Intermittent bug fixes

spatialprofilingtoolbox/db/importance_score_transcriber.py

@@ -133,8 +133,6 @@ def _upload(
             get_feature_description("gnn importance score"),
             len(importance_score_set_indexer),
         ),
-        impute_zeros=True,
-        upload_anyway=True,
     ) as feature_uploader:
         for histological_structure, row in df.iterrows():
             feature_uploader.stage_feature_value(

spatialprofilingtoolbox/db/source_file_parser_interface.py

@@ -53,7 +53,7 @@ def get_field_names(self, tablename):
 
     def generate_basic_insert_query(self, tablename):
         fields_sorted = self.get_field_names(tablename)
-        if tablename == 'quantitative_feature_value':
+        if tablename in ('quantitative_feature_value', 'feature_specification'):
             fields_sorted = fields_sorted[1:]
         handle_duplicates = 'ON CONFLICT DO NOTHING '
         query = (

spatialprofilingtoolbox/ondemand/providers/pending_provider.py

@@ -94,7 +94,7 @@ def _warn_no_value(self) -> None:
         specification = str(self.job.feature_specification)
         study = self.job.study
         sample = self.job.sample
-        logger.warning(f'Feature {specification} ({sample}, {study}) could not be computed, worker generated None.')
+        logger.warning(f'Feature {specification} ({sample}, {study}) could not be computed, worker generated None. May insert None.')
 
     def _insert_value(self, value: float | int) -> None:
         study = self.job.study

spatialprofilingtoolbox/ondemand/request_scheduling.py

@@ -2,10 +2,10 @@
 
 from typing import cast
 from typing import Callable
+import signal
 
 from psycopg import Connection as PsycopgConnection
 
-from spatialprofilingtoolbox.ondemand.job_reference import ComputationJobReference
 from spatialprofilingtoolbox.db.database_connection import DBConnection
 from spatialprofilingtoolbox.db.database_connection import DBCursor
 from spatialprofilingtoolbox.ondemand.providers.counts_provider import CountsProvider
@@ -18,7 +18,8 @@
     CompositePhenotype,
     UnivariateMetricsComputationResult,
 )
-from spatialprofilingtoolbox.ondemand.relevant_specimens import relevant_specimens_query
+from spatialprofilingtoolbox.ondemand.timeout import create_timeout_handler
+from spatialprofilingtoolbox.ondemand.timeout import SPTTimeoutError
 from spatialprofilingtoolbox.standalone_utilities.log_formats import colorized_logger
 Metrics1D = UnivariateMetricsComputationResult
 
@@ -40,6 +41,51 @@ def _nonempty(string: str) -> bool:
     return string != ''
 
 
+class FeatureComputationTimeoutHandler:
+    feature: str
+    study: str
+
+    def __init__(self, feature: str, study: str):
+        self.feature = feature
+        self.study = study
+
+    def handle(self) -> None:
+        message = f'Timed out waiting for the feature {self.feature} to complete. Aborting.'
+        logger.error(message)
+        if self._queue_size() == 0 and self._completed_size() < self._expected_size():
+            self._delete_feature()
+
+    def _queue_size(self) -> int:
+        with DBCursor(study=self.study) as cursor:
+            query = 'SELECT COUNT(*) FROM quantitative_feature_value_queue WHERE feature=%s ;'
+            cursor.execute(query, (self.feature,))
+            count = tuple(cursor.fetchall())[0][0]
+        return count
+
+    def _completed_size(self) -> int:
+        with DBCursor(study=self.study) as cursor:
+            query = 'SELECT COUNT(*) FROM quantitative_feature_value WHERE feature=%s ;'
+            cursor.execute(query, (self.feature,))
+            count = tuple(cursor.fetchall())[0][0]
+        return count
+
+    def _expected_size(self) -> int:
+        with DBCursor(study=self.study) as cursor:
+            query = 'SELECT COUNT(*) FROM specimen_data_measurement_process ;'
+            cursor.execute(query)
+            count = tuple(cursor.fetchall())[0][0]
+        return count
+
+    def _delete_feature(self) -> None:
+        logger.error('Also deleting the feature, since the queue was empty; we assume the remaining jobs failed.')
+        with DBCursor(study=self.study) as cursor:
+            param = (self.feature,)
+            cursor.execute('DELETE FROM quantitative_feature_value WHERE feature=%s ;', param)
+            cursor.execute('DELETE FROM feature_specifier WHERE feature_specification=%s ;', param)
+            cursor.execute('DELETE FROM feature_specification WHERE identifier=%s ;', param)
+
+
+
 class OnDemandRequester:
     """Entry point for requesting computation by the on-demand service."""
 
