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https://issues.apache.org/jira/browse/AVRO-3408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17515309#comment-17515309
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Oscar Westra van Holthe - Kind commented on AVRO-3408:
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Another bit of the spec, when introducing logical types, says:
{quote}Language implementations may choose to represent logical types with an
appropriate native type, although this is not required.
Language implementations must ignore unknown logical types when reading, and
should use the underlying Avro type. If a logical type is invalid, for example
a decimal with scale greater than its precision, then implementations should
ignore the logical type and use the underlying Avro type.
{quote}
This can be read in multiple ways:
* Logical types are used to represent the data as Avro reads it using an
appropriate type in [language]
* Logical types are used to represent the raw bytes using an appropriate type
in [language], using the underlying Avro type as fallback
* ... (something else)
The first sees logical types as an add-on conversion after schema resolution.
They are ignored for schema resolution, and you must hope that the data makes
sense after reading it.
The second sees logical types as a way to represent the raw bytes differently.
They are an active part of schema resolution, {_}but as "language
implementations must ... underlying Avro type.", schema resolution is not
guaranteed to always work the same across all languages{_}.
So the question is: do we want to allow more flexibility, or do we want to
guarantee (a more limited form of) schema resolution?
> Schema evolution with logical types
> ------------------------------------
>
> Key: AVRO-3408
> URL: https://issues.apache.org/jira/browse/AVRO-3408
> Project: Apache Avro
> Issue Type: Improvement
> Components: java
> Affects Versions: 1.11.0
> Reporter: Ivan Zemlyanskiy
> Assignee: Ivan Zemlyanskiy
> Priority: Major
> Labels: pull-request-available
> Fix For: 1.12.0
>
> Time Spent: 4h 10m
> Remaining Estimate: 0h
>
> Hello!
> First of all, thank you for this project. I love Avro encoding from both
> technology and code culture points of view. (y)
> I know you recommend migrating schema by adding a new field and removing the
> old one in the future, but please-please-please consider my case as well.
> In my company, we have some DTOs, and it's about 200+ fields in total that we
> encode with Avro and send over the network. About a third of them have type
> `java.math.BigDecimal`. At some point, we discovered we send them with a
> schema like
> {code:json}
> {
> "name":"performancePrice",
> "type":{
> "type":"string",
> "java-class":"java.math.BigDecimal"
> }
> }
> {code}
> That's a kind of disaster for us cos we have pretty much a high load with ~2
> million RPS.
> So we start to think about migrating to something lighter than strings (no
> blame for choosing it as a default, I know BigDecimal has a lot of pitfalls,
> and string is the easiest way for encoding/decoding).
> It was fine to make a standard precision for all such fields, so we found
> `Conversions.DecimalConversion` and decided at the end of the day we were
> going to use this logical type with a recommended schema like
> {code:java}
> @Override
> public Schema getRecommendedSchema() {
> Schema schema = Schema.create(Schema.Type.BYTES);
> LogicalTypes.Decimal decimalType =
> LogicalTypes.decimal(MathContext.DECIMAL32.getPrecision(),
> DecimalUtils.MONEY_ROUNDING_SCALE);
> decimalType.addToSchema(schema);
> return schema;
> }
> {code}
> (we use `org.apache.avro.reflect.ReflectData`)
> It all looks good and promising, but the question is how to migrate to such
> schema?
> As I said, we have a lot of such fields, and migrating all of them with
> duplication fields with future removal might be painful and would cost us a
> considerable overhead.
> I made some tests and found out if two applications register the same
> `BigDecimalConversion` but for one application the `getRecommendedSchema()`
> is like the method above and for another application the
> `getRecommendedSchema()` is
> {code:java}
> @Override
> public Schema getRecommendedSchema() {
> Schema schema = Schema.create(Schema.Type.STRING);
> schema.addProp(SpecificData.CLASS_PROP, BigDecimal.class.getName());
> return schema;
> }
> {code}
> so they can easily read each other messages using _SERVER_ schema.
> So, I made two applications and wired them up with `ProtocolRepository`,
> `ReflectResponder` and all that stuff, I found out it doesn't work. Because
> `org.apache.avro.io.ResolvingDecoder` totally ignores logical types for some
> reason.
> So as a result, one application specifically told "I encode this field as a
> byte array which supposed to be a logical type 'decimal' with precision N",
> but another application just tries to convert those bytes to a string and
> make a BigDecimal based on the result string. As a result, we got
> {code:java}
> java.lang.NumberFormatException: Character ' is neither a decimal digit
> number, decimal point, nor "e" notation exponential mark.
> {code}
> In my humble opinion, `org.apache.avro.io.ResolvingDecoder` should respect
> logical types in _SERVER_ (_ACTUAL_) schema and use a corresponding
> conversion instance for reading values. In my example, I'd say it might be
> {code}
> ResolvingDecoder#readString() -> read the actual logical type -> find
> BigDecimalConversion instance ->
> conversion.fromBytes(readValueWithActualSchema()) ->
> conversion.toCharSequence(readValueWithConversion)
> {code}
> I'd love to read your opinion on all of that.
> Thank you in advance for your time, and sorry for the long issue description.
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