Issue #656: make sure CsvJobDatabase uses expected dtypes

CHANGELOG.md

@@ -20,6 +20,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `MultiBackendJobManager`: Fix issue with duplicate job starting across multiple backends ([#654](https://github.com/Open-EO/openeo-python-client/pull/654))
 - `MultiBackendJobManager`: Fix encoding issue of job metadata in `on_job_done` ([#657](https://github.com/Open-EO/openeo-python-client/issues/657))
 - `MultiBackendJobManager`: Avoid `SettingWithCopyWarning` ([#641](https://github.com/Open-EO/openeo-python-client/issues/641))
+- `MultiBackendJobManager`: avoid dtype loading mistakes in `CsvJobDatabase` on empty columns ([#656](https://github.com/Open-EO/openeo-python-client/issues/656))
 
 
 ## [0.34.0] - 2024-10-31

openeo/extra/job_management.py

@@ -1,6 +1,7 @@
 import abc
 import collections
 import contextlib
+import dataclasses
 import datetime
 import json
 import logging
@@ -9,7 +10,7 @@
 import warnings
 from pathlib import Path
 from threading import Thread
-from typing import Callable, Dict, List, NamedTuple, Optional, Union
+from typing import Any, Callable, Dict, List, Mapping, NamedTuple, Optional, Union
 
 import numpy
 import pandas as pd
@@ -104,6 +105,14 @@ def _start_job_default(row: pd.Series, connection: Connection, *args, **kwargs):
     raise NotImplementedError("No 'start_job' callable provided")
 
 
+@dataclasses.dataclass(frozen=True)
+class _ColumnProperties:
+    """Expected/required properties of a column in the job manager related dataframes"""
+
+    dtype: str = "object"
+    default: Any = None
+
+
 class MultiBackendJobManager:
     """
     Tracker for multiple jobs on multiple backends.
@@ -171,6 +180,24 @@ def start_job(
         Added ``cancel_running_job_after`` parameter.
     """
 
+    # Expected columns in the job DB dataframes.
+    # Mapping of column name to (dtype, default value)
+    # TODO: make this part of public API when settled?
+    _COLUMN_REQUIREMENTS: Mapping[str, _ColumnProperties] = {
+        "id": _ColumnProperties(dtype="str"),
+        "backend_name": _ColumnProperties(dtype="str"),
+        "status": _ColumnProperties(dtype="str", default="not_started"),
+        # TODO: use proper date/time dtype instead of leagacy str for start times?
+        "start_time": _ColumnProperties(dtype="str"),
+        "running_start_time": _ColumnProperties(dtype="str"),
+        # TODO: these columns "cpu", "memory", "duration" are not referenced explicitly from MultiBackendJobManager,
+        #       but are indirectly coupled through handling of VITO-specific "usage" metadata in `_track_statuses`.
+        #       Since bfd99e34 they are not really required to be present anymore, can we make that more explicit?
+        "cpu": _ColumnProperties(dtype="str"),
+        "memory": _ColumnProperties(dtype="str"),
+        "duration": _ColumnProperties(dtype="str"),
+    }
+
     def __init__(
         self,
         poll_sleep: int = 60,
@@ -267,31 +294,16 @@ def _make_resilient(connection):
         connection.session.mount("https://", HTTPAdapter(max_retries=retries))
         connection.session.mount("http://", HTTPAdapter(max_retries=retries))
 
-    @staticmethod
-    def _normalize_df(df: pd.DataFrame) -> pd.DataFrame:
+    @classmethod
+    def _normalize_df(cls, df: pd.DataFrame) -> pd.DataFrame:
         """
         Normalize given pandas dataframe (creating a new one):
         ensure we have the required columns.
 
         :param df: The dataframe to normalize.
         :return: a new dataframe that is normalized.
         """
-        # check for some required columns.
-        required_with_default = [
-            ("status", "not_started"),
-            ("id", None),
-            ("start_time", None),
-            ("running_start_time", None),
-            # TODO: columns "cpu", "memory", "duration" are not referenced directly
-            #       within MultiBackendJobManager making it confusing to claim they are required.
-            #       However, they are through assumptions about job "usage" metadata in `_track_statuses`.
-            #       => proposed solution: allow to configure usage columns when adding a backend
-            ("cpu", None),
-            ("memory", None),
-            ("duration", None),
-            ("backend_name", None),
-        ]
-        new_columns = {col: val for (col, val) in required_with_default if col not in df.columns}
+        new_columns = {col: req.default for (col, req) in cls._COLUMN_REQUIREMENTS.items() if col not in df.columns}
         df = df.assign(**new_columns)
 
         return df
@@ -832,7 +844,10 @@ def _is_valid_wkt(self, wkt: str) -> bool:
             return False
 
     def read(self) -> pd.DataFrame:
-        df = pd.read_csv(self.path)
+        df = pd.read_csv(
+            self.path,
+            dtype={c: r.dtype for (c, r) in MultiBackendJobManager._COLUMN_REQUIREMENTS.items()},
+        )
         if (
             "geometry" in df.columns
             and df["geometry"].dtype.name != "geometry"

tests/extra/test_job_management.py

@@ -702,6 +702,37 @@ def test_automatic_cancel_of_too_long_running_jobs(
 
         assert fake_backend.cancel_job_mock.called == (expected_status == "canceled")
 
+    def test_empty_csv_handling(self, tmp_path, requests_mock, sleep_mock, recwarn):
+        """
+        Check how starting from an empty CSV is handled: will columns accepts string values without warning/error?
+        """
+        manager = self._create_basic_mocked_manager(requests_mock, tmp_path)
+
+        df = pd.DataFrame({"year": [2021, 2022]})
+
+        job_db_path = tmp_path / "jobs.csv"
+        # Initialize job db and trigger writing it to CSV file
+        _ = CsvJobDatabase(job_db_path).initialize_from_df(df)
+
+        assert job_db_path.exists()
+        # Simple check for empty columns in the CSV file
+        assert ",,,,," in job_db_path.read_text()
+
+        # Start over with existing file
+        job_db = CsvJobDatabase(job_db_path)
+
+        def start_job(row, connection, **kwargs):
+            year = int(row["year"])
+            return BatchJob(job_id=f"job-{year}", connection=connection)
+
+        run_stats = manager.run_jobs(job_db=job_db, start_job=start_job)
+        assert run_stats == dirty_equals.IsPartialDict({"start_job call": 2, "job finished": 2})
+
+        result = pd.read_csv(job_db_path)
+        assert [(r.id, r.status) for r in result.itertuples()] == [("job-2021", "finished"), ("job-2022", "finished")]
+
+        assert [(w.category, w.message, str(w)) for w in recwarn.list] == []
+