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https://github.com/ansible/awx.git
synced 2026-05-12 03:47:36 -02:30
Per-service metrics http server
* Organize metrics into their respective service * Server per-service metrics on a per-service http server * Increase prometheus client usage over our custom metrics fields
This commit is contained in:
Chris Meyers
committed by
Chris Meyers
Chris Meyers
parent
6dcaa09dfb
commit
8a902debd5
@@ -1,10 +1,15 @@
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import itertools
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import redis
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import json
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import time
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import logging
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import prometheus_client
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from prometheus_client.core import GaugeMetricFamily, HistogramMetricFamily
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from prometheus_client.registry import CollectorRegistry
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from django.conf import settings
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from django.apps import apps
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from django.http import HttpRequest
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from rest_framework.request import Request
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from awx.main.consumers import emit_channel_notification
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from awx.main.utils import is_testing
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@@ -13,6 +18,30 @@ root_key = settings.SUBSYSTEM_METRICS_REDIS_KEY_PREFIX
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logger = logging.getLogger('awx.main.analytics')
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class MetricsNamespace:
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def __init__(self, namespace):
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self._namespace = namespace
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class MetricsServerSettings(MetricsNamespace):
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def port(self):
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return settings.METRICS_SUBSYSTEM_CONFIG['server'][self._namespace]['port']
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class MetricsServer(MetricsServerSettings):
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def __init__(self, namespace, registry):
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MetricsNamespace.__init__(self, namespace)
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self._registry = registry
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def start(self):
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try:
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# TODO: addr for ipv6 ?
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prometheus_client.start_http_server(self.port(), addr='localhost', registry=self._registry)
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except Exception:
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logger.error(f"MetricsServer failed to start for service '{self._namespace}.")
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raise
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class BaseM:
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def __init__(self, field, help_text):
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self.field = field
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@@ -148,76 +177,40 @@ class HistogramM(BaseM):
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return output_text
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class Metrics:
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def __init__(self, auto_pipe_execute=False, instance_name=None):
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class Metrics(MetricsNamespace):
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# metric name, help_text
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METRICSLIST = []
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_METRICSLIST = [
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FloatM('subsystem_metrics_pipe_execute_seconds', 'Time spent saving metrics to redis'),
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IntM('subsystem_metrics_pipe_execute_calls', 'Number of calls to pipe_execute'),
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FloatM('subsystem_metrics_send_metrics_seconds', 'Time spent sending metrics to other nodes'),
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]
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def __init__(self, namespace, auto_pipe_execute=False, instance_name=None, metrics_have_changed=True, **kwargs):
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MetricsNamespace.__init__(self, namespace)
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self.pipe = redis.Redis.from_url(settings.BROKER_URL).pipeline()
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self.conn = redis.Redis.from_url(settings.BROKER_URL)
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self.last_pipe_execute = time.time()
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# track if metrics have been modified since last saved to redis
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# start with True so that we get an initial save to redis
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self.metrics_have_changed = True
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self.metrics_have_changed = metrics_have_changed
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self.pipe_execute_interval = settings.SUBSYSTEM_METRICS_INTERVAL_SAVE_TO_REDIS
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self.send_metrics_interval = settings.SUBSYSTEM_METRICS_INTERVAL_SEND_METRICS
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# auto pipe execute will commit transaction of metric data to redis
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# at a regular interval (pipe_execute_interval). If set to False,
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# the calling function should call .pipe_execute() explicitly
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self.auto_pipe_execute = auto_pipe_execute
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Instance = apps.get_model('main', 'Instance')
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if instance_name:
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self.instance_name = instance_name
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elif is_testing():
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self.instance_name = "awx_testing"
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else:
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self.instance_name = Instance.objects.my_hostname()
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self.instance_name = settings.CLUSTER_HOST_ID # Same as Instance.objects.my_hostname() BUT we do not need to import Instance
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# metric name, help_text
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METRICSLIST = [
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SetIntM('callback_receiver_events_queue_size_redis', 'Current number of events in redis queue'),
