Add initial producer-sender metrics

kafka/producer/buffer.py

@@ -1,4 +1,4 @@
-from __future__ import absolute_import
+from __future__ import absolute_import, division
 
 import collections
 import io
@@ -55,6 +55,8 @@ class MessageSetBuffer(object):
         self._batch_size = batch_size
         self._closed = False
         self._messages = 0
+        self._bytes_written = 4 # Int32 header is 4 bytes
+        self._final_size = None
 
     def append(self, offset, message):
         """Apend a Message to the MessageSet.
@@ -62,6 +64,8 @@ class MessageSetBuffer(object):
         Arguments:
             offset (int): offset of the message
             message (Message or bytes): message struct or encoded bytes
+
+        Returns: bytes written
         """
         if isinstance(message, Message):
             encoded = message.encode()
@@ -70,6 +74,8 @@ class MessageSetBuffer(object):
         msg = Int64.encode(offset) + Int32.encode(len(encoded)) + encoded
         self._buffer.write(msg)
         self._messages += 1
+        self._bytes_written += len(msg)
+        return len(msg)
 
     def has_room_for(self, key, value):
         if self._closed:
@@ -107,16 +113,20 @@ class MessageSetBuffer(object):
                 self._buffer.write(Int32.encode(len(encoded)))
                 self._buffer.write(encoded)
 
-            # Update the message set size, and return ready for full read()
+            # Update the message set size (less the 4 byte header),
-            size = self._buffer.tell() - 4
+            # and return with buffer ready for full read()
+            self._final_size = self._buffer.tell()
             self._buffer.seek(0)
-            self._buffer.write(Int32.encode(size))
+            self._buffer.write(Int32.encode(self._final_size - 4))
 
         self._buffer.seek(0)
         self._closed = True
 
     def size_in_bytes(self):
-        return self._buffer.tell()
+        return self._final_size or self._buffer.tell()
+
+    def compression_rate(self):
+        return self.size_in_bytes() / self._bytes_written
 
     def buffer(self):
         return self._buffer

kafka/producer/kafka.py

@@ -9,6 +9,7 @@ import weakref
 
 from .. import errors as Errors
 from ..client_async import KafkaClient
+from ..metrics import MetricConfig, Metrics
 from ..partitioner.default import DefaultPartitioner
 from ..protocol.message import Message, MessageSet
 from ..structs import TopicPartition
@@ -220,6 +221,13 @@ class KafkaProducer(object):
         api_version_auto_timeout_ms (int): number of milliseconds to throw a
             timeout exception from the constructor when checking the broker
             api version. Only applies if api_version set to 'auto'
+        metric_reporters (list): A list of classes to use as metrics reporters.
+            Implementing the AbstractMetricsReporter interface allows plugging
+            in classes that will be notified of new metric creation. Default: []
+        metrics_num_samples (int): The number of samples maintained to compute
+            metrics. Default: 2
+        metrics_sample_window_ms (int): The maximum age in milliseconds of
+            samples used to compute metrics. Default: 30000
 
     Note:
         Configuration parameters are described in more detail at
@@ -255,7 +263,10 @@ class KafkaProducer(object):
         'ssl_keyfile': None,
         'ssl_crlfile': None,
         'api_version': None,
-        'api_version_auto_timeout_ms': 2000
+        'api_version_auto_timeout_ms': 2000,
+        'metric_reporters': [],
+        'metrics_num_samples': 2,
+        'metrics_sample_window_ms': 30000,
     }
 
     def __init__(self, **configs):
@@ -285,6 +296,14 @@ class KafkaProducer(object):
             log.warning('use api_version=%s (%s is deprecated)',
                         str(self.config['api_version']), deprecated)
 
+        # Configure metrics
+        metrics_tags = {'client-id': self.config['client_id']}
+        metric_config = MetricConfig(samples=self.config['metrics_num_samples'],
+                                     time_window_ms=self.config['metrics_sample_window_ms'],
+                                     tags=metrics_tags)
+        reporters = [reporter() for reporter in self.config['metric_reporters']]
+        self._metrics = Metrics(metric_config, reporters)
+
         client = KafkaClient(**self.config)
 
