Avro schema definition in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the type of the Avro field.

Kafka

{
  "name": "age",
  "type": "[1]"
}

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

Aint

Bstring

Cboolean

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Using string instead of int for numeric fields.

Using float when decimals are not needed.

The field age should be an integer, so the type is int.

2fill in blank

medium

Complete the code to define a nullable string field in Avro.

Kafka

{
  "name": "nickname",
  "type": ["null", "[1]"]
}

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

Aboolean

Bfloat

Cstring

Dint

Attempts:

3 left

💡 Hint

Common Mistakes

Using only string without null for nullable fields.

Using int or boolean instead of string for text fields.

The field nickname can be missing or a string, so the type is a union of null and string.

3fill in blank

hard

Fix the error in the Avro schema type definition for a boolean field.

Kafka

{
  "name": "isActive",
  "type": "[1]"
}

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

Aboolean

Bint

Cstring

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Using string or int instead of boolean for true/false fields.

Using float for boolean fields.

The field isActive should be a boolean type, so the correct type is boolean.

4fill in blank

hard

Fill both blanks to define an Avro record with two fields: a string name and an int age.

Kafka

{
  "type": "record",
  "name": "Person",
  "fields": [
    {"name": "name", "type": "[1]"},
    {"name": "age", "type": "[2]"}
  ]
}

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

Astring

Bboolean

Cint

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up types for name and age fields.

Using boolean or float instead of string or int.

The name field is text, so it uses string. The age field is a whole number, so it uses int.

5fill in blank

hard

Fill all three blanks to define an Avro schema with a record named Employee having fields: id (int), name (string), and active (boolean).

Kafka

{
  "type": "record",
  "name": "Employee",
  "fields": [
    {"name": "id", "type": "[1]"},
    {"name": "name", "type": "[2]"},
    {"name": "active", "type": "[3]"}
  ]
}

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

Astring

Bint

Cboolean

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping types between fields.

Using float instead of int for id.

The id is a number without decimals, so int. The name is text, so string. The active field is true/false, so boolean.

Practice

1. What is the main purpose of an Avro schema in Kafka?

easy

A. To route messages to different topics

B. To define the structure and data types of messages

C. To compress messages for faster transmission

D. To encrypt messages for security

Solution

1. Step 1: Understand Avro schema role

   An Avro schema describes how data is organized and typed in messages.

2. Step 2: Compare options with schema purpose

   Only defining structure and data types matches the schema's role.

3. Final Answer:

   To define the structure and data types of messages -> Option B

4. Quick Check:

   Avro schema = data structure definition [OK]

Hint: Avro schema describes message format, not security or routing [OK]

Common Mistakes:

• Confusing schema with encryption or compression
• Thinking schema controls message routing
• Assuming schema is for message storage

2. Which of the following is the correct way to start an Avro record schema definition?

easy

A. {"type": "enum", "symbols": ["A", "B"]}

B. {"type": "array", "items": "string"}

C. {"type": "map", "values": "int"}

D. {"type": "record", "name": "User", "fields": []}

Solution

1. Step 1: Identify record schema syntax

   An Avro record schema starts with type "record", a name, and a fields array.

2. Step 2: Match options to record schema

   Only {"type": "record", "name": "User", "fields": []} matches the record schema format correctly.

3. Final Answer:

   {"type": "record", "name": "User", "fields": []} -> Option D

4. Quick Check:

   Record schema = type "record" with name and fields [OK]

Hint: Record schemas always start with type "record" and have fields [OK]

Common Mistakes:

• Using array or map types instead of record
• Missing the name or fields keys
• Confusing enum with record

3. Given this Avro schema snippet:

{"type": "record", "name": "Person", "fields": [{"name": "age", "type": "int"}]}

What is the expected type of the age field in messages?

medium

A. int

B. boolean

C. string

D. float

Solution

1. Step 1: Read the field definition

   The field "age" has type "int" in the schema.

2. Step 2: Determine expected data type

   Messages must have an integer value for "age" as per schema.

3. Final Answer:

   int -> Option A

4. Quick Check:

   Field type "int" means integer value expected [OK]

Hint: Field type in schema directly shows expected data type [OK]

Common Mistakes:

• Assuming string type by default
• Confusing int with float or boolean
• Ignoring the schema field type

4. Identify the error in this Avro schema snippet:

{"type": "record", "name": "Product", "fields": [{"name": "price", "type": "double"}, {"name": "available"}]}

medium

A. Missing type for field "available"

B. Invalid record name

C. Fields array should be empty

D. Type "double" is not allowed

Solution

1. Step 1: Check each field for required keys

   Each field must have a "name" and a "type" key.

2. Step 2: Find missing keys

   The field "available" lacks a "type" key, causing an error.

3. Final Answer:

   Missing type for field "available" -> Option A

4. Quick Check:

   Every field needs a type [OK]

Hint: Every field must have a type key defined [OK]

Common Mistakes:

• Forgetting to add type for all fields
• Thinking record name can be empty
• Assuming double type is invalid

5. You want to add an optional field email of type string with a default empty value to an Avro record schema. Which field definition is correct?

hard

A. {"name": "email", "type": "string"}

B. {"name": "email", "type": ["null", "string"], "default": ""}

C. {"name": "email", "type": ["string", "null"], "default": ""}

D. {"name": "email", "type": ["null", "string"], "default": null}

Solution

1. Step 1: Understand optional field in Avro

   Optional fields use a union type with "null" and the actual type, with a default value matching the first type in the union.

2. Step 2: Check default value and type order

   For default empty string "", the first type must be "string" followed by "null"; {"name": "email", "type": ["string", "null"], "default": ""} matches.

3. Final Answer:

   {"name": "email", "type": ["string", "null"], "default": ""} -> Option C

4. Quick Check:

   Optional with default "" = ["string", "null"] union [OK]

Hint: Union first type must match default; include null for optional [OK]

Common Mistakes:

• Not using union with null for optional fields
• Using null first with string default (invalid)
• Setting default to null when empty string needed