[AMLII-2019] Max samples per context for Histogram, Distribution and Timing metrics (Experimental Feature)

datadog/__init__.py

@@ -43,6 +43,7 @@ def initialize(
     statsd_use_default_route=False,  # type: bool
     statsd_socket_path=None,  # type: Optional[str]
     statsd_namespace=None,  # type: Optional[str]
+    statsd_max_samples_per_context=0,  # type: Optional[int]
     statsd_constant_tags=None,  # type: Optional[List[str]]
     return_raw_response=False,  # type: bool
     hostname_from_config=True,  # type: bool
@@ -82,8 +83,12 @@ def initialize(
                                      (default: True).
     :type statsd_disable_aggregation: boolean
 
+    :param statsd_max_samples_per_context: Set the max samples per context for Histogram,
+    Distribution and Timing metrics. Use with the statsd_disable_aggregation set to False.
+    :type statsd_max_samples_per_context: int
+
     :param statsd_aggregation_flush_interval: If aggregation is enabled, set the flush interval for
-                    aggregation/buffering
+                    aggregation/buffering (This feature is experimental)
                                      (default: 0.3 seconds)
     :type statsd_aggregation_flush_interval: float
 
@@ -142,7 +147,7 @@ def initialize(
     if statsd_disable_aggregation:
         statsd.disable_aggregation()
     else:
-        statsd.enable_aggregation(statsd_aggregation_flush_interval)
+        statsd.enable_aggregation(statsd_aggregation_flush_interval, statsd_max_samples_per_context)
     statsd.disable_buffering = statsd_disable_buffering
     api._return_raw_response = return_raw_response
 

datadog/dogstatsd/aggregator.py

@@ -4,16 +4,28 @@
     GaugeMetric,
     SetMetric,
 )
+from datadog.dogstatsd.max_sample_metric import (
+    HistogramMetric,
+    DistributionMetric,
+    TimingMetric
+)
 from datadog.dogstatsd.metric_types import MetricType
+from datadog.dogstatsd.max_sample_metric_context import MaxSampleMetricContexts
 
 
 class Aggregator(object):
-    def __init__(self):
+    def __init__(self, max_samples_per_context=0):
+        self.max_samples_per_context = max_samples_per_context
         self.metrics_map = {
             MetricType.COUNT: {},
             MetricType.GAUGE: {},
             MetricType.SET: {},
         }
+        self.max_sample_metric_map = {
+            MetricType.HISTOGRAM: MaxSampleMetricContexts(HistogramMetric),
+            MetricType.DISTRIBUTION: MaxSampleMetricContexts(DistributionMetric),
+            MetricType.TIMING: MaxSampleMetricContexts(TimingMetric)
+        }
         self._locks = {
             MetricType.COUNT: threading.RLock(),
             MetricType.GAUGE: threading.RLock(),
@@ -30,6 +42,17 @@ def flush_aggregated_metrics(self):
                 metrics.extend(metric.get_data() if isinstance(metric, SetMetric) else [metric])
         return metrics
 
+    def set_max_samples_per_context(self, max_samples_per_context=0):
+        self.max_samples_per_context = max_samples_per_context
+
+    def flush_aggregated_sampled_metrics(self):
+        metrics = []
+        for metric_type in self.max_sample_metric_map.keys():
+            metric_context = self.max_sample_metric_map[metric_type]
+            for metricList in metric_context.flush():
+                metrics.extend(metricList)
+        return metrics
+
     def get_context(self, name, tags):
         tags_str = ",".join(tags) if tags is not None else ""
         return "{}:{}".format(name, tags_str)
@@ -60,3 +83,27 @@ def add_metric(
                 self.metrics_map[metric_type][context] = metric_class(
                     name, value, tags, rate, timestamp
                 )
+
+    def histogram(self, name, value, tags, rate):
+        return self.add_max_sample_metric(
+            MetricType.HISTOGRAM, name, value, tags, rate
+        )
+
+    def distribution(self, name, value, tags, rate):
+        return self.add_max_sample_metric(
+            MetricType.DISTRIBUTION, name, value, tags, rate
+        )
+
+    def timing(self, name, value, tags, rate):
+        return self.add_max_sample_metric(
+            MetricType.TIMING, name, value, tags, rate
+        )
+
+    def add_max_sample_metric(
+        self, metric_type, name, value, tags, rate
+    ):
+        if rate is None:
+            rate = 1
+        context_key = self.get_context(name, tags)
+        metric_context = self.max_sample_metric_map[metric_type]
+        return metric_context.sample(name, value, tags, rate, context_key, self.max_samples_per_context)

datadog/dogstatsd/base.py

@@ -160,6 +160,7 @@ def __init__(
         telemetry_port=None,                    # type: Union[str, int]
         telemetry_socket_path=None,             # type: Text
         max_buffer_len=0,                       # type: int
+        max_metric_samples_per_context=0,       # type: int
         container_id=None,                      # type: Optional[Text]
         origin_detection_enabled=True,          # type: bool
         socket_timeout=0,                       # type: Optional[float]
@@ -236,9 +237,14 @@ def __init__(
         it overrides the default value.
         :type flush_interval: float
 