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IntM('callback_receiver_events_popped_redis', 'Number of events popped from redis'),
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IntM('callback_receiver_events_in_memory', 'Current number of events in memory (in transfer from redis to db)'),
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IntM('callback_receiver_batch_events_errors', 'Number of times batch insertion failed'),
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FloatM('callback_receiver_events_insert_db_seconds', 'Total time spent saving events to database'),
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IntM('callback_receiver_events_insert_db', 'Number of events batch inserted into database'),
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IntM('callback_receiver_events_broadcast', 'Number of events broadcast to other control plane nodes'),
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HistogramM(
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'callback_receiver_batch_events_insert_db', 'Number of events batch inserted into database', settings.SUBSYSTEM_METRICS_BATCH_INSERT_BUCKETS
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),
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SetFloatM('callback_receiver_event_processing_avg_seconds', 'Average processing time per event per callback receiver batch'),
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FloatM('subsystem_metrics_pipe_execute_seconds', 'Time spent saving metrics to redis'),
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IntM('subsystem_metrics_pipe_execute_calls', 'Number of calls to pipe_execute'),
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FloatM('subsystem_metrics_send_metrics_seconds', 'Time spent sending metrics to other nodes'),
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SetFloatM('task_manager_get_tasks_seconds', 'Time spent in loading tasks from db'),
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SetFloatM('task_manager_start_task_seconds', 'Time spent starting task'),
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SetFloatM('task_manager_process_running_tasks_seconds', 'Time spent processing running tasks'),
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SetFloatM('task_manager_process_pending_tasks_seconds', 'Time spent processing pending tasks'),
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SetFloatM('task_manager__schedule_seconds', 'Time spent in running the entire _schedule'),
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IntM('task_manager__schedule_calls', 'Number of calls to _schedule, after lock is acquired'),
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SetFloatM('task_manager_recorded_timestamp', 'Unix timestamp when metrics were last recorded'),
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SetIntM('task_manager_tasks_started', 'Number of tasks started'),
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SetIntM('task_manager_running_processed', 'Number of running tasks processed'),
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SetIntM('task_manager_pending_processed', 'Number of pending tasks processed'),
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SetIntM('task_manager_tasks_blocked', 'Number of tasks blocked from running'),
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SetFloatM('task_manager_commit_seconds', 'Time spent in db transaction, including on_commit calls'),
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SetFloatM('dependency_manager_get_tasks_seconds', 'Time spent loading pending tasks from db'),
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SetFloatM('dependency_manager_generate_dependencies_seconds', 'Time spent generating dependencies for pending tasks'),
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SetFloatM('dependency_manager__schedule_seconds', 'Time spent in running the entire _schedule'),
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IntM('dependency_manager__schedule_calls', 'Number of calls to _schedule, after lock is acquired'),
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SetFloatM('dependency_manager_recorded_timestamp', 'Unix timestamp when metrics were last recorded'),
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SetIntM('dependency_manager_pending_processed', 'Number of pending tasks processed'),
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SetFloatM('workflow_manager__schedule_seconds', 'Time spent in running the entire _schedule'),
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IntM('workflow_manager__schedule_calls', 'Number of calls to _schedule, after lock is acquired'),
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SetFloatM('workflow_manager_recorded_timestamp', 'Unix timestamp when metrics were last recorded'),
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SetFloatM('workflow_manager_spawn_workflow_graph_jobs_seconds', 'Time spent spawning workflow tasks'),
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SetFloatM('workflow_manager_get_tasks_seconds', 'Time spent loading workflow tasks from db'),
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# dispatcher subsystem metrics
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SetIntM('dispatcher_pool_scale_up_events', 'Number of times local dispatcher scaled up a worker since startup'),
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SetIntM('dispatcher_pool_active_task_count', 'Number of active tasks in the worker pool when last task was submitted'),
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SetIntM('dispatcher_pool_max_worker_count', 'Highest number of workers in worker pool in last collection interval, about 20s'),
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SetFloatM('dispatcher_availability', 'Fraction of time (in last collection interval) dispatcher was able to receive messages'),
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]
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# turn metric list into dictionary with the metric name as a key
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self.METRICS = {}
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for m in METRICSLIST:
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for m in itertools.chain(self.METRICSLIST, self._METRICSLIST):
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self.METRICS[m.field] = m
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# track last time metrics were sent to other nodes
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@@ -230,7 +223,7 @@ class Metrics:
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m.reset_value(self.conn)
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self.metrics_have_changed = True
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self.conn.delete(root_key + "_lock")
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for m in self.conn.scan_iter(root_key + '_instance_*'):
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for m in self.conn.scan_iter(root_key + '-' + self._namespace + '_instance_*'):
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self.conn.delete(m)
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def inc(self, field, value):
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@@ -297,7 +290,7 @@ class Metrics:
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def send_metrics(self):
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# more than one thread could be calling this at the same time, so should
