JSON Schema and Protobuf support in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the serializer for JSON Schema in Kafka producer configuration.

Kafka

props.put("value.serializer", [1]);

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

A"org.apache.kafka.common.serialization.ByteArraySerializer"

B"org.apache.kafka.common.serialization.StringSerializer"

C"io.confluent.kafka.serializers.KafkaAvroSerializer"

D"io.confluent.kafka.serializers.KafkaJsonSchemaSerializer"

Attempts:

3 left

💡 Hint

Common Mistakes

Using StringSerializer instead of KafkaJsonSchemaSerializer

Confusing Avro serializer with JSON Schema serializer

To use JSON Schema serialization in Kafka, you must set the value.serializer to io.confluent.kafka.serializers.KafkaJsonSchemaSerializer.

2fill in blank

medium

Complete the code to specify the deserializer for Protobuf in Kafka consumer configuration.

Kafka

props.put("value.deserializer", [1]);

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

A"io.confluent.kafka.serializers.KafkaJsonSchemaDeserializer"

B"org.apache.kafka.common.serialization.StringDeserializer"

C"io.confluent.kafka.serializers.KafkaProtobufDeserializer"

D"io.confluent.kafka.serializers.KafkaAvroDeserializer"

Attempts:

3 left

💡 Hint

Common Mistakes

Using JSON Schema deserializer for Protobuf data

Using StringDeserializer which does not handle Protobuf

For Protobuf deserialization in Kafka consumers, use io.confluent.kafka.serializers.KafkaProtobufDeserializer as the value.deserializer.

3fill in blank

hard

Fix the error in the schema registry URL configuration for Kafka producer.

Kafka

props.put("schema.registry.url", [1]);

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

A"http://localhost:8081"

B"localhost:8081"

C"https://schema-registry:8081"

D"http://schema-registry"

Attempts:

3 left

💡 Hint

Common Mistakes

Omitting the protocol (http://)

Using only hostname without port

The schema registry URL must include the protocol (http:// or https://) and the port number, for example http://localhost:8081.

4fill in blank

hard

Fill both blanks to configure Kafka producer for Protobuf serialization with schema registry.

Kafka

props.put("value.serializer", [1]);
props.put("schema.registry.url", [2]);

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

A"io.confluent.kafka.serializers.KafkaProtobufSerializer"

B"http://localhost:8081"

C"io.confluent.kafka.serializers.KafkaJsonSchemaSerializer"

D"localhost:8081"

Attempts:

3 left

💡 Hint

Common Mistakes

Using JSON Schema serializer for Protobuf data

Omitting protocol in schema registry URL

For Protobuf serialization, use KafkaProtobufSerializer and specify the schema registry URL with protocol and port.

5fill in blank

hard

Fill all three blanks to create a Kafka consumer configuration for JSON Schema deserialization with schema registry URL and group ID.

Kafka

props.put("value.deserializer", [1]);
props.put("schema.registry.url", [2]);
props.put("group.id", [3]);

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

A"io.confluent.kafka.serializers.KafkaJsonSchemaDeserializer"

B"http://localhost:8081"

C"json-schema-consumer-group"

D"io.confluent.kafka.serializers.KafkaProtobufDeserializer"

Attempts:

3 left

💡 Hint

Common Mistakes

Using Protobuf deserializer for JSON Schema data

Omitting group ID or schema registry URL protocol

Use KafkaJsonSchemaDeserializer for JSON Schema deserialization, specify the schema registry URL with protocol and port, and set a group ID for the consumer.

Practice

1. What is the main purpose of using JSON Schema or Protobuf with Kafka messages?

easy

A. To define the structure and format of messages so producers and consumers agree

B. To encrypt Kafka messages for security

C. To speed up Kafka message delivery by skipping validation

D. To store Kafka messages permanently in a database

Solution

1. Step 1: Understand message format agreement

   JSON Schema and Protobuf define how messages look so both sender and receiver understand the data.

2. Step 2: Identify the main purpose

   This agreement prevents errors and confusion when reading messages.

