Refactoring: move preprocessing to a new celery task

server/website/website/forms.py

@@ -158,4 +158,4 @@ class SessionKnobForm(forms.ModelForm):
 
     class Meta:  # pylint: disable=no-init
         model = SessionKnob
-        fields = ['session', 'knob', 'minval', 'maxval', 'tunable']
+        fields = ['session', 'knob', 'minval', 'maxval', 'lowerbound', 'upperbound', 'tunable']

server/website/website/tasks/async_tasks.py

@@ -179,17 +179,19 @@ def save_execution_time(start_ts, fn, result):
 
 
 def choose_value_in_range(num1, num2):
-    if num1 < 1:
+    # It is important to add 1 to avoid log(0)
-        num1 = num1 + 1
+    log_num1 = np.log(num1 + 1)
-    if num2 < 1:
+    log_num2 = np.log(num2 + 1)
-        num2 = num2 + 1
-    log_num1 = np.log(num1)
-    log_num2 = np.log(num2)
     return np.exp((log_num1 + log_num2) / 2)
 
 
-def test_knob_range(knob_info, newest_result, good_val, bad_val, mode):
+def calc_next_knob_range(algorithm, knob_info, newest_result, good_val, bad_val, mode):
     session = newest_result.session
+    # The metric should not be used for learning because the driver runs nothing
+    # We tag the metric as invalid, so later they will be set to the worst result
+    metric_file = newest_result.metric_data
+    metric_file.name = metric_file.name + '*'
+    metric_file.save()
     knob = KnobCatalog.objects.get(name=knob_info['name'], dbms=session.dbms)
     knob_file = newest_result.knob_data
     knob_values = JSONUtil.loads(knob_file.data)
@@ -225,9 +227,13 @@ def test_knob_range(knob_info, newest_result, good_val, bad_val, mode):
         else:
             if mode == 'lowerbound':
                 session_knob.minval = str(int(expected_value))
+                # Terminate the search if the observed value is very different from the set one
                 if expected_value < last_value / 10:
                     session_knob.minval = str(int(last_value))
                     session_knob.lowerbound = str(int(last_value))
+                    session_knob.save()
+                    # The return value means we will not generate next config to test this knob
+                    return False, None
             else:
                 session_knob.maxval = str(int(expected_value))
             next_value = choose_value_in_range(expected_value, bad_val)
@@ -241,43 +247,50 @@ def test_knob_range(knob_info, newest_result, good_val, bad_val, mode):
         knobs = SessionKnob.objects.get_knobs_for_session(session)
         next_config = gen_test_maxval_data(knobs, knob.name, next_value)
 
-    agg_data = DataUtil.aggregate_data(Result.objects.filter(pk=newest_result.pk))
+    target_data = {}
-    agg_data['newest_result_id'] = newest_result.pk
+    target_data['newest_result_id'] = newest_result.pk
-    agg_data['status'] = 'range_test'
+    target_data['status'] = 'range_test'
-    agg_data['config_recommend'] = next_config
+    target_data['config_recommend'] = next_config
-    LOG.debug('Testing %s of %s.\n\ndata=%s\n', mode, knob.name,
+    LOG.debug('%s: Generated a config to test %s of %s.\n\ndata=%s\n',
-              JSONUtil.dumps(agg_data, pprint=True))
+              AlgorithmType.name(algorithm), mode, knob.name,
-    save_execution_time(time.time(), "aggregate_target_results", newest_result)
+              JSONUtil.dumps(target_data, pprint=True))
-    return agg_data
+    return True, target_data
 
 
-@shared_task(base=IgnoreResultTask, name='aggregate_target_results')
+@shared_task(base=IgnoreResultTask, name='preprocessing')
-def aggregate_target_results(result_id, algorithm):
+def preprocessing(result_id, algorithm):
     start_ts = time.time()
-    agg_data = DataUtil.aggregate_data(Result.objects.filter(pk=result_id))
+    target_data = {}
+    target_data['newest_result_id'] = result_id
     newest_result = Result.objects.get(pk=result_id)
     session = newest_result.session
     knobs = SessionKnob.objects.get_knobs_for_session(session)
 
     # Check that the minvals of tunable knobs are all decided
     for knob_info in knobs:
-        if 'lowerbound' in knob_info and knob_info['lowerbound'] is not None:
+        if knob_info.get('lowerbound', None) is not None:
             lowerbound = float(knob_info['lowerbound'])
             minval = float(knob_info['minval'])
             if lowerbound < minval * 0.7:
                 # We need to do binary search to determine the minval of this knob
-                return test_knob_range(knob_info,
+                successful, target_data = calc_next_knob_range(
-                                       newest_result, minval, lowerbound, 'lowerbound'), algorithm
+                    algorithm, knob_info, newest_result, minval, lowerbound, 'lowerbound')
+                if successful:
+                    save_execution_time(start_ts, "preprocessing", newest_result)
+                    return result_id, algorithm, target_data
 
