[SARC-329] Implémenter les alertes : Proportion de jobs CPU avec stat…

…s prometheus sur un noeud donné plus bas qu’un threshold X (#132)

* [SARC-329] Implémenter les alertes : Proportion de jobs CPU avec stats prometheus sur un noeud donné plus bas qu’un threshold X

* Use file_regression for tests.

* (cleanup) Remove unused code in tests for SARC-328

sarc/alerts/usage_alerts/prometheus_stats_occurrences.py

@@ -0,0 +1,177 @@
+import logging
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Sequence, Union
+
+from sarc.config import MTL
+from sarc.jobs.series import compute_time_frames, load_job_series
+
+logger = logging.getLogger(__name__)
+
+
+class PrometheusStatInfo:
+    """Prometheus stat context, used in checking below."""
+
+    def __init__(self, name):
+        self.name = name
+        self.col_has = f"has_{name}"
+        self.col_ratio = f"ratio_{name}"
+        self.avg = None
+        self.stddev = None
+        self.threshold = None
+
+
+def check_prometheus_stats_occurrences(
+    time_interval: Optional[timedelta] = timedelta(days=7),
+    time_unit=timedelta(days=1),
+    minimum_runtime: Optional[timedelta] = timedelta(minutes=5),
+    cluster_names: Optional[List[str]] = None,
+    group_by_node: Optional[Sequence[str]] = ("mila",),
+    min_jobs_per_group: Optional[Union[int, Dict[str, int]]] = None,
+    nb_stddev=2,
+):
+    """
+    Check if we have scrapped Prometheus stats for enough jobs per node per cluster per time unit.
+    Log a warning for each node / cluster where ratio of jobs with Prometheus stats is lower than
+    a threshold computed using mean and standard deviation statistics from all clusters.
+
+    Parameters
+    ----------
+    time_interval: timedelta
+        If given, only jobs which ran in [now - time_interval, time_interval] will be used for checking.
+        Default is last 7 days.
+        If None, all jobs are used.
+    time_unit: timedelta
+        Time unit in which we must check cluster usage through time_interval. Default is 1 day.
+    minimum_runtime: timedelta
+        If given, only jobs which ran at least for this minimum runtime will be used for checking.
+        Default is 5 minutes.
+        If None, set to 0.
+    cluster_names: list
+        Optional list of clusters to check.
+
+        There may have clusters we don't want to check among retrieved jobs (eg. clusters in maintenance).
+        On the opposite, we may expect to see jobs in a cluster, but there are actually no jobs in this cluster.
+        To cover such cases, one can specify the complete list of expected clusters with `cluster_names`.
+        Jobs from clusters not in this list will be ignored both to compute statistics and in checking phase.
+        If a cluster in this list does not appear in jobs, a warning will be logged.
+
+        If empty (or not specified), use all clusters available among jobs retrieved with time_interval.
+    group_by_node: Sequence
+        Optional sequence of clusters to group by node.
+        For clusters in this list, we will check each node separately (ie. a "group" is a cluster node).
+        By default, we check the entire cluster (i.e. the "group" is the cluster itself).
+    min_jobs_per_group: int | dict
+        Minimum number of jobs required for checking in each group.
+        Either an integer, as minimum number for any group,
+        or a dictionary mapping a cluster name to minimum number in each group of this cluster
+        A group is either a cluster node, if cluster name is in `group_by_node`,
+        or the entire cluster otherwise.
+        Default is 1 job per group.
+    nb_stddev: float
+        Amount of standard deviation to remove from average statistics to compute checking threshold.
+        Threshold is computed as:
+        max(0, average - nb_stddev * stddev)
+    """
+
+    # Parse time_interval and get data frame
+    start, end, clip_time = None, None, False
+    if time_interval is not None:
+        end = datetime.now(tz=MTL)
+        start = end - time_interval
+        clip_time = True
+    df = load_job_series(start=start, end=end, clip_time=clip_time)
+
+    # Parse minimum_runtime, and select only jobs where
+    # elapsed time >= minimum runtime and allocated.gres_gpu == 0
+    if minimum_runtime is None:
+        minimum_runtime = timedelta(seconds=0)
+    df = df[
+        (df["elapsed_time"] >= minimum_runtime.total_seconds())
+        & (df["allocated.gres_gpu"] == 0)
+    ]
+
+    # List clusters
+    cluster_names = cluster_names or sorted(df["cluster_name"].unique())
+
+    # Split data frame into time frames using `time_unit`
+    df = compute_time_frames(df, frame_size=time_unit)
+
+    # Duplicates lines per node to count each job for each node where it runs
+    df = df.explode("nodes")
+
+    # If cluster not in group_by_node,
+    # then we must count jobs for the entire cluster, not per node.
+    # To simplify the code, let's just define 1 common node for all cluster jobs
+    cluster_node_name = "(all)"
+    df.loc[~df["cluster_name"].isin(group_by_node), "nodes"] = cluster_node_name
+
+    # Add a column to ease job count
+    df.loc[:, "task_"] = 1
+
+    # Generate Prometheus context for each Prometheus stat we want to check.
+    prom_contexts = [
+        PrometheusStatInfo(name=prom_col)
+        for prom_col in ["cpu_utilization", "system_memory"]
+    ]
+
+    # Add columns to check if job has prometheus stats
+    for prom in prom_contexts:
+        df.loc[:, prom.col_has] = ~df[prom.name].isnull()
+
+    # Group per timestamp per cluster per node, and count jobs and prometheus stats.
+    # If "cluster_names" are given, use only jobs in these clusters.
+    f_stats = (
+        df[df["cluster_name"].isin(cluster_names)]
+        .groupby(["timestamp", "cluster_name", "nodes"])[
+            [prom_info.col_has for prom_info in prom_contexts] + ["task_"]
+        ]
+        .sum()
+    )
+
+    # Compute ratio of job with Prometheus stat for each group,
+    # then compute threshold for each Prometheus stat.
+    for prom in prom_contexts:
+        f_stats[prom.col_ratio] = f_stats[prom.col_has] / f_stats["task_"]
+        prom.avg = f_stats[prom.col_ratio].mean()
+        prom.stddev = f_stats[prom.col_ratio].std()
+        prom.threshold = max(0, prom.avg - nb_stddev * prom.stddev)
+
+    # Parse min_jobs_per_group
+    default_min_jobs = 1
+    if min_jobs_per_group is None:
+        min_jobs_per_group = {}
+    elif isinstance(min_jobs_per_group, int):
+        default_min_jobs = min_jobs_per_group
+        min_jobs_per_group = {}
+    assert isinstance(min_jobs_per_group, dict)
+
+    # Now we can check
+    clusters_seen = set()
+    for row in f_stats.itertuples():
+        timestamp, cluster_name, node = row.Index
+        clusters_seen.add(cluster_name)
+        nb_jobs = row.task_
+        if nb_jobs >= min_jobs_per_group.get(cluster_name, default_min_jobs):
+            grouping_type = "cluster" if node == cluster_node_name else "node / cluster"
+            grouping_name = (
+                f"[{cluster_name}]"
+                if node == cluster_node_name
+                else f"[{cluster_name}][{node}]"
+            )
+            for prom in prom_contexts:
+                local_stat = getattr(row, prom.col_has) / nb_jobs
+                if local_stat < prom.threshold:
+                    logger.warning(
+                        f"[{timestamp}]{grouping_name} insufficient Prometheus data for {prom.name}: "
+                        f"{round(local_stat * 100, 2)} % of CPU jobs / {grouping_type} / time unit; "
+                        f"minimum required: {prom.threshold} ({prom.avg} - {nb_stddev} * {prom.stddev}); "
+                        f"time unit: {time_unit}"
+                    )
+
+    # Check clusters listed in `cluster_names` but not found in jobs.
+    for cluster_name in cluster_names:
+        if cluster_name not in clusters_seen:
+            # No stats found for this cluster. Warning
+            logger.warning(
+                f"[{cluster_name}] no Prometheus data available: no job found"
+            )