         # Get auto-discovered version from client if necessary
@@ -298,7 +317,8 @@ class KafkaProducer(object):
         self._accumulator = RecordAccumulator(message_version=message_version, **self.config)
         self._metadata = client.cluster
         guarantee_message_order = bool(self.config['max_in_flight_requests_per_connection'] == 1)
-        self._sender = Sender(client, self._metadata, self._accumulator,
+        self._sender = Sender(client, self._metadata,
+                              self._accumulator, self._metrics,
                               guarantee_message_order=guarantee_message_order,
                               **self.config)
         self._sender.daemon = True
@@ -382,6 +402,7 @@ class KafkaProducer(object):
             if not invoked_from_callback:
                 self._sender.join()
 
+        self._metrics.close()
         try:
             self.config['key_serializer'].close()
         except AttributeError:

kafka/producer/record_accumulator.py

@@ -38,7 +38,7 @@ class AtomicInteger(object):
 class RecordBatch(object):
     def __init__(self, tp, records, message_version=0):
         self.record_count = 0
-        #self.max_record_size = 0 # for metrics only
+        self.max_record_size = 0
         now = time.time()
         self.created = now
         self.drained = None
@@ -56,8 +56,8 @@ class RecordBatch(object):
             return None
 
         msg = Message(value, key=key, magic=self.message_version)
-        self.records.append(self.record_count, msg)
+        record_size = self.records.append(self.record_count, msg)
-        # self.max_record_size = max(self.max_record_size, Record.record_size(key, value)) # for metrics only
+        self.max_record_size = max(self.max_record_size, record_size)
         self.last_append = time.time()
         future = FutureRecordMetadata(self.produce_future, self.record_count,
                                       timestamp_ms)

kafka/producer/sender.py

@@ -1,4 +1,4 @@
-from __future__ import absolute_import
+from __future__ import absolute_import, division
 
 import collections
 import copy
@@ -8,9 +8,11 @@ import threading
 import six
 
 from .. import errors as Errors
+from ..metrics.measurable import AnonMeasurable
+from ..metrics.stats import Avg, Count, Max, Rate
+from ..protocol.produce import ProduceRequest
 from ..structs import TopicPartition
 from ..version import __version__
-from ..protocol.produce import ProduceRequest
 
 log = logging.getLogger(__name__)
 
@@ -31,7 +33,7 @@ class Sender(threading.Thread):
         'api_version': (0, 8, 0),
     }
 
-    def __init__(self, client, metadata, accumulator, **configs):
+    def __init__(self, client, metadata, accumulator, metrics, **configs):
         super(Sender, self).__init__()
         self.config = copy.copy(self._DEFAULT_CONFIG)
         for key in self.config:
@@ -45,6 +47,7 @@ class Sender(threading.Thread):
         self._running = True
         self._force_close = False
         self._topics_to_add = set()
+        self._sensors = SenderMetrics(metrics, self._client, self._metadata)
 
     def run(self):
         """The main run loop for the sender thread."""
@@ -119,7 +122,10 @@ class Sender(threading.Thread):
 
         expired_batches = self._accumulator.abort_expired_batches(
             self.config['request_timeout_ms'], self._metadata)
+        for expired_batch in expired_batches:
+            self._sensors.record_errors(expired_batch.topic_partition.topic, expired_batch.record_count)
 
+        self._sensors.update_produce_request_metrics(batches_by_node)
         requests = self._create_produce_requests(batches_by_node)
         # If we have any nodes that are ready to send + have sendable data,
         # poll with 0 timeout so this can immediately loop and try sending more
@@ -223,6 +229,7 @@ class Sender(threading.Thread):
                         self.config['retries'] - batch.attempts - 1,
                         error)
             self._accumulator.reenqueue(batch)
+            self._sensors.record_retries(batch.topic_partition.topic, batch.record_count)
         else:
             if error is Errors.TopicAuthorizationFailedError:
                 error = error(batch.topic_partition.topic)
@@ -230,6 +237,8 @@ class Sender(threading.Thread):
             # tell the user the result of their request
             batch.done(base_offset, timestamp_ms, error)
             self._accumulator.deallocate(batch)
+            if error is not None:
+                self._sensors.record_errors(batch.topic_partition.topic, batch.record_count)
 
