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Protobuf Field Changes - Risks, Experiments, and Best Practices

· 11 min read


Protocol Buffers, commonly known as Protobuf, have emerged as a potent tool for efficient serialization of structured data. Developed by Google, Protobuf not only ensures smaller payloads compared to formats like JSON and XML but also boasts faster serialization and deserialization times. This combination of speed and efficiency has led developers worldwide to adopt it, especially in performance-critical applications.

However, with its power comes complexity. One of the intricacies often faced by developers is managing changes within their Protobuf schemas, particularly when it comes to altering field types. The crux of the challenge lies in maintaining backward compatibility, ensuring data integrity, and minimizing disruption to services relying on the schema.

In this piece, we delve into the world of field type changes in Protobuf, exploring the potential risks, sharing insights from real-world experiments, and providing recommendations to navigate these waters safely. Whether you're a novice just starting with Protobuf or an expert who's managed large schemas, this exploration aims to enlighten and guide.

Protobuf's Stand on Field Changes

Field changes are almost inevitable in evolving systems. As new requirements emerge or data structures get refined, developers often face the task of modifying their Protobuf schemas. But how does Protobuf itself view these changes, especially when it comes to altering field types?

The Official Word

According to the Google Protobuf Documentation on Updating A Message Type, several critical guidelines are provided for those looking to modify their schemas:

  1. Field Numbers: Once a field number is used, it cannot be reused in the future. This ensures backward compatibility as older serialized data can still be read by new versions of the schema.
  2. Field Removal: While you can remove a field, the field number should be reserved to prevent future reuse, thus safeguarding against unforeseen issues.
  3. Changing Field Types: This is where things get tricky. The documentation explicitly mentions that changing types, even if they're wire-compatible, can lead to issues. Some languages might have stricter rules, and subtle differences in type semantics can introduce runtime errors or change the program's behavior.

Risks of Changing Field Types

Changing field types is not a trivial endeavor, and here's why:

  1. Backward Compatibility: Even if new data serialized using the changed schema works fine, older serialized data might lead to errors or unexpected behavior when read using the updated schema.
  2. Multiple Language Support: Protobuf supports multiple programming languages. A change that works seamlessly in one language might introduce issues in another due to differences in type representations or semantics.
  3. Runtime Errors: Especially in dynamically-typed languages, a change in field type might not manifest errors at compile time but could introduce runtime exceptions.
  4. Data Integrity: Altering field types without a proper understanding can lead to data corruption, loss, or misinterpretation.

A Real-World Experiment

Amit, from our team, conducted an experiment to explore the implications of changing field types in Protobuf using Python:

# First run: Uncomment this block to write a serialized message using the old schema
# from generated.some_pb2 import Mother, Person, Baby
# mother_old = Mother(child=Person(name="Amit"))
# with open('./test_schema.pb', 'wb') as file:
# file.write(mother_old.SerializeToString())

# Second run: Uncomment this block to read the serialized message using the new schema
# from generated2.some_pb2 import Mother, Baby, Person
# with open('./test_schema.pb', 'rb') as file:
# content =
# mother_new = Mother.FromString(content)
# print(mother_new)

From this test, a key observation was made: While the new schema could correctly deserialize messages serialized with the old schema, such an outcome should not be taken as a guarantee. The specific context and intricacies of each system can vary, and what works in one scenario might not hold true in another.

To sum it up, while Protobuf provides guidelines, the onus lies on developers to tread carefully, understand their specific requirements, and test comprehensively before committing to changes in their schema.

An Experimental Deep Dive

Protobuf's documentation provides us with a comprehensive set of guidelines on schema evolution. Still, sometimes, the best way to truly understand the nuances and potential pitfalls is to get our hands dirty with some practical experimentation. Let's delve deeper into the experiment Amit carried out and see what insights we can glean.

Setting Up the Experiment

The primary aim of this experiment was to explore how changing the field type in a Protobuf schema might affect the serialization and deserialization processes. In our specific test, a Person type was changed to a Baby type within a Mother message, despite both types having the same internal structure.

Old Schema:

message Mother{
Person child = 1;

message Person{
string name = 1;

New Schema:

message Mother{
Baby child = 1;

message Baby{
string name = 1;

Conducting the Test

With the schemas defined, the experiment was executed in two stages:

1. Serialization using the Old Schema: A Mother message was created using the Person type for the child, serialized to a binary format, and saved to a file.

from generated.some_pb2 import Mother, Person
mother_old = Mother(child=Person(name="Amit"))
with open('./test_schema.pb', 'wb') as file:

2. Deserialization using the New Schema: The saved binary data was then read using the new schema, attempting to deserialize it into a Mother message using the Baby type for the child.

from generated2.some_pb2 import Mother, Baby
with open('./test_schema.pb', 'rb') as file:
content =
mother_new = Mother.FromString(content)

Observations and Findings

The key takeaway from this experiment was that, even after changing the field type, the new schema successfully deserialized messages serialized with the old schema. The internal wire format, given the identical structure of Person and Baby, remained consistent.

However, it's essential to consider a few caveats:

  1. Scope of the Test: This test was carried out in Python. Protobuf's behavior might differ across various languages and their respective implementations.
  2. Limited Context: The successful outcome of this experiment does not universally guarantee safety in all scenarios. The specific intricacies of each system, combined with other potential changes in the schema, can lead to different results.
  3. Not a Green Light: Just because it worked in this test doesn't mean it's always a good idea. As mentioned before, changing field types, even if wire-compatible, introduces potential risk.

In essence, while this experiment provided some useful insights, it also emphasized the need for comprehensive testing and a careful approach when considering changes to Protobuf schemas.