tests/functional/conftest.py

@@ -41,9 +41,9 @@ def clear_db(db):
     db.clusters.drop()
 
 
-def fill_db(db, with_users=False, with_clusters=False):
+def fill_db(db, with_users=False, with_clusters=False, job_patch=None):
     db.allocations.insert_many(create_allocations())
-    db.jobs.insert_many(create_jobs())
+    db.jobs.insert_many(create_jobs(job_patch=job_patch))
     db.diskusage.insert_many(create_diskusages())
     if with_users:
         db.users.insert_many(create_users())
@@ -58,15 +58,15 @@ def fill_db(db, with_users=False, with_clusters=False):
 
 
 def create_db_configuration_fixture(
-    db_name, empty=False, with_users=False, scope="function"
+    db_name, empty=False, with_users=False, job_patch=None, scope="function"
 ):
     @pytest.fixture(scope=scope)
     def fixture(standard_config_object):
         cfg = custom_db_config(standard_config_object, db_name)
         db = cfg.mongo.database_instance
         clear_db(db)
         if not empty:
-            fill_db(db, with_users=with_users)
+            fill_db(db, with_users=with_users, job_patch=job_patch)
         yield
 
     return fixture
@@ -106,6 +106,16 @@ def fixture(client_config_object):
 )
 