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# acquire redis lock before sending metrics
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lock = self.conn.lock(root_key + '_lock')
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lock = self.conn.lock(root_key + '-' + self._namespace + '_lock')
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if not lock.acquire(blocking=False):
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return
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try:
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@@ -307,9 +300,10 @@ class Metrics:
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payload = {
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'instance': self.instance_name,
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'metrics': serialized_metrics,
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'metrics_namespace': self._namespace,
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}
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# store the serialized data locally as well, so that load_other_metrics will read it
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self.conn.set(root_key + '_instance_' + self.instance_name, serialized_metrics)
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self.conn.set(root_key + '-' + self._namespace + '_instance_' + self.instance_name, serialized_metrics)
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emit_channel_notification("metrics", payload)
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self.previous_send_metrics.set(current_time)
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@@ -331,14 +325,14 @@ class Metrics:
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instances_filter = request.query_params.getlist("node")
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# get a sorted list of instance names
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instance_names = [self.instance_name]
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for m in self.conn.scan_iter(root_key + '_instance_*'):
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for m in self.conn.scan_iter(root_key + '-' + self._namespace + '_instance_*'):
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instance_names.append(m.decode('UTF-8').split('_instance_')[1])
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instance_names.sort()
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# load data, including data from the this local instance
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instance_data = {}
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for instance in instance_names:
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if len(instances_filter) == 0 or instance in instances_filter:
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instance_data_from_redis = self.conn.get(root_key + '_instance_' + instance)
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instance_data_from_redis = self.conn.get(root_key + '-' + self._namespace + '_instance_' + instance)
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# data from other instances may not be available. That is OK.
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if instance_data_from_redis:
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instance_data[instance] = json.loads(instance_data_from_redis.decode('UTF-8'))
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@@ -357,6 +351,120 @@ class Metrics:
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return output_text
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class DispatcherMetrics(Metrics):
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METRICSLIST = [
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SetFloatM('task_manager_get_tasks_seconds', 'Time spent in loading tasks from db'),
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SetFloatM('task_manager_start_task_seconds', 'Time spent starting task'),
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SetFloatM('task_manager_process_running_tasks_seconds', 'Time spent processing running tasks'),
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SetFloatM('task_manager_process_pending_tasks_seconds', 'Time spent processing pending tasks'),
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SetFloatM('task_manager__schedule_seconds', 'Time spent in running the entire _schedule'),
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IntM('task_manager__schedule_calls', 'Number of calls to _schedule, after lock is acquired'),
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SetFloatM('task_manager_recorded_timestamp', 'Unix timestamp when metrics were last recorded'),
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SetIntM('task_manager_tasks_started', 'Number of tasks started'),
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SetIntM('task_manager_running_processed', 'Number of running tasks processed'),
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SetIntM('task_manager_pending_processed', 'Number of pending tasks processed'),
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SetIntM('task_manager_tasks_blocked', 'Number of tasks blocked from running'),
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SetFloatM('task_manager_commit_seconds', 'Time spent in db transaction, including on_commit calls'),
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SetFloatM('dependency_manager_get_tasks_seconds', 'Time spent loading pending tasks from db'),
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SetFloatM('dependency_manager_generate_dependencies_seconds', 'Time spent generating dependencies for pending tasks'),
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SetFloatM('dependency_manager__schedule_seconds', 'Time spent in running the entire _schedule'),
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IntM('dependency_manager__schedule_calls', 'Number of calls to _schedule, after lock is acquired'),
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SetFloatM('dependency_manager_recorded_timestamp', 'Unix timestamp when metrics were last recorded'),
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SetIntM('dependency_manager_pending_processed', 'Number of pending tasks processed'),
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SetFloatM('workflow_manager__schedule_seconds', 'Time spent in running the entire _schedule'),
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IntM('workflow_manager__schedule_calls', 'Number of calls to _schedule, after lock is acquired'),
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SetFloatM('workflow_manager_recorded_timestamp', 'Unix timestamp when metrics were last recorded'),
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SetFloatM('workflow_manager_spawn_workflow_graph_jobs_seconds', 'Time spent spawning workflow tasks'),
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SetFloatM('workflow_manager_get_tasks_seconds', 'Time spent loading workflow tasks from db'),
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# dispatcher subsystem metrics
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SetIntM('dispatcher_pool_scale_up_events', 'Number of times local dispatcher scaled up a worker since startup'),
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SetIntM('dispatcher_pool_active_task_count', 'Number of active tasks in the worker pool when last task was submitted'),
