Windowed operations in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a tumbling window of 5 minutes.

Kafka

KStream<String, String> windowedStream = stream.groupByKey().windowedBy(TimeWindows.of(Duration.[1](5))).count();

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

Aminutes

Bseconds

Chours

Ddays

Attempts:

3 left

💡 Hint

Common Mistakes

Using seconds instead of minutes causes a shorter window.

Using hours or days creates a much larger window than intended.

The TimeWindows.of(Duration.minutes(5)) creates a tumbling window of 5 minutes.

2fill in blank

medium

Complete the code to create a hopping window with size 10 minutes and advance interval 5 minutes.

Kafka

KStream<String, String> hoppingWindowedStream = stream.groupByKey().windowedBy(SlidingWindows.ofTimeDifferenceWithNoGrace(Duration.ofMinutes(10)).[1](Duration.ofMinutes(5))).count();

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

Agrace

BadvanceBy

Cuntil

Dsuppress

Attempts:

3 left

💡 Hint

Common Mistakes

Using grace() instead of advanceBy() changes grace period, not advance interval.

Using until() or suppress() are unrelated to window advance.

The advanceBy() method sets the advance interval for hopping windows.

3fill in blank

hard

Fix the error in the code to set a session window with 15 minutes inactivity gap.

Kafka

KStream<String, String> sessionWindowedStream = stream.groupByKey().windowedBy(SessionWindows.[1](Duration.ofMinutes(15))).count();

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

AwithGap

BwithInactivityGap

Cwith

Dof

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like withGap() or withInactivityGap() causes errors.

The correct method to set the inactivity gap for session windows is of().

4fill in blank

hard

Fill both blanks to create a tumbling window of 1 minute and set a grace period of 30 seconds.

Kafka

KStream<String, String> windowedStream = stream.groupByKey().windowedBy(TimeWindows.of(Duration.[1](1)).[2](Duration.ofSeconds(30))).count();

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

Aminutes

Bseconds

Cgrace

DadvanceBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using advanceBy instead of grace for grace period.

Using seconds for window size instead of minutes.

The of(Duration.minutes(1)) sets the window size to 1 minute, and grace(Duration.ofSeconds(30)) sets the grace period to 30 seconds.

5fill in blank

hard

Fill all three blanks to create a hopping window of 15 minutes size, 5 minutes advance, and set a grace period of 1 minute.

Kafka

KStream<String, String> hoppingWindowedStream = stream.groupByKey().windowedBy(SlidingWindows.ofTimeDifferenceWithNoGrace(Duration.of[1](15)).[2](Duration.of[3](5))).grace(Duration.ofMinutes(1)).count();

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

AMinutes

BSeconds

Cminutes

DadvanceBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using uppercase Minutes instead of lowercase minutes causes errors.

Confusing grace period setting with advance interval.

The window size and advance interval use Duration.ofMinutes(), and the advance interval is set by advanceBy(). The grace period is set separately.

Practice

1. What is the main purpose of windowed operations in Kafka Streams?

easy

A. To compress data for storage

B. To group data into time intervals for easier analysis

C. To encrypt data streams

D. To sort data alphabetically

Solution

1. Step 1: Understand windowed operations

   Windowed operations group data based on time intervals, making it easier to analyze events occurring within those intervals.

2. Step 2: Compare options

   Sorting, encrypting, and compressing are unrelated to windowing, which focuses on time-based grouping.

3. Final Answer:

   To group data into time intervals for easier analysis -> Option B

4. Quick Check:

   Windowed operations = time grouping [OK]

Hint: Windowed means grouping by time intervals [OK]

Common Mistakes:

• Confusing windowing with sorting
• Thinking windowing encrypts data
• Assuming windowing compresses data

2. Which of the following is the correct syntax to create a tumbling window of 5 minutes in Kafka Streams DSL?

easy

A. SlidingWindows.ofTimeDifferenceWithNoGrace(Duration.ofMinutes(5))

B. SessionWindows.of(Duration.ofMinutes(5))

C. TimeWindows.of(Duration.ofMinutes(5))

D. Windowed.of(Duration.ofMinutes(5))

Solution

1. Step 1: Identify tumbling window syntax

   Tumbling windows in Kafka Streams are created using TimeWindows.of() with a duration.

