first version of output thread

openeo/extra/job_management/__init__.py

@@ -544,28 +544,20 @@ def _output_queue_worker(self, df, job_db):
                     print(f"Failed to load dataframe from job_db: {e}", flush=True)
                     return
 
+
             while True:
                 item = self.job_output_queue.get(timeout=1)
                 if item is None:
-                    print("Output worker received shutdown signal.", flush=True)
-                    self.job_output_queue.task_done()
                     break
-
-                i, status = item
-                print(f"Output worker processing row {i}: '{status}'", flush=True)
-
+                i, updates = item  # Now expecting a dictionary of updates
+                print(f"Processing updates for row {i}: {updates}", flush=True)
                 with self.df_lock:
-                    # Update the dataframe
-                    df.loc[i, "status"] = status
-                    print(f"Updated dataframe row {i}: {df.loc[i].to_dict()}", flush=True)
-                    # Persist the updated row to the job database
+                    for col, value in updates.items():
+                        df.loc[i, col] = value
                     job_db.persist(df.loc[[i]])
-
-                print(f"Persisted row {i} to job database.", flush=True)
                 self.job_output_queue.task_done()
-
         except Exception as e:
-            print(f"Output worker crashed: {e}", flush=True)
+            print(f"Output worker error: {e}", flush=True)
 
     def _job_update_loop(
         self, job_db: JobDatabaseInterface, start_job: Callable[[], BatchJob], stats: Optional[dict] = None
@@ -607,7 +599,6 @@ def _job_update_loop(
                     with self.df_lock:
                         job_db.persist(not_started.loc[i : i + 1])
                     stats["job_db persist"] += 1
-                    total_added += 1
 
                 except Exception as e:
                     _log.error(f"Job launch failed for index {i}: {e}")
@@ -710,7 +701,7 @@ def _launch_job(self, i, connection, job_id, stats):
                     stats["job start error"] += 1
 
         # Instead of updating the dataframe, push the outcome to the output queue.
-        self.job_output_queue.put((i, status))
+        self.job_output_queue.put((i, {'status': status, 'id': job_id}))
 
 
     def on_job_done(self, job: BatchJob, row):
@@ -724,6 +715,23 @@ def on_job_done(self, job: BatchJob, row):
         """
         # TODO: param `row` is never accessed in this method. Remove it? Is this intended for future use?
 
+
+        # final metric update to queue
+        updates = {
+        'status': 'finished',
+        'cpu': row.get('cpu'),
+        'memory': row.get('memory'),
+        'duration': row.get('duration'),
+        'costs': row.get('costs')
+        }
+
+        with self.df_lock:
+            for key, value in updates.items():
+                row[key] = value
+        # Send updates to the output queue
+        self.job_output_queue.put((row.name, updates))
+
+        #download the data
         job_metadata = job.describe()
         job_dir = self.get_job_dir(job.job_id)
         metadata_path = self.get_job_metadata_path(job.job_id)