Why State stores in Kafka? - Purpose & Use Cases

The Big Idea

What if you could track live data instantly without slow database calls?

The Scenario

Imagine you are building a system that processes streams of data, like tracking user clicks or orders in real time. Without state stores, you have to manually keep track of all past events in external databases or files, constantly fetching and updating data as new events arrive.

The Problem

This manual approach is slow and error-prone because every time you want to know the current count or status, you must query an external database, which adds delay and complexity. Also, managing consistency and updates yourself can lead to bugs and lost data.

The Solution

State stores in Kafka let you keep track of data directly inside your stream processing application. They provide fast, local storage that updates automatically as new events come in, so you can quickly access and update the current state without extra database calls.

Before vs After

✗ Before

count = db.query('SELECT count FROM clicks WHERE user_id=123')
count += 1
db.update('UPDATE clicks SET count=? WHERE user_id=123', count)

✓ After

store = context.getStateStore('clickCounts')
count = store.get(user_id) or 0
store.put(user_id, count + 1)

What It Enables

State stores enable real-time, efficient, and reliable tracking of data changes directly within your streaming application, making complex event processing smooth and scalable.

Real Life Example

For example, an online shop can use state stores to keep a live count of items in users' shopping carts as they add or remove products, without needing to query a database every time.

Key Takeaways

Manual tracking of streaming data is slow and complex.

State stores keep data locally and update it automatically.

This makes real-time data processing faster and more reliable.

Practice

1. What is the main purpose of a state store in Kafka Streams?

easy

A. To monitor Kafka cluster health

B. To send messages to Kafka topics

C. To configure Kafka brokers

D. To keep data between processing of different messages

Solution

1. Step 1: Understand state store role and compare options

   A state store holds data locally so the app can remember information across messages. Sending messages, configuring brokers, and monitoring health are unrelated to state stores.

2. Final Answer:

   To keep data between processing of different messages -> Option D

3. Quick Check:

   State store = remembers data between messages [OK]

Hint: State stores remember data across messages [OK]

Common Mistakes:

• Confusing state store with message sending
• Thinking state store configures Kafka brokers
• Assuming state store monitors cluster health

2. Which of the following is the correct way to add a persistent key-value state store named myStore to a StreamsBuilder in Kafka Streams?

easy

A. builder.addStateStore(Stores.keyValueStoreBuilder(Stores.persistentKeyValueStore("myStore"), Serdes.String(), Serdes.String()))

B. builder.addStore("myStore", persistentKeyValueStore())

C. builder.createStateStore("myStore", KeyValueStore.class)

D. builder.addKeyValueStore("myStore", Serdes.String(), Serdes.String())

Solution

1. Step 1: Recall correct syntax for adding state store and check options

   The correct method uses addStateStore with a store builder from Stores.keyValueStoreBuilder and Stores.persistentKeyValueStore. Only builder.addStateStore(Stores.keyValueStoreBuilder(Stores.persistentKeyValueStore("myStore"), Serdes.String(), Serdes.String())) matches the correct method signature and usage.

2. Final Answer:

   builder.addStateStore(Stores.keyValueStoreBuilder(Stores.persistentKeyValueStore("myStore"), Serdes.String(), Serdes.String())) -> Option A

3. Quick Check:

   Use addStateStore with keyValueStoreBuilder [OK]

Hint: Use addStateStore with keyValueStoreBuilder [OK]

Common Mistakes:

• Using incorrect method names like addStore or createStateStore
• Missing Serdes parameters for key and value
• Confusing persistent and in-memory store creation

3. Given the following Kafka Streams processor code snippet, what will be the output stored in the state store after processing the input key-value pairs ("key1", 1) and ("key1", 2)?

KeyValueStore<String, Integer> store = context.getStateStore("countStore");
Integer current = store.get("key1");
if (current == null) current = 0;
store.put("key1", current + value);

medium

A. The state store will have key "key1" with value 1

B. The state store will have key "key1" with value 2

C. The state store will have key "key1" with value 3

D. The state store will be empty

Solution

1. Step 1: Trace code execution for both inputs

   First ("key1", 1): store.get("key1") returns null, current = 0, store.put("key1", 0 + 1) = 1. Second ("key1", 2): store.get("key1") = 1, current = 1, store.put("key1", 1 + 2) = 3.

2. Final Answer:

   The state store will have key "key1" with value 3 -> Option C

3. Quick Check:

   Sum values for key1 = 3 [OK]

Hint: Add new value to existing store value for same key [OK]

Common Mistakes:

• Assuming store resets between messages
• Not handling null initial value
• Overwriting instead of adding values

4. Identify the error in this Kafka Streams processor code that tries to update a state store named myStore:

KeyValueStore<String, Long> store = context.getStateStore("myStore");
Long count = store.get(key);
count += 1L;
store.put(key, count);

medium

A. The getStateStore method is called incorrectly

B. The variable count may be null causing a NullPointerException

C. The put method should be called before incrementing count

D. The store should be cast to ReadOnlyKeyValueStore

Solution

1. Step 1: Check store.get(key) handling and increment operation

   If the key does not exist, store.get(key) returns null, so count is null. Trying to do count += 1L when count is null causes a NullPointerException.

2. Final Answer:

   The variable count may be null causing a NullPointerException -> Option B

3. Quick Check:

   Null check needed before incrementing count [OK]

Hint: Always check for null before incrementing store value [OK]

Common Mistakes:

• Ignoring null values from store.get()
• Incorrect method usage for state store access
• Wrong store type casting

5. You want to build a Kafka Streams application that counts the number of times each word appears in a stream of sentences. Which approach correctly uses a state store to maintain the counts?

hard

A. Use a persistent key-value state store to keep counts, update counts on each word, and query the store for results

B. Use an in-memory state store without updating counts, just print words as they arrive

C. Use a state store only to store the last processed sentence, ignoring counts

D. Use a state store to buffer all sentences without processing counts

Solution

1. Step 1: Understand word count requirement and evaluate options

   Counting words requires storing and updating counts per word, so a key-value store is needed. Use a persistent key-value state store to keep counts, update counts on each word, and query the store for results fits the requirement.

2. Final Answer:

   Use a persistent key-value state store to keep counts, update counts on each word, and query the store for results -> Option A

3. Quick Check:

   Persistent store + update counts = correct word count approach [OK]

Hint: Use persistent store and update counts per word [OK]

Common Mistakes:

• Using in-memory store without updating counts
• Storing sentences instead of counts
• Buffering data without processing