Add WAL for direct deployment state recovery

acceptance/bundle/deploy/wal/chain-10-jobs/databricks.yml

@@ -0,0 +1,117 @@
+bundle:
+  name: wal-chain-test
+
+resources:
+  jobs:
+    # Linear chain: job_01 -> job_02 -> ... -> job_10
+    # Execution order: job_01 first, job_10 last
+    job_01:
+      name: "job-01"
+      description: "first in chain"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_02:
+      name: "job-02"
+      description: "depends on ${resources.jobs.job_01.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_03:
+      name: "job-03"
+      description: "depends on ${resources.jobs.job_02.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_04:
+      name: "job-04"
+      description: "depends on ${resources.jobs.job_03.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_05:
+      name: "job-05"
+      description: "depends on ${resources.jobs.job_04.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_06:
+      name: "job-06"
+      description: "depends on ${resources.jobs.job_05.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_07:
+      name: "job-07"
+      description: "depends on ${resources.jobs.job_06.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_08:
+      name: "job-08"
+      description: "depends on ${resources.jobs.job_07.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_09:
+      name: "job-09"
+      description: "depends on ${resources.jobs.job_08.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    job_10:
+      name: "job-10"
+      description: "depends on ${resources.jobs.job_09.id}"
+      tasks:
+        - task_key: "task"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0

acceptance/bundle/deploy/wal/chain-10-jobs/output.txt

@@ -0,0 +1,73 @@
+=== First deploy (crashes on job_10) ===
+
+>>> errcode [CLI] bundle deploy
+Uploading bundle files to /Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files...
+Deploying resources...
+[PROCESS_KILLED]
+
+Exit code: [KILLED]
+
+=== WAL content after crash ===
+{"lineage":"[UUID]","serial": [SERIAL]}
+{"k":"resources.jobs.job_01","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"first in chain","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-01","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]}}}
+{"k":"resources.jobs.job_02","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-02","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_01","label":"${resources.jobs.job_01.id}"}]}}
+{"k":"resources.jobs.job_03","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-03","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_02","label":"${resources.jobs.job_02.id}"}]}}
+{"k":"resources.jobs.job_04","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-04","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_03","label":"${resources.jobs.job_03.id}"}]}}
+{"k":"resources.jobs.job_05","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-05","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_04","label":"${resources.jobs.job_04.id}"}]}}
+{"k":"resources.jobs.job_06","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-06","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_05","label":"${resources.jobs.job_05.id}"}]}}
+{"k":"resources.jobs.job_07","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-07","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_06","label":"${resources.jobs.job_06.id}"}]}}
+{"k":"resources.jobs.job_08","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-08","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_07","label":"${resources.jobs.job_07.id}"}]}}
+{"k":"resources.jobs.job_09","v":{"__id__": "[ID]","state":{"deployment":{"kind":"BUNDLE","metadata_file_path":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/state/metadata.json"},"description":"depends on 1001","edit_mode":"UI_LOCKED","format":"MULTI_TASK","max_concurrent_runs":1,"name":"job-09","queue":{"enabled":true},"tasks":[{"new_cluster":{"node_type_id":"[NODE_TYPE_ID]","num_workers":0,"spark_version":"15.4.x-scala2.12"},"spark_python_task":{"python_file":"/Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files/test.py"},"task_key":"task"}]},"depends_on":[{"node":"resources.jobs.job_08","label":"${resources.jobs.job_08.id}"}]}}
+
+=== Number of jobs saved in WAL ===
+9
+
+=== Bundle summary (reads from WAL) ===
+Name: wal-chain-test
+Target: default
+Workspace:
+  User: [USERNAME]
+  Path: /Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default
+Resources:
+  Jobs:
+    job_01:
+      Name: job-01
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_02:
+      Name: job-02
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_03:
+      Name: job-03
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_04:
+      Name: job-04
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_05:
+      Name: job-05
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_06:
+      Name: job-06
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_07:
+      Name: job-07
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_08:
+      Name: job-08
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_09:
+      Name: job-09
+      URL:  [DATABRICKS_URL]/jobs/1001?o=[NUMID]
+    job_10:
+      Name: job-10
+      URL:  (not deployed)
+
+=== Second deploy (recovery) ===
+
+>>> [CLI] bundle deploy --force-lock
+Uploading bundle files to /Workspace/Users/[USERNAME]/.bundle/wal-chain-test/default/files...
+Deploying resources...
+Updating deployment state...
+Deployment complete!
+
+=== WAL after successful deploy ===
+WAL deleted (expected)

acceptance/bundle/deploy/wal/chain-10-jobs/script

@@ -0,0 +1,22 @@
+echo "=== First deploy (crashes on job_10) ==="
+trace errcode $CLI bundle deploy
+
+echo ""
+echo "=== WAL content after crash ==="
+cat .databricks/bundle/default/resources.json.wal 2>/dev/null || echo "No WAL file"
+
+echo ""
+echo "=== Number of jobs saved in WAL ==="
+grep -c '"k":"resources.jobs' .databricks/bundle/default/resources.json.wal 2>/dev/null || echo "0"
+
+echo ""
+echo "=== Bundle summary (reads from WAL) ==="
+$CLI bundle summary
+
+echo ""
+echo "=== Second deploy (recovery) ==="
+trace $CLI bundle deploy --force-lock
+
+echo ""
+echo "=== WAL after successful deploy ==="
+cat .databricks/bundle/default/resources.json.wal 2>/dev/null || echo "WAL deleted (expected)"

