Refactor workspace_client_mock to have combine fixtures stored in separate JSON files

src/databricks/labs/ucx/assessment/jobs.py

@@ -57,6 +57,24 @@
         return self._assess_jobs(all_jobs, all_clusters)
 
     def _assess_jobs(self, all_jobs: list[BaseJob], all_clusters_by_id) -> Iterable[JobInfo]:
+        job_assessment, job_details = self._prepare(all_jobs)
+        for job, cluster_config in self._get_cluster_configs_from_all_jobs(all_jobs, all_clusters_by_id):
+            job_id = job.job_id
+            if not job_id:
+                continue
+            cluster_details = ClusterDetails.from_dict(cluster_config.as_dict())
+            cluster_failures = self.check_cluster_failures(cluster_details, "Job cluster")
+            job_assessment[job_id].update(cluster_failures)
+
+        # TODO: next person looking at this - rewrite, as this code makes no sense
+        for job_key in job_details.keys():  # pylint: disable=consider-using-dict-items,consider-iterating-dictionary
+            job_details[job_key].failures = json.dumps(list(job_assessment[job_key]))
+            if len(job_assessment[job_key]) > 0:
+                job_details[job_key].success = 0
+        return list(job_details.values())
+
+    @staticmethod
+    def _prepare(all_jobs):
         job_assessment: dict[int, set[str]] = {}
         job_details: dict[int, JobInfo] = {}
         for job in all_jobs:
@@ -82,21 +100,7 @@
                 success=1,
                 failures="[]",
             )
-
-        for job, cluster_config in self._get_cluster_configs_from_all_jobs(all_jobs, all_clusters_by_id):
-            job_id = job.job_id
-            if not job_id:
-                continue
-            cluster_details = ClusterDetails.from_dict(cluster_config.as_dict())
-            cluster_failures = self.check_cluster_failures(cluster_details, "Job cluster")
-            job_assessment[job_id].update(cluster_failures)
-
-        # TODO: next person looking at this - rewrite, as this code makes no sense
-        for job_key in job_details.keys():  # pylint: disable=consider-using-dict-items,consider-iterating-dictionary
-            job_details[job_key].failures = json.dumps(list(job_assessment[job_key]))
-            if len(job_assessment[job_key]) > 0:
-                job_details[job_key].success = 0
-        return list(job_details.values())
+        return job_assessment, job_details
 
     def snapshot(self) -> Iterable[JobInfo]:
         return self._snapshot(self._try_fetch, self._crawl)

tests/unit/assessment/__init__.py

@@ -15,8 +15,11 @@
 
 
 def _base64(filename: str):
-    with (__dir / filename).open("rb") as f:
-        return base64.b64encode(f.read())
+    try:
+        with (__dir / filename).open("rb") as f:
+            return base64.b64encode(f.read())
+    except FileNotFoundError as err:
+        raise NotFound(filename) from err
 
 
 def _workspace_export(filename: str):
@@ -25,12 +28,30 @@ def _workspace_export(filename: str):
 
 
 def _load_fixture(filename: str):
-    with (__dir / filename).open("r") as f:
-        return json.load(f)
+    try:
+        with (__dir / filename).open("r") as f:
+            return json.load(f)
+    except FileNotFoundError as err:
+        raise NotFound(filename) from err
 
 
-def _load_list(cls: type, filename: str):
-    return [cls.from_dict(_) for _ in _load_fixture(filename)]  # type: ignore[attr-defined]
+_FOLDERS = {
+    BaseJob: '../assessment/jobs',
+    ClusterDetails: '../assessment/clusters',
+    PipelineStateInfo: '../assessment/pipelines',
+}
+
+
+def _load_list(cls: type, filename: str, ids=None):
+    if not ids:  # TODO: remove
+        return [cls.from_dict(_) for _ in _load_fixture(filename)]  # type: ignore[attr-defined]
+    return _id_list(cls, ids)
+
+
+def _id_list(cls: type, ids=None):
+    if not ids:
+        return []
+    return [cls.from_dict(_load_fixture(f'{_FOLDERS[cls]}/{_}.json')) for _ in ids]  # type: ignore[attr-defined]
 
 
 def _cluster_policy(policy_id: str):
@@ -51,18 +72,18 @@ def _secret_not_found(secret_scope, _):
 
