rahil-c commented on code in PR #13743:
URL: https://github.com/apache/hudi/pull/13743#discussion_r2412141810


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rfc/rfc-99/rfc-99.md:
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+
+# RFC-99: Hudi Type System
+
+## Proposers
+
+- @bvaradar
+
+## Approvers
+
+- @vinothchandar
+
+
+## Status
+
+Umbrella ticket: [HUDI-9730](https://issues.apache.org/jira/browse/HUDI-9730)
+
+
+## Abstract
+The main goal is to propose a native Hudi type system as the authoritative 
representation for Hudi data types, making the system more extensible and the 
semantics of data types clear and unified. While Hudi currently uses Avro for 
schema representation, introducing a more comprehensive, Arrow-based type 
system will make it easier to provide consistent handling and implementation of 
data types across different engines and improve support for modern data 
paradigms like multi-modal and semi-structured data.
+
+There is [earlier attempt](https://github.com/apache/hudi/pull/12795/files) to 
define a common schema but it was geared towards building more general 
abstractions. This RFC relooks at the specific need for defining a type system 
model for Hudi to become more extensible fnd also support non-traditional 
usecases.
+   
+## Background
+Apache Hudi currently uses Apache Avro as the canonical representation for its 
schema. While this has served the project well, introducing a native, 
engine-agnostic type system offers a strategic opportunity to evolve Hudi's 
core abstractions for the future. The primary motivations for this evolution 
are:
+
+- A common type system allows us to build richer functionalities and common 
interface across engines and non-JVM clients to interact with Hudi data 
directly and efficiently.
+- A native type system provides a formal framework for introducing new, 
complex data types. This will accelerate Hudi's ability to offer first-class 
support for emerging use cases in AI/ML (vectors, tensors) and semi-structured 
data analysis (VARIANT), keeping Hudi at the forefront of data lakehouse 
technology
+- By standardizing on an in-memory format, Hudi can eliminate costly 
serialization and deserialization steps when exchanging data with a growing 
number of Arrow-native tools and engines. This unlocks zero-copy data access, 
significantly boosting performance for both read and write paths.
+
+## Design
+
+The canonical in-memory representation for all types will be based on the 
Apache Arrow specification. The main reasons for this is that :
+
+- Apache Arrow provides a standard in-memory format that eliminates the costly 
process of data serialization and deserialization when moving data across 
system boundaries. This enables "zero-copy" data exchange, which radically 
reduces computational overhead and query latency.
+- This helps us more easily achieve seamless data exchange with ecosystem of 
Arrow-native tools.

Review Comment:
   Sorry about that i think you cover spark, and flink further in the 
`Interoperability Mapping` section



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