[CI] Upstream metrics script and container definition (#117461)

This patch includes the script that pulls information from Github and
pushes it to Grafana. This is currently running in the cluster and
pushes information to
https://llvm.grafana.net/public-dashboards/6a1c1969b6794e0a8ee5d494c72ce2cd.
This script is designed to accept other jobs relatively easily and can
be easily modified to look at other metrics.

.ci/metrics/Dockerfile

@@ -0,0 +1,7 @@
+FROM docker.io/python:3.12
+
+COPY requirements.lock.txt ./
+RUN pip3 install --no-cache-dir -r requirements.lock.txt
+COPY metrics.py ./
+
+CMD ["python3", "metrics.py"]

.ci/metrics/metrics.py

@@ -0,0 +1,182 @@
+import requests
+import time
+import os
+from dataclasses import dataclass
+import sys
+
+import github
+from github import Github
+from github import Auth
+
+GRAFANA_URL = (
+    "https://influx-prod-13-prod-us-east-0.grafana.net/api/v1/push/influx/write"
+)
+GITHUB_PROJECT = "llvm/llvm-project"
+WORKFLOWS_TO_TRACK = ["Check code formatting"]
+SCRAPE_INTERVAL_SECONDS = 5 * 60
+
+
+@dataclass
+class JobMetrics:
+    job_name: str
+    queue_time: int
+    run_time: int
+    status: int
+    created_at_ns: int
+    workflow_id: int
+
+
+def get_metrics(github_repo: github.Repository, workflows_to_track: dict[str, int]):
+    """Gets the metrics for specified Github workflows.
+
+    This function takes in a list of workflows to track, and optionally the
+    workflow ID of the last tracked invocation. It grabs the relevant data
+    from Github, returning it to the caller.
+
+    Args:
+      github_repo: A github repo object to use to query the relevant information.
+      workflows_to_track: A dictionary mapping workflow names to the last
+        invocation ID where metrics have been collected, or None to collect the
+        last five results.
+
+    Returns:
+      Returns a list of JobMetrics objects, containing the relevant metrics about
+      the workflow.
+    """
+    workflow_runs = iter(github_repo.get_workflow_runs())
+
+    workflow_metrics = []
+
+    workflows_to_include = set(workflows_to_track.keys())
+
+    while len(workflows_to_include) > 0:
+        workflow_run = next(workflow_runs)
+        if workflow_run.status != "completed":
+            continue
+
+        # This workflow was already sampled for this run, or is not tracked at
+        # all. Ignoring.
+        if workflow_run.name not in workflows_to_include:
+            continue
+
+        # There were no new workflow invocations since the previous scrape.
+        # The API returns a sorted list with the most recent invocations first,
+        # so we can stop looking for this particular workflow. Continue to grab
+        # information on the other workflows of interest, if present.
+        if workflows_to_track[workflow_run.name] == workflow_run.id:
+            workflows_to_include.remove(workflow_run.name)
+            continue
+
+        workflow_jobs = workflow_run.jobs()
+        if workflow_jobs.totalCount == 0:
+            continue
+        if workflow_jobs.totalCount > 1:
+            raise ValueError(
+                f"Encountered an unexpected number of jobs: {workflow_jobs.totalCount}"
+            )
+
+        created_at = workflow_jobs[0].created_at
+        started_at = workflow_jobs[0].started_at
+        completed_at = workflow_jobs[0].completed_at
+
+        job_result = int(workflow_jobs[0].conclusion == "success")
+
+        queue_time = started_at - created_at
+        run_time = completed_at - started_at
+
+        if run_time.seconds == 0:
+            continue
+
+        if (
+            workflows_to_track[workflow_run.name] is None
+            or workflows_to_track[workflow_run.name] == workflow_run.id
+        ):
+            workflows_to_include.remove(workflow_run.name)
+        if (
+            workflows_to_track[workflow_run.name] is not None
+            and len(workflows_to_include) == 0
+        ):
+            break
+
+        # The timestamp associated with the event is expected by Grafana to be
+        # in nanoseconds.
+        created_at_ns = int(created_at.timestamp()) * 10**9
+
+        workflow_metrics.append(
+            JobMetrics(
+                workflow_run.name,
+                queue_time.seconds,
+                run_time.seconds,
+                job_result,
+                created_at_ns,
+                workflow_run.id,
+            )
+        )
+
+    return workflow_metrics
+
+
+def upload_metrics(workflow_metrics, metrics_userid, api_key):
+    """Upload metrics to Grafana.
+
+    Takes in a list of workflow metrics and then uploads them to Grafana
+    through a REST request.
+
+    Args:
+      workflow_metrics: A list of metrics to upload to Grafana.
+      metrics_userid: The userid to use for the upload.
+      api_key: The API key to use for the upload.
+    """
+    metrics_batch = []
+    for workflow_metric in workflow_metrics:
+        workflow_formatted_name = workflow_metric.job_name.lower().replace(" ", "_")
+        metrics_batch.append(
+            f"{workflow_formatted_name} queue_time={workflow_metric.queue_time},run_time={workflow_metric.run_time},status={workflow_metric.status} {workflow_metric.created_at_ns}"
+        )
+
+    request_data = "\n".join(metrics_batch)
+    response = requests.post(
+        GRAFANA_URL,
+        headers={"Content-Type": "text/plain"},
+        data=request_data,
+        auth=(metrics_userid, api_key),
+    )
+
+    if response.status_code < 200 or response.status_code >= 300:
+        print(
+            f"Failed to submit data to Grafana: {response.status_code}", file=sys.stderr
+        )
+
+
+def main():
+    # Authenticate with Github
+    auth = Auth.Token(os.environ["GITHUB_TOKEN"])
+    github_object = Github(auth=auth)
+    github_repo = github_object.get_repo("llvm/llvm-project")
+
+    grafana_api_key = os.environ["GRAFANA_API_KEY"]
+    grafana_metrics_userid = os.environ["GRAFANA_METRICS_USERID"]
+
+    workflows_to_track = {}
+    for workflow_to_track in WORKFLOWS_TO_TRACK:
+        workflows_to_track[workflow_to_track] = None
+
+    # Enter the main loop. Every five minutes we wake up and dump metrics for
+    # the relevant jobs.
+    while True:
+        current_metrics = get_metrics(github_repo, workflows_to_track)
+        if len(current_metrics) == 0:
+            print("No metrics found to upload.", file=sys.stderr)
+            continue
+
+        upload_metrics(current_metrics, grafana_metrics_userid, grafana_api_key)
+        print(f"Uploaded {len(current_metrics)} metrics", file=sys.stderr)
+
+        for workflow_metric in reversed(current_metrics):
+            workflows_to_track[workflow_metric.job_name] = workflow_metric.workflow_id
+
+        time.sleep(SCRAPE_INTERVAL_SECONDS)
+
+
+if __name__ == "__main__":
+    main()