diff options
| -rw-r--r-- | kafka/producer.py | 15 |
1 files changed, 11 insertions, 4 deletions
diff --git a/kafka/producer.py b/kafka/producer.py index 7a7c48f..8f35963 100644 --- a/kafka/producer.py +++ b/kafka/producer.py @@ -181,14 +181,20 @@ class SimpleProducer(Producer): batch_send - If True, messages are send in batches batch_send_every_n - If set, messages are send in batches of this size batch_send_every_t - If set, messages are send after this timeout + random_start - If true, randomize the initial partition which the + the first message block will be published to, otherwise + if false, the first message block will always publish + to partition 0 before cycling through each partition """ def __init__(self, client, async=False, req_acks=Producer.ACK_AFTER_LOCAL_WRITE, ack_timeout=Producer.DEFAULT_ACK_TIMEOUT, batch_send=False, batch_send_every_n=BATCH_SEND_MSG_COUNT, - batch_send_every_t=BATCH_SEND_DEFAULT_INTERVAL): + batch_send_every_t=BATCH_SEND_DEFAULT_INTERVAL, + random_start=False): self.partition_cycles = {} + self.random_start = random_start super(SimpleProducer, self).__init__(client, async, req_acks, ack_timeout, batch_send, batch_send_every_n, @@ -201,9 +207,10 @@ class SimpleProducer(Producer): self.partition_cycles[topic] = cycle(self.client.topic_partitions[topic]) # Randomize the initial partition that is returned - num_partitions = len(self.client.topic_partitions[topic]) - for _ in xrange(random.randint(0, num_partitions-1)): - self.partition_cycles[topic].next() + if self.random_start: + num_partitions = len(self.client.topic_partitions[topic]) + for _ in xrange(random.randint(0, num_partitions-1)): + self.partition_cycles[topic].next() return self.partition_cycles[topic].next() |