Extract command codes and unify the checks for spark_conf, cluster_policy, init_scripts

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

@@ -1,22 +1,30 @@
+import base64
 import json
+import logging
 from collections.abc import Iterable
 from dataclasses import dataclass
 
 from databricks.sdk import WorkspaceClient
 from databricks.sdk.errors import NotFound
-from databricks.sdk.service.compute import ClusterDetails, ClusterSource, Policy
+from databricks.sdk.service.compute import (
+    ClusterDetails,
+    ClusterSource,
+    InitScriptInfo,
+    Policy,
+)
 
 from databricks.labs.ucx.assessment.crawlers import (
     _AZURE_SP_CONF_FAILURE_MSG,
+    _INIT_SCRIPT_DBFS_PATH,
     INCOMPATIBLE_SPARK_CONFIG_KEYS,
-    _azure_sp_conf_in_init_scripts,
     _azure_sp_conf_present_check,
-    _get_init_script_data,
-    logger,
     spark_version_compatibility,
 )
+from databricks.labs.ucx.assessment.init_scripts import CheckInitScriptMixin
 from databricks.labs.ucx.framework.crawlers import CrawlerBase, SqlBackend
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class ClusterInfo:
@@ -27,7 +35,7 @@
     creator: str | None = None
 
 
-class ClustersMixin:
+class CheckClusterMixin(CheckInitScriptMixin):
     _ws: WorkspaceClient
 
     def _safe_get_cluster_policy(self, policy_id: str) -> Policy | None:
@@ -37,62 +45,77 @@
             logger.warning(f"The cluster policy was deleted: {policy_id}")
             return None
 
-    def _check_spark_conf(self, cluster, failures):
+    def _check_cluster_policy(self, policy_id: str, source: str) -> list[str]:
+        failures: list[str] = []
+        policy = self._safe_get_cluster_policy(policy_id)
+        if policy:
+            if policy.definition:
+                if _azure_sp_conf_present_check(json.loads(policy.definition)):
+                    failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} {source}.")
+            if policy.policy_family_definition_overrides:
+                if _azure_sp_conf_present_check(json.loads(policy.policy_family_definition_overrides)):
+                    failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} {source}.")
+        return failures
+
+    def _get_init_script_data(self, init_script_info: InitScriptInfo) -> str | None:
+        if init_script_info.dbfs is not None and init_script_info.dbfs.destination is not None:
+            if len(init_script_info.dbfs.destination.split(":")) == _INIT_SCRIPT_DBFS_PATH:
+                file_api_format_destination = init_script_info.dbfs.destination.split(":")[1]
+                if file_api_format_destination:
+                    try:
+                        data = self._ws.dbfs.read(file_api_format_destination).data
+                        if data is not None:
+                            return base64.b64decode(data).decode("utf-8")
+                    except NotFound:
+                        return None
+        if init_script_info.workspace is not None and init_script_info.workspace.destination is not None:
+            workspace_file_destination = init_script_info.workspace.destination
+            try:
+                data = self._ws.workspace.export(workspace_file_destination).content
+                if data is not None:
+                    return base64.b64decode(data).decode("utf-8")
+            except NotFound:
+                return None
+        return None
+
+    def _check_cluster_init_script(self, init_scripts: list[InitScriptInfo], source: str) -> list[str]:
+        failures: list[str] = []
+        for init_script_info in init_scripts:
+            init_script_data = self._get_init_script_data(init_script_info)
+            failures.extend(self.check_init_script(init_script_data, source))
+        return failures
+
+    def check_spark_conf(self, conf: dict[str, str], source: str) -> list[str]:
+        failures: list[str] = []
         for k in INCOMPATIBLE_SPARK_CONFIG_KEYS:
-            if k in cluster.spark_conf:
+            if k in conf:
                 failures.append(f"unsupported config: {k}")
-        for value in cluster.spark_conf.values():
+        for value in conf.values():
             if "dbfs:/mnt" in value or "/dbfs/mnt" in value:
                 failures.append(f"using DBFS mount in configuration: {value}")
         # Checking if Azure cluster config is present in spark config
-        if _azure_sp_conf_present_check(cluster.spark_conf):
-            failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} cluster.")
+        if _azure_sp_conf_present_check(conf):
+            failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} {source}.")
+        return failures
 
