yes, after processing each record, the store is updated with the new count. For example, after step 3, 'apple' count is updated from 1 to 2.

What happens if we query the store for a key not seen before?

The store returns null, meaning no data yet. We then treat it as zero count before incrementing. See steps with 'cherry' in execution_table.

Visual Quiz - 3 Questions

Look at the execution table, what is the store value for 'banana' after step 5?

A 2

B 3

C 1

D null

💡 Hint

Check the 'Updated Store Value' column at step 5 for 'banana'.

At which step does the store first get a value for 'cherry'?

A Step 4

B Step 6

C Step 5

D Step 7

💡 Hint

Look for the first time 'cherry' appears in the 'Input Record Key' column.

If the input had no 'apple' records, what would be the final store value for 'apple'?

A 1

B 0

C null

D undefined

💡 Hint

Refer to variable_tracker for 'store["apple"]' start and final values when no updates occur.

Concept Snapshot

State stores hold data locally in Kafka Streams apps.
They keep track of information like counts or aggregates.
You read from the store, update it, then write back.
If no data exists for a key, treat it as zero or empty.
State stores enable fast, fault-tolerant stateful processing.

Full Transcript

This visual trace shows how Kafka Streams uses state stores to keep track of counts for keys. The app starts by creating a state store. For each input record, it reads the current count from the store. If no count exists, it treats it as zero. Then it increments the count and updates the store. The updated count is output. This process repeats for each record. The variable tracker shows how counts for keys like 'apple', 'banana', and 'cherry' change step-by-step. Key moments clarify why we check for null counts and how the store updates immediately. The quiz tests understanding of store values at different steps and what happens if keys are missing. Overall, state stores let Kafka Streams apps remember information across records to do stateful processing.