 
+read_only_db_with_many_cpu_jobs_config_object = create_db_configuration_fixture(
+    db_name="sarc-read-only-with-many-cpu-jobs-test",
+    scope="session",
+    job_patch={
+        "allocated": {"billing": 0, "cpu": 0, "gres_gpu": 0, "mem": 0, "node": 0},
+        "requested": {"billing": 0, "cpu": 0, "gres_gpu": 0, "mem": 0, "node": 0},
+    },
+)
+
+
 read_only_db_with_users_config_object = create_db_configuration_fixture(
     db_name="sarc-read-only-with-users-test",
     with_users=True,
@@ -141,6 +151,15 @@ def read_only_db(standard_config, read_only_db_config_object):
         yield cfg.mongo.database_instance
 
 
+@pytest.fixture
+def read_only_db_with_many_cpu_jobs(
+    standard_config, read_only_db_with_many_cpu_jobs_config_object
+):
+    cfg = custom_db_config(standard_config, "sarc-read-only-with-many-cpu-jobs-test")
+    with using_config(cfg) as cfg:
+        yield cfg.mongo.database_instance
+
+
 @pytest.fixture
 def read_only_db_with_users(standard_config, read_only_db_with_users_config_object):
     cfg = custom_db_config(standard_config, "sarc-read-only-with-users-test")

tests/functional/jobs/factory.py

@@ -45,13 +45,17 @@
 
 class JobFactory:
     def __init__(
-        self, first_submit_time: None | datetime = None, first_job_id: int = 1
+        self,
+        first_submit_time: None | datetime = None,
+        first_job_id: int = 1,
+        job_patch: dict | None = None,
     ):
         self.jobs = []
         self._first_submit_time = first_submit_time or datetime(
             2023, 2, 14, tzinfo=MTL
         ).astimezone(UTC)
         self._first_job_id = first_job_id
+        self.job_patch = job_patch or {}
 
     @property
     def next_job_id(self):
@@ -96,6 +100,8 @@ def format_kwargs(self, kwargs):
 
     def create_job(self, **kwargs):
         job = copy.deepcopy(base_job)
+        if self.job_patch:
+            job.update(self.job_patch)
         job.update(self.format_kwargs(kwargs))
 
         return job
@@ -189,9 +195,9 @@ def _create_user(username: str, with_drac=True):
     }
 
 
-def create_jobs(job_factory: JobFactory | None = None):
+def create_jobs(job_factory: JobFactory | None = None, job_patch: dict | None = None):
     if job_factory is None:
-        job_factory = JobFactory()
+        job_factory = JobFactory(job_patch=job_patch)
 
     for status in [
         "CANCELLED",

tests/functional/usage_alerts/test_alert_cluster_scraping.py

@@ -6,12 +6,6 @@
 from sarc.alerts.usage_alerts.cluster_scraping import check_nb_jobs_per_cluster_per_time
 
 from ..jobs.test_func_load_job_series import MOCK_TIME
-from .common import _get_warnings
-
-get_warnings = functools.partial(
-    _get_warnings,
-    module="sarc.alerts.usage_alerts.cluster_scraping:cluster_scraping.py",
-)
 
 
 @pytest.mark.freeze_time(MOCK_TIME)

