Drop unused kafka.producer.buffer / SimpleBufferPool

kafka/producer/kafka.py

@@ -6,6 +6,7 @@
 import socket
 import threading
 import time
+import warnings
 import weakref
 
 from kafka.vendor import six
@@ -72,11 +73,6 @@ class KafkaProducer(object):
     can lead to fewer, more efficient requests when not under maximal load at
     the cost of a small amount of latency.
 
-    The buffer_memory controls the total amount of memory available to the
-    producer for buffering. If records are sent faster than they can be
-    transmitted to the server then this buffer space will be exhausted. When
-    the buffer space is exhausted additional send calls will block.
-
     The key_serializer and value_serializer instruct how to turn the key and
     value objects the user provides into bytes.
 
@@ -166,12 +162,6 @@ class KafkaProducer(object):
             messages with the same key are assigned to the same partition.
             When a key is None, the message is delivered to a random partition
             (filtered to partitions with available leaders only, if possible).
-        buffer_memory (int): The total bytes of memory the producer should use
-            to buffer records waiting to be sent to the server. If records are
-            sent faster than they can be delivered to the server the producer
-            will block up to max_block_ms, raising an exception on timeout.
-            In the current implementation, this setting is an approximation.
-            Default: 33554432 (32MB)
         connections_max_idle_ms: Close idle connections after the number of
             milliseconds specified by this config. The broker closes idle
             connections after connections.max.idle.ms, so this avoids hitting
@@ -319,7 +309,6 @@ class KafkaProducer(object):
         'batch_size': 16384,
         'linger_ms': 0,
         'partitioner': DefaultPartitioner(),
-        'buffer_memory': 33554432,
         'connections_max_idle_ms': 9 * 60 * 1000,
         'max_block_ms': 60000,
         'max_request_size': 1048576,
@@ -361,6 +350,8 @@ class KafkaProducer(object):
         'kafka_client': KafkaClient,
     }
 
+    DEPRECATED_CONFIGS = ('buffer_memory',)
+
     _COMPRESSORS = {
         'gzip': (has_gzip, LegacyRecordBatchBuilder.CODEC_GZIP),
         'snappy': (has_snappy, LegacyRecordBatchBuilder.CODEC_SNAPPY),
@@ -376,6 +367,11 @@ def __init__(self, **configs):
             if key in configs:
                 self.config[key] = configs.pop(key)
 
+        for key in self.DEPRECATED_CONFIGS:
+            if key in configs:
+                configs.pop(key)
+                warnings.warn('Deprecated Producer config: %s' % (key,), DeprecationWarning)
+
         # Only check for extra config keys in top-level class
         assert not configs, 'Unrecognized configs: %s' % (configs,)
 
@@ -640,9 +636,7 @@ def send(self, topic, value=None, key=None, headers=None, partition=None, timest
             tp = TopicPartition(topic, partition)
             log.debug("Sending (key=%r value=%r headers=%r) to %s", key, value, headers, tp)
             result = self._accumulator.append(tp, timestamp_ms,
-                                              key_bytes, value_bytes, headers,
-                                              self.config['max_block_ms'],
-                                              estimated_size=message_size)
+                                              key_bytes, value_bytes, headers)
             future, batch_is_full, new_batch_created = result
             if batch_is_full or new_batch_created:
                 log.debug("Waking up the sender since %s is either full or"
@@ -697,11 +691,6 @@ def _ensure_valid_record_size(self, size):
                 "The message is %d bytes when serialized which is larger than"
                 " the maximum request size you have configured with the"
                 " max_request_size configuration" % (size,))
-        if size > self.config['buffer_memory']:
-            raise Errors.MessageSizeTooLargeError(
-                "The message is %d bytes when serialized which is larger than"
-                " the total memory buffer you have configured with the"
-                " buffer_memory configuration." % (size,))
 
     def _wait_on_metadata(self, topic, max_wait):
         """

kafka/producer/record_accumulator.py

@@ -7,7 +7,6 @@
 import time
 
 import kafka.errors as Errors
-from kafka.producer.buffer import SimpleBufferPool
 from kafka.producer.future import FutureRecordMetadata, FutureProduceResult
 from kafka.record.memory_records import MemoryRecordsBuilder
 from kafka.structs import TopicPartition
@@ -36,7 +35,7 @@ def get(self):
 
 
 class ProducerBatch(object):
-    def __init__(self, tp, records, buffer):
+    def __init__(self, tp, records):
         self.max_record_size = 0
         now = time.time()
         self.created = now
@@ -48,7 +47,6 @@ def __init__(self, tp, records, buffer):
         self.topic_partition = tp
         self.produce_future = FutureProduceResult(tp)
         self._retry = False
-        self._buffer = buffer  # We only save it, we don't write to it
 
     @property
     def record_count(self):
@@ -123,9 +121,6 @@ def in_retry(self):
     def set_retry(self):
         self._retry = True
 
