Support for custom MetricsFetcher in Perf tooling.

.github/workflows/run_perf_alert_tool.yml

@@ -30,7 +30,7 @@ on:
 jobs:
   python_run_change_point_analysis:
     name: Run Change Point Analysis.
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-20.04, main]
     permissions:
       issues: write
     steps:

sdks/python/apache_beam/testing/analyzers/README.md

@@ -35,16 +35,13 @@ update already created GitHub issue or ignore performance alert by not creating
 
 ## Config file structure
 
-The config file defines the structure to run change point analysis on a given test. To add a test to the config file,
+The yaml defines the structure to run change point analysis on a given test. To add a test config to the yaml file,
 please follow the below structure.
 
-**NOTE**: The Change point analysis only supports reading the metric data from Big Query for now.
+**NOTE**: The Change point analysis only supports reading the metric data from `BigQuery` only.
 
 ```
-# the test_1 must be a unique id.
-test_1:
-  test_description: Pytorch image classification on 50k images of size 224 x 224 with resnet 152
-  test_target: apache_beam.testing.benchmarks.inference.pytorch_image_classification_benchmarks
+test_1: # a unique id for each test config.
   metrics_dataset: beam_run_inference
   metrics_table: torch_inference_imagenet_results_resnet152
   project: apache-beam-testing
@@ -55,11 +52,17 @@ test_1:
   num_runs_in_change_point_window: 30 # optional parameter
 ```
 
-**NOTE**: `test_target` is optional. It is used for identifying the test that was causing the regression.
+#### Optional Parameters:
 
-**Note**: By default, the tool fetches metrics from BigQuery tables. `metrics_dataset`, `metrics_table`, `project` and `metric_name` should match with the values defined for performance/load tests.
-The above example uses this [test configuration](https://github.com/apache/beam/blob/0a91d139dea4276dc46176c4cdcdfce210fc50c4/.test-infra/jenkins/job_InferenceBenchmarkTests_Python.groovy#L30)
-to fill up the values required to fetch the data from source.
+These are the optional parameters that can be added to the test config in addition to the parameters mentioned above.
+
+- `test_target`: Identifies the test responsible for the regression.
+
+- `test_description`: Provides a brief overview of the test's function.
+
+- `test_name`: Denotes the name of the test as stored in the BigQuery table.
+
+**Note**: The tool, by default, pulls metrics from BigQuery tables. Ensure that the values for `metrics_dataset`, `metrics_table`, `project`, and `metric_name` align with those defined for performance/load tests. The provided example utilizes this [test configuration](https://github.com/apache/beam/blob/0a91d139dea4276dc46176c4cdcdfce210fc50c4/.test-infra/jenkins/job_InferenceBenchmarkTests_Python.groovy#L30) to populate the necessary values for data retrieval.
 
 ### Different ways to avoid false positive change points
 
@@ -76,8 +79,35 @@ setting `num_runs_in_change_point_window=7` will achieve it.
 
 ## Register a test for performance alerts
 
-If a new test needs to be registered for the performance alerting tool, please add the required test parameters to the
-config file.
+If a new test needs to be registered for the performance alerting tool,
+
+- You can either add it to the config file that is already present.
+- You can define your own yaml file and call the [perf_analysis.run()](https://github.com/apache/beam/blob/a46bc12a256dcaa3ae2cc9e5d6fdcaa82b59738b/sdks/python/apache_beam/testing/analyzers/perf_analysis.py#L152) method.
+
+
+## Integrating the Perf Alert Tool with a Custom BigQuery Schema
+
+By default, the Perf Alert Tool retrieves metrics from the `apache-beam-testing` BigQuery projects. All performance and load tests within Beam utilize a standard [schema](https://github.com/apache/beam/blob/a7e12db9b5977c4a7b13554605c0300389a3d6da/sdks/python/apache_beam/testing/load_tests/load_test_metrics_utils.py#L70) for metrics publication. The tool inherently recognizes and operates with this schema when extracting metrics from BigQuery tables.
+
+To fetch the data from a BigQuery dataset that is not a default setting of the Apache Beam's setting, One can inherit the `MetricsFetcher` class and implement the abstract method `fetch_metric_data`. This method should return a tuple of desired metric values and timestamps of the metric values of when it was published.
+
+```
+from apache_beam.testing.analyzers import perf_analysis
+config_file_path = <path_to_config_file>
+my_metric_fetcher = MyMetricsFetcher() # inherited from MetricsFetcher
+perf_analysis.run(config_file_path, my_metrics_fetcher)
+```
+
+``Note``: The metrics and timestamps should be sorted based on the timestamps values in ascending order.
+
+### Configuring GitHub Parameters
+
+Out of the box, the performance alert tool targets the `apache/beam` repository when raising issues. If you wish to utilize this tool for another repository, you'll need to pre-set a couple of environment variables:
+
+- `REPO_OWNER`: Represents the owner of the repository. (e.g., `apache`)
+- `REPO_NAME`: Specifies the repository name itself. (e.g., `beam`)
+
+Before initiating the tool, also ensure that the `GITHUB_TOKEN` is set to an authenticated GitHub token. This permits the tool to generate GitHub issues whenever performance alerts arise.
 