tests/functional/usage_alerts/test_prometheus_scraping_stats.py

@@ -0,0 +1,82 @@
+import functools
+import re
+
+import pytest
+
+from sarc.alerts.usage_alerts.prometheus_stats_occurrences import (
+    check_prometheus_stats_occurrences,
+)
+from sarc.client import get_jobs
+from tests.functional.jobs.test_func_load_job_series import MOCK_TIME
+
+from ..jobs.test_func_job_statistics import generate_fake_timeseries
+
+
+@pytest.mark.freeze_time(MOCK_TIME)
+@pytest.mark.usefixtures("read_only_db_with_many_cpu_jobs", "tzlocal_is_mtl")
+@pytest.mark.parametrize(
+    "params",
+    [
+        # Check with default params. In last 7 days from now (mock time: 2023-11-22),
+        # there is only 2 jobs from 1 cluster in 1 timestamp, both with no cpu_utilization
+        # and no system_memory. So threshold will be 0 everywhere, and no warning will be printed.
+        dict(),
+        # Check with no time_interval.
+        dict(time_interval=None),
+        # Check with no time_interval and low amount of stddev (0.25), to get more warnings.
+        dict(time_interval=None, nb_stddev=0.25),
+        # Check with no time_interval, 0.25 stddev, and 1 extra cluster.
+        # Expected 1 more warning, no other changes .
+        dict(
+            time_interval=None,
+            nb_stddev=0.25,
+            cluster_names=[
+                "raisin",
+                "patate",
+                "fromage",
+                "mila",
+                "invisible-cluster",
+            ],
+        ),
+        # Check with no time_interval, 0.25 stddev, with only 2 clusters. Thresholds will change.
+        dict(time_interval=None, nb_stddev=0.25, cluster_names=["raisin", "mila"]),
+        # Check with no time_interval, 0.25 stddev, and no group_by_node.
+        dict(time_interval=None, nb_stddev=0.25, group_by_node=()),
+        # Check with no time_interval, 0.25 stddev, and group_by_node for all clusters.
+        # Many changes.
+        dict(
+            time_interval=None,
+            nb_stddev=0.25,
+            group_by_node=["raisin", "patate", "fromage", "mila"],
+        ),
+        # Check with no time_interval, 0.25 stddev, group_by_node for all clusters, and min jobs to 2.
+        dict(
+            time_interval=None,
+            nb_stddev=0.25,
+            group_by_node=["raisin", "patate", "fromage", "mila"],
+            min_jobs_per_group=2,
+        ),
+        # Check with no time_interval, 0.25 stddev, group_by_node for all clusters, and min jobs set for one cluster.
+        dict(
+            time_interval=None,
+            nb_stddev=0.25,
+            group_by_node=["raisin", "patate", "fromage", "mila"],
+            min_jobs_per_group={"raisin": 3},
+        ),
+    ],
+)
+def test_check_prometheus_scraping_stats(params, monkeypatch, caplog, file_regression):
+    monkeypatch.setattr(
+        "sarc.jobs.series.get_job_time_series", generate_fake_timeseries
+    )
+
+    for job in get_jobs():
+        job.statistics(save=True)
+    check_prometheus_stats_occurrences(**params)
+    file_regression.check(
+        re.sub(
+            r"WARNING +sarc\.alerts\.usage_alerts\.prometheus_stats_occurrences:prometheus_stats_occurrences.py:[0-9]+ +",
+            "",
+            caplog.text,
+        )
+    )

tests/functional/usage_alerts/test_prometheus_scraping_stats/test_check_prometheus_scraping_stats_params1_.txt

@@ -0,0 +1 @@
+[2023-11-21 00:01:00-05:00][raisin] insufficient Prometheus data for cpu_utilization: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.368376726851694 (0.9230769230769231 - 2 * 0.2773500981126146); time unit: 1 day, 0:00:00

tests/functional/usage_alerts/test_prometheus_scraping_stats/test_check_prometheus_scraping_stats_params2_.txt

@@ -0,0 +1,12 @@
+[2023-02-14 00:01:00-05:00][raisin] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-15 00:01:00-05:00][raisin] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-16 00:01:00-05:00][raisin] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-17 00:01:00-05:00][fromage] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-17 00:01:00-05:00][patate] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-17 00:01:00-05:00][raisin] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-18 00:01:00-05:00][mila][cn-c021] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / node / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-18 00:01:00-05:00][patate] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-18 00:01:00-05:00][raisin] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-02-19 00:01:00-05:00][raisin] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00
+[2023-11-21 00:01:00-05:00][raisin] insufficient Prometheus data for cpu_utilization: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.8537393985487695 (0.9230769230769231 - 0.25 * 0.2773500981126146); time unit: 1 day, 0:00:00
+[2023-11-21 00:01:00-05:00][raisin] insufficient Prometheus data for system_memory: 0.0 % of CPU jobs / cluster / time unit; minimum required: 0.059962701821302505 (0.15384615384615385 - 0.25 * 0.3755338080994054); time unit: 1 day, 0:00:00