Confluent Schema Registry

Question 1

What is the purpose of the Schema Registry in the Kafka ecosystem?

Answer 1

The Schema Registry stores and retrieves Avro schemas for producers and consumers, enforces compatibility (backward, forward, or full), and reduces message size since schemas are not sent with the payload.

Question 2

What is Kafka REST Proxy, and how does it interact with Avro and Kafka?

Answer 2

Kafka REST Proxy allows non-Java clients to interact with Kafka using HTTP requests. It integrates with the Schema Registry for Avro and supports producers and consumers through REST endpoints.

Question 3

What are the advantages of using Avro?

Answer 3

• Fully typed data.
• Automatic compression.
• Schema travels with the data.
• Embedded schema documentation.
• Language-agnostic data readability.
• Safe schema evolution.

Question 4

What are the disadvantages of using Avro?

Answer 4

• Limited support for some languages.
• Data cannot be printed directly without using Avro tools, as it’s compressed and serialized.

Question 5

Name some primitive types supported by Avro.

Answer 5

• null
• boolean
• int
• long
• float
• bytes
• string

Question 6

What are the common fields in an Avro Record Schema?

Answer 6

• Name
• Namespace
• Doc (optional)
• Aliases (optional)
• Fields: Name, Doc, Type, Default

Question 7

What is the key rule when using Enums in Avro?

Answer 7

Once set, enum values cannot be changed to maintain compatibility.

Question 8

How does Avro define arrays?

Answer 8

As a list of items of the same schema, e.g., {“type”: “array”, “items”: “string”}.

Question 9

What is a map in Avro, and how is it defined?

Answer 9

A map is a list of string keys and associated values of the same type, e.g., {“type”: “map”, “values”: “string”}.

Question 10

What is the most common use case for Unions in Avro?

Answer 10

Defining optional fields, e.g., {“name”: “middle_name”, “type”: [“null”, “string”], “default”: null}.

Question 11

Name four common Avro logical types.

Answer 11

• Decimal
• Date
• Time-millis
• Timestamp-millis

Question 12

What is the difference between Generic and Specific Records in Avro?

Answer 12

• Generic Record uses schemas at runtime and is less safe.
• Specific Record is generated from the schema using build tools like Maven, providing type safety.

Question 13

What is backward compatibility in Avro schema evolution?

Answer 13

A new schema can read old data.

Question 14

What is forward compatibility in Avro schema evolution?

Answer 14

An old schema can read new data, with additional fields in the new schema being ignored.

Question 15

What is full compatibility in Avro schema evolution?

Answer 15

Changes allow both forward and backward compatibility. Fields added must have defaults, and fields removed must also have defaults.

Question 16

Give examples of breaking changes in Avro schema evolution

Answer 16

• Adding/removing elements from an Enum.
• Changing a field’s type.
• Renaming a required field.

Question 17

What is the format of the Content-Type header for Avro with Kafka REST Proxy?

Answer 17

application/vnd.kafka.avro.v2+json.

Question 18

List the steps for producing Avro data using Kafka REST Proxy.

Answer 18

Define schema in JSON format.
Produce data with the schema.
Retrieve schema ID for subsequent smaller requests.

Question 19

What are the steps to consume data using Kafka REST Proxy?

Answer 19

• Create a consumer in a consumer group.
• Subscribe to topics.
• Retrieve and process records.
• Commit offsets.
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Question 20

What are the topic operations in Kafka REST Proxy?

Answer 20

• Get a list of topics
• Get a specific topic