Add DiskProvisionedIops/ThroughputMibps options and update Go client libraries

[bambadiouf1]

This pull request introduces two new pipeline options for the Google Cloud Dataflow runner for the Java, Python and Go SDKs. These options allow users to specify provisioned performance for worker VM disks:

disk_provisioned_iops: Sets the provisioned IOPS for the disk. If unspecified, the service chooses a default
disk_provisioned_throughput_mibps: Sets the provisioned throughput in MiB/s for the disk.

Tests have been added/updated to verify that these options are correctly parsed and translated.

More context:
we need to add these pipeline options before submitting this cl: https://critique.corp.google.com/cl/858930428
Issue: #37374

Additionally, the Go Google API client dependencies were updated to their latest versions to allow the Go SDK to recognize the newly introduced DiskProvisionedIops and DiskProvisionedThroughputMibps fields in the Dataflow API. Several other Go client libraries and dependencies in sdks/go.mod have also been indirectly updated to their latest versions as well.

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[gemini-code-assist]

Summary of Changes

Hello @bambadiouf1, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances Google Cloud Dataflow's capabilities by allowing users to specify detailed performance characteristics for worker VM root disks. By introducing options for provisioned IOPS and throughput, it provides greater control over the underlying infrastructure, which can be crucial for optimizing the performance and cost-efficiency of data processing pipelines. This change is integrated across all supported SDKs, ensuring a consistent experience for developers.

Highlights

• New Pipeline Options: Introduced DiskProvisionedIops and DiskProvisionedThroughputMibps as new pipeline options for Google Cloud Dataflow.
• Cross-SDK Implementation: These new options are implemented across the Python, Java, and Go SDKs, allowing users to specify provisioned performance for worker VM root disks regardless of the SDK used.
• Worker Disk Performance Control: Users can now explicitly set the provisioned IOPS (Input/Output Operations Per Second) and throughput (in MiB/s) for Dataflow worker root disks, offering finer control over job performance.
• Comprehensive Testing: New tests have been added for each SDK to ensure that these options are correctly parsed, translated, and applied to the Dataflow worker pool configuration.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 71.94%. Comparing base (a615657) to head (ca353fb).
⚠️ Report is 13 commits behind head on master.

Additional details and impacted files

@@              Coverage Diff              @@
##             master   #37377       +/-   ##
=============================================
+ Coverage     58.52%   71.94%   +13.42%     
+ Complexity    15434     3427    -12007     
=============================================
  Files          2851      329     -2522     
  Lines        280086    29836   -250250     
  Branches      12337     3593     -8744     
=============================================
- Hits         163917    21466   -142451     
+ Misses       109756     6729   -103027     
+ Partials       6413     1641     -4772     

Flag	Coverage Δ
go	?
java	71.94% <ø> (+7.33%)	⬆️
python	?

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[damccorm]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces support for provisioned IOPS and throughput for worker disks across the Java, Go, and Python SDKs for Google Cloud Dataflow. It includes updates to pipeline options, translator logic, and associated tests, alongside dependency updates in the Go SDK. Feedback indicates critical issues within the Python SDK's autogenerated Dataflow client, specifically the removal of essential JSON field mappings for Flex Templates and the renumbering of message field indices, which could lead to serialization errors and breaking changes.

I am having trouble creating individual review comments. Click here to see my feedback.

sdks/python/apache_beam/runners/dataflow/internal/clients/dataflow/dataflow_v1b3_messages.py (8050-8057)

The custom JSON field mappings for dynamicTemplate_gcsPath and dynamicTemplate_stagingLocation have been removed. These mappings are critical for Flex Template functionality in the Python SDK, as they ensure that attribute names with underscores are correctly mapped to JSON keys containing dots (e.g., dynamicTemplate.gcsPath). Removing them will break Flex Template job submissions.

sdks/python/apache_beam/runners/dataflow/internal/clients/dataflow/dataflow_v1b3_messages.py (7954-7974)

The field indices in the WorkerPool message class (and others like Job and RuntimeUpdatableParams) have been renumbered. In apitools generated message classes, these integer indices are significant for serialization. Manually shifting existing indices is a breaking change that can cause binary incompatibility or serialization errors when communicating with the Dataflow service. New fields should be added at the end of the message definition with new unique indices, or the file should be regenerated using the official generator to ensure consistency with the discovery document.

[damccorm]

Thanks - this is looking pretty good, just a few more minor comments

[damccorm]

/gemini review

[gemini-code-assist]

Code Review

This pull request adds support for setting disk provisioned IOPS and throughput in the Dataflow runner across the Java, Python, and Go SDKs. It introduces new pipeline options, updates job translation logic, and includes relevant tests for each SDK. The PR also updates several Go dependencies and refreshes the autogenerated Dataflow API client in Python. Feedback was provided to generalize the feature description in CHANGES.md, as the originally listed flag names were specific to the Java SDK and did not reflect the snake_case convention used in Python and Go.

[damccorm]

Thanks! Retrying some precommits to try to get a green signal, but I will eventually merge (unless they continue to fail and seem to represent a real problem)

[bambadiouf1]

Thanks! Retrying some precommits to try to get a green signal, but I will eventually merge (unless they continue to fail and seem to represent a real problem)

Thanks a lot, Danny :)!

[jrmccluskey]

Hey, this PR actually breaks the Dataflow protos in python by changing field numbers for existing fields. Additionally, this is changing the internally generated python client that we're in the process of deprecating. I'd suggest reverting this PR and removing the Python-specific code

[damccorm]

Hey, this PR actually breaks the Dataflow protos in python by changing field numbers for existing fields. Additionally, this is changing the internally generated python client that we're in the process of deprecating. I'd suggest reverting this PR and removing the Python-specific code

This is my fault, I should've caught the conflict. The relevant PR making this change is #37639, which would basically need to undo all the Python pieces here.

I agree, lets:

1. Revert
2. Put up another PR with just the Java and Go side changes. We can merge that immediately.
3. Address the Python pieces separately. @jrmccluskey could you help Bamba with that after you merge your PR? (either by just doing it or providing code pointers on what changes need to happen)