Why stream processing transforms data in Kafka - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to transform each message value to uppercase in a Kafka Streams application.

Kafka

KStream<String, String> stream = builder.stream("input-topic");
KStream<String, String> transformed = stream.mapValues(value -> value.[1]());

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

AtoLowerCase

BtoUpperCase

Ctrim

Dsubstring

Attempts:

3 left

💡 Hint

Common Mistakes

Using toLowerCase() instead of toUpperCase()

Forgetting parentheses after method name

The toUpperCase() method converts the string to uppercase, which is a common transformation in stream processing.

2fill in blank

medium

Complete the code to filter messages where the value length is greater than 5.

Kafka

KStream<String, String> filtered = stream.filter((key, value) -> value.[1]() > 5);

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Asize

BlengthOf

Clength

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using size() which is for collections, not strings

Using count() which is not a String method

The length() method returns the number of characters in a string, which is used to filter messages by length.

3fill in blank

hard

Fix the error in the code to correctly convert the message value to an integer before doubling it.

Kafka

KStream<String, Integer> doubled = stream.mapValues(value -> Integer.[1](value) * 2);

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AparseInt

BvalueOf

CtoInt

Dconvert

Attempts:

3 left

💡 Hint

Common Mistakes

Using valueOf() which returns Integer object, not int

Using non-existent methods like toInt() or convert()

Integer.parseInt() converts a string to a primitive int, which can then be used in arithmetic operations.

4fill in blank

hard

Fill both blanks to create a map of word counts from a stream of sentences.

Kafka

KTable<String, Long> wordCounts = stream
  .flatMapValues(value -> Arrays.asList(value.toLowerCase().split(" ")))
  .groupBy((key, word) -> word)
  .[1]()
  .[2]();

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Acount

Breduce

Caggregate

DgroupByKey

Attempts:

3 left

💡 Hint

Common Mistakes

Using reduce() or aggregate() without grouping by key

Swapping the order of count() and groupByKey()

The groupByKey() groups the stream by the word key, and count() method counts the occurrences of each word.

5fill in blank

hard

Fill all three blanks to filter, transform, and write the stream to an output topic.

Kafka

stream
  .filter((key, value) -> value.[1]() > 10)
  .mapValues(value -> value.[2]())
  .to("[3]", Produced.with(Serdes.String(), Serdes.String()));

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Alength

BtoUpperCase

Coutput-topic

DtoLowerCase

Attempts:

3 left

💡 Hint

Common Mistakes

Using toLowerCase() instead of toUpperCase()

Using wrong topic name

Forgetting parentheses after method names

The code filters messages with values longer than 10 characters, converts them to uppercase, and writes to the output-topic.