 
 def workspace_client_mock(
-    clusters="no-spark-conf.json",
-    pipelines="single-pipeline.json",
-    jobs="single-job.json",
+    cluster_ids: list[str] | None = None,
+    pipeline_ids: list[str] | None = None,
+    job_ids: list[str] | None = None,
     warehouse_config="single-config.json",
     secret_exists=True,
 ):
     ws = create_autospec(WorkspaceClient)
-    ws.clusters.list.return_value = _load_list(ClusterDetails, f"../assessment/clusters/{clusters}")
+    ws.clusters.list.return_value = _id_list(ClusterDetails, cluster_ids)
     ws.cluster_policies.get = _cluster_policy
-    ws.pipelines.list_pipelines.return_value = _load_list(PipelineStateInfo, f"../assessment/pipelines/{pipelines}")
+    ws.pipelines.list_pipelines.return_value = _id_list(PipelineStateInfo, pipeline_ids)
     ws.pipelines.get = _pipeline
-    ws.jobs.list.return_value = _load_list(BaseJob, f"../assessment/jobs/{jobs}")
+    ws.jobs.list.return_value = _id_list(BaseJob, job_ids)
     ws.warehouses.get_workspace_warehouse_config().data_access_config = _load_list(
         EndpointConfPair, f"../assessment/warehouses/{warehouse_config}"
     )

tests/unit/assessment/clusters/azure-spn-secret.json

@@ -0,0 +1,16 @@
+{
+  "autoscale": {
+    "min_workers": 1,
+    "max_workers": 6
+  },
+  "cluster_id": "azure-spn-secret",
+  "cluster_name": "Azure SPN Secret",
+  "cluster_source": "UI",
+  "spark_conf": {
+    "spark.hadoop.fs.azure.account.oauth2.client.id.abcde.dfs.core.windows.net": "{{secrets/abcff/sp_app_client_id}}",
+    "spark.hadoop.fs.azure.account.oauth2.client.endpoint.abcde.dfs.core.windows.net": "https://login.microsoftonline.com/dedededede/oauth2/token",
+    "spark.hadoop.fs.azure.account.oauth2.client.secret.abcde.dfs.core.windows.net": "{{secrets/abcff/sp_secret}}"
+  },
+  "spark_context_id": 5134472582179565315,
+  "spark_version": "13.3.x-cpu-ml-scala2.12"
+}

tests/unit/assessment/clusters/init-scripts-dbfs.json

@@ -0,0 +1,32 @@
+{
+  "autoscale": {
+    "max_workers": 6,
+    "min_workers": 1
+  },
+  "cluster_id": "01234-11223344-1122334455",
+  "cluster_name": "UCX Cluster",
+  "policy_id": "single-user-with-spn",
+  "spark_version": "13.3.x-cpu-ml-scala2.12",
+  "init_scripts": [
+    {
+      "dbfs": {
+        "destination": ":/users/test@test.com/init_scripts/test.sh"
+      }
+    },
+    {
+      "dbfs": {
+        "destination": "dbfs"
+      }
+    },
+    {
+      "dbfs": {
+        "destination": "dbfs:"
+      }
+    },
+    {
+      "workspace": {
+        "destination": "init.sh"
+      }
+    }
+  ]
+}

tests/unit/assessment/clusters/job-cluster.json

@@ -0,0 +1,16 @@
+{
+  "autoscale": {
+    "max_workers": 6,
+    "min_workers": 1
+  },
+  "cluster_source": "JOB",
+  "creator_user_name":"fake@fake.org",
+  "cluster_id": "0123-190044-1122334422",
+  "cluster_name": "Single User Cluster Name",
+  "policy_id": "single-user-with-spn",
+  "spark_version": "9.3.x-cpu-ml-scala2.12",
+  "spark_conf" : {
+    "spark.databricks.delta.preview.enabled": "true"
+  },
+  "spark_context_id":"5134472582179565315"
+}

tests/unit/assessment/clusters/legacy-passthrough.json

@@ -0,0 +1,5 @@
+{
+  "cluster_name": "Passthrough cluster",
+  "spark_version": "12.3.x-cpu-ml-scala2.12",
+  "data_security_mode": "LEGACY_PASSTHROUGH"
+}

tests/unit/assessment/clusters/no-isolation.json

@@ -0,0 +1,5 @@
+{
+  "cluster_name": "No isolation shared",
+  "spark_version": "12.3.x-cpu-ml-scala2.12",
+  "data_security_mode": "NONE"
+}