Event sourcing pattern in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to produce an event to Kafka topic.

Kafka

producer.send([1], value=event_data)

Drag options to blanks, or click blank then click option'

Aevents_topic

B"events-topic"

Cevents-topic

DeventsTopic

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names without quotes instead of string topic names.

Using invalid topic name formats.

Kafka topic names are strings and usually enclosed in quotes. Here, "events-topic" is the correct string format.

2fill in blank

medium

Complete the code to consume events from a Kafka topic.

Kafka

consumer.subscribe([1])

Drag options to blanks, or click blank then click option'

A"events-topic"

Bevents_topic

Cevents-topic

D["events-topic"]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string directly instead of a list of strings.

Using variable names without quotes.

Kafka consumer.subscribe expects a list of topic names, so the topic name must be inside a list like ["events-topic"].

3fill in blank

hard

Fix the error in the event processing loop to correctly decode event data.

Kafka

for message in consumer:
    event = message.value.[1]('utf-8')

Drag options to blanks, or click blank then click option'

AtoString

Bencode

Cdecode

Dparse

Attempts:

3 left

💡 Hint

Common Mistakes

Using encode instead of decode.

Trying to parse bytes directly without decoding.

Kafka message values are bytes and need to be decoded to strings using decode('utf-8').

4fill in blank

hard

Fill both blanks to filter and store only events with type 'UserCreated'.

Kafka

filtered_events = [event for event in events if event[1] 'type' and event['type'] [2] 'UserCreated']

Drag options to blanks, or click blank then click option'

A.__contains__

B==

C!=

Din

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'in' instead of __contains__ for key check.

Using != instead of == for filtering.

First, check if 'type' key exists using __contains__, then check if its value equals 'UserCreated'.

5fill in blank

hard

Fill all three blanks to create a dictionary of event IDs and their payloads for events with version > 1.

Kafka

event_dict = [1]: [2] for event in events if event['version'] [3] 1

Drag options to blanks, or click blank then click option'

Aevent['id']

Bevent['payload']

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '>' for version filtering.

Swapping key and value in dictionary comprehension.

Use event['id'] as key, event['payload'] as value, and filter events where version is greater than 1.

Practice

1. What is the main purpose of the event sourcing pattern in Kafka?

easy

A. To save every change as an event for a full history

B. To delete old data to save storage space

C. To process only the latest state without history

D. To send events only to one consumer

Solution

1. Step 1: Understand event sourcing concept

   Event sourcing means saving every change as a separate event instead of just the current state.

2. Step 2: Relate to Kafka usage

   Kafka topics store these events so consumers can read the full history and rebuild state if needed.

3. Final Answer:

   To save every change as an event for a full history -> Option A

4. Quick Check:

   Event sourcing = full history saved as events [OK]

Hint: Event sourcing always stores all changes, not just current state [OK]

Common Mistakes:

• Thinking event sourcing deletes old data
• Believing it only keeps latest state
• Assuming events go to only one consumer

2. Which Kafka component is primarily used to store events in event sourcing?

easy

A. Kafka Topic

B. Kafka Producer

C. Kafka Consumer

D. Kafka Broker

Solution

1. Step 1: Identify Kafka components roles

   Kafka Producer sends data, Consumer reads data, Broker manages servers, Topic stores data.

2. Step 2: Match storage role

   Events are stored in Kafka Topics, which keep the event log for consumers.

3. Final Answer:

   Kafka Topic -> Option A

4. Quick Check:

   Events stored in Kafka Topic [OK]

Hint: Events are stored in topics, not producers or consumers [OK]

Common Mistakes:

• Confusing producer with storage
• Thinking consumer stores events
• Mixing broker with event storage

3. Given this Kafka event stream, what will the consumer state be after processing all events?

events = [
  {"type": "create", "id": 1, "value": 10},
  {"type": "update", "id": 1, "value": 20},
  {"type": "create", "id": 2, "value": 5},
  {"type": "delete", "id": 1}
]

state = {}
for event in events:
    if event["type"] == "create" or event["type"] == "update":
        state[event["id"]] = event["value"]
    elif event["type"] == "delete":
        state.pop(event["id"], None)

print(state)

medium

A. {}

B. {"1": 20, "2": 5}

C. {"1": 10, "2": 5}

D. {"2": 5}

Solution

1. Step 1: Process each event in order

   First create id 1 with value 10, then update id 1 to 20, create id 2 with 5, then delete id 1.

2. Step 2: Determine final state

   After deletion, id 1 is removed, only id 2 with value 5 remains.

3. Final Answer:

   {"2": 5} -> Option D

4. Quick Check:

   Delete removes id 1, only id 2 left [OK]

Hint: Track events step-by-step to update state correctly [OK]

Common Mistakes:

• Forgetting the delete event removes id 1
• Confusing update with create
• Assuming old values remain after update

4. Identify the bug in this Kafka event consumer code snippet for event sourcing:

state = {}
for event in events:
    if event["type"] == "create":
        state[event["id"]] = event["value"]
    elif event["type"] == "update":
        if event["id"] in state:
            state[event["id"]] = event["value"]
    elif event["type"] == "delete":
        del state[event["id"]]

medium

A. Missing else clause for unknown event types

B. Using del without checking if id exists causes KeyError

C. Create should not overwrite existing id

D. Update should create new id if not present

Solution

1. Step 1: Analyze delete operation

   Using del without checking if the key exists can cause a KeyError if id is missing.

2. Step 2: Check other conditions

   Update correctly checks if id exists before updating; create overwrites which is allowed; else clause is optional.

3. Final Answer:

   Using del without checking if id exists causes KeyError -> Option B

4. Quick Check:

   del without check = KeyError risk [OK]

Hint: Always check key exists before deleting from dict [OK]

Common Mistakes:

• Ignoring KeyError on delete
• Thinking update must create new id
• Believing create cannot overwrite

5. You want to rebuild the current state from a Kafka topic storing events using event sourcing. Which approach is best to handle a large number of events efficiently?

hard

A. Read all events from the beginning every time to rebuild state

B. Only read the latest event for each entity from the topic

C. Use snapshots periodically and then replay only recent events

D. Delete old events from the topic to reduce size

Solution

1. Step 1: Understand rebuilding state from events

   Replaying all events from the start is slow for large data; deleting events loses history.

2. Step 2: Use snapshots to optimize

   Snapshots save a recent state, so only new events after snapshot need replay, improving efficiency.

3. Final Answer:

   Use snapshots periodically and then replay only recent events -> Option C

4. Quick Check:

   Snapshots + replay recent events = efficient rebuild [OK]

Hint: Snapshots speed up rebuilding by avoiding full replay [OK]

Common Mistakes:

• Replaying all events every time (slow)
• Deleting events loses full history
• Reading only latest event misses changes