feat: Add basic performance tests of list_experiments and fetch_metrics#71
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feat: Add basic performance tests of list_experiments and fetch_metrics#71
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| description: Timeout tolerance in as float; e.g. 1.2 means 20% tolerance | ||
| required: false | ||
| default: 1.1 | ||
| push: |
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TODO: Drop the on-push trigger from perf workflow
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| @@ -74,7 +74,7 @@ def fetch_metrics( | |||
| restricted_attributes = validation.restrict_attribute_filter_type(attributes, type_in={"float_series"}) | |||
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| valid_context = validate_context(context or get_context()) | |||
| client = get_client(context=valid_context) | |||
| client = _client.get_client(context=valid_context) | |||
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Related & required. I'm currently mocking get_client in perf tests in order to inject the x-perf-request header.
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| min_val = random.uniform(MIN_NUMERIC_VALUE, MAX_NUMERIC_VALUE) | ||
| max_val = random.uniform(min_val, MAX_NUMERIC_VALUE) |
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| min_val = random.uniform(MIN_NUMERIC_VALUE, MAX_NUMERIC_VALUE) | |
| max_val = random.uniform(min_val, MAX_NUMERIC_VALUE) | |
| min_val, max_val = sorted(random.uniform(MIN_NUMERIC_VALUE, MAX_NUMERIC_VALUE) for _ in (1, 2)) |
| proto_attr.float_series_properties.max = max_val | ||
| proto_attr.float_series_properties.last = random.uniform(min_val, max_val) | ||
| proto_attr.float_series_properties.average = random.uniform(min_val, max_val) | ||
| proto_attr.float_series_properties.variance = random.uniform(MIN_VARIANCE, MAX_NUMERIC_VALUE) |
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| proto_attr.float_series_properties.variance = random.uniform(MIN_VARIANCE, MAX_NUMERIC_VALUE) | |
| proto_attr.float_series_properties.variance = random.uniform(MIN_VARIANCE, MAX_VARIANCE) |
tests/performance/backend/endpoints/get_multiple_float_series_values.py
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| initial_step = 1 if after_step is None else (after_step + 1) | ||
| total_remaining_steps = series_cardinality - (initial_step - 1) | ||
| steps_in_current_request = min(total_remaining_steps, max_values) | ||
| max_step = initial_step + steps_in_current_request - 1 | ||
| step_range = range(int(initial_step), int(max_step) + 1) | ||
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| return [(1600000000 + step, step, random.uniform(-1e6, 1e6)) for step in step_range] |
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| initial_step = 1 if after_step is None else (after_step + 1) | |
| total_remaining_steps = series_cardinality - (initial_step - 1) | |
| steps_in_current_request = min(total_remaining_steps, max_values) | |
| max_step = initial_step + steps_in_current_request - 1 | |
| step_range = range(int(initial_step), int(max_step) + 1) | |
| return [(1600000000 + step, step, random.uniform(-1e6, 1e6)) for step in step_range] | |
| initial_step = 1 if after_step is None else (after_step + 1) | |
| initial_timestamp = 1600000000 # some time in the year 2020 | |
| total_remaining_steps = series_cardinality - (initial_step - 1) | |
| steps_in_current_request = min(total_remaining_steps, max_values) | |
| max_step = initial_step + steps_in_current_request - 1 | |
| step_range = range(int(initial_step), int(max_step) + 1) | |
| return [(initial_timestamp + step, step, random.uniform(-1e6, 1e6)) for step in step_range] |
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Contributes to PY-257
Completes PY-258
Completes PY-264