         if getattr(error, 'invalid_metadata', False):
             self._metadata.request_update()
@@ -296,3 +305,200 @@ class Sender(threading.Thread):
     def wakeup(self):
         """Wake up the selector associated with this send thread."""
         self._client.wakeup()
+
+
+class SenderMetrics(object):
+
+    def __init__(self, metrics, client, metadata):
+        self.metrics = metrics
+        self._client = client
+        self._metadata = metadata
+
+        sensor_name = 'batch-size'
+        self.batch_size_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('batch-size-avg', Avg(),
+                        sensor_name=sensor_name,
+                        description='The average number of bytes sent per partition per-request.')
+        self.add_metric('batch-size-max', Max(),
+                        sensor_name=sensor_name,
+                        description='The max number of bytes sent per partition per-request.')
+
+        sensor_name = 'compression-rate'
+        self.compression_rate_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('compression-rate-avg', Avg(),
+                        sensor_name=sensor_name,
+                        description='The average compression rate of record batches.')
+
+        sensor_name = 'queue-time'
+        self.queue_time_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('record-queue-time-avg', Avg(),
+                        sensor_name=sensor_name,
+                        description='The average time in ms record batches spent in the record accumulator.')
+        self.add_metric('record-queue-time-max', Max(),
+                        sensor_name=sensor_name,
+                        description='The maximum time in ms record batches spent in the record accumulator.')
+
+        sensor_name = 'request-time'
+        self.request_time_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('request-latency-avg', Avg(),
+                        sensor_name=sensor_name,
+                        description='The average request latency in ms')
+        self.add_metric('request-latency-max', Max(),
+                        sensor_name=sensor_name,
+                        description='The maximum request latency in ms')
+
+        sensor_name = 'produce-throttle-time'
+        self.produce_throttle_time_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('produce-throttle-time-avg', Avg(),
+                        sensor_name=sensor_name,
+                        description='The average throttle time in ms')
+        self.add_metric('produce-throttle-time-max', Max(),
+                        sensor_name=sensor_name,
+                        description='The maximum throttle time in ms')
+
+        sensor_name = 'records-per-request'
+        self.records_per_request_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('record-send-rate', Rate(),
+                        sensor_name=sensor_name,
+                        description='The average number of records sent per second.')
+        self.add_metric('records-per-request-avg', Avg(),
+                        sensor_name=sensor_name,
+                        description='The average number of records per request.')
+
+        sensor_name = 'bytes'
+        self.byte_rate_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('byte-rate', Rate(),
+                        sensor_name=sensor_name,
+                        description='The average number of bytes sent per second.')
+
+        sensor_name = 'record-retries'
+        self.retry_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('record-retry-rate', Rate(),
+                        sensor_name=sensor_name,
+                        description='The average per-second number of retried record sends')
+
+        sensor_name = 'errors'
+        self.error_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('record-error-rate', Rate(),
+                        sensor_name=sensor_name,
+                        description='The average per-second number of record sends that resulted in errors')
+
+        sensor_name = 'record-size-max'
+        self.max_record_size_sensor = self.metrics.sensor(sensor_name)
+        self.add_metric('record-size-max', Max(),
+                        sensor_name=sensor_name,
+                        description='The maximum record size across all batches')
+        self.add_metric('record-size-avg', Avg(),
+                        sensor_name=sensor_name,
+                        description='The average maximum record size per batch')
+
+        self.add_metric('requests-in-flight',
+                        AnonMeasurable(lambda *_: self._client.in_flight_request_count()),
+                        description='The current number of in-flight requests awaiting a response.')
+
+        self.add_metric('metadata-age',
+                        AnonMeasurable(lambda _, now: (now - self._metadata._last_successful_refresh_ms) / 1000),
+                        description='The age in seconds of the current producer metadata being used.')
+
+    def add_metric(self, metric_name, measurable, group_name='producer-metrics',
+                   description=None, tags=None,
+                   sensor_name=None):
+        m = self.metrics
+        metric = m.metric_name(metric_name, group_name, description, tags)
+        if sensor_name:
+            sensor = m.sensor(sensor_name)
+            sensor.add(metric, measurable)
+        else:
+            m.add_metric(metric, measurable)