 ## Triage performance alert issues
 

sdks/python/apache_beam/testing/analyzers/github_issues_utils.py

@@ -34,8 +34,8 @@
       'A Github Personal Access token is required '
       'to create Github Issues.')
 
-_BEAM_GITHUB_REPO_OWNER = 'apache'
-_BEAM_GITHUB_REPO_NAME = 'beam'
+_GITHUB_REPO_OWNER = os.environ.get('REPO_OWNER', 'apache')
+_GITHUB_REPO_NAME = os.environ.get('REPO_NAME', 'beam')
 # Adding GitHub Rest API version to the header to maintain version stability.
 # For more information, please look at
 # https://github.blog/2022-11-28-to-infinity-and-beyond-enabling-the-future-of-githubs-rest-api-with-api-versioning/ # pylint: disable=line-too-long
@@ -77,10 +77,10 @@ def create_issue(
     Tuple containing GitHub issue number and issue URL.
   """
   url = "https://api.github.com/repos/{}/{}/issues".format(
-      _BEAM_GITHUB_REPO_OWNER, _BEAM_GITHUB_REPO_NAME)
+      _GITHUB_REPO_OWNER, _GITHUB_REPO_NAME)
   data = {
-      'owner': _BEAM_GITHUB_REPO_OWNER,
-      'repo': _BEAM_GITHUB_REPO_NAME,
+      'owner': _GITHUB_REPO_OWNER,
+      'repo': _GITHUB_REPO_NAME,
       'title': title,
       'body': description,
       'labels': [_AWAITING_TRIAGE_LABEL, _PERF_ALERT_LABEL]
@@ -108,20 +108,20 @@ def comment_on_issue(issue_number: int,
       issue, and the comment URL.
   """
   url = 'https://api.github.com/repos/{}/{}/issues/{}'.format(
-      _BEAM_GITHUB_REPO_OWNER, _BEAM_GITHUB_REPO_NAME, issue_number)
+      _GITHUB_REPO_OWNER, _GITHUB_REPO_NAME, issue_number)
   open_issue_response = requests.get(
       url,
       json.dumps({
-          'owner': _BEAM_GITHUB_REPO_OWNER,
-          'repo': _BEAM_GITHUB_REPO_NAME,
+          'owner': _GITHUB_REPO_OWNER,
+          'repo': _GITHUB_REPO_NAME,
           'issue_number': issue_number
       },
                  default=str),
       headers=_HEADERS).json()
   if open_issue_response['state'] == 'open':
     data = {
-        'owner': _BEAM_GITHUB_REPO_OWNER,
-        'repo': _BEAM_GITHUB_REPO_NAME,
+        'owner': _GITHUB_REPO_OWNER,
+        'repo': _GITHUB_REPO_NAME,
         'body': comment_description,
         issue_number: issue_number,
     }
@@ -134,13 +134,14 @@ def comment_on_issue(issue_number: int,
 
