def map_workload(target_data):
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
dbms = newest_result.dbms.pk
hardware = newest_result.application.hardware.pk
workload_data = PipelineResult.get_latest(
dbms, hardware, PipelineTaskType.WORKLOAD_MAPPING_DATA)
if workload_data is None:
target_data['scores'] = None
return target_data
data_values = JSONUtil.loads(workload_data.value)
X_scaler = np.load(data_values['X_scaler'])
y_scaler = np.load(data_values['y_scaler'])
y_deciles = np.load(data_values['y_deciles'])['deciles']
X_columnlabels = data_values['X_columnlabels']
y_columnlabels = data_values['y_columnlabels']
X_idxs = [
i for i in range(target_data['X_matrix'].shape[1])
if target_data['X_columnlabels'][i] in X_columnlabels
]
y_idxs = [
i for i in range(target_data['y_matrix'].shape[1])
if target_data['y_columnlabels'][i] in y_columnlabels
]
X_target = target_data['X_matrix'][:, X_idxs]
y_target = target_data['y_matrix'][:, y_idxs]
X_target = (X_target - X_scaler['mean']) / X_scaler['scale']
y_target = (y_target - y_scaler['mean']) / y_scaler['scale']
y_binned = np.empty_like(y_target)
for i in range(y_target.shape[1]):
y_binned[:, i] = bin_by_decile(y_target[:, i], y_deciles[i])
scores = {}
for wkld_id, wkld_entry_path in data_values['data'].iteritems():
wkld_entry = np.load(wkld_entry_path)
preds = np.empty_like(y_target)
X_wkld = wkld_entry['X_matrix']
for j in range(y_target.shape[1]):
y_col = wkld_entry['y_matrix'][:, j].reshape(X_wkld.shape[0], 1)
model = GPR()
model.fit(X_wkld, y_col, ridge=0.01)
preds[:, j] = bin_by_decile(
model.predict(X_target).ypreds.ravel(), y_deciles[j])
dists = np.sqrt(np.sum(np.square(np.subtract(preds, y_target)),
axis=1))
scores[wkld_id] = np.mean(dists)
# Find the best (minimum) score
best_score = np.inf
best_wkld_id = None
for wkld_id, similarity_score in scores.iteritems():
if similarity_score < best_score:
best_score = similarity_score
best_wkld_id = wkld_id
target_data['mapped_workload'] = (best_wkld_id, best_score)
target_data['scores'] = scores
return target_data
def map_workload(target_data):
# Get the latest version of pipeline data that's been computed so far.
latest_pipeline_run = PipelineRun.objects.get_latest()
assert latest_pipeline_run is not None
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
target_workload = newest_result.workload
X_columnlabels = np.array(target_data['X_columnlabels'])
y_columnlabels = np.array(target_data['y_columnlabels'])
# Find all pipeline data belonging to the latest version with the same
# DBMS and hardware as the target
pipeline_data = PipelineData.objects.filter(
pipeline_run=latest_pipeline_run,
workload__dbms=target_workload.dbms,
workload__hardware=target_workload.hardware)
# FIXME (dva): we should also compute the global (i.e., overall) ranked_knobs
# and pruned metrics but we just use those from the first workload for now
initialized = False
global_ranked_knobs = None
global_pruned_metrics = None
ranked_knob_idxs = None
pruned_metric_idxs = None
# Compute workload mapping data for each unique workload
unique_workloads = pipeline_data.values_list('workload',
flat=True).distinct()
assert len(unique_workloads) > 0
workload_data = {}
for unique_workload in unique_workloads:
# Load knob & metric data for this workload
knob_data = load_data_helper(pipeline_data, unique_workload,
PipelineTaskType.KNOB_DATA)
metric_data = load_data_helper(pipeline_data, unique_workload,
PipelineTaskType.METRIC_DATA)
X_matrix = np.array(knob_data["data"])
y_matrix = np.array(metric_data["data"])
rowlabels = np.array(knob_data["rowlabels"])
assert np.array_equal(rowlabels, metric_data["rowlabels"])
if not initialized:
# For now set ranked knobs & pruned metrics to be those computed
# for the first workload
global_ranked_knobs = load_data_helper(
pipeline_data, unique_workload,
PipelineTaskType.RANKED_KNOBS)[:10] # FIXME (dva)
global_pruned_metrics = load_data_helper(
pipeline_data, unique_workload,
PipelineTaskType.PRUNED_METRICS)
ranked_knob_idxs = [
i for i in range(X_matrix.shape[1])
if X_columnlabels[i] in global_ranked_knobs
]
pruned_metric_idxs = [
i for i in range(y_matrix.shape[1])
if y_columnlabels[i] in global_pruned_metrics
]
# Filter X & y columnlabels by top 10 ranked_knobs & pruned_metrics
X_columnlabels = X_columnlabels[ranked_knob_idxs]
y_columnlabels = y_columnlabels[pruned_metric_idxs]
initialized = True
# Filter X & y matrices by top 10 ranked_knobs & pruned_metrics
X_matrix = X_matrix[:, ranked_knob_idxs]
y_matrix = y_matrix[:, pruned_metric_idxs]
# Combine duplicate rows (rows with same knob settings)
X_matrix, y_matrix, rowlabels = DataUtil.combine_duplicate_rows(
X_matrix, y_matrix, rowlabels)
workload_data[unique_workload] = {
'X_matrix': X_matrix,
'y_matrix': y_matrix,
'rowlabels': rowlabels,
}
# Stack all X & y matrices for preprocessing
Xs = np.vstack([entry['X_matrix'] for entry in workload_data.values()])
ys = np.vstack([entry['y_matrix'] for entry in workload_data.values()])
# Scale the X & y values, then compute the deciles for each column in y
X_scaler = StandardScaler(copy=False)
X_scaler.fit(Xs)
y_scaler = StandardScaler(copy=False)
y_scaler.fit_transform(ys)
y_binner = Bin(bin_start=1, axis=0)
y_binner.fit(ys)
del Xs
del ys
# Filter the target's X & y data by the ranked knobs & pruned metrics.
X_target = target_data['X_matrix'][:, ranked_knob_idxs]
y_target = target_data['y_matrix'][:, pruned_metric_idxs]
# Now standardize the target's data and bin it by the deciles we just
# calculated
X_target = X_scaler.transform(X_target)
y_target = y_scaler.transform(y_target)
y_target = y_binner.transform(y_target)
scores = {}
for workload_id, workload_entry in workload_data.iteritems():
predictions = np.empty_like(y_target)
X_workload = workload_entry['X_matrix']
y_workload = workload_entry['y_matrix']
for j, y_col in enumerate(y_workload.T):
# Using this workload's data, train a Gaussian process model
# and then predict the performance of each metric for each of
# the knob configurations attempted so far by the target.
y_col = y_col.reshape(-1, 1)
model = GPR()
model.fit(X_workload, y_col, ridge=0.01)
predictions[:, j] = model.predict(X_target).ypreds.ravel()
# Bin each of the predicted metric columns by deciles and then
# compute the score (i.e., distance) between the target workload
# and each of the known workloads
predictions = y_binner.transform(predictions)
dists = np.sqrt(
np.sum(np.square(np.subtract(predictions, y_target)), axis=1))
scores[workload_id] = np.mean(dists)
# Find the best (minimum) score
best_score = np.inf
best_workload_id = None
for workload_id, similarity_score in scores.iteritems():
if similarity_score < best_score:
best_score = similarity_score
best_workload_id = workload_id
target_data['mapped_workload'] = (best_workload_id, best_score)
target_data['scores'] = scores
return target_data