Schema evolution (backward, forward, full) in Kafka - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is backward schema evolution in Kafka?

Backward schema evolution means new schemas can read data written with older schemas. It allows consumers using the new schema to understand old data without errors.

beginner

Define forward schema evolution in Kafka.

Forward schema evolution means old schemas can read data written with newer schemas. It allows older consumers to process new data without breaking.

intermediate

What does full schema evolution mean?

Full schema evolution means schemas are compatible both backward and forward. New and old schemas can read data written by each other without errors.

intermediate

Give an example of a schema change that is backward compatible.

Adding a new optional field with a default value is backward compatible. Old data without this field can still be read by the new schema.

beginner

Why is schema evolution important in Kafka?

Schema evolution allows Kafka producers and consumers to change data formats over time without breaking the system. It supports smooth upgrades and data compatibility.

Which schema evolution type allows new consumers to read old data?

A No evolution

B Forward evolution

C Backward evolution

D Full evolution

Backward evolution means new schemas can read data written with older schemas.

What does forward schema evolution ensure?

A New schemas read old data

B Schemas are identical

C Schemas are incompatible

D Old schemas read new data

Forward evolution means old schemas can read data written with newer schemas.

Which change is usually backward compatible?

A Adding a new optional field with default

B Removing a required field

C Changing field type

D Removing a field without default

Adding an optional field with a default value does not break old data reading.

Full schema evolution means compatibility is:

A Only backward

B Both backward and forward

C Only forward

D None

Full evolution means schemas are compatible both backward and forward.

Why is schema evolution critical in Kafka?

A To allow schema changes without breaking consumers

B To prevent data loss

C To speed up message delivery

D To encrypt messages

Schema evolution allows changing data formats without breaking consumers.

Explain the differences between backward, forward, and full schema evolution in Kafka.

Think about who can read whose data in each type.

Describe a schema change that is backward compatible and why it is safe.

Consider what happens when old data meets a new schema.