Hv issue719 job manager threaded job start

openeo/extra/job_management/__init__.py

@@ -32,12 +32,16 @@
 from requests.adapters import HTTPAdapter, Retry
 
 from openeo import BatchJob, Connection
+from openeo.extra.job_management._thread_worker import ( _JobManagerWorkerThreadPool,
+                                                         _JobStartTask)
+
 from openeo.internal.processes.parse import (
     Parameter,
     Process,
     parse_remote_process_definition,
 )
 from openeo.rest import OpenEoApiError
+from openeo.rest.auth.auth import BearerAuth
 from openeo.util import LazyLoadCache, deep_get, repr_truncate, rfc3339
 
 _log = logging.getLogger(__name__)
@@ -105,6 +109,7 @@ def get_by_status(self, statuses: List[str], max=None) -> pd.DataFrame:
         """
         ...
 
+
 def _start_job_default(row: pd.Series, connection: Connection, *args, **kwargs):
     raise NotImplementedError("No 'start_job' callable provided")
 
@@ -186,6 +191,7 @@ def start_job(
 
     # Expected columns in the job DB dataframes.
     # TODO: make this part of public API when settled?
+    # TODO: move non official statuses to seperate column (not_started, queued_for_start)
     _COLUMN_REQUIREMENTS: Mapping[str, _ColumnProperties] = {
         "id": _ColumnProperties(dtype="str"),
         "backend_name": _ColumnProperties(dtype="str"),
@@ -222,6 +228,7 @@ def __init__(
             datetime.timedelta(seconds=cancel_running_job_after) if cancel_running_job_after is not None else None
         )
         self._thread = None
+        self._worker_pool = None
 
     def add_backend(
         self,
@@ -358,21 +365,27 @@ def start_job_thread(self, start_job: Callable[[], BatchJob], job_db: JobDatabas
         _log.info(f"Resuming `run_jobs` from existing {job_db}")
 
         self._stop_thread = False
+        self._worker_pool = _JobManagerWorkerThreadPool()
 
         def run_loop():
 
             # TODO: support user-provided `stats`
             stats = collections.defaultdict(int)
 
             while (
-                sum(job_db.count_by_status(statuses=["not_started", "created", "queued", "running"]).values()) > 0
+                sum(
+                    job_db.count_by_status(
+                        statuses=["not_started", "created", "queued", "queued_for_start", "running"]
+                    ).values()
+                )
+                > 0
                 and not self._stop_thread
             ):
-                self._job_update_loop(job_db=job_db, start_job=start_job)
+                self._job_update_loop(job_db=job_db, start_job=start_job, stats=stats)
                 stats["run_jobs loop"] += 1
 
+                # Show current stats and sleep
                 _log.info(f"Job status histogram: {job_db.count_by_status()}. Run stats: {dict(stats)}")
-                # Do sequence of micro-sleeps to allow for quick thread exit
                 for _ in range(int(max(1, self.poll_sleep))):
                     time.sleep(1)
                     if self._stop_thread:
@@ -391,6 +404,8 @@ def stop_job_thread(self, timeout_seconds: Optional[float] = _UNSET):
 
         .. versionadded:: 0.32.0
         """
+        self._worker_pool.shutdown()
+
         if self._thread is not None:
             self._stop_thread = True
             if timeout_seconds is _UNSET:
@@ -493,7 +508,16 @@ def run_jobs(
         # TODO: support user-provided `stats`
         stats = collections.defaultdict(int)
 
-        while sum(job_db.count_by_status(statuses=["not_started", "created", "queued", "running"]).values()) > 0:
+        self._worker_pool = _JobManagerWorkerThreadPool()
+
+        while (
+            sum(
+                job_db.count_by_status(
+                    statuses=["not_started", "created", "queued_for_start", "queued", "running"]
+                ).values()
+            )
+            > 0
+        ):
             self._job_update_loop(job_db=job_db, start_job=start_job, stats=stats)
             stats["run_jobs loop"] += 1
 
@@ -502,6 +526,10 @@ def run_jobs(
             time.sleep(self.poll_sleep)
             stats["sleep"] += 1
 