3. Final Answer:

   To define the structure and format of messages so producers and consumers agree -> Option A

4. Quick Check:

   Message format agreement = C [OK]

Hint: They define message format so systems agree on data [OK]

Common Mistakes:

• Thinking JSON Schema encrypts messages
• Believing Protobuf speeds delivery by skipping validation
• Confusing schema with storage

2. Which of the following is the correct way to specify a Protobuf schema for a Kafka message?

easy

A. { 'name': 'string', 'id': 'int' }

B. schema { field name: string; field id: int; }

C. stringint

D. message Person { string name = 1; int32 id = 2; }

Solution

1. Step 1: Recognize Protobuf syntax

   Protobuf uses the 'message' keyword with fields and their types and numbers.

2. Step 2: Match the correct syntax

   message Person { string name = 1; int32 id = 2; } shows correct Protobuf message definition syntax.

3. Final Answer:

   message Person { string name = 1; int32 id = 2; } -> Option D

4. Quick Check:

   Protobuf syntax = B [OK]

Hint: Protobuf uses 'message' keyword and numbered fields [OK]

Common Mistakes:

• Confusing JSON or XML syntax with Protobuf
• Missing field numbers in Protobuf
• Using invalid keywords like 'schema'

3. Given this JSON Schema snippet for a Kafka message:

{
  "type": "object",
  "properties": {
    "userId": {"type": "string"},
    "age": {"type": "integer"}
  },
  "required": ["userId"]
}

Which message is valid according to this schema?

medium

A. {"userId": "abc123", "age": 30}

B. {"age": 30}

C. {"userId": 123, "age": 30}

D. {"userId": "abc123", "age": "30"}

Solution

1. Step 1: Check required fields and types

   The schema requires 'userId' as a string and 'age' as an integer (optional).

2. Step 2: Validate each option

   {"userId": "abc123", "age": 30} has 'userId' as string and 'age' as integer, so it is valid.

3. Final Answer:

   {"userId": "abc123", "age": 30} -> Option A

4. Quick Check:

   Required string userId and integer age = A [OK]

Hint: Required fields must be present with correct types [OK]

Common Mistakes:

• Missing required 'userId' field
• Using wrong data types for fields
• Confusing string and integer types

4. You have this Protobuf schema:

message Event {
  string id = 1;
  int32 count = 2;
}

Which of the following is CORRECT about using it with Kafka?

medium

A. Using 'int32' instead of 'integer' causes error

B. Field numbers must be unique and start from 1

C. Missing a root 'schema' keyword causes failure

D. Field names cannot be strings

Solution

1. Step 1: Check Protobuf field numbering rules

   Field numbers must be unique and start from 1, which is true here.

2. Step 2: Identify the correct statement

   Field numbers must be unique and start from 1 correctly describes the field numbering rule. Options B, C, D are incorrect statements.

3. Final Answer:

   Field numbers must be unique and start from 1 -> Option B

4. Quick Check:

   Protobuf field numbering rule = A [OK]

Hint: Field numbers must be unique and start at 1 [OK]

Common Mistakes:

• Thinking 'int32' is invalid
• Expecting a 'schema' keyword in Protobuf
• Confusing field names with types

5. You want to optimize Kafka message size and speed. Which schema choice is best and why?

hard

A. Use JSON Schema because it is human-readable and easy to debug

B. Use XML schema because it supports complex data types

C. Use Protobuf because it is compact and faster to serialize/deserialize

D. Use plain text messages without schema for maximum speed

Solution

1. Step 1: Compare JSON Schema and Protobuf

   JSON Schema is readable but larger; Protobuf is compact and faster.

2. Step 2: Choose based on optimization goal

   For smaller size and speed, Protobuf is better.

3. Final Answer:

   Use Protobuf because it is compact and faster to serialize/deserialize -> Option C

4. Quick Check:

   Compact and fast = D [OK]

Hint: Protobuf is compact and faster than JSON Schema [OK]

Common Mistakes:

• Choosing JSON Schema for speed optimization
• Ignoring schema benefits and using plain text
• Confusing XML with Protobuf advantages