+
+    def maybe_register_topic_metrics(self, topic):
+
+        def sensor_name(name):
+            return 'topic.{0}.{1}'.format(topic, name)
+
+        # if one sensor of the metrics has been registered for the topic,
+        # then all other sensors should have been registered; and vice versa
+        if not self.metrics.get_sensor(sensor_name('records-per-batch')):
+
+            self.add_metric('record-send-rate', Rate(),
+                            sensor_name=sensor_name('records-per-batch'),
+                            group_name='producer-topic-metrics.' + topic,
+                            description= 'Records sent per second for topic ' + topic)
+
+            self.add_metric('byte-rate', Rate(),
+                            sensor_name=sensor_name('bytes'),
+                            group_name='producer-topic-metrics.' + topic,
+                            description='Bytes per second for topic ' + topic)
+
+            self.add_metric('compression-rate', Avg(),
+                            sensor_name=sensor_name('compression-rate'),
+                            group_name='producer-topic-metrics.' + topic,
+                            description='Average Compression ratio for topic ' + topic)
+
+            self.add_metric('record-retry-rate', Rate(),
+                            sensor_name=sensor_name('record-retries'),
+                            group_name='producer-topic-metrics.' + topic,
+                            description='Record retries per second for topic ' + topic)
+
+            self.add_metric('record-error-rate', Rate(),
+                            sensor_name=sensor_name('record-errors'),
+                            group_name='producer-topic-metrics.' + topic,
+                            description='Record errors per second for topic ' + topic)
+
+    def update_produce_request_metrics(self, batches_map):
+        for node_batch in batches_map.values():
+            records = 0
+            total_bytes = 0
+            for batch in node_batch:
+                # register all per-topic metrics at once
+                topic = batch.topic_partition.topic
+                self.maybe_register_topic_metrics(topic)
+
+                # per-topic record send rate
+                topic_records_count = self.metrics.get_sensor(
+                    'topic.' + topic + '.records-per-batch')
+                topic_records_count.record(batch.record_count)
+
+                # per-topic bytes send rate
+                topic_byte_rate = self.metrics.get_sensor(
+                    'topic.' + topic + '.bytes')
+                topic_byte_rate.record(batch.records.size_in_bytes())
+
+                # per-topic compression rate
+                topic_compression_rate = self.metrics.get_sensor(
+                    'topic.' + topic + '.compression-rate')
+                topic_compression_rate.record(batch.records.compression_rate())
+
+                # global metrics
+                self.batch_size_sensor.record(batch.records.size_in_bytes())
+                if batch.drained:
+                    self.queue_time_sensor.record(batch.drained - batch.created)
+                self.compression_rate_sensor.record(batch.records.compression_rate())
+                self.max_record_size_sensor.record(batch.max_record_size)
+                records += batch.record_count
+                total_bytes += batch.records.size_in_bytes()
+
+            self.records_per_request_sensor.record(records)
+            self.byte_rate_sensor.record(total_bytes)
+
+    def record_retries(self, topic, count):
+        self.retry_sensor.record(count)
+        sensor = self.metrics.get_sensor('topic.' + topic + '.record-retries')
+        if sensor:
+            sensor.record(count)
+
+    def record_errors(self, topic, count):
+        self.error_sensor.record(count)
+        sensor = self.metrics.get_sensor('topic.' + topic + '.record-errors')
+        if sensor:
+            sensor.record(count)
+
+    def record_latency(self, latency, node=None):
+        self.request_time_sensor.record(latency)
+        if node:
+            sensor = self.metrics.get_sensor('node-' + node + '.latency')
+            if sensor:
+                sensor.record(latency)
+
+    def record_throttle_time(self, throttle_time_ms, node=None):
+        self.produce_throttle_time_sensor.record(throttle_time_ms)

test/test_sender.py

@@ -7,12 +7,13 @@ import pytest
 
 from kafka.client_async import KafkaClient
 from kafka.cluster import ClusterMetadata
-from kafka.producer.buffer import MessageSetBuffer
-from kafka.producer.sender import Sender
-from kafka.producer.record_accumulator import RecordAccumulator, RecordBatch
 import kafka.errors as Errors
 from kafka.future import Future
+from kafka.metrics import Metrics
+from kafka.producer.buffer import MessageSetBuffer
 from kafka.protocol.produce import ProduceRequest
+from kafka.producer.record_accumulator import RecordAccumulator, RecordBatch
+from kafka.producer.sender import Sender
 from kafka.structs import TopicPartition, OffsetAndMetadata
 
 
@@ -29,8 +30,13 @@ def accumulator():
 
 
 @pytest.fixture
-def sender(client, accumulator):
+def metrics():
-    return Sender(client, client.cluster, accumulator)
+    return Metrics()
+
+
+@pytest.fixture
+def sender(client, accumulator, metrics):
+    return Sender(client, client.cluster, accumulator, metrics)
 
 
 @pytest.mark.parametrize(("api_version", "produce_version"), [