2. Step 2: Check options

   SessionWindows is for session windows, SlidingWindows is for sliding windows, and Windowed is not a window type class.

3. Final Answer:

   TimeWindows.of(Duration.ofMinutes(5)) -> Option C

4. Quick Check:

   Tumbling window = TimeWindows.of() [OK]

Hint: Tumbling windows use TimeWindows.of() syntax [OK]

Common Mistakes:

• Using SessionWindows for tumbling windows
• Confusing SlidingWindows with tumbling windows
• Using non-existent Windowed class

3. Given the following Kafka Streams code snippet, what will be the output count for the window ending at timestamp 60000?

stream.groupByKey()
      .windowedBy(TimeWindows.of(Duration.ofSeconds(30)))
      .count()
      .toStream()
      .foreach((windowedKey, count) -> System.out.println(windowedKey.window().end() + ": " + count));

Assume events with keys arrive at timestamps 10000, 20000, 35000, 40000, and 55000.

medium

A. 3

B. 4

C. 2

D. 5

Solution

1. Step 1: Identify window intervals

   Windows are 30 seconds long: [0-30000), [30000-60000), etc. Events at 10000 and 20000 fall in first window; 35000, 40000, 55000 fall in second window ending at 60000.

2. Step 2: Count events in window ending at 60000

   Events at 35000, 40000, and 55000 are inside the window ending at 60000, so count is 3.

3. Final Answer:

   3 -> Option A

4. Quick Check:

   Events in 30s window = 3 [OK]

Hint: Count events in the correct time window interval [OK]

Common Mistakes:

• Counting events from previous window
• Misunderstanding window boundaries
• Including events outside the window

4. Identify the error in this Kafka Streams windowed count code snippet:

stream.groupByKey()
      .windowedBy(SessionWindows.of(Duration.ofMinutes(1)))
      .count()
      .toStream()
      .foreach((windowedKey, count) -> System.out.println(windowedKey.window().start() + ": " + count));

medium

A. windowedBy() cannot be used with groupByKey()

B. toStream() is not available after count()

C. count() must be called before windowedBy()

D. SessionWindows.of() requires a gap duration, not a window size

Solution

1. Step 1: Check SessionWindows.of() usage

   SessionWindows require a gap duration (inactivity gap), not a fixed window size in of().

2. Step 2: Validate other method calls

   groupByKey() works with windowedBy(), count() is correctly placed, and toStream() is valid after count().

3. Final Answer:

   SessionWindows.of() requires a gap duration, not a window size -> Option D

4. Quick Check:

   SessionWindows need gap duration [OK]

Hint: SessionWindows need gap duration, not fixed size [OK]

Common Mistakes:

• Using fixed duration in SessionWindows.of()
• Misplacing count() before windowedBy()
• Assuming toStream() is invalid after count()

5. You want to count user clicks in overlapping 1-minute windows that advance every 30 seconds using Kafka Streams. Which window type and configuration should you use?

hard

A. Use TimeWindows.of(Duration.ofMinutes(1)).advanceBy(Duration.ofSeconds(30))

B. Use SlidingWindows.ofTimeDifferenceAndGrace(Duration.ofMinutes(1), Duration.ofSeconds(0)).advanceBy(Duration.ofSeconds(30))

C. Use SessionWindows.withGap(Duration.ofSeconds(30))

D. Use TimeWindows.of(Duration.ofSeconds(30)).advanceBy(Duration.ofMinutes(1))

Solution

1. Step 1: Identify overlapping window type

   Overlapping windows that slide are created with TimeWindows.of() and advanceBy() to set the slide interval.

2. Step 2: Match configuration to requirement

   Window size is 1 minute, sliding every 30 seconds, so TimeWindows.of(1 minute).advanceBy(30 seconds) fits perfectly.

3. Final Answer:

   Use TimeWindows.of(Duration.ofMinutes(1)).advanceBy(Duration.ofSeconds(30)) -> Option A

4. Quick Check:

   Sliding windows = TimeWindows with advanceBy [OK]

Hint: Sliding windows use TimeWindows with advanceBy() [OK]

Common Mistakes:

• Using SlidingWindows class which is deprecated
• Confusing session windows with sliding windows
• Swapping window size and advanceBy durations