     # Check that the maxvals of tunable knobs are all decided
     for knob_info in knobs:
-        if 'upperbound' in knob_info and knob_info['upperbound'] is not None:
+        if knob_info.get('upperbound', None) is not None:
             upperbound = float(knob_info['upperbound'])
             maxval = float(knob_info['maxval'])
-            if upperbound > maxval * 1.5:
+            if upperbound > maxval * 1.3:
                 # We need to do binary search to determine the maxval of this knob
-                return test_knob_range(knob_info,
+                successful, target_data = calc_next_knob_range(
-                                       newest_result, maxval, upperbound, 'upperbound'), algorithm
+                    algorithm, knob_info, newest_result, maxval, upperbound, 'upperbound')
+                if successful:
+                    save_execution_time(start_ts, "preprocessing", newest_result)
+                    return result_id, algorithm, target_data
 
     # Check that we've completed the background tasks at least once. We need
     # this data in order to make a configuration recommendation (until we
@@ -296,24 +309,38 @@ def aggregate_target_results(result_id, algorithm):
             LOG.debug('%s: Generated LHS.\n\ndata=%s\n',
                       AlgorithmType.name(algorithm), JSONUtil.dumps(all_samples[:5], pprint=True))
         samples = all_samples.pop()
-        agg_data['newest_result_id'] = result_id
+        target_data['status'] = 'lhs'
-        agg_data['status'] = 'lhs'
+        target_data['config_recommend'] = samples
-        agg_data['config_recommend'] = samples
         session.lhs_samples = JSONUtil.dumps(all_samples)
         session.save()
         LOG.debug('%s: Got LHS config.\n\ndata=%s\n',
-                  AlgorithmType.name(algorithm), JSONUtil.dumps(agg_data, pprint=True))
+                  AlgorithmType.name(algorithm), JSONUtil.dumps(target_data, pprint=True))
 
     elif session.tuning_session == 'randomly_generate':
         # generate a config randomly
         random_knob_result = gen_random_data(knobs)
-        agg_data['newest_result_id'] = result_id
+        target_data['status'] = 'random'
-        agg_data['status'] = 'random'
+        target_data['config_recommend'] = random_knob_result
-        agg_data['config_recommend'] = random_knob_result
+        LOG.debug('%s: Generated a random config.\n\ndata=%s\n',
-        LOG.debug('%s: Finished generating a random config.\n\ndata=%s\n',
+                  AlgorithmType.name(algorithm), JSONUtil.dumps(target_data, pprint=True))
-                  AlgorithmType.name(algorithm), JSONUtil.dumps(agg_data, pprint=True))
 
-    else:
+    save_execution_time(start_ts, "preprocessing", newest_result)
+    return result_id, algorithm, target_data
+
+
+@shared_task(base=IgnoreResultTask, name='aggregate_target_results')
+def aggregate_target_results(aggregate_target_results_input):
+    start_ts = time.time()
+    result_id, algorithm, target_data = aggregate_target_results_input
+    # If the preprocessing method has already generated a config, bypass this method.
+    if 'config_recommend' in target_data:
+        assert 'newest_result_id' in target_data and 'status' in target_data
+        LOG.debug('%s: Skipping aggregate_target_results.\nData:\n%s\n\n',
+                  AlgorithmType.name(algorithm), target_data)
+        return target_data, algorithm
+
+    newest_result = Result.objects.get(pk=result_id)
+    session = newest_result.session
     # Aggregate all knob config results tried by the target so far in this
     # tuning session and this tuning workload.
     target_results = Result.objects.filter(session=session,
@@ -433,16 +460,23 @@ def gen_lhs_samples(knobs, nsamples):
 
 
 @shared_task(base=IgnoreResultTask, name='train_ddpg')
-def train_ddpg(result_id):
+def train_ddpg(train_ddpg_input):
     start_ts = time.time()
+    result_id, algorithm, target_data = train_ddpg_input
+    # If the preprocessing method has already generated a config, bypass this method.
+    if 'config_recommend' in target_data:
+        assert 'newest_result_id' in target_data and 'status' in target_data
+        LOG.debug('Skipping training DDPG.\nData:\n%s\n\n', target_data)
+        return target_data, algorithm
+
     LOG.info('Add training data to ddpg and train ddpg')
     result = Result.objects.get(pk=result_id)
-    session = Result.objects.get(pk=result_id).session
+    session = result.session
     params = JSONUtil.loads(session.hyperparameters)
     session_results = Result.objects.filter(session=session,
                                             creation_time__lt=result.creation_time)
-    result_info = {}
+    target_data = {}
-    result_info['newest_result_id'] = result_id
+    target_data['newest_result_id'] = result_id
 