@@ -89,21 +135,14 @@ def _counts(
     ) -> tuple[str, Metrics1D, Metrics1D]:
         get = CountsProvider.get_metrics_or_schedule
 
-        def get_results() -> tuple[Metrics1D, str]:
+        def get_results1() -> tuple[Metrics1D, str]:
             counts, feature1 = get(
                 study_name,
                 phenotype=phenotype,
                 cells_selected=selected,
             )
             return (counts, feature1)
 
-        with DBConnection() as connection:
-            connection._set_autocommit(True)
-            connection.execute('LISTEN new_items_in_queue ;')
-            connection.execute('LISTEN one_job_complete ;')
-            cls._wait_for_wrappedup(connection, get_results)
-            counts, feature1 =  get_results()
-
         def get_results2() -> tuple[Metrics1D, str]:
             counts_all, feature2 = get(
                 study_name,
@@ -114,44 +153,49 @@ def get_results2() -> tuple[Metrics1D, str]:
 
         with DBConnection() as connection:
             connection._set_autocommit(True)
-            connection.execute('LISTEN new_items_in_queue ;')
-            connection.execute('LISTEN one_job_complete ;')
-            cls._wait_for_wrappedup(connection, get_results2)
-            counts_all, _ =  get_results2()
-
-        cls._request_check_for_failed_jobs()
-        return (feature1, counts, counts_all)
+            cls._wait_for_wrappedup(connection, get_results1, study_name)
+            counts, feature1 =  get_results1()
 
-    @classmethod
-    def _request_check_for_failed_jobs(cls) -> None:
         with DBConnection() as connection:
             connection._set_autocommit(True)
-            connection.execute('NOTIFY check_for_failed_jobs ;')
+            cls._wait_for_wrappedup(connection, get_results2, study_name)
+            counts_all, _ =  get_results2()
+
+        return (feature1, counts, counts_all)
 
     @classmethod
     def _wait_for_wrappedup(
         cls,
         connection: PsycopgConnection,
         get_results: Callable[[], tuple[Metrics1D, str]],
+        study_name: str,
     ) -> None:
-        counts, feature = get_results()
-        if not get_results()[0].is_pending:
-            logger.debug(f'Feature {feature} already complete.')
-            return
-        logger.debug(f'Waiting for signal that feature {feature} may be ready.')
+        connection.execute('LISTEN new_items_in_queue ;')
+        connection.execute('LISTEN one_job_complete ;')
         notifications = connection.notifies()
-        for notification in notifications:
-            if not get_results()[0].is_pending:
-                logger.debug(f'Closing notification processing, {feature} ready.')
-                notifications.close()
-                break
-            channel = notification.channel
-            if channel == 'one_job_complete':
-                logger.debug(f'A job is complete, so {feature} may be ready.')
-                if not get_results()[0].is_pending:
-                    logger.debug(f'And {feature} was ready. Closing.')
+
+        counts, feature = get_results()
+        handler = FeatureComputationTimeoutHandler(feature, study_name)
+        generic_handler = create_timeout_handler(handler.handle)
+        try:
+            if not counts.is_pending:
+                logger.debug(f'Feature {feature} already complete.')
+                return
+            logger.debug(f'Waiting for signal that feature {feature} may be ready, because the result is not ready yet.')
+
+            for notification in notifications:
+                channel = notification.channel
+                if channel == 'one_job_complete':
+                    logger.debug(f'A job is complete, so {feature} may be ready. (PID: {notification.pid})')
+                _result = get_results()
+                if not _result[0].is_pending:
+                    logger.debug(f'Closing notification processing, {feature} ready.')
                     notifications.close()
                     break
+        except SPTTimeoutError:
+            pass
+        finally:
+            generic_handler.disalarm()
 