+
+        # TODO; run post process after shutdown once more to ensure completion? 
+        self._worker_pool.shutdown()
+
         return stats
 
     def _job_update_loop(
@@ -524,7 +552,7 @@ def _job_update_loop(
         not_started = job_db.get_by_status(statuses=["not_started"], max=200).copy()
         if len(not_started) > 0:
             # Check number of jobs running at each backend
-            running = job_db.get_by_status(statuses=["created", "queued", "running"])
+            running = job_db.get_by_status(statuses=["created", "queued", "queued_for_start", "running"])
             stats["job_db get_by_status"] += 1
             per_backend = running.groupby("backend_name").size().to_dict()
             _log.info(f"Running per backend: {per_backend}")
@@ -541,7 +569,9 @@ def _job_update_loop(
                         stats["job_db persist"] += 1
                         total_added += 1
 
-        # Act on jobs
+        self._process_threadworker_updates(self._worker_pool, job_db, stats)
+
+        # TODO: move this back closer to the `_track_statuses` call above, once job done/error handling is also handled in threads?
         for job, row in jobs_done:
             self.on_job_done(job, row)
 
@@ -551,7 +581,6 @@ def _job_update_loop(
         for job, row in jobs_cancel:
             self.on_job_cancel(job, row)
 
-
     def _launch_job(self, start_job, df, i, backend_name, stats: Optional[dict] = None):
         """Helper method for launching jobs
 
@@ -598,26 +627,92 @@ def _launch_job(self, start_job, df, i, backend_name, stats: Optional[dict] = No
             df.loc[i, "start_time"] = rfc3339.utcnow()
             if job:
                 df.loc[i, "id"] = job.job_id
+                _log.info(f"Job created: {job.job_id}")
                 with ignore_connection_errors(context="get status"):
                     status = job.status()
                     stats["job get status"] += 1
                     df.loc[i, "status"] = status
                     if status == "created":
                         # start job if not yet done by callback
                         try:
-                            job.start()
-                            stats["job start"] += 1
-                            df.loc[i, "status"] = job.status()
-                            stats["job get status"] += 1
+                            job_con = job.connection
+                            task = _JobStartTask(
+                                root_url=job_con.root_url,
+                                bearer_token=job_con.auth.bearer if isinstance(job_con.auth, BearerAuth) else None,
+                                job_id=job.job_id,
+                            )
+                            _log.info(f"Submitting task {task} to thread pool")
+                            self._worker_pool.submit_task(task)
+
+                            stats["job_queued_for_start"] += 1
+                            df.loc[i, "status"] = "queued_for_start"
                         except OpenEoApiError as e:
-                            _log.error(e)
-                            df.loc[i, "status"] = "start_failed"
-                            stats["job start error"] += 1
+                            _log.info(f"Failed submitting task {task} to thread pool with error: {e}")
+                            df.loc[i, "status"] = "queued_for_start_failed"
+                            stats["job queued for start failed"] += 1
             else:
                 # TODO: what is this "skipping" about actually?
                 df.loc[i, "status"] = "skipped"
                 stats["start_job skipped"] += 1
 
+    def _process_threadworker_updates(
+        self,
+        worker_pool: _JobManagerWorkerThreadPool,
+        job_db: JobDatabaseInterface,
+        stats: dict
+    ) -> None:
+        """Processes asynchronous job updates from worker threads and applies them to the job database and statistics.
+
+        This wrapper function is responsible for:
+        1. Collecting completed results from the worker thread pool
+        2. applying database updates for each job result
+        3. applying statistics updates
+        4. Handles errors with comprehensive logging
+
+        :param worker_pool:
+            Thread pool instance managing the asynchronous job operations.
+            Should provide a `process_futures()` method returning completed job results.
+
+        :param job_db:
+            Job database implementing the :py:class:`JobDatabaseInterface` interface.
+            Used to persist job status updates and metadata.
+            Must support the `_update_row(job_id: str, updates: dict)` method.
+
+        :param stats:
+            Dictionary tracking operational statistics that will be updated in-place.
+            Expected to handle string keys with integer values.
+            Statistics will be updated with counts from completed job results.
+
+        :return: 
+            None: All updates are applied in-place to the job_db and stats parameters.
+.
+        """
+        results = worker_pool.process_futures()
+        stats_updates = collections.defaultdict(int)
+
+        for result in results: 
+            try:
+                # Handle job database updates
+                if result.db_update:
+                    _log.debug(f"Processing update for job {result.job_id}")
+                    job_db._update_row(job_id=result.job_id, updates=result.db_update)
+
+                # Aggregate statistics updates
+                if result.stats_update:
+                    for key, count in result.stats_update.items():
+                        stats_updates[key] += int(count)
+
+
+            except Exception as e:
+                _log.error(
+                    f"Failed aggregating the updates for update for job {result.job_id}: {str(e)}")
+
+        # Apply all stat updates
+        for key, count in stats_updates.items():
+            stats[key] = stats.get(key, 0) + count
+
+
+
     def on_job_done(self, job: BatchJob, row):
         """
         Handles jobs that have finished. Can be overridden to provide custom behaviour.
@@ -673,20 +768,19 @@ def _cancel_prolonged_job(self, job: BatchJob, row):
         try:
             # Ensure running start time is valid
             job_running_start_time = rfc3339.parse_datetime(row.get("running_start_time"), with_timezone=True)
-            
+
             # Parse the current time into a datetime object with timezone info
             current_time = rfc3339.parse_datetime(rfc3339.utcnow(), with_timezone=True)
 
