feat(spark): unify Databricks connector auth with PAT, OAuth M2M, and OIDC federation strategies

[rohitrsh]

Tracking issue

Related to flyteorg/flyte#7319

Why are the changes needed?

Databricks has marked Personal Access Tokens (PATs) as a legacy authentication method and is steering customers toward OAuth machine-to-machine (M2M) credentials and OIDC workload-identity federation. Today the Flyte Databricks connector only supports PAT, which means:

• Operators have to provision and rotate PATs for every Databricks Service Principal that backs a Flyte tenant.
• There is no path to truly secret-less authentication via Kubernetes ServiceAccount JWTs.
• The per-namespace tenancy model that landed in Add multi-tenant Databricks token support via cross-namespace K8s secrets #3394 (Closes [Core feature] Add Databricks Serverless Compute Support to Databricks Connector flyte#6911) cannot be extended to OAuth or OIDC without copy-paste work in connector.py.

This PR brings OAuth M2M and OIDC federation to the connector while preserving the existing per-namespace tenancy story, and refactors the existing PAT path so all four modes share one extension point.

What changes were proposed in this pull request?

A unified DatabricksAuth strategy abstraction in a new databricks_auth.py module, plus three new strategies and a refactor of the existing PAT path:

Strategy	What it uses	Where credentials live
PATAuth	Personal Access Token (existing flow)	databricks-token k8s secret in workflow namespace, with FLYTE_DATABRICKS_ACCESS_TOKEN fallback
OAuthM2MAuth	Service Principal client_id + client_secret	databricks-oauth k8s secret in workflow namespace, with env-var fallback
OIDCConnectorIRSAAuth (Model 1)	The connector pod's own projected JWT (e.g. EKS IRSA)	Federation policy in Databricks bound to the connector SA
OIDCNamespaceSAAuth (Model 2)	Per-workflow-namespace ServiceAccount JWT minted via Kubernetes TokenRequest API	Federation policy in Databricks bound to the workflow SA, discovered from SA labels and annotations

Each strategy implements auth_type, get_bearer_token(session), invalidate_cache(), and describe(). select_auth(...) resolves task config -> connector env var -> auto-detect; build_auth(...) reconstructs the strategy from DatabricksJobMetadata so long-running jobs can refresh their token transparently on a 401 response.

Important note for reviewers: PAT is being refactored

This PR is more than a feature addition. It refactors the PAT support that landed via #3394 from a direct call into get_databricks_token(...) into a PATAuth strategy that lives alongside the new modes. The behaviour, env vars, custom-secret-name task field, and cross-namespace lookup semantics are preserved end to end (new tests cover all of those scenarios), but the code path now flows through select_auth -> PATAuth.get_bearer_token -> get_databricks_token. If you reviewed #3394 you may want to read connector.py and the new tests/test_databricks_token.py with this lens; the goal is one extension point for all four modes instead of two.

Files changed

• plugins/flytekit-spark/flytekitplugins/spark/databricks_auth.py (new): strategy abstraction, four strategies, select_auth, build_auth, token cache, OIDC discovery cache, validators.
• plugins/flytekit-spark/flytekitplugins/spark/connector.py: DatabricksJobMetadata carries auth context for refresh; create calls select_auth; get and delete go through a new _request_with_auth helper that refreshes on 401 and falls back to the stored token for older job metadata.
• plugins/flytekit-spark/flytekitplugins/spark/task.py: DatabricksV2 gains optional per-task overrides (databricks_auth_type, databricks_client_id, databricks_oauth_secret, databricks_oidc_token_file, databricks_oidc_audience). All optional, all backward compatible.
• plugins/flytekit-spark/tests/test_databricks_auth.py (new): 56 tests covering every strategy, the resolver, auto-detection, fallback rules, and the OIDC Model 2 SA discovery cache.
• plugins/flytekit-spark/tests/test_databricks_token.py: refactored to call into the strategy via select_auth while preserving the multi-tenant PAT scenarios from Add multi-tenant Databricks token support via cross-namespace K8s secrets #3394.
• plugins/flytekit-spark/tests/test_connector.py: updated DatabricksJobMetadata constructions for the new fields.
• plugins/flytekit-spark/README.md: full Authentication section, env-var matrix, RBAC manifest for OIDC Model 2, migration guide from PAT.

Zero workflow-code changes

A core constraint of this PR is that existing Databricks workflows keep working without edits. Operators flip auth modes by setting connector-level env vars; task authors only touch DatabricksV2 fields if a single workflow needs to diverge from the connector default.

How was this patch tested?

Unit tests

$ cd plugins/flytekit-spark
$ pytest tests/test_databricks_auth.py tests/test_databricks_token.py tests/test_connector.py
============================ 100 passed in 3.04s ============================

The full plugin suite (pytest tests/ --ignore=tests/test_remote_register.py) shows 119 passed. The 5 failures in test_wf.py and test_pyspark_transformers.py are a pre-existing PySpark + Java 18+ compatibility issue (Exception: getSubject is not supported) on the local dev machine running Java 25; they pass on upstream CI which uses Java 11/17. None of those tests touch the files in this PR.

End-to-end on a development EKS cluster

Each auth mode was exercised against a real Databricks workspace by patching the connector deployment with an image built from this branch:

• pat: per-namespace databricks-token k8s secret. Existing path.
• oauth_m2m: per-namespace databricks-oauth k8s secret with client_id and client_secret keys, federation against a Databricks Service Principal.
• oidc_federation Model 2: per-workflow-namespace ServiceAccount annotated with flyte.org/databricks-enabled, flyte.org/databricks-client-id, flyte.org/databricks-audience. Connector minted a JWT via Kubernetes TokenRequest, exchanged it for a Databricks bearer token, and the resulting Jobs run in the Databricks console was attributed to the per-namespace Service Principal (verifying Unity Catalog tenancy is preserved).

Pre-commit

$ pre-commit run --files <the seven changed files>
ruff..................Passed
ruff-format...........Passed
codespell.............Passed
pydoclint.............Passed

pydoclint-errors-baseline.txt was not modified by this PR.

Setup process

The plugin README's Authentication section now documents the full operator setup, including the RBAC manifest for OIDC Model 2 and a migration guide from PAT. No infra changes are required for existing PAT users; the connector defaults still resolve PAT first.

Screenshots

n/a

Check all the applicable boxes

• I updated the documentation accordingly.
• All new and existing tests passed.
• All commits are signed-off.

Related PRs

• Add multi-tenant Databricks token support via cross-namespace K8s secrets #3394: original multi-tenant PAT support. This PR refactors that path into the strategy abstraction; the public behaviour (env vars, secret names, fallback order) is preserved.
• Add Databricks Serverless Compute Support #3392: Databricks Serverless compute. Orthogonal feature; preserved in task.py through this rebase.

Docs link

The plugin README in this PR (plugins/flytekit-spark/README.md) is the documentation; there is no separate docs site change.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 73.19%. Comparing base (39d4a9f) to head (ba19fbf).
⚠️ Report is 1 commits behind head on master.

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Additional details and impacted files

@@             Coverage Diff             @@
##           master    #3429       +/-   ##
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- Coverage   84.08%   73.19%   -10.90%     
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  Files         388      216      -172     
  Lines       31182    22855     -8327     
  Branches     3016     3016               
===========================================
- Hits        26220    16729     -9491     
- Misses       4082     5287     +1205     
+ Partials      880      839       -41     

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