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SetIntM('dispatcher_pool_max_worker_count', 'Highest number of workers in worker pool in last collection interval, about 20s'),
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SetFloatM('dispatcher_availability', 'Fraction of time (in last collection interval) dispatcher was able to receive messages'),
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]
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def __init__(self, *args, **kwargs):
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super().__init__(settings.METRICS_SERVICE_DISPATCHER, *args, **kwargs)
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class CallbackReceiverMetrics(Metrics):
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METRICSLIST = [
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SetIntM('callback_receiver_events_queue_size_redis', 'Current number of events in redis queue'),
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IntM('callback_receiver_events_popped_redis', 'Number of events popped from redis'),
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IntM('callback_receiver_events_in_memory', 'Current number of events in memory (in transfer from redis to db)'),
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IntM('callback_receiver_batch_events_errors', 'Number of times batch insertion failed'),
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FloatM('callback_receiver_events_insert_db_seconds', 'Total time spent saving events to database'),
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IntM('callback_receiver_events_insert_db', 'Number of events batch inserted into database'),
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IntM('callback_receiver_events_broadcast', 'Number of events broadcast to other control plane nodes'),
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HistogramM(
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'callback_receiver_batch_events_insert_db', 'Number of events batch inserted into database', settings.SUBSYSTEM_METRICS_BATCH_INSERT_BUCKETS
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),
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SetFloatM('callback_receiver_event_processing_avg_seconds', 'Average processing time per event per callback receiver batch'),
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]
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def __init__(self, *args, **kwargs):
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super().__init__(settings.METRICS_SERVICE_CALLBACK_RECEIVER, *args, **kwargs)
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def metrics(request):
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m = Metrics()
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return m.generate_metrics(request)
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output_text = ''
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for m in [DispatcherMetrics(), CallbackReceiverMetrics()]:
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output_text += m.generate_metrics(request)
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return output_text
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class CustomToPrometheusMetricsCollector(prometheus_client.registry.Collector):
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"""
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Takes the metric data from redis -> our custom metric fields -> prometheus
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library metric fields.
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The plan is to get rid of the use of redis, our custom metric fields, and
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to switch fully to the prometheus library. At that point, this translation
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code will be deleted.
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"""
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def __init__(self, metrics_obj, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._metrics = metrics_obj
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def collect(self):
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my_hostname = settings.CLUSTER_HOST_ID
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instance_data = self._metrics.load_other_metrics(Request(HttpRequest()))
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if not instance_data:
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logger.debug(f"No metric data not found in redis for metric namespace '{self._metrics._namespace}'")
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return None
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host_metrics = instance_data.get(my_hostname)
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for _, metric in self._metrics.METRICS.items():
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entry = host_metrics.get(metric.field)
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if not entry:
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logger.debug(f"{self._metrics._namespace} metric '{metric.field}' not found in redis data payload {json.dumps(instance_data, indent=2)}")
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continue
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if isinstance(metric, HistogramM):
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buckets = list(zip(metric.buckets, entry['counts']))
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buckets = [[str(i[0]), str(i[1])] for i in buckets]
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yield HistogramMetricFamily(metric.field, metric.help_text, buckets=buckets, sum_value=entry['sum'])
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else:
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yield GaugeMetricFamily(metric.field, metric.help_text, value=entry)
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class CallbackReceiverMetricsServer(MetricsServer):
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def __init__(self):
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registry = CollectorRegistry(auto_describe=True)
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registry.register(CustomToPrometheusMetricsCollector(DispatcherMetrics(metrics_have_changed=False)))
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super().__init__(settings.METRICS_SERVICE_CALLBACK_RECEIVER, registry)
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class DispatcherMetricsServer(MetricsServer):
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def __init__(self):
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registry = CollectorRegistry(auto_describe=True)
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registry.register(CustomToPrometheusMetricsCollector(CallbackReceiverMetrics(metrics_have_changed=False)))
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super().__init__(settings.METRICS_SERVICE_DISPATCHER, registry)
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class WebsocketsMetricsServer(MetricsServer):
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def __init__(self):
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registry = CollectorRegistry(auto_describe=True)
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# registry.register()
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super().__init__(settings.METRICS_SERVICE_WEBSOCKETS, registry)
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