 def add_awaiting_triage_label(issue_number: int):
   url = 'https://api.github.com/repos/{}/{}/issues/{}/labels'.format(
-      _BEAM_GITHUB_REPO_OWNER, _BEAM_GITHUB_REPO_NAME, issue_number)
+      _GITHUB_REPO_OWNER, _GITHUB_REPO_NAME, issue_number)
   requests.post(
       url, json.dumps({'labels': [_AWAITING_TRIAGE_LABEL]}), headers=_HEADERS)
 
 
 def get_issue_description(
-    test_name: str,
+    test_id: str,
+    test_name: Optional[str],
     metric_name: str,
     timestamps: List[pd.Timestamp],
     metric_values: List,
@@ -167,10 +168,13 @@ def get_issue_description(
 
   description = []
 
-  description.append(_ISSUE_DESCRIPTION_TEMPLATE.format(test_name, metric_name))
+  description.append(_ISSUE_DESCRIPTION_TEMPLATE.format(test_id, metric_name))
 
-  description.append(("`Test description:` " +
-                      f'{test_description}') if test_description else '')
+  if test_name:
+    description.append(("`test_name:` " + f'{test_name}'))
+
+  if test_description:
+    description.append(("`Test description:` " + f'{test_description}'))
 
   description.append('```')
 

sdks/python/apache_beam/testing/analyzers/perf_analysis.py

@@ -23,7 +23,6 @@
 import argparse
 import logging
 import os
-import uuid
 from datetime import datetime
 from datetime import timezone
 from typing import Any
@@ -33,20 +32,21 @@
 import pandas as pd
 
 from apache_beam.testing.analyzers import constants
+from apache_beam.testing.analyzers.perf_analysis_utils import BigQueryMetricsFetcher
 from apache_beam.testing.analyzers.perf_analysis_utils import GitHubIssueMetaData
+from apache_beam.testing.analyzers.perf_analysis_utils import MetricsFetcher
 from apache_beam.testing.analyzers.perf_analysis_utils import create_performance_alert
-from apache_beam.testing.analyzers.perf_analysis_utils import fetch_metric_data
 from apache_beam.testing.analyzers.perf_analysis_utils import find_latest_change_point_index
 from apache_beam.testing.analyzers.perf_analysis_utils import get_existing_issues_data
 from apache_beam.testing.analyzers.perf_analysis_utils import is_change_point_in_valid_window
 from apache_beam.testing.analyzers.perf_analysis_utils import is_perf_alert
 from apache_beam.testing.analyzers.perf_analysis_utils import publish_issue_metadata_to_big_query
 from apache_beam.testing.analyzers.perf_analysis_utils import read_test_config
 from apache_beam.testing.analyzers.perf_analysis_utils import validate_config
-from apache_beam.testing.load_tests.load_test_metrics_utils import BigQueryMetricsFetcher
 
 
-def run_change_point_analysis(params, test_name, big_query_metrics_fetcher):
+def run_change_point_analysis(
+    params, test_id, big_query_metrics_fetcher: MetricsFetcher):
   """
   Args:
    params: Dict containing parameters to run change point analysis.
@@ -56,14 +56,21 @@ def run_change_point_analysis(params, test_name, big_query_metrics_fetcher):
   Returns:
      bool indicating if a change point is observed and alerted on GitHub.
   """
-  logging.info("Running change point analysis for test %s" % test_name)
+  logging.info("Running change point analysis for test ID %s" % test_id)
   if not validate_config(params.keys()):
     raise ValueError(
         f"Please make sure all these keys {constants._PERF_TEST_KEYS} "
-        f"are specified for the {test_name}")
+        f"are specified for the {test_id}")
 
   metric_name = params['metric_name']
 
+  # test_name will be used to query a single test from
+  # multiple tests in a single BQ table. Right now, the default
+  # assumption is that all the test have an individual BQ table
+  # but this might not be case for other tests(such as IO tests where
+  # a single BQ tables stores all the data)
+  test_name = params.get('test_name', None)
+
   min_runs_between_change_points = (
       constants._DEFAULT_MIN_RUNS_BETWEEN_CHANGE_POINTS)
   if 'min_runs_between_change_points' in params:
@@ -74,15 +81,18 @@ def run_change_point_analysis(params, test_name, big_query_metrics_fetcher):
   if 'num_runs_in_change_point_window' in params:
     num_runs_in_change_point_window = params['num_runs_in_change_point_window']
 