     # Extract data from result and previous results
     result = Result.objects.filter(pk=result_id)
@@ -459,8 +493,7 @@ def train_ddpg(result_id):
     base_metric_data = (DataUtil.aggregate_data(base_result))['y_matrix'].flatten()
     prev_metric_data = (DataUtil.aggregate_data(prev_result))['y_matrix'].flatten()
 
-    result = Result.objects.get(pk=result_id)
+    target_objective = session.target_objective
-    target_objective = result.session.target_objective
     prev_obj_idx = [i for i, n in enumerate(agg_data['y_columnlabels']) if n == target_objective]
 
     # Clean metric data
@@ -492,8 +525,8 @@ def train_ddpg(result_id):
     objective = metric_data[target_obj_idx]
     base_objective = base_metric_data[prev_obj_idx]
     prev_objective = prev_metric_data[prev_obj_idx]
-    metric_meta = db.target_objectives.get_metric_metadata(
+    metric_meta = db.target_objectives.get_metric_metadata(session.dbms.pk,
-        result.session.dbms.pk, result.session.target_objective)
+                                                           session.target_objective)
 
     # Calculate the reward
     if params['DDPG_SIMPLE_REWARD']:
@@ -535,7 +568,7 @@ def train_ddpg(result_id):
     session.ddpg_reply_memory = ddpg.replay_memory.get()
     save_execution_time(start_ts, "train_ddpg", Result.objects.get(pk=result_id))
     session.save()
-    return result_info
+    return target_data, algorithm
 
 
 def create_and_save_recommendation(recommended_knobs, result, status, **kwargs):
@@ -555,11 +588,38 @@ def create_and_save_recommendation(recommended_knobs, result, status, **kwargs):
     return retval
 
 
+def check_early_return(target_data, algorithm):
+    result_id = target_data['newest_result_id']
+    newest_result = Result.objects.get(pk=result_id)
+    if target_data.get('status', 'good') != 'good':  # No status or status is not 'good'
+        if target_data['status'] == 'random':
+            info = 'The config is generated by Random'
+        elif target_data['status'] == 'lhs':
+            info = 'The config is generated by LHS'
+        elif target_data['status'] == 'range_test':
+            info = 'Searching for valid knob ranges'
+        else:
+            info = 'Unknown'
+        target_data_res = create_and_save_recommendation(
+            recommended_knobs=target_data['config_recommend'], result=newest_result,
+            status=target_data['status'], info=info, pipeline_run=None)
+        LOG.debug('%s: Skipping configuration recommendation.\nData:\n%s\n\n',
+                  AlgorithmType.name(algorithm), target_data)
+        return True, target_data_res
+    return False, None
+
+
 @shared_task(base=ConfigurationRecommendation, name='configuration_recommendation_ddpg')
-def configuration_recommendation_ddpg(result_info):  # pylint: disable=invalid-name
+def configuration_recommendation_ddpg(recommendation_ddpg_input):  # pylint: disable=invalid-name
     start_ts = time.time()
-    LOG.info('Use ddpg to recommend configuration')
+    LOG.info('configuration_recommendation called (DDPG)')
-    result_id = result_info['newest_result_id']
+    target_data, algorithm = recommendation_ddpg_input
+
+    early_return, target_data_res = check_early_return(target_data, algorithm)
+    if early_return:
+        return target_data_res
+
+    result_id = target_data['newest_result_id']
     result_list = Result.objects.filter(pk=result_id)
     result = result_list.first()
     session = result.session
@@ -588,11 +648,11 @@ def configuration_recommendation_ddpg(result_info):  # pylint: disable=invalid-n
     knob_data = MinMaxScaler().fit(knob_bounds).inverse_transform(knob_data.reshape(1, -1))[0]
     conf_map = {k: knob_data[i] for i, k in enumerate(knob_labels)}
 
-    conf_map_res = create_and_save_recommendation(recommended_knobs=conf_map, result=result,
+    target_data_res = create_and_save_recommendation(recommended_knobs=conf_map, result=result,
                                                      status='good', info='INFO: ddpg')
 
     save_execution_time(start_ts, "configuration_recommendation_ddpg", result)
-    return conf_map_res
+    return target_data_res
 
 
 def combine_workload(target_data):
@@ -796,28 +856,15 @@ def configuration_recommendation(recommendation_input):
     start_ts = time.time()
     target_data, algorithm = recommendation_input
     LOG.info('configuration_recommendation called')
+
+    early_return, target_data_res = check_early_return(target_data, algorithm)
+    if early_return:
+        return target_data_res
+
     newest_result = Result.objects.get(pk=target_data['newest_result_id'])
     session = newest_result.session
     params = JSONUtil.loads(session.hyperparameters)
 
