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Towards a Stream-Centered Enterprise
Kafka Summit 2016
Gabriel Commeau
Stream Data Platform Overview
Data Ingest
Multi-Datacenter Deployment
o Avro versus json, protobuf, xml, thrift
o Some guidelines for Avro schema:
• Document it all
• Use explicit names
• Provide default value
• Avoid complex union type
• If possible avoid byte arrays
o Use:
• Common core schemas
• Proper nesting
• Enum + String
Schema Management
Avro Schema Evolution
Enum
• Reader can add value
• Writer can remove value
Don’t
• Change namespace
• Change name
• Unless default value
• Change type
Do
• Use default value
• Use union type
containing null
Union Type
• Change type order ok
• Reader can add type
• Writer can remove type
Compatibility
Ok
• Change order
• Change, add, remove,
reorder alias

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  • 5. o Avro versus json, protobuf, xml, thrift o Some guidelines for Avro schema: • Document it all • Use explicit names • Provide default value • Avoid complex union type • If possible avoid byte arrays o Use: • Common core schemas • Proper nesting • Enum + String Schema Management
  • 6. Avro Schema Evolution Enum • Reader can add value • Writer can remove value Don’t • Change namespace • Change name • Unless default value • Change type Do • Use default value • Use union type containing null Union Type • Change type order ok • Reader can add type • Writer can remove type Compatibility Ok • Change order • Change, add, remove, reorder alias
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