chore: remove the optional purge_jobs in schedule_jobs

Author: ttngu207

datajoint/autopopulate.py

@@ -528,7 +528,7 @@ def jobs(self):
         return self._Jobs & {"table_name": self.target.table_name}
 
     def schedule_jobs(
-        self, *restrictions, purge_jobs=False, min_scheduling_interval=None
+        self, *restrictions, min_scheduling_interval=None
     ):
         """
         Schedule new jobs for this autopopulate table by finding keys that need computation.
@@ -538,12 +538,8 @@ def schedule_jobs(
         2. If recent scheduling event exists, skips scheduling to prevent database load
         3. Otherwise, finds keys that need computation and schedules them
 
-        The method also optionally purges invalid jobs (jobs that no longer exist in key_source)
-        to maintain database cleanliness.
-
         Args:
             restrictions: a list of restrictions each restrict (table.key_source - target.proj())
-            purge_jobs: if True, remove orphaned jobs from the jobs table (potentially expensive operation)
             min_scheduling_interval: minimum time in seconds that must have passed since last job scheduling.
                 If None, uses the value from dj.config["min_scheduling_interval"] (default: None)
 
@@ -589,11 +585,8 @@ def schedule_jobs(
             logger.info(
                 f"{schedule_count} new jobs scheduled for `{to_camel_case(self.target.table_name)}`"
             )
-        finally:
-            if purge_jobs:
-                self.purge_jobs()
 
-    def purge_jobs(self):
+    def cleanup_jobs(self):
         """
         Check and remove any orphaned/outdated jobs in the JobTable for this autopopulate table.
 

docs/jobs_orchestration.md

@@ -77,11 +77,11 @@ The `populate()` method orchestrates the job execution process:
    - Records errors and execution metrics
 
 4. **Cleanup**:
-   - Optionally purges orphaned/outdated jobs
+   - Optionally clean up orphaned/outdated jobs
 
 ## Job Cleanup Process
 
-The `purge_jobs` method maintains database consistency by removing orphaned jobs:
+The `cleanup_jobs` method maintains database consistency by removing orphaned jobs:
 
 1. **Orphaned Success Jobs**:
    - Identifies jobs marked as `success` but not present in the target table
@@ -107,7 +107,7 @@ The "freshness" and consistency of the jobs table depends on regular maintenance
    - Example: Run every few minutes in a cron job for active pipelines
    - Event-driven approach: `inserts` in upstream tables auto trigger this step
 
-2. **Cleanup** (`purge_jobs`):
+2. **Cleanup** (`cleanup_jobs`):
    - Removes orphaned or outdated jobs
    - Should be run periodically to maintain consistency
    - More resource-intensive than scheduling
@@ -129,5 +129,5 @@ dj.config["min_scheduling_interval"] = 300  # 5 minutes
 # (implement as a cron job or scheduled task)
 def daily_cleanup():
     for table in your_pipeline_tables:
-        table.purge_jobs()
+        table.cleanup_jobs()
 ```