-    if target_data['status'] != 'good':
-        LOG.info(target_data['status'])
-        if target_data['status'] == 'random':
-            info = 'The config is generated by Random'
-        elif target_data['status'] == 'lhs':
-            info = 'The config is generated by LHS'
-        elif target_data['status'] == 'range_test':
-            info = 'Searching for the valid ranges of knobs'
-        else:
-            info = 'Unknown'
-        target_data_res = create_and_save_recommendation(
-            recommended_knobs=target_data['config_recommend'], result=newest_result,
-            status=target_data['status'], info=info,
-            pipeline_run=target_data['pipeline_run'])
-        LOG.debug('%s: Skipping configuration recommendation.\nData:\n%s\n\n',
-                  AlgorithmType.name(algorithm), target_data)
-        return target_data_res
-
     X_columnlabels, X_scaler, X_scaled, y_scaled, X_max, X_min,\
         dummy_encoder, constraint_helper, pipeline_knobs,\
         pipeline_metrics = combine_workload(target_data)
@@ -950,9 +997,8 @@ def map_workload(map_workload_input):
     start_ts = time.time()
     target_data, algorithm = map_workload_input
 
-    if target_data['status'] != 'good':
     assert target_data is not None
-        target_data['pipeline_run'] = None
+    if target_data['status'] != 'good':
         LOG.debug('%s: Skipping workload mapping.\n\ndata=%s\n',
                   AlgorithmType.name(algorithm), JSONUtil.dumps(target_data, pprint=True))
 

server/website/website/templates/edit_knobs.html

@@ -11,6 +11,8 @@
             <td>Name</td>
 	          <td>Min</td>
             <td>Max</td>
+            <td>Lowerbound</td>
+            <td>Upperbound</td>
             <td>Tunable</td>
             <td></td>
         </tr>
@@ -21,6 +23,8 @@
                     <td>{{ form.name }}</td>
                     <td>{{ form.minval }}</td>
                     <td>{{ form.maxval }}</td>
+                    <td>{{ form.lowerbound }}</td>
+                    <td>{{ form.upperbound }}</td>
                     <td>{{ form.tunable }}</td>
                     <td><button type="submit">Save</button></td>
                 </tr>

server/website/website/views.py

@@ -484,10 +484,6 @@ def handle_result_files(session, files, execution_times=None):
     # Load the contents of the controller's summary file
     summary = JSONUtil.loads(files['summary'])
 
-    # If database crashed on restart, pull latest result and worst throughput so far
-    if 'error' in summary and summary['error'] == "DB_RESTART_ERROR":
-
-        LOG.debug("Error in restarting database")
     # Find worst throughput
     past_metrics = MetricData.objects.filter(session=session)
     metric_meta = target_objectives.get_instance(session.dbms.pk, session.target_objective)
@@ -520,6 +516,11 @@ def handle_result_files(session, files, execution_times=None):
             past_metric.data = JSONUtil.dumps(past_metric_data)
             past_metric.save()
 
+    # If database crashed on restart, pull latest result and worst throughput so far
+    if 'error' in summary and summary['error'] == "DB_RESTART_ERROR":
+
+        LOG.debug("Error in restarting database")
+
         worst_result = Result.objects.filter(metric_data=worst_metric).first()
         last_result = Result.objects.filter(session=session).order_by("-id").first()
         backup_data = BackupData.objects.filter(result=worst_result).first()
@@ -677,18 +678,21 @@ def handle_result_files(session, files, execution_times=None):
     response = None
     if session.algorithm == AlgorithmType.GPR:
         subtask_list = [
-            ('aggregate_target_results', (result_id, session.algorithm)),
+            ('preprocessing', (result_id, session.algorithm)),
+            ('aggregate_target_results', ()),
             ('map_workload', ()),
             ('configuration_recommendation', ()),
         ]
     elif session.algorithm == AlgorithmType.DDPG:
         subtask_list = [
-            ('train_ddpg', (result_id,)),
+            ('preprocessing', (result_id, session.algorithm)),
+            ('train_ddpg', ()),
             ('configuration_recommendation_ddpg', ()),
         ]
     elif session.algorithm == AlgorithmType.DNN:
         subtask_list = [
-            ('aggregate_target_results', (result_id, session.algorithm)),
+            ('preprocessing', (result_id, session.algorithm)),
+            ('aggregate_target_results', ()),
             ('map_workload', ()),
             ('configuration_recommendation', ()),
         ]