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[dev] Add an Agent Developer Mode for collecting fine-grained perform…
…ance metrics
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,151 @@ | ||
| import os | ||
| import threading | ||
|
|
||
| # 3p | ||
| try: | ||
| import psutil | ||
| except ImportError: | ||
| psutil = None | ||
|
|
||
| # project | ||
| from checks import AgentCheck | ||
| from checks.metric_types import MetricTypes | ||
| from config import _is_affirmative | ||
|
|
||
| MAX_THREADS_COUNT = 50 | ||
| MAX_COLLECTION_TIME = 30 | ||
| MAX_EMIT_TIME = 5 | ||
| MAX_CPU_PCT = 10 | ||
|
|
||
|
|
||
| class UnsupportedMetricType(Exception): | ||
| """ | ||
| Raised by :class:`AgentMetrics` when a metric type outside outside of AgentMetrics.ALLOWED_METRIC_TYPES | ||
| is requested for measurement of a particular statistic | ||
| """ | ||
| def __init__(self, metric_name, metric_type): | ||
| message = 'Unsupported Metric Type for {0} : {1}'.format(metric_name, metric_type) | ||
| Exception.__init__(self, message) | ||
|
|
||
| class AgentMetrics(AgentCheck): | ||
| """ | ||
| New-style version of `CollectorMetrics` | ||
| Gets information about agent performance on every collector loop | ||
| """ | ||
|
|
||
| def __init__(self, *args, **kwargs): | ||
| AgentCheck.__init__(self, *args, **kwargs) | ||
| self._collector_payload = {} | ||
| self._metric_context = {} | ||
|
|
||
| def _psutil_config_to_stats(self, instance): | ||
| """ | ||
| Reads `init_config` for `psutil` methods to call on the current process | ||
| Calls those methods and stores the raw output | ||
| :returns a dictionary of statistic_name: value | ||
| """ | ||
| process_metrics = instance.get('process_metrics', self.init_config.get('process_metrics', None)) | ||
| if not process_metrics: | ||
| self.log.error('No metrics configured for AgentMetrics check!') | ||
| return {} | ||
|
|
||
| methods, metric_types = zip( | ||
| *[(p['name'], p.get('type', MetricTypes.GAUGE)) | ||
| for p in process_metrics if _is_affirmative(p.get('active'))] | ||
| ) | ||
|
|
||
| names_to_metric_types = {} | ||
| for i, m in enumerate(methods): | ||
| names_to_metric_types[AgentCheck._get_statistic_name_from_method(m)] = metric_types[i] | ||
|
|
||
| stats = AgentCheck._collect_internal_stats(methods) | ||
| return stats, names_to_metric_types | ||
|
|
||
| def _send_single_metric(self, metric_name, metric_value, metric_type): | ||
| if metric_type == MetricTypes.GAUGE: | ||
| self.gauge(metric_name, metric_value) | ||
| elif metric_type == MetricTypes.RATE: | ||
| self.rate(metric_name, metric_value) | ||
| else: | ||
| raise UnsupportedMetricType(metric_name, metric_type) | ||
|
|
||
| def _register_psutil_metrics(self, stats, names_to_metric_types): | ||
| """ | ||
| Saves sample metrics from psutil | ||
| :param stats: a dictionary that looks like: | ||
| { | ||
| 'memory_info': OrderedDict([('rss', 24395776), ('vms', 144666624)]), | ||
| 'io_counters': OrderedDict([('read_count', 4536), | ||
| ('write_count', 100), | ||
| ('read_bytes', 0), | ||
| ('write_bytes', 61440)]) | ||
| ... | ||
| } | ||
| This creates a metric like `datadog.agent.collector.{key_1}.{key_2}` where key_1 is a top-level | ||
| key in `stats`, and key_2 is a nested key. | ||
| E.g. datadog.agent.collector.memory_info.rss | ||
| """ | ||
|
|
||
| base_metric = 'datadog.agent.collector.{0}.{1}' | ||
| #TODO: May have to call self.normalize(metric_name) to get a compliant name | ||
| for k, v in stats.iteritems(): | ||
| metric_type = names_to_metric_types[k] | ||
| if isinstance(v, dict): | ||
| for _k, _v in v.iteritems(): | ||
| full_metric_name = base_metric.format(k, _k) | ||
| self._send_single_metric(full_metric_name, _v, metric_type) | ||
| else: | ||
| full_metric_name = 'datadog.agent.collector.{0}'.format(k) | ||
| self._send_single_metric(full_metric_name, v, metric_type) | ||
|
|
||
| def set_metric_context(self, payload, context): | ||
| self._collector_payload = payload | ||
| self._metric_context = context | ||
|
|
||
| def get_metric_context(self): | ||
| return self._collector_payload, self._metric_context | ||
|
|
||
| def check(self, instance): | ||
| if self.in_developer_mode: | ||
| stats, names_to_metric_types = self._psutil_config_to_stats(instance) | ||
| self._register_psutil_metrics(stats, names_to_metric_types) | ||
|
|
||
| payload, context = self.get_metric_context() | ||
| collection_time = context.get('collection_time', None) | ||
| emit_time = context.get('emit_time', None) | ||
| cpu_time = context.get('cpu_time', None) | ||
|
|
||
| if threading.activeCount() > MAX_THREADS_COUNT: | ||
| self.gauge('datadog.agent.collector.threads.count', threading.activeCount()) | ||
| self.log.info("Thread count is high: %d" % threading.activeCount()) | ||
|
|
||
| collect_time_exceeds_threshold = collection_time > MAX_COLLECTION_TIME | ||
| if collection_time is not None and \ | ||
| (collect_time_exceeds_threshold or self.in_developer_mode): | ||
|
|
||
| self.gauge('datadog.agent.collector.collection.time', collection_time) | ||
| if collect_time_exceeds_threshold: | ||
| self.log.info("Collection time (s) is high: %.1f, metrics count: %d, events count: %d" | ||
| % (collection_time, len(payload['metrics']), len(payload['events']))) | ||
|
|
||
| emit_time_exceeds_threshold = emit_time > MAX_EMIT_TIME | ||
| if emit_time is not None and \ | ||
| (emit_time_exceeds_threshold or self.in_developer_mode): | ||
| self.gauge('datadog.agent.emitter.emit.time', emit_time) | ||
| if emit_time_exceeds_threshold: | ||
| self.log.info("Emit time (s) is high: %.1f, metrics count: %d, events count: %d" | ||
| % (emit_time, len(payload['metrics']), len(payload['events']))) | ||
|
|
||
| if cpu_time is not None: | ||
| try: | ||
| cpu_used_pct = 100.0 * float(cpu_time)/float(collection_time) | ||
| if cpu_used_pct > MAX_CPU_PCT: | ||
| self.gauge('datadog.agent.collector.cpu.used', cpu_used_pct) | ||
| self.log.info("CPU consumed (%%) is high: %.1f, metrics count: %d, events count: %d" | ||
| % (cpu_used_pct, len(payload['metrics']), len(payload['events']))) | ||
| except Exception, e: | ||
| self.log.debug("Couldn't compute cpu used by collector with values %s %s %s" | ||
| % (cpu_time, collection_time, str(e))) |
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