-    def _check_cluster_policy(self, cluster, failures):
-        policy = self._safe_get_cluster_policy(cluster.policy_id)
-        if policy:
-            if policy.definition:
-                if _azure_sp_conf_present_check(json.loads(policy.definition)):
-                    failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} cluster.")
-            if policy.policy_family_definition_overrides:
-                if _azure_sp_conf_present_check(json.loads(policy.policy_family_definition_overrides)):
-                    failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} cluster.")
+    def check_cluster_failures(self, cluster: ClusterDetails, source: str) -> list[str]:
+        failures: list[str] = []
 
-    def _check_init_scripts(self, cluster, failures):
-        for init_script_info in cluster.init_scripts:
-            init_script_data = _get_init_script_data(self._ws, init_script_info)
-            if not init_script_data:
-                continue
-            if not _azure_sp_conf_in_init_scripts(init_script_data):
-                continue
-            failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} cluster.")
-
-    def _check_cluster_failures(self, cluster: ClusterDetails):
-        failures = []
-        cluster_info = ClusterInfo(
-            cluster_id=cluster.cluster_id if cluster.cluster_id else "",
-            cluster_name=cluster.cluster_name,
-            creator=cluster.creator_user_name,
-            success=1,
-            failures="[]",
-        )
         support_status = spark_version_compatibility(cluster.spark_version)
         if support_status != "supported":
             failures.append(f"not supported DBR: {cluster.spark_version}")
         if cluster.spark_conf is not None:
-            self._check_spark_conf(cluster, failures)
+            failures.extend(self.check_spark_conf(cluster.spark_conf, source))
         # Checking if Azure cluster config is present in cluster policies
-        if cluster.policy_id:
-            self._check_cluster_policy(cluster, failures)
-        if cluster.init_scripts:
-            self._check_init_scripts(cluster, failures)
-        cluster_info.failures = json.dumps(failures)
-        if len(failures) > 0:
-            cluster_info.success = 0
-        return cluster_info
+        if cluster.policy_id is not None:
+            failures.extend(self._check_cluster_policy(cluster.policy_id, source))
+        if cluster.init_scripts is not None:
+            failures.extend(self._check_cluster_init_script(cluster.init_scripts, source))
+
+        return failures
 
 
-class ClustersCrawler(CrawlerBase[ClusterInfo], ClustersMixin):
+class ClustersCrawler(CrawlerBase[ClusterInfo], CheckClusterMixin):
     def __init__(self, ws: WorkspaceClient, sbe: SqlBackend, schema):
         super().__init__(sbe, "hive_metastore", schema, "clusters", ClusterInfo)
         self._ws = ws
@@ -110,7 +133,18 @@
                     f"Cluster {cluster.cluster_id} have Unknown creator, it means that the original creator "
                     f"has been deleted and should be re-created"
                 )
-            yield self._check_cluster_failures(cluster)
+            cluster_info = ClusterInfo(
+                cluster_id=cluster.cluster_id if cluster.cluster_id else "",
+                cluster_name=cluster.cluster_name,
+                creator=cluster.creator_user_name,
+                success=1,
+                failures="[]",
+            )
+            failures = self.check_cluster_failures(cluster, "cluster")
+            if len(failures) > 0:
+                cluster_info.success = 0
+                cluster_info.failures = json.dumps(failures)
+            yield cluster_info
 
     def snapshot(self) -> Iterable[ClusterInfo]:
         return self._snapshot(self._try_fetch, self._crawl)

