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https://issues.apache.org/jira/browse/AVRO-2779?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mateusz Mrozewski updated AVRO-2779:
------------------------------------
Description:
I have a producer that sometimes adds new fields to schema. Producer usually
gets updated first and starts producing serialized records with new fields
(data is sent via Kafka).
I have a consumer, that should be able to read the data from Kafka, even when
produced with newer schema - new fields can be ignored until consumer gets
updated.
I noticed that adding two fields, one at the top level and one in the nested
records yields unexpected results.
Old schema:
{code:java}
{
"namespace" : "some.namespace",
"name" : "MyRecord",
"type" : "record",
"fields" : [
{"name": "field1", "type": "long"},
{
"name": "nested",
"type": {
"type" : "record",
"name" : "nestedRecord",
"fields" : [
{"name": "nestedField1", "type": "long"}
]
}
}
]
}
{code}
New Schema:
{code:java}
{
"namespace" : "some.namespace",
"name" : "MyRecord",
"type" : "record",
"fields" : [
{"name": "field1", "type": "long"},
{"name": "field2", "type": "long"},
{
"name": "nested",
"type": {
"type" : "record",
"name" : "nestedRecord",
"fields" : [
{"name": "nestedField1", "type": "long"},
{"name": "nestedField2", "type": "long"}
]
}
}
]
}
{code}
And example code:
{code:java}
Schema.Parser parser = new Schema.Parser();
InputStream fin = new FileInputStream("src/main/resources/schemas/old.json");
Schema oldSchema = parser.parse(fin);Schema.Parser parser2 = new
Schema.Parser();
fin = new FileInputStream("src/main/resources/schemas/new.json");
Schema newSchema = parser2.parse(fin);GenericData.Record nested = new
GenericRecordBuilder(newSchema.getField("nested").schema())
.set("nestedField1", 3L)
.set("nestedField2", 4L)
.build();
GenericData.Record newRecord = new GenericRecordBuilder(newSchema)
.set("field1", 1L)
.set("field2", 2L)
.set("nested", nested)
.build();GenericData gd1 = new GenericData();
RawMessageEncoder<GenericRecord> encoder = new RawMessageEncoder<>(gd1,
newSchema);
ByteBuffer encoded = encoder.encode(newRecord);GenericData gd2 = new
GenericData();
RawMessageDecoder<GenericRecord> decoder = new RawMessageDecoder<>(gd2,
oldSchema);
GenericRecord record =
decoder.decode(encoded);System.out.println(record.get("field1")); // prints 1
System.out.println(record.get("field2")); // prints null
System.out.println(record.get("totally-fake-field")); // prints
nullSystem.out.println(((GenericRecord)
record.get("nested")).get("nestedField1")); // prints 2!
System.out.println(((GenericRecord) record.get("nested")).get("nestedField2"));
// prints null
{code}
Is this an expected behavior? Should such schema evolution be supported?
was:
I have a producer that sometimes adds new fields to schema. Producer usually
gets updated first and starts producing serialized records with new fields
(data is sent via Kafka).
I have a consumer, that should be able to read the data from Kafka, even when
produced with newer schema - new fields can be ignored until consumer gets
updated.
I noticed that adding two fields, one at the top level and one in the nested
records yields unexpected results.
Old schema:
{code:java}
{
"namespace" : "some.namespace",
"name" : "MyRecord",
"type" : "record",
"fields" : [
{"name": "field1", "type": "long"},
{
"name": "nested",
"type": {
"type" : "record",
"name" : "nestedRecord",
"fields" : [
{"name": "nestedField1", "type": "long"}
]
}
}
]
}
{code}
New Schema:
{code:java}
{
"namespace" : "some.namespace",
"name" : "MyRecord",
"type" : "record",
"fields" : [
{"name": "field1", "type": "long"},
{"name": "field2", "type": "long"},
{
"name": "nested",
"type": {
"type" : "record",
"name" : "nestedRecord",
"fields" : [
{"name": "nestedField1", "type": "long"},
{"name": "nestedField2", "type": "long"}
]
}
}
]
}
{code}
And example code:
{code:java}
Schema.Parser parser = new Schema.Parser();
InputStream fin = new FileInputStream("src/main/resources/schemas/old.json");
Schema oldSchema = parser.parse(fin);Schema.Parser parser2 = new
Schema.Parser();
fin = new FileInputStream("src/main/resources/schemas/new.json");
Schema newSchema = parser2.parse(fin);GenericData.Record nested = new
GenericRecordBuilder(newSchema.getField("nested").schema())
.set("nestedField1", 3L)
.set("nestedField2", 4L)
.build();GenericData.Record newRecord = new
GenericRecordBuilder(newSchema)
.set("field1", 1L)
.set("field2", 2L)
.set("nested", nested)
.build();GenericData gd1 = new GenericData();
RawMessageEncoder<GenericRecord> encoder = new RawMessageEncoder<>(gd1,
newSchema);
ByteBuffer encoded = encoder.encode(newRecord);GenericData gd2 = new
GenericData();
RawMessageDecoder<GenericRecord> decoder = new RawMessageDecoder<>(gd2,
oldSchema);
GenericRecord record =
decoder.decode(encoded);System.out.println(record.get("field1")); // prints 1
System.out.println(record.get("field2")); // prints null
System.out.println(record.get("totally-fake-field")); // prints
nullSystem.out.println(((GenericRecord)
record.get("nested")).get("nestedField1")); // prints 2!