acceptance/bundle/deploy/wal/chain-10-jobs/test.py

@@ -0,0 +1,2 @@
+# Placeholder for Spark task
+print("Hello from test job")

acceptance/bundle/deploy/wal/chain-10-jobs/test.toml

@@ -0,0 +1,21 @@
+# Linear chain: job_01 -> job_02 -> ... -> job_10
+# Let first 9 jobs/create succeed, then kill on the 10th
+
+[[Server]]
+Pattern = "POST /api/2.2/jobs/create"
+KillCallerOffset = 9
+KillCaller = 1
+Response.Body = '{"job_id": 1001}'
+
+[[Server]]
+Pattern = "POST /api/2.2/jobs/reset"
+Response.Body = '{}'
+
+[[Server]]
+Pattern = "GET /api/2.2/jobs/get"
+Response.Body = '{"job_id": 1001, "settings": {"name": "test-job"}}'
+
+# Strip single-node cluster warnings
+[[Repls]]
+Old = '(?s)Warning: Single node cluster is not correctly configured.*?ResourceClass: SingleNode\n  \n\n'
+New = ''

acceptance/bundle/deploy/wal/corrupted-wal-entry/databricks.yml

@@ -0,0 +1,25 @@
+bundle:
+  name: wal-corrupted-test
+
+resources:
+  jobs:
+    valid_job:
+      name: "valid-job"
+      tasks:
+        - task_key: "task-a"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0
+    another_valid:
+      name: "another-valid"
+      tasks:
+        - task_key: "task-b"
+          spark_python_task:
+            python_file: ./test.py
+          new_cluster:
+            spark_version: 15.4.x-scala2.12
+            node_type_id: i3.xlarge
+            num_workers: 0

acceptance/bundle/deploy/wal/corrupted-wal-entry/output.txt

@@ -0,0 +1,56 @@
+=== Creating state file with serial 5 ===
+=== Creating WAL with corrupted entry ===
+=== WAL content ===
+{"lineage":"test-lineage-123","serial": [SERIAL]}
+{"k":"resources.jobs.valid_job","v":{"__id__": "[ID]","state":{"name":"valid-job"}}}
+not valid json - this line should be skipped
+{"k":"resources.jobs.another_valid","v":{"__id__": "[ID]","state":{"name":"another-valid"}}}
+=== Deploy (should recover valid entries, skip corrupted) ===
+
+>>> [CLI] bundle deploy
+Warning: Single node cluster is not correctly configured
+  at resources.jobs.another_valid.tasks[0].new_cluster
+  in databricks.yml:23:13
+
+num_workers should be 0 only for single-node clusters. To create a
+valid single node cluster please ensure that the following properties
+are correctly set in the cluster specification:
+
+  spark_conf:
+    spark.databricks.cluster.profile: singleNode
+    spark.master: local[*]
+
+  custom_tags:
+    ResourceClass: SingleNode
+
+
+Warning: Single node cluster is not correctly configured
+  at resources.jobs.valid_job.tasks[0].new_cluster
+  in databricks.yml:13:13
+
+num_workers should be 0 only for single-node clusters. To create a
+valid single node cluster please ensure that the following properties
+are correctly set in the cluster specification:
+
+  spark_conf:
+    spark.databricks.cluster.profile: singleNode
+    spark.master: local[*]
+
+  custom_tags:
+    ResourceClass: SingleNode
+
+
+Uploading bundle files to /Workspace/Users/[USERNAME]/.bundle/wal-corrupted-test/default/files...
+Deploying resources...
+Updating deployment state...
+Deployment complete!
+=== Final state (should have recovered entries) ===
+{
+  "serial": [SERIAL],
+  "state_keys": [
+    "resources.jobs.another_valid",
+    "resources.jobs.valid_job"
+  ]
+}
+=== WAL after successful deploy ===
+WAL deleted (expected)

acceptance/bundle/deploy/wal/corrupted-wal-entry/script

@@ -0,0 +1,35 @@
+echo "=== Creating state file with serial 5 ==="
+mkdir -p .databricks/bundle/default
+cat > .databricks/bundle/default/resources.json << 'EOF'
+{
+  "state_version": 1,
+  "cli_version": "0.0.0",
+  "lineage": "test-lineage-123",
+  "serial": 5,
+  "state": {}
+}
+EOF
+
+echo "=== Creating WAL with corrupted entry ==="
+cat > .databricks/bundle/default/resources.json.wal << 'EOF'
+{"lineage":"test-lineage-123","serial":6}
+{"k":"resources.jobs.valid_job","v":{"__id__":"1111","state":{"name":"valid-job"}}}
+not valid json - this line should be skipped
+{"k":"resources.jobs.another_valid","v":{"__id__":"2222","state":{"name":"another-valid"}}}
+EOF
+
+echo "=== WAL content ==="
+cat .databricks/bundle/default/resources.json.wal
+
+echo "=== Deploy (should recover valid entries, skip corrupted) ==="
+trace $CLI bundle deploy 2>&1 | python3 sort_warnings.py
+
+echo "=== Final state (should have recovered entries) ==="
+cat .databricks/bundle/default/resources.json | jq -S '{serial: .serial, state_keys: (.state | keys | sort)}'
+
+echo "=== WAL after successful deploy ==="
+if [ -f ".databricks/bundle/default/resources.json.wal" ]; then
+    echo "WAL exists (unexpected)"
+else
+    echo "WAL deleted (expected)"
+fi