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

@@ -1,9 +1,6 @@
-import base64
 import logging
 import re
 
-from databricks.sdk.errors import NotFound
-
 logger = logging.getLogger(__name__)
 
 INCOMPATIBLE_SPARK_CONFIG_KEYS = [
@@ -27,27 +24,6 @@
 _INIT_SCRIPT_DBFS_PATH = 2
 
 
-def _get_init_script_data(w, init_script_info):
-    if init_script_info.dbfs:
-        if len(init_script_info.dbfs.destination.split(":")) == _INIT_SCRIPT_DBFS_PATH:
-            file_api_format_destination = init_script_info.dbfs.destination.split(":")[1]
-            if file_api_format_destination:
-                try:
-                    data = w.dbfs.read(file_api_format_destination).data
-                    return base64.b64decode(data).decode("utf-8")
-                except NotFound:
-                    return None
-    if init_script_info.workspace:
-        workspace_file_destination = init_script_info.workspace.destination
-        if workspace_file_destination:
-            try:
-                data = w.workspace.export(workspace_file_destination).content
-                return base64.b64decode(data).decode("utf-8")
-            except NotFound:
-                return None
-    return None
-
-
 def _azure_sp_conf_in_init_scripts(init_script_data: str) -> bool:
     for conf in _AZURE_SP_CONF:
         if re.search(conf, init_script_data):

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

@@ -1,5 +1,6 @@
 import base64
 import json
+import logging
 from collections.abc import Iterable
 from dataclasses import dataclass
 
@@ -8,10 +9,11 @@
 from databricks.labs.ucx.assessment.crawlers import (
     _AZURE_SP_CONF_FAILURE_MSG,
     _azure_sp_conf_in_init_scripts,
-    logger,
 )
 from databricks.labs.ucx.framework.crawlers import CrawlerBase, SqlBackend
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class GlobalInitScriptInfo:
@@ -23,7 +25,19 @@ class GlobalInitScriptInfo:
     enabled: bool | None = None
 
 
-class GlobalInitScriptCrawler(CrawlerBase[GlobalInitScriptInfo]):
+class CheckInitScriptMixin:
+    _ws: WorkspaceClient
+
+    def check_init_script(self, init_script_data: str | None, source: str) -> list[str]:
+        failures: list[str] = []
+        if not init_script_data:
+            return failures
+        if _azure_sp_conf_in_init_scripts(init_script_data):
+            failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} {source}.")
+        return failures
+
+
+class GlobalInitScriptCrawler(CrawlerBase[GlobalInitScriptInfo], CheckInitScriptMixin):
     def __init__(self, ws: WorkspaceClient, sbe: SqlBackend, schema):
         super().__init__(sbe, "hive_metastore", schema, "global_init_scripts", GlobalInitScriptInfo)
         self._ws = ws
@@ -52,9 +66,8 @@ def _assess_global_init_scripts(self, all_global_init_scripts):
             global_init_script = base64.b64decode(script.script).decode("utf-8")
             if not global_init_script:
                 continue
-            if _azure_sp_conf_in_init_scripts(global_init_script):
-                failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} global init script.")
-                global_init_script_info.failures = json.dumps(failures)
+            failures.extend(self.check_init_script(global_init_script, "global init script"))
+            global_init_script_info.failures = json.dumps(failures)
 
             if len(failures) > 0:
                 global_init_script_info.success = 0

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

@@ -1,15 +1,17 @@
 import json
+import logging
 from collections.abc import Iterable
 from dataclasses import dataclass
 
 from databricks.sdk import WorkspaceClient
 from databricks.sdk.service.compute import ClusterDetails
 from databricks.sdk.service.jobs import BaseJob
 