-        :disable_aggregation: If true, metrics (Count, Gauge, Set) are no longered aggregated by the client
+        :disable_aggregation: If true, metrics (Count, Gauge, Set) are no longer aggregated by the client
         :type disable_aggregation: bool
 
+        :max_metric_samples_per_context: Sets the maximum amount of samples for Histogram, Distribution
+        and Timings metrics (default 0). This feature should be used alongside aggregation. This feature
+        is experimental.
+        :type max_metric_samples_per_context: int
+
         :disable_buffering: If set, metrics are no longered buffered by the client and
         all data is sent synchronously to the server
         :type disable_buffering: bool
@@ -450,7 +456,7 @@ def __init__(
         self._flush_interval = flush_interval
         self._flush_thread = None
         self._flush_thread_stop = threading.Event()
-        self.aggregator = Aggregator()
+        self.aggregator = Aggregator(max_metric_samples_per_context)
         # Indicates if the process is about to fork, so we shouldn't start any new threads yet.
         self._forking = False
 
@@ -643,10 +649,11 @@ def disable_aggregation(self):
                 self._stop_flush_thread()
             log.debug("Statsd aggregation is disabled")
 
-    def enable_aggregation(self, flush_interval=DEFAULT_BUFFERING_FLUSH_INTERVAL):
+    def enable_aggregation(self, flush_interval=DEFAULT_BUFFERING_FLUSH_INTERVAL, max_samples_per_context=0):
         with self._config_lock:
             if not self._disable_aggregation:
                 return
+            self.aggregator.set_max_samples_per_context(max_samples_per_context)
             self._disable_aggregation = False
             self._flush_interval = flush_interval
             if self._disable_buffering:
@@ -826,6 +833,10 @@ def flush_aggregated_metrics(self):
         for m in metrics:
             self._report(m.name, m.metric_type, m.value, m.tags, m.rate, m.timestamp)
 
+        sampled_metrics = self.aggregator.flush_aggregated_sampled_metrics()
+        for m in sampled_metrics:
+            self._report(m.name, m.metric_type, m.value, m.tags, m.rate, m.timestamp, False)
+
     def gauge(
         self,
         metric,  # type: Text
@@ -960,7 +971,12 @@ def histogram(
         >>> statsd.histogram("uploaded.file.size", 1445)
         >>> statsd.histogram("album.photo.count", 26, tags=["gender:female"])
         """
-        self._report(metric, "h", value, tags, sample_rate)
+        if not self._disable_aggregation and self.aggregator.max_samples_per_context != 0:
+            print("Aggregated histogram")
+            self.aggregator.histogram(metric, value, tags, sample_rate)
+        else:
+            print("Regular histogram")
+            self._report(metric, "h", value, tags, sample_rate)
 
     def distribution(
         self,
@@ -975,7 +991,10 @@ def distribution(
         >>> statsd.distribution("uploaded.file.size", 1445)
         >>> statsd.distribution("album.photo.count", 26, tags=["gender:female"])
         """
-        self._report(metric, "d", value, tags, sample_rate)
+        if not self._disable_aggregation and self.aggregator.max_samples_per_context != 0:
+            self.aggregator.distribution(metric, value, tags, sample_rate)
+        else:
+            self._report(metric, "d", value, tags, sample_rate)
 
     def timing(
         self,
@@ -989,7 +1008,10 @@ def timing(
 
         >>> statsd.timing("query.response.time", 1234)
         """
-        self._report(metric, "ms", value, tags, sample_rate)
+        if not self._disable_aggregation and self.aggregator.max_samples_per_context != 0:
+            self.aggregator.timing(metric, value, tags, sample_rate)
+        else:
+            self._report(metric, "ms", value, tags, sample_rate)
 
     def timed(self, metric=None, tags=None, sample_rate=None, use_ms=None):
         """
@@ -1093,7 +1115,7 @@ def _serialize_metric(
             ("|T" + text(timestamp)) if timestamp > 0 else "",
         )
 
-    def _report(self, metric, metric_type, value, tags, sample_rate, timestamp=0):
+    def _report(self, metric, metric_type, value, tags, sample_rate, timestamp=0, sampling=True):
         """
         Create a metric packet and send it.
 
@@ -1109,11 +1131,12 @@ def _report(self, metric, metric_type, value, tags, sample_rate, timestamp=0):
         if self._telemetry:
             self.metrics_count += 1
 
-        if sample_rate is None:
-            sample_rate = self.default_sample_rate
+        if sampling:
+            if sample_rate is None:
+                sample_rate = self.default_sample_rate
 
-        if sample_rate != 1 and random() > sample_rate:
-            return
+            if sample_rate != 1 and random() > sample_rate:
+                return
         # timestamps (protocol v1.3) only allowed on gauges and counts
         allows_timestamp = metric_type == MetricType.GAUGE or metric_type == MetricType.COUNT
 

datadog/dogstatsd/max_sample_metric.py

@@ -0,0 +1,60 @@
+import random
+from datadog.dogstatsd.metric_types import MetricType
+from datadog.dogstatsd.metrics import MetricAggregator
+
+
+class MaxSampleMetric(object):
+    def __init__(self, name, tags, metric_type, specified_rate=1.0, max_metric_samples=0):
+        self.name = name
+        self.tags = tags
+        self.metric_type = metric_type
+        self.max_metric_samples = max_metric_samples
+        self.specified_rate = specified_rate
+        self.data = [None] * max_metric_samples if max_metric_samples > 0 else []
+        self.stored_metric_samples = 0
+        self.total_metric_samples = 0
+
+    def sample(self, value):
+        if self.max_metric_samples == 0:
+            self.data.append(value)
+        else:
+            self.data[self.stored_metric_samples] = value
+        self.stored_metric_samples += 1
+        self.total_metric_samples += 1
+
+    def maybe_keep_sample(self, value):
+        if self.max_metric_samples > 0:
+            self.total_metric_samples += 1
+            if self.stored_metric_samples < self.max_metric_samples:
+                self.data[self.stored_metric_samples] = value
+                self.stored_metric_samples += 1
+            else:
+                i = random.randint(0, self.total_metric_samples - 1)
+                if i < self.max_metric_samples:
+                    self.data[i] = value
+        else:
+            self.sample(value)
+
+    def skip_sample(self):
+        self.total_metric_samples += 1
+
+    def flush(self):
+        return [
+            MetricAggregator(self.name, self.tags, self.specified_rate, self.metric_type, value)
+            for value in self.data
+        ]
+
+
+class HistogramMetric(MaxSampleMetric):
+    def __init__(self, name, tags, rate=1.0, max_metric_samples=0):
+        super(HistogramMetric, self).__init__(name, tags, MetricType.HISTOGRAM, rate, max_metric_samples)
+
+
+class DistributionMetric(MaxSampleMetric):
+    def __init__(self, name, tags, rate=1.0, max_metric_samples=0):
+        super(DistributionMetric, self).__init__(name, tags, MetricType.DISTRIBUTION, rate, max_metric_samples)
+
+
+class TimingMetric(MaxSampleMetric):
+    def __init__(self, name, tags, rate=1.0, max_metric_samples=0):
+        super(TimingMetric, self).__init__(name, tags, MetricType.TIMING, rate, max_metric_samples)

datadog/dogstatsd/max_sample_metric_context.py

@@ -0,0 +1,40 @@
+from threading import Lock
+import random
+
+
+class MaxSampleMetricContexts:
+    def __init__(self, max_sample_metric_type):
+        self.lock = Lock()
+        self.values = {}
+        self.max_sample_metric_type = max_sample_metric_type
+
+    def flush(self):
+        metrics = []
+        """Flush the metrics and reset the stored values."""
+        with self.lock:
+            copiedValues = self.values.copy()
+            self.values.clear()
+        self.values = {}
+        for _, metric in copiedValues.items():
+            metrics.append(metric.flush())
+
+        return metrics
+
+    def sample(self, name, value, tags, rate, context_key, max_samples_per_context):
+        """Sample a metric and store it if it meets the criteria."""
+        keeping_sample = self.should_sample(rate)
+        with self.lock:
+            if context_key not in self.values:
+                # Create a new metric if it doesn't exist
+                self.values[context_key] = self.max_sample_metric_type(name, tags, rate, max_samples_per_context)
+            metric = self.values[context_key]
+        if keeping_sample:
+            metric.maybe_keep_sample(value)
+        else:
+            metric.skip_sample()
+
+    def should_sample(self, rate):
+        """Determine if a sample should be kept based on the specified rate."""
+        if rate >= 1:
+            return True
+        return random.random() < rate

datadog/dogstatsd/metric_types.py

@@ -2,3 +2,6 @@ class MetricType:
     COUNT = "c"
     GAUGE = "g"
     SET = "s"
+    HISTOGRAM = "h"
+    DISTRIBUTION = "d"
+    TIMING = "ms"