At LiveRamp we are considering replacing Scribe with Kafka, and as a first step we run some tests to evaluate producer performance. You can find our preliminary results here: https://blog.liveramp.com/2013/04/08/kafka-0-8-producer-performance-2/. We hope this will be useful for some folks, and If anyone has comments or suggestions about what to do differently to obtain better results your feedback will be very welcome.
Thank you for your comments. I'll reply point by point for clