ray-project--ray/release/benchmarks/distributed/many_nodes_tests/dashboard_test.py

import asyncio
import time
import urllib
from typing import Dict, Optional, List
from pprint import pprint

import requests
import ray
import logging
import os

from collections import defaultdict
from ray.util.state import list_nodes
from ray._private.test_utils import get_system_metric_for_component
from pydantic import BaseModel
from ray.dashboard.utils import get_address_for_submission_client
from ray.dashboard.modules.metrics.metrics_head import (
    DEFAULT_PROMETHEUS_HOST,
    PROMETHEUS_HOST_ENV_VAR,
)

logger = logging.getLogger(__name__)


def calc_p(latencies, percent):
    if len(latencies) == 0:
        return 0

    return round(sorted(latencies)[int(len(latencies) / 100 * percent)] * 1000, 3)


class Result(BaseModel):
    success: bool
    # endpoints -> list of latencies
    result: Dict[str, List[float]]
    # Dashboard memory usage in MB.
    memory_mb: Optional[float]


# Currently every endpoint is GET endpoints.
endpoints = [
    "/logical/actors",
    "/nodes?view=summary",
    "/",
    "/api/cluster_status",
    "/events",
    "/api/jobs/",
    "/api/v0/logs",
    "/api/prometheus_health",
]


@ray.remote(num_cpus=0)
class DashboardTester:
    def __init__(self, interval_s: int = 1):
        self.dashboard_url = get_address_for_submission_client(None)
        # Ping interval for all endpoints.
        self.interval_s = interval_s
        # endpoint -> a list of latencies
        self.result = defaultdict(list)

    async def run(self):
        await asyncio.gather(*[self.ping(endpoint) for endpoint in endpoints])

    async def ping(self, endpoint):
        """Synchronously call an endpoint."""
        node_id = ray.get_runtime_context().get_node_id()
        while True:
            start = time.monotonic()
            # for logs API, we should append node ID and glob.
            if "/api/v0/logs" in endpoint:
                glob_filter = "*"

                options_dict = {"node_id": node_id, "glob": glob_filter}
                url = (
                    f"{self.dashboard_url}{endpoint}?"
                    f"{urllib.parse.urlencode(options_dict)}"
                )
            else:
                url = f"{self.dashboard_url}{endpoint}"

            resp = requests.get(url, timeout=30)
            elapsed = time.monotonic() - start

            if resp.status_code == 200:
                self.result[endpoint].append(time.monotonic() - start)
            else:
                try:
                    resp.raise_for_status()
                except Exception as e:
                    logger.exception(e)
            await asyncio.sleep(max(0, self.interval_s, elapsed))

    def get_result(self):
        return self.result


class DashboardTestAtScale:
    """This is piggybacked into existing scalability tests."""

    def __init__(self, addr: ray._private.worker.RayContext):
        self.addr = addr

        # Schedule the actor on the current node (which is a head node).
        current_node_ip = ray._private.worker.global_worker.node_ip_address
        nodes = list_nodes(filters=[("node_ip", "=", current_node_ip)])
        assert len(nodes) > 0, f"{current_node_ip} not found in the cluster"
        node = nodes[0]
        # Schedule on a head node.
        self.tester = DashboardTester.options(
            label_selector={ray._raylet.RAY_NODE_ID_KEY: node["node_id"]}
        ).remote()

        self.tester.run.remote()

    def get_result(self):
        """Get the result from the test.

        Returns:
            A tuple of success, and the result (Result object).
        """
        try:
            result = ray.get(self.tester.get_result.remote(), timeout=60)
        except ray.exceptions.GetTimeoutError:
            return Result(success=False)

        # Get the memory usage.
        memories = get_system_metric_for_component(
            "ray_component_uss_bytes",
            "dashboard",
            os.environ.get(PROMETHEUS_HOST_ENV_VAR, DEFAULT_PROMETHEUS_HOST),
        )

        return Result(
            success=True,
            result=result,
            memory_mb=max(memories) / 1.0e6 if memories else None,
        )

    def update_release_test_result(self, release_result: dict):
        test_result = self.get_result()

        def calc_endpoints_p(result, percent):
            return {
                # sort -> get PX -> convert second to ms -> round up.
                endpoint: calc_p(latencies, percent)
                for endpoint, latencies in result.items()
            }

        print("======Print per dashboard endpoint latencies======")
        print("=====================P50==========================")
        pprint(calc_endpoints_p(test_result.result, 50))
        print("=====================P95==========================")
        pprint(calc_endpoints_p(test_result.result, 95))
        print("=====================P99==========================")
        pprint(calc_endpoints_p(test_result.result, 99))

        latencies = []
        for per_endpoint_latencies in test_result.result.values():
            latencies.extend(per_endpoint_latencies)
        aggregated_metrics = {
            "p50": calc_p(latencies, 50),
            "p95": calc_p(latencies, 95),
            "p99": calc_p(latencies, 99),
        }

        print("=====================Aggregated====================")
        pprint(aggregated_metrics)

        release_result["_dashboard_test_success"] = test_result.success
        if test_result.success:
            if "perf_metrics" not in release_result:
                release_result["perf_metrics"] = []

            release_result["perf_metrics"].extend(
                [
                    {
                        "perf_metric_name": f"dashboard_{p}_latency_ms",
                        "perf_metric_value": value,
                        "perf_metric_type": "LATENCY",
                    }
                    for p, value in aggregated_metrics.items()
                ]
            )
            release_result["_dashboard_memory_usage_mb"] = test_result.memory_mb