added costs as a column to tracking databases

CHANGELOG.md

@@ -13,6 +13,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
+- `MultiBackendJobManager`: costs has been added as a column in tracking databases ([[#588](https://github.com/Open-EO/openeo-python-client/issues/588)])
+
 ### Removed
 
 ### Fixed

openeo/extra/job_management.py

@@ -207,6 +207,7 @@ def start_job(
         "cpu": _ColumnProperties(dtype="str"),
         "memory": _ColumnProperties(dtype="str"),
         "duration": _ColumnProperties(dtype="str"),
+        "costs": _ColumnProperties(dtype="float64"),
     }
 
     def __init__(
@@ -744,6 +745,8 @@ def _track_statuses(self, job_db: JobDatabaseInterface, stats: Optional[dict] =
                 for key in job_metadata.get("usage", {}).keys():
                     if key in active.columns:
                         active.loc[i, key] = _format_usage_stat(job_metadata, key)
+                if "costs" in job_metadata.keys():
+                    active.loc[i, "costs"] = job_metadata.get("costs")
 
             except OpenEoApiError as e:
                 # TODO: inspect status code and e.g. differentiate between 4xx/5xx

openeo/rest/_testing.py

@@ -225,11 +225,19 @@ def _handle_get_job(self, request, context):
             self.batch_jobs[job_id]["status"] = self._get_job_status(
                 job_id=job_id, current_status=self.batch_jobs[job_id]["status"]
             )
-        return {
+        result = {
             # TODO: add some more required fields like "process" and "created"?
             "id": job_id,
             "status": self.batch_jobs[job_id]["status"],
         }
+        if self.batch_jobs[job_id]["status"] == "finished":  # HACK some realistic values for a small job
+            result["costs"] = 123
+            result["usage"] = {
+                "cpu": {"unit": "cpu-seconds", "value": 1234.5},
+                "memory": {"unit": "mb-seconds", "value": 34567.89},
+                "duration": {"unit": "seconds", "value": 2345},
+            }
+        return result
 
     def _handle_get_job_results(self, request, context):
         """Handler of `GET /job/{job_id}/results` (list batch job results)."""

tests/extra/test_job_management.py

@@ -18,6 +18,7 @@
 #   httpretty avoids this specific problem because it mocks at the socket level,
 #   But I would rather not have two dependencies with almost the same goal.
 import httpretty
+import numpy as np
 import pandas
 import pandas as pd
 import pytest
@@ -166,12 +167,15 @@ def test_basic(self, tmp_path, job_manager, job_manager_root_dir, sleep_mock):
             }
         )
 
-        assert [(r.id, r.status, r.backend_name) for r in pd.read_csv(job_db_path).itertuples()] == [
-            ("job-2018", "finished", "foo"),
-            ("job-2019", "finished", "foo"),
-            ("job-2020", "finished", "bar"),
-            ("job-2021", "finished", "bar"),
-            ("job-2022", "finished", "foo"),
+        assert [
+            (r.id, r.status, r.backend_name, r.cpu, r.memory, r.duration, r.costs)
+            for r in pd.read_csv(job_db_path).itertuples()
+        ] == [
+            ("job-2018", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2019", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2020", "finished", "bar", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2021", "finished", "bar", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2022", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
         ]
 
         # Check downloaded results and metadata.
@@ -204,6 +208,10 @@ def test_db_class(self, tmp_path, job_manager, job_manager_root_dir, sleep_mock,
         assert len(result) == 5
         assert set(result.status) == {"finished"}
         assert set(result.backend_name) == {"foo", "bar"}
+        assert set(result.cpu) == {"1234.5 cpu-seconds"}
+        assert set(result.memory) == {"34567.89 mb-seconds"}
+        assert set(result.duration) == {"2345 seconds"}
+        assert set(result.costs) == {123}
 
     @pytest.mark.parametrize(
         ["filename", "expected_db_class"],
@@ -254,12 +262,15 @@ def test_basic_threading(self, tmp_path, job_manager, job_manager_root_dir, slee
         # TODO #645 how to collect stats with the threaded run_job?
         assert sleep_mock.call_count > 10
 
-        assert [(r.id, r.status, r.backend_name) for r in pd.read_csv(job_db_path).itertuples()] == [
-            ("job-2018", "finished", "foo"),
-            ("job-2019", "finished", "foo"),
-            ("job-2020", "finished", "bar"),
-            ("job-2021", "finished", "bar"),
-            ("job-2022", "finished", "foo"),
+        assert [
+            (r.id, r.status, r.backend_name, r.cpu, r.memory, r.duration, r.costs)
+            for r in pd.read_csv(job_db_path).itertuples()
+        ] == [
+            ("job-2018", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2019", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2020", "finished", "bar", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2021", "finished", "bar", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2022", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
         ]
 
         # Check downloaded results and metadata.
@@ -283,6 +294,7 @@ def test_normalize_df(self):
                 "memory",
                 "duration",
                 "backend_name",
+                "costs",
             ]
         )
 
@@ -333,12 +345,15 @@ def start_worker_thread():
         )
 
         # Also check that we got sensible end results in the job db.
-        assert [(r.id, r.status, r.backend_name) for r in pd.read_csv(job_db_path).itertuples()] == [
-            ("job-2018", "finished", "foo"),
-            ("job-2019", "finished", "foo"),
-            ("job-2020", "finished", "bar"),
-            ("job-2021", "finished", "bar"),
-            ("job-2022", "error", "foo"),
+        results = pd.read_csv(job_db_path).replace({np.nan: None})  # np.nan's are replaced by None for easy comparison
+        assert [
+            (r.id, r.status, r.backend_name, r.cpu, r.memory, r.duration, r.costs) for r in results.itertuples()
+        ] == [
+            ("job-2018", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2019", "finished", "foo", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2020", "finished", "bar", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2021", "finished", "bar", "1234.5 cpu-seconds", "34567.89 mb-seconds", "2345 seconds", 123),
+            ("job-2022", "error", "foo", None, None, None, None),
         ]
 
         # Check downloaded results and metadata.
@@ -673,6 +688,7 @@ def test_initialize_from_df(self, tmp_path, db_class):
             "memory",
             "duration",
             "backend_name",
+            "costs",
         }
 
         actual_columns = set(db_class(path).read().columns)
@@ -852,6 +868,7 @@ def test_initialize_from_df(self, tmp_path):
             "memory",
             "duration",
             "backend_name",
+            "costs",
         }
 
         # Raw file content check
@@ -930,6 +947,7 @@ def test_initialize_from_df(self, tmp_path):
             "memory",
             "duration",
             "backend_name",
+            "costs",
         }
 
         df_from_disk = ParquetJobDatabase(path).read()