Schema evolution (backward, forward, full) in Kafka - Step-by-Step Execution

Process Flow - Schema evolution (backward, forward, full)

Start: Define initial schema

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Add new fields?

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Check backward compatibility

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Remove or rename fields?

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Check forward compatibility

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Both backward & forward?

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Full compatibility

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Schema accepted or rejected

Shows how schema changes are checked step-by-step for backward, forward, and full compatibility before acceptance.

Execution Sample

Kafka

Initial schema: {name:string}
Add field: age:int (default=0)
Check backward compatibility
Check forward compatibility
Result: Full compatibility

This example adds a new field with a default value and checks all compatibility types.

Process Table

Step	Schema Change	Backward Compatible?	Forward Compatible?	Full Compatible?	Result
1	Initial schema {name:string}	Yes (base)	Yes (base)	Yes (base)	Schema accepted
2	Add field age:int with default=0	Yes (old data valid)	Yes (new data valid)	Yes	Schema accepted
3	Remove field name	No (old data missing)	Yes	No	Schema rejected
4	Rename field name to fullname	No (field missing)	No (field missing)	No	Schema rejected
5	Add field email:string no default	No (old data missing field)	Yes	No	Schema rejected

💡 Schema changes are accepted only if compatibility checks pass according to the evolution type.

Status Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5
Schema Fields	{name:string}	{name:string, age:int=0}	{age:int=0}	{fullname:string}	{fullname:string, email:string}
Backward Compatible	Yes	Yes	No	No	No
Forward Compatible	Yes	Yes	Yes	No	Yes
Full Compatible	Yes	Yes	No	No	No

Key Moments - 3 Insights

Why is adding a new field with no default value not backward compatible?

Because old data does not have the new field, so deserialization fails without a default. See step 5 in execution_table where backward compatibility is No.

Why does renaming a field break both backward and forward compatibility?

Renaming removes the old field and adds a new one, so old data can't find the renamed field and new data can't find the old field. See step 4 in execution_table.

What does full compatibility require?

Both backward and forward compatibility must be true. See step 2 where full compatibility is Yes because both backward and forward are Yes.

Visual Quiz - 3 Questions

Look at the execution_table at step 3. What is the backward compatibility result?

A Yes

B No

C Partial

D Unknown

💡 Hint

Check the 'Backward Compatible?' column at step 3 in execution_table.

At which step does the schema become fully compatible again after the initial schema?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look for 'Full Compatible?' column with 'Yes' after step 1.

If you add a new field with a default value, how does it affect backward compatibility?

A It breaks backward compatibility

B It keeps backward compatibility

C It breaks forward compatibility

D It breaks full compatibility

💡 Hint

See step 2 in execution_table where adding a field with default keeps backward compatibility.

Concept Snapshot

Schema evolution rules:
- Backward compatible: new schema reads old data
- Forward compatible: old schema reads new data
- Full compatible: both backward and forward
- Adding fields with defaults is safe
- Removing or renaming fields breaks compatibility

Full Transcript

Schema evolution in Kafka means changing data formats without breaking existing data or applications. Backward compatibility means new schema can read old data. Forward compatibility means old schema can read new data. Full compatibility means both backward and forward are true. Adding new fields with default values keeps compatibility. Removing or renaming fields usually breaks it. This visual shows step-by-step how schema changes affect compatibility and when schemas are accepted or rejected.