-    def buffer(self):
-        return self._buffer
-
     def __str__(self):
         return 'ProducerBatch(topic_partition=%s, record_count=%d)' % (
             self.topic_partition, self.records.next_offset())
@@ -145,12 +140,6 @@ class RecordAccumulator(object):
             A small batch size will make batching less common and may reduce
             throughput (a batch size of zero will disable batching entirely).
             Default: 16384
-        buffer_memory (int): The total bytes of memory the producer should use
-            to buffer records waiting to be sent to the server. If records are
-            sent faster than they can be delivered to the server the producer
-            will block up to max_block_ms, raising an exception on timeout.
-            In the current implementation, this setting is an approximation.
-            Default: 33554432 (32MB)
         compression_attrs (int): The compression type for all data generated by
             the producer. Valid values are gzip(1), snappy(2), lz4(3), or
             none(0).
@@ -168,7 +157,6 @@ class RecordAccumulator(object):
             all retries in a short period of time. Default: 100
     """
     DEFAULT_CONFIG = {
-        'buffer_memory': 33554432,
         'batch_size': 16384,
         'compression_attrs': 0,
         'linger_ms': 0,
@@ -189,18 +177,13 @@ def __init__(self, **configs):
         self._appends_in_progress = AtomicInteger()
         self._batches = collections.defaultdict(collections.deque) # TopicPartition: [ProducerBatch]
         self._tp_locks = {None: threading.Lock()} # TopicPartition: Lock, plus a lock to add entries
-        self._free = SimpleBufferPool(self.config['buffer_memory'],
-                                      self.config['batch_size'],
-                                      metrics=self.config['metrics'],
-                                      metric_group_prefix=self.config['metric_group_prefix'])
         self._incomplete = IncompleteProducerBatches()
         # The following variables should only be accessed by the sender thread,
         # so we don't need to protect them w/ locking.
         self.muted = set()
         self._drain_index = 0
 
-    def append(self, tp, timestamp_ms, key, value, headers, max_time_to_block_ms,
-               estimated_size=0):
+    def append(self, tp, timestamp_ms, key, value, headers):
         """Add a record to the accumulator, return the append result.
 
         The append result will contain the future metadata, and flag for
@@ -213,8 +196,6 @@ def append(self, tp, timestamp_ms, key, value, headers, max_time_to_block_ms,
             key (bytes): The key for the record
             value (bytes): The value for the record
             headers (List[Tuple[str, bytes]]): The header fields for the record
-            max_time_to_block_ms (int): The maximum time in milliseconds to
-                block for buffer memory to be available
 
         Returns:
             tuple: (future, batch_is_full, new_batch_created)
@@ -240,9 +221,6 @@ def append(self, tp, timestamp_ms, key, value, headers, max_time_to_block_ms,
                         batch_is_full = len(dq) > 1 or last.records.is_full()
                         return future, batch_is_full, False
 
-            size = max(self.config['batch_size'], estimated_size)
-            log.debug("Allocating a new %d byte message buffer for %s", size, tp) # trace
-            buf = self._free.allocate(size, max_time_to_block_ms)
             with self._tp_locks[tp]:
                 # Need to check if producer is closed again after grabbing the
                 # dequeue lock.
@@ -254,7 +232,6 @@ def append(self, tp, timestamp_ms, key, value, headers, max_time_to_block_ms,
                     if future is not None:
                         # Somebody else found us a batch, return the one we
                         # waited for! Hopefully this doesn't happen often...
-                        self._free.deallocate(buf)
                         batch_is_full = len(dq) > 1 or last.records.is_full()
                         return future, batch_is_full, False
 
@@ -264,7 +241,7 @@ def append(self, tp, timestamp_ms, key, value, headers, max_time_to_block_ms,
                     self.config['batch_size']
                 )
 
-                batch = ProducerBatch(tp, records, buf)
+                batch = ProducerBatch(tp, records)
                 future = batch.try_append(timestamp_ms, key, value, headers)
                 if not future:
                     raise Exception()
@@ -384,7 +361,6 @@ def ready(self, cluster):
         unknown_leaders_exist = False
         now = time.time()
 
-        exhausted = bool(self._free.queued() > 0)
         # several threads are accessing self._batches -- to simplify
         # concurrent access, we iterate over a snapshot of partitions
         # and lock each partition separately as needed
@@ -414,7 +390,7 @@ def ready(self, cluster):
                 full = bool(len(dq) > 1 or batch.records.is_full())
                 expired = bool(waited_time >= time_to_wait)
 
-                sendable = (full or expired or exhausted or self._closed or
+                sendable = (full or expired or self._closed or
                             self._flush_in_progress())
 
                 if sendable and not backing_off:
@@ -506,7 +482,6 @@ def drain(self, cluster, nodes, max_size):
     def deallocate(self, batch):
         """Deallocate the record batch."""
         self._incomplete.remove(batch)
-        self._free.deallocate(batch.buffer())
 
     def _flush_in_progress(self):
         """Are there any threads currently waiting on a flush?"""

test/test_producer.py

@@ -7,22 +7,9 @@
 import pytest
 
 from kafka import KafkaConsumer, KafkaProducer, TopicPartition
-from kafka.producer.buffer import SimpleBufferPool
 from test.testutil import env_kafka_version, random_string, maybe_skip_unsupported_compression
 
 
-def test_buffer_pool():
-    pool = SimpleBufferPool(1000, 1000)
-
-    buf1 = pool.allocate(1000, 1000)
-    message = ''.join(map(str, range(100)))
-    buf1.write(message.encode('utf-8'))
-    pool.deallocate(buf1)
-
-    buf2 = pool.allocate(1000, 1000)
-    assert buf2.read() == b''
-
-
 @contextmanager
 def producer_factory(**kwargs):
     producer = KafkaProducer(**kwargs)

test/test_sender.py

@@ -42,10 +42,9 @@ def sender(client, accumulator, metrics, mocker):
 def test_produce_request(sender, mocker, api_version, produce_version):
     sender._client._api_versions = BROKER_API_VERSIONS[api_version]
     tp = TopicPartition('foo', 0)
-    buffer = io.BytesIO()
     records = MemoryRecordsBuilder(
         magic=1, compression_type=0, batch_size=100000)
-    batch = ProducerBatch(tp, records, buffer)
+    batch = ProducerBatch(tp, records)
     records.close()
     produce_request = sender._produce_request(0, 0, 0, [batch])
     assert isinstance(produce_request, ProduceRequest[produce_version])