     @classmethod
     def get_proximity_metrics(

spatialprofilingtoolbox/ondemand/timeout.py

@@ -0,0 +1,45 @@
+"""General-purpose one-time timeout functionality based on Unix signal alarm."""
+from typing import Callable
+import signal
+
+from spatialprofilingtoolbox.db.database_connection import DBCursor
+from spatialprofilingtoolbox.standalone_utilities.log_formats import colorized_logger
+
+logger = colorized_logger(__name__)
+
+
+TIMEOUT_SECONDS_DEFAULT = 300
+
+
+class SPTTimeoutError(RuntimeError):
+    def __init__(self, message: str):
+        super().__init__(message)
+        self.message = message
+
+
+class TimeoutHandler:
+    active: bool
+    callback: Callable
+    timeout: int
+
+    def __init__(self, callback: Callable, timeout: int):
+        self.active = True
+        self.callback = callback
+        self.timeout = timeout
+
+    def handle(self, signum, frame) -> None:
+        if self.active:
+            message = f'Waited {self.timeout} seconds, timed out.'
+            logger.error(message)
+            self.callback()
+            raise TimeoutError(message)
+
+    def disalarm(self) -> None:
+        self.active = False
+
+
+def create_timeout_handler(callback: Callable, timeout_seconds: int = TIMEOUT_SECONDS_DEFAULT) -> TimeoutHandler:
+    handler = TimeoutHandler(callback, timeout_seconds)
+    signal.signal(signal.SIGALRM, handler.handle)
+    signal.alarm(timeout_seconds)
+    return handler

spatialprofilingtoolbox/ondemand/worker.py

@@ -14,6 +14,8 @@
 from spatialprofilingtoolbox.db.describe_features import get_handle
 from spatialprofilingtoolbox.ondemand.job_reference import ComputationJobReference
 from spatialprofilingtoolbox.ondemand.scheduler import MetricComputationScheduler
+from spatialprofilingtoolbox.ondemand.timeout import create_timeout_handler
+from spatialprofilingtoolbox.ondemand.timeout import SPTTimeoutError
 from spatialprofilingtoolbox.standalone_utilities.log_formats import colorized_logger
 Job = ComputationJobReference
 
@@ -33,17 +35,17 @@ def start(self) -> None:
 
     def _listen_for_queue_activity(self) -> None:
         with DBConnection() as connection:
-            connection._set_autocommit(True)
+            self.connection = connection
+            self.connection._set_autocommit(True)
+            self.connection.execute('LISTEN new_items_in_queue ;')
+            logger.info('Listening on new_items_in_queue channel.')
+            self.notifications = self.connection.notifies()
             while True:
-                self._wait_for_queue_activity_on(connection)
+                self._wait_for_queue_activity_on_connection()
                 self._work_until_complete()
 
-    def _wait_for_queue_activity_on(self, connection: PsycopgConnection) -> None:
-        connection.execute('LISTEN new_items_in_queue ;')
-        logger.info('Listening on new_items_in_queue channel.')
-        notifications = connection.notifies()
-        for notification in notifications:
-            notifications.close()
+    def _wait_for_queue_activity_on_connection(self) -> None:
+        for _ in self.notifications:
             logger.info('Received notice of new items in the job queue.')
             break
 
@@ -57,26 +59,35 @@ def _work_until_complete(self) -> None:
         logger.info(f'Finished jobs {" ".join(completed_pids)}.')
 
     def _one_job(self) -> tuple[bool, int]:
-        with DBConnection() as connection:
-            connection._set_autocommit(True)
-            self.connection = connection
-            pid = self.connection.info.backend_pid
-            job = self.queue.pop_uncomputed()
-            if job is None:
-                return (False, pid)
-            logger.info(f'{pid} doing job {job.feature_specification} {job.sample}.')
-            self._compute(job)
-            self._notify_complete(job)
-            return (True, pid)
+        pid = self.connection.info.backend_pid
+        job = self.queue.pop_uncomputed()
+        if job is None:
+            return (False, pid)
+        logger.info(f'{pid} doing job {job.feature_specification} {job.sample}.')
+        self._compute(job)
+        self._notify_complete(job)
+        return (True, pid)
+
+    def _no_value_wrapup(self, job) -> None:
+        provider = self._get_provider(job)
+        provider._warn_no_value()
+        provider._insert_null()
 
     def _compute(self, job: Job) -> None:
         provider = self._get_provider(job)
+        generic_handler = create_timeout_handler(
+            lambda *arg: self._no_value_wrapup(job),
+            timeout_seconds=150,
+        )
         try:
             provider.compute()
+        except SPTTimeoutError:
+            pass
         except Exception as error:
             logger.error(error)
             print_exception(type(error), error, error.__traceback__)
-            self._get_provider(job)._warn_no_value()
+        finally:
+            generic_handler.disalarm()
 
     def _notify_complete(self, job: Job) -> None:
         self.connection.execute('NOTIFY one_job_complete ;')