Schema compatibility rules in Kafka - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is schema compatibility in Kafka?

Schema compatibility ensures that new versions of a schema can work with data written using older versions without errors.

beginner

Name the four main types of schema compatibility rules in Kafka.

Backward, Forward, Full, and None compatibility.

beginner

What does Backward compatibility mean?

New schema can read data written with the previous schema version, so consumers using the new schema can read old data.

beginner

Explain Forward compatibility in Kafka schemas.

Old schema can read data written with the new schema version, so consumers using the old schema can read new data.

intermediate

What is Full compatibility?

It means the schema is both backward and forward compatible, so both old and new consumers can read data from either schema version.

Which compatibility rule allows new consumers to read old data but not vice versa?

A Backward compatibility

B Forward compatibility

C Full compatibility

D None compatibility

Backward compatibility means new schema can read old data, but old consumers may not read new data.

If a schema change breaks both backward and forward compatibility, which compatibility setting should be used?

A Backward

B Forward

C Full

D None

None compatibility means no compatibility checks are enforced, allowing any schema changes.

What does full compatibility guarantee?

A Only new consumers can read old data

B Both old and new consumers can read data from either schema version

C Only old consumers can read new data

D No compatibility between schema versions

Full compatibility means both backward and forward compatibility are ensured.

Which schema compatibility rule is the strictest?

A Full

B Forward

C None

D Backward

Full compatibility requires both backward and forward compatibility, making it the strictest.

Adding a new optional field to a schema is usually compatible with which rule?

A Backward compatibility

B Forward compatibility

C Full compatibility

D None compatibility

Adding an optional field is both backward and forward compatible, so it fits full compatibility.

Describe the differences between backward, forward, and full schema compatibility in Kafka.

Think about who can read data written by whom.

Explain why schema compatibility is important in Kafka data pipelines.

Consider what happens if schema changes break data reading.

Practice

1. What does the BACKWARD compatibility rule in Kafka Schema Registry ensure?

easy

A. Both old and new schemas can read each other's data

B. Old schema can read data produced with the new schema

C. No compatibility checks are enforced

D. New schema can read data produced with the previous schema

Solution

1. Step 1: Understand BACKWARD compatibility meaning

   BACKWARD compatibility means new schema can read data written with the old schema.

2. Step 2: Compare options with definition

   New schema can read data produced with the previous schema matches this definition exactly, others describe different rules.

3. Final Answer:

   New schema can read data produced with the previous schema -> Option D

4. Quick Check:

   BACKWARD = New reads old [OK]

Hint: Backward means new schema reads old data [OK]

Common Mistakes:

• Confusing backward with forward compatibility
• Thinking backward means old reads new
• Assuming no compatibility checks

2. Which of the following is the correct way to set schema compatibility to FULL using Kafka Schema Registry REST API?

easy

A. POST /config -d '{"compatibilityLevel": "FULL"}'

B. PUT /config -d '{"compatibilityLevel": "FULL"}'

C. PUT /config -d '{"compatibility": "FULL"}'

D. GET /config -d '{"compatibility": "FULL"}'

Solution

1. Step 1: Identify correct HTTP method and endpoint

   Setting compatibility uses PUT on /config endpoint.

2. Step 2: Check correct JSON field name

   The field is "compatibilityLevel", not "compatibility".

3. Final Answer:

   PUT /config -d '{"compatibilityLevel": "FULL"}' -> Option B

4. Quick Check:

   PUT + compatibilityLevel = FULL [OK]

Hint: Use PUT and compatibilityLevel field [OK]

Common Mistakes:

• Using POST instead of PUT
• Using wrong JSON field name
• Using GET method to set config

3. Given the following schemas, which is the most restrictive compatibility rule that will allow the new schema to be registered without error?

Old schema: {"type": "record", "name": "User", "fields": [{"name": "name", "type": "string"}]}
New schema: {"type": "record", "name": "User", "fields": [{"name": "name", "type": "string"}, {"name": "age", "type": ["null", "int"], "default": null}]}

medium

A. FULL

B. FORWARD

C. NONE

D. BACKWARD

Solution

1. Step 1: Analyze schema change

   The new schema adds a field with a default value, which is backward and forward compatible.

2. Step 2: Determine compatibility rules allowing this

   FULL compatibility requires both backward and forward compatibility, which this change satisfies.

3. Final Answer:

   FULL -> Option A

4. Quick Check:

   Adding default field = FULL compatible [OK]

Hint: Adding default fields supports full compatibility [OK]

Common Mistakes:

• Thinking adding fields breaks backward compatibility
• Confusing NONE with allowing all changes
• Ignoring default values in new fields

4. You try to register a new schema that removes a required field from the old schema under BACKWARD compatibility. What is the likely result?

medium

A. Schema registration succeeds without errors

B. Schema registration succeeds but consumers break at runtime

C. Schema registration fails due to compatibility violation

D. Schema registration succeeds but producers reject data

Solution

1. Step 1: Understand backward compatibility rules

   Backward compatibility requires new schema to read old data, so removing required fields breaks this.

2. Step 2: Predict schema registry behavior

   Registry rejects schemas violating compatibility rules, so registration fails.

3. Final Answer:

   Schema registration fails due to compatibility violation -> Option C

4. Quick Check:

   Removing required field breaks backward = fail [OK]

Hint: Removing required fields breaks backward compatibility [OK]

Common Mistakes:

• Assuming registration always succeeds
• Confusing runtime errors with registration errors
• Thinking producers reject data on schema mismatch

5. You have a Kafka topic with schema compatibility set to FORWARD. You want to add a new optional field to the schema without breaking existing consumers. Which approach should you take?

hard

A. Add the new field with a default value or make it nullable

B. Remove an existing required field to simplify the schema

C. Add the new field without a default value

D. Change the type of an existing field to a different primitive type

Solution

1. Step 1: Understand FORWARD compatibility

   FORWARD compatibility means old schema can read data written with new schema.

2. Step 2: Determine safe schema changes

   Adding optional fields with defaults or nullable types preserves forward compatibility.

3. Step 3: Evaluate options

   Only Add the new field with a default value or make it nullable adds a field safely; others break compatibility or cause errors.

4. Final Answer:

   Add the new field with a default value or make it nullable -> Option A

5. Quick Check:

   Forward compatible additions need defaults or nullable [OK]

Hint: Add optional fields with defaults for forward compatibility [OK]

Common Mistakes:

• Adding required fields without defaults
• Removing fields under forward compatibility
• Changing field types without compatibility checks