Schema compatibility rules in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the schema compatibility to backward.

Kafka

schema_registry_client.update_compatibility(subject_name, '[1]')

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

ABACKWARD

BFORWARD

CFULL

DNONE

Attempts:

3 left

💡 Hint

Common Mistakes

Using FORWARD instead of BACKWARD

Using NONE disables compatibility checks

Setting compatibility to BACKWARD allows new schemas to read data produced with the last schema.

2fill in blank

medium

Complete the code to check if a new schema is compatible with the existing one.

Kafka

is_compatible = schema_registry_client.test_compatibility([1], new_schema)

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

Aschema_type

Bsubject_name

Cschema_version

Dschema_id

Attempts:

3 left

💡 Hint

Common Mistakes

Passing schema_id instead of subject_name

Confusing schema_version with subject_name

The subject_name identifies the schema subject to check compatibility against.

3fill in blank

hard

Fix the error in the code to set compatibility to FULL_TRANSITIVE.

Kafka

schema_registry_client.update_compatibility(subject_name, '[1]')

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

AFULL

BBACKWARD

CFORWARD_TRANSITIVE

DFULL_TRANSITIVE

Attempts:

3 left

💡 Hint

Common Mistakes

Using FULL instead of FULL_TRANSITIVE

Using FORWARD_TRANSITIVE which is different

FULL_TRANSITIVE ensures compatibility with all previous schema versions, not just the last one.

4fill in blank

hard

Fill both blanks to create a schema compatibility check that ensures the new schema is backward compatible with the latest version.

Kafka

result = schema_registry_client.test_compatibility([2], [1])

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

Anew_schema

Bold_schema

Csubject_name

Dschema_version

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping new_schema and old_schema

Using schema_version instead of subject_name

The new_schema is checked against the subject_name to ensure backward compatibility with the latest schema version.

5fill in blank

hard

Fill all three blanks to define a compatibility rule that sets the compatibility level to FORWARD and verifies it.

Kafka

schema_registry_client.update_compatibility(subject_name, [1])
current_level = schema_registry_client.get_compatibility(subject_name)
assert current_level == [2] and current_level == [3]

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

A'FORWARD'

D'BACKWARD'

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing FORWARD and BACKWARD strings

Forgetting quotes around the string values

Setting compatibility to FORWARD and verifying ensures previous schemas can read data produced by new schemas.

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