-  metric_values, timestamps = fetch_metric_data(
-    params=params,
-    big_query_metrics_fetcher=big_query_metrics_fetcher
+  metric_values, timestamps = big_query_metrics_fetcher.fetch_metric_data(
+    project=params['project'],
+    metrics_dataset=params['metrics_dataset'],
+    metrics_table=params['metrics_table'],
+    metric_name=params['metric_name'],
+    test_name=test_name
   )
 
   change_point_index = find_latest_change_point_index(
       metric_values=metric_values)
   if not change_point_index:
-    logging.info("Change point is not detected for the test %s" % test_name)
+    logging.info("Change point is not detected for the test ID %s" % test_id)
     return False
   # since timestamps are ordered in ascending order and
   # num_runs_in_change_point_window refers to the latest runs,
@@ -92,11 +102,11 @@ def run_change_point_analysis(params, test_name, big_query_metrics_fetcher):
   if not is_change_point_in_valid_window(num_runs_in_change_point_window,
                                          latest_change_point_run):
     logging.info(
-        'Performance regression/improvement found for the test: %s. '
+        'Performance regression/improvement found for the test ID: %s. '
         'on metric %s. Since the change point run %s '
         'lies outside the num_runs_in_change_point_window distance: %s, '
         'alert is not raised.' % (
-            test_name,
+            test_id,
             metric_name,
             latest_change_point_run + 1,
             num_runs_in_change_point_window))
@@ -106,8 +116,7 @@ def run_change_point_analysis(params, test_name, big_query_metrics_fetcher):
   last_reported_issue_number = None
   issue_metadata_table_name = f'{params.get("metrics_table")}_{metric_name}'
   existing_issue_data = get_existing_issues_data(
-      table_name=issue_metadata_table_name,
-      big_query_metrics_fetcher=big_query_metrics_fetcher)
+      table_name=issue_metadata_table_name)
 
   if existing_issue_data is not None:
     existing_issue_timestamps = existing_issue_data[
@@ -124,20 +133,21 @@ def run_change_point_analysis(params, test_name, big_query_metrics_fetcher):
         min_runs_between_change_points=min_runs_between_change_points)
   if is_alert:
     issue_number, issue_url = create_performance_alert(
-    metric_name, test_name, timestamps,
+    metric_name, test_id, timestamps,
     metric_values, change_point_index,
     params.get('labels', None),
     last_reported_issue_number,
     test_description = params.get('test_description', None),
+    test_name = test_name
     )
 
     issue_metadata = GitHubIssueMetaData(
         issue_timestamp=pd.Timestamp(
             datetime.now().replace(tzinfo=timezone.utc)),
         # BQ doesn't allow '.' in table name
-        test_name=test_name.replace('.', '_'),
+        test_id=test_id.replace('.', '_'),
+        test_name=test_name,
         metric_name=metric_name,
-        test_id=uuid.uuid4().hex,
         change_point=metric_values[change_point_index],
         issue_number=issue_number,
         issue_url=issue_url,
@@ -149,7 +159,10 @@ def run_change_point_analysis(params, test_name, big_query_metrics_fetcher):
   return is_alert
 
 
-def run(config_file_path: Optional[str] = None) -> None:
+def run(
+    big_query_metrics_fetcher: MetricsFetcher = BigQueryMetricsFetcher(),
+    config_file_path: Optional[str] = None,
+) -> None:
   """
   run is the entry point to run change point analysis on test metric
   data, which is read from config file, and if there is a performance
@@ -169,12 +182,10 @@ def run(config_file_path: Optional[str] = None) -> None:
 
   tests_config: Dict[str, Dict[str, Any]] = read_test_config(config_file_path)
 
-  big_query_metrics_fetcher = BigQueryMetricsFetcher()
-
-  for test_name, params in tests_config.items():
+  for test_id, params in tests_config.items():
     run_change_point_analysis(
         params=params,
-        test_name=test_name,
+        test_id=test_id,
         big_query_metrics_fetcher=big_query_metrics_fetcher)