             # Calculate the elapsed time between job start and now
             elapsed = current_time - job_running_start_time
 
             if elapsed > self._cancel_running_job_after:
-
                 _log.info(
                     f"Cancelling long-running job {job.job_id} (after {elapsed}, running since {job_running_start_time})"
                 )
                 job.stop()
-                
+
         except Exception as e:
             _log.error(f"Unexpected error while handling job {job.job_id}: {e}")
 
@@ -715,7 +809,7 @@ def _track_statuses(self, job_db: JobDatabaseInterface, stats: Optional[dict] =
         """
         stats = stats if stats is not None else collections.defaultdict(int)
 
-        active = job_db.get_by_status(statuses=["created", "queued", "running"]).copy()
+        active = job_db.get_by_status(statuses=["created", "queued", "queued_for_start", "running"]).copy()
 
         jobs_done = []
         jobs_error = []
@@ -749,7 +843,7 @@ def _track_statuses(self, job_db: JobDatabaseInterface, stats: Optional[dict] =
                     stats["job canceled"] += 1
                     jobs_cancel.append((the_job, active.loc[i]))
 
-                if previous_status in {"created", "queued"} and new_status == "running":
+                if previous_status in {"created", "queued", "queued_for_start"} and new_status == "running":
                     stats["job started running"] += 1
                     active.loc[i, "running_start_time"] = rfc3339.utcnow()
 
@@ -782,7 +876,6 @@ def _track_statuses(self, job_db: JobDatabaseInterface, stats: Optional[dict] =
 
         return jobs_done, jobs_error, jobs_cancel
 
-
 def _format_usage_stat(job_metadata: dict, field: str) -> str:
     value = deep_get(job_metadata, "usage", field, "value", default=0)
     unit = deep_get(job_metadata, "usage", field, "unit", default="")
@@ -877,6 +970,55 @@ def _merge_into_df(self, df: pd.DataFrame):
         else:
             self._df = df
 
+    def _update_row(self, job_id: str, updates: dict):
+        """
+        Propagates dataframe updates provided in a dictionary to the row relevant for said job_id.
+
+        :param job_id: a job_id.
+        :param updates: a dictionary containing status updates.
+
+        :return: DataFrame with jobs filtered by status.
+        """
+        if self._df is None:
+            raise ValueError("Job database not initialized")
+
+        # Create boolean mask for target row
+        mask = self._df["id"] == job_id
+        match_count = mask.sum()
+
+        # Handle row identification issues
+        #TODO: make this more robust, e.g. falling back on the row index?
+        if match_count == 0:
+            _log.error(f"Job {job_id!r} not found in database")
+            return
+        if match_count > 1:
+            _log.error(f"Duplicate job ID {job_id!r} found in database")
+            return
+
+        # Get valid columns 
+        valid_columns = set(self._df.columns)  
+        filtered_updates = {}
+
+        # Validate update keys s
+        for key, value in updates.items():
+            if key in valid_columns:
+                filtered_updates[key] = value
+            else:
+                _log.warning(f"Ignoring invalid column {key!r} in update for job {job_id}")
+
+        # Bulk update 
+        if  not filtered_updates:
+            return
+        try:
+            # Update all columns in a single operation
+            self._df.loc[mask, list(filtered_updates.keys())] = list(filtered_updates.values())
+            self.persist(self._df)
+        except Exception as e:
+            _log.error(f"Failed to persist row update for job {job_id}: {e}")
+
+
+
+
 
 class CsvJobDatabase(FullDataFrameJobDatabase):
     """
@@ -932,6 +1074,8 @@ def persist(self, df: pd.DataFrame):
         self.path.parent.mkdir(parents=True, exist_ok=True)
         self.df.to_csv(self.path, index=False)
 
+
+
 
 class ParquetJobDatabase(FullDataFrameJobDatabase):
     """