Potential Pitfalls

Making changes to Protobuf schemas, especially to field types, might seem benign on the surface, especially if initial tests show successful outcomes. However, diving deeper into the complexities of software development, we quickly realize that there are numerous pitfalls to be wary of. Here's an exploration of these potential risks:

1. Language-specific Behaviors

Protobuf is designed to be language-agnostic, enabling developers to use it across a multitude of platforms and programming languages. While this is one of its strengths, it also introduces variability:

  • Different Implementations: Each language might have its own peculiarities in its Protobuf library. A change that seems harmless in Python might cause unexpected behavior in, say, Go or Java.
  • Deserialization Concerns: Some languages might have stricter rules when deserializing data, causing failures when encountering unexpected field types, even if wire formats align.

2. Versioning Issues

In distributed systems, not all components might be updated simultaneously. If one service uses an older version of a schema while another adopts the new version, discrepancies can arise:

  • Data Mismatches: Even if Person and Baby are identical in structure, semantically they might represent different entities. This could lead to logical errors in systems relying on these distinctions.
  • Deprecation Dangers: If a field is deprecated in one version and not in another, or vice versa, complications can ensue when older systems interact with newer ones.

3. Expansion Concerns

While the current structure of Person and Baby might be identical, future changes to either could introduce incompatibilities. If Baby were to be expanded with additional fields, older systems might struggle to handle this data.

4. Overconfidence from Limited Tests

Successful outcomes from isolated experiments might give developers a false sense of security:

  • Test Scope: A small-scale test might not cover all edge cases or real-world scenarios.
  • Data Variability: Real-world data is often more complex and varied than test data. There's a risk that some unexpected data formats or values could lead to deserialization errors or logical issues.

5. Documentation and Collaboration Overhead

Changes to field types, even if technically feasible, can introduce confusion among teams:

  • Documentation Discrepancies: All documentation needs to be updated to reflect changes, a task that's often overlooked.
  • Collaboration Challenges: Teams working on different parts of the system need to be in sync about changes. Without clear communication, discrepancies can arise.

Closing Thoughts on Pitfalls

While Protobuf offers a robust mechanism for defining data structures, its very flexibility demands caution. Each change, however minor it seems, can have ripple effects across a system. The key takeaway? Always approach schema modifications with thorough testing, clear documentation, and open communication.

Safe Alternatives

While the lure of modifying a Protobuf schema might be tempting, especially for small tweaks, the potential fallout, as discussed, can be significant. Instead, developers can adopt certain strategies that allow for the evolution of Protobuf schemas without risking existing functionality.

1. Schema Versioning

Versioning is a time-tested strategy in software development, and it can be applied to Protobuf schemas as well:

  • Package Versioning: By adding a version component to the package directive of .proto files, schemas can evolve without conflicts. For instance, a package can be named my_package.v1, and a subsequent version can be my_package.v2.
  • Directory Structure: The versioning can be mirrored in the directory structure as well. For instance, a new version of a schema can be placed in a /v2 folder.
  • Clear Migration Paths: Ensure that there's a clear pathway for migrating from one version to another, especially if there are significant changes.

2. Deprecation Instead of Replacement

Rather than replacing an old field, consider marking it as deprecated. This allows older systems that still rely on this field to function correctly, while new implementations can shift to using the newer field:

message Mother {
Person child = 1 [deprecated=true];
Baby new_child = 2;

This way, you can gradually phase out the use of the deprecated field over time without introducing sudden breaks.

3. Utilize Reserved Fields

If you decide to remove a field altogether, ensure that its number is not reused for a new field. Instead, mark that field number as reserved:

message Mother {
reserved 1;
Baby child = 2;

By doing this, you safeguard against future accidental reuse of field numbers, which could cause compatibility issues.

4. Embrace Documentation

It might sound mundane, but maintaining updated documentation for every schema change is paramount:

  • Change Logs: Maintain a log of all changes, so developers can trace the evolution of the schema.
  • Annotation: Use comments within .proto files to clearly indicate changes, reasons, and potential implications.

5. Collaboration and Testing

Given that schema changes can affect various parts of a system, it's crucial to foster a culture of collaboration:

  • Review Mechanisms: Establish a clear review process for any schema modifications. Peer reviews can catch potential pitfalls early.
  • Comprehensive Testing: Before rolling out any changes, ensure that they undergo thorough testing – both in isolation and in integrated scenarios. Unit tests, integration tests, and even real-world scenario simulations can provide insights into potential issues.

Final Thoughts on Safe Alternatives

The nature of distributed systems and microservices architecture makes Protobuf schema changes inherently tricky. However, with a blend of best practices, thorough testing, and clear communication, it's possible to evolve schemas without compromising system integrity.


Protobuf, as an integral part of many contemporary software architectures, brings with it a responsibility. The allure of its efficiency, cross-language compatibility, and compactness should never obscure the fundamental principle of stable data contract management.

The act of modifying a schema isn't inherently perilous. Instead, it's the manner in which we approach these modifications that can set the course for success or lead us astray. As demonstrated, even seemingly harmless changes, when made without due diligence, can ripple through a system, producing unintended consequences.

Thus, a cautious approach, rooted in understanding and validated through rigorous testing, isn't just recommended—it's essential. Whether you're new to Protobuf or have been navigating its waters for years, the principle remains the same: your schemas are the contracts of your software, and their integrity is paramount.

In the ever-evolving world of software, continuous learning is a given. So, we invite you to join the conversation. Have you faced challenges with your Protobuf schemas? Do you have insights or tales from the trenches? We'd love to hear from you. Your experiences not only enrich the community's collective knowledge but also pave the way for more informed decisions in the future. Let's learn together.


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