System.out.println(((GenericRecord) record.get("nested")).get("nestedField2"));
// prints null
{code}
Is this an expected behavior? Should such schema evolution be supported?
> Schema evolution and adding fields to nested records
> ----------------------------------------------------
>
> Key: AVRO-2779
> URL: https://issues.apache.org/jira/browse/AVRO-2779
> Project: Apache Avro
> Issue Type: Bug
> Components: java
> Affects Versions: 1.9.2
> Reporter: Mateusz Mrozewski
> Priority: Major
>
> I have a producer that sometimes adds new fields to schema. Producer usually
> gets updated first and starts producing serialized records with new fields
> (data is sent via Kafka).
> I have a consumer, that should be able to read the data from Kafka, even when
> produced with newer schema - new fields can be ignored until consumer gets
> updated.
> I noticed that adding two fields, one at the top level and one in the nested
> records yields unexpected results.
> Old schema:
> {code:java}
> {
> "namespace" : "some.namespace",
> "name" : "MyRecord",
> "type" : "record",
> "fields" : [
> {"name": "field1", "type": "long"},
> {
> "name": "nested",
> "type": {
> "type" : "record",
> "name" : "nestedRecord",
> "fields" : [
> {"name": "nestedField1", "type": "long"}
> ]
> }
> }
> ]
> }
> {code}
> New Schema:
> {code:java}
> {
> "namespace" : "some.namespace",
> "name" : "MyRecord",
> "type" : "record",
> "fields" : [
> {"name": "field1", "type": "long"},
> {"name": "field2", "type": "long"},
> {
> "name": "nested",
> "type": {
> "type" : "record",
> "name" : "nestedRecord",
> "fields" : [
> {"name": "nestedField1", "type": "long"},
> {"name": "nestedField2", "type": "long"}
> ]
> }
> }
> ]
> }
> {code}
> And example code:
> {code:java}
> Schema.Parser parser = new Schema.Parser();
> InputStream fin = new FileInputStream("src/main/resources/schemas/old.json");
> Schema oldSchema = parser.parse(fin);Schema.Parser parser2 = new
> Schema.Parser();
> fin = new FileInputStream("src/main/resources/schemas/new.json");
> Schema newSchema = parser2.parse(fin);GenericData.Record nested = new
> GenericRecordBuilder(newSchema.getField("nested").schema())
> .set("nestedField1", 3L)
> .set("nestedField2", 4L)
> .build();
> GenericData.Record newRecord = new GenericRecordBuilder(newSchema)
> .set("field1", 1L)
> .set("field2", 2L)
> .set("nested", nested)
> .build();GenericData gd1 = new GenericData();
> RawMessageEncoder<GenericRecord> encoder = new RawMessageEncoder<>(gd1,
> newSchema);
> ByteBuffer encoded = encoder.encode(newRecord);GenericData gd2 = new
> GenericData();
> RawMessageDecoder<GenericRecord> decoder = new RawMessageDecoder<>(gd2,
> oldSchema);
> GenericRecord record =
> decoder.decode(encoded);System.out.println(record.get("field1")); // prints 1
> System.out.println(record.get("field2")); // prints null
> System.out.println(record.get("totally-fake-field")); // prints
> nullSystem.out.println(((GenericRecord)
> record.get("nested")).get("nestedField1")); // prints 2!
> System.out.println(((GenericRecord)
> record.get("nested")).get("nestedField2")); // prints null
> {code}
> Is this an expected behavior? Should such schema evolution be supported?
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