-from databricks.labs.ucx.assessment.clusters import ClustersMixin
-from databricks.labs.ucx.assessment.crawlers import logger
+from databricks.labs.ucx.assessment.clusters import CheckClusterMixin
 from databricks.labs.ucx.framework.crawlers import CrawlerBase, SqlBackend
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class JobInfo:
@@ -20,7 +22,7 @@ class JobInfo:
     creator: str | None = None
 
 
-class JobsMixin(ClustersMixin):
+class JobsMixin:
     @staticmethod
     def _get_cluster_configs_from_all_jobs(all_jobs, all_clusters_by_id):
         for j in all_jobs:
@@ -44,7 +46,7 @@ def _get_cluster_configs_from_all_jobs(all_jobs, all_clusters_by_id):
                     yield j, t.new_cluster
 
 
-class JobsCrawler(CrawlerBase[JobInfo], JobsMixin):
+class JobsCrawler(CrawlerBase[JobInfo], JobsMixin, CheckClusterMixin):
     def __init__(self, ws: WorkspaceClient, sbe: SqlBackend, schema):
         super().__init__(sbe, "hive_metastore", schema, "jobs", JobInfo)
         self._ws = ws
@@ -86,9 +88,8 @@ def _assess_jobs(self, all_jobs: list[BaseJob], all_clusters_by_id) -> Iterable[
             if not job_id:
                 continue
             cluster_details = ClusterDetails.from_dict(cluster_config.as_dict())
-            cluster_failures = self._check_cluster_failures(cluster_details)
-            for failure in json.loads(cluster_failures.failures):
-                job_assessment[job_id].add(failure)
+            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

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

@@ -1,16 +1,15 @@
 import json
+import logging
 from collections.abc import Iterable
 from dataclasses import dataclass
 
 from databricks.sdk import WorkspaceClient
 
-from databricks.labs.ucx.assessment.crawlers import (
-    _AZURE_SP_CONF_FAILURE_MSG,
-    _azure_sp_conf_present_check,
-    logger,
-)
+from databricks.labs.ucx.assessment.clusters import CheckClusterMixin
 from databricks.labs.ucx.framework.crawlers import CrawlerBase, SqlBackend
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class PipelineInfo:
@@ -21,7 +20,7 @@ class PipelineInfo:
     creator_name: str | None = None
 
 
-class PipelinesCrawler(CrawlerBase[PipelineInfo]):
+class PipelinesCrawler(CrawlerBase[PipelineInfo], CheckClusterMixin):
     def __init__(self, ws: WorkspaceClient, sbe: SqlBackend, schema):
         super().__init__(sbe, "hive_metastore", schema, "pipelines", PipelineInfo)
         self._ws = ws
@@ -50,8 +49,7 @@ def _assess_pipelines(self, all_pipelines) -> Iterable[PipelineInfo]:
             assert pipeline_response.spec is not None
             pipeline_config = pipeline_response.spec.configuration
             if pipeline_config:
-                if _azure_sp_conf_present_check(pipeline_config):
-                    failures.append(f"{_AZURE_SP_CONF_FAILURE_MSG} pipeline.")
+                failures.extend(self.check_spark_conf(pipeline_config, "pipeline"))
 
             pipeline_info.failures = json.dumps(failures)
             if len(failures) > 0:

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

@@ -0,0 +1,33 @@
+[
+  {
+    "autoscale": {
+      "max_workers": 6,
+      "min_workers": 1
+    },
+    "cluster_source": "JOB",
+    "creator_user_name":"[email protected]",
+    "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"
+  },
+  {
+    "autoscale": {
+    "max_workers": 6,
+    "min_workers": 1
+    },
+    "creator_user_name":"[email protected]",
+    "cluster_id": "0123-190044-1122334411",
+    "cluster_name": "Single User Cluster Name",
+    "policy_id": "azure-oauth",
+    "spark_version": "13.3.x-cpu-ml-scala2.12",
+    "spark_conf" : {
+    "spark.databricks.delta.preview.enabled": "true"
+    },
+    "spark_context_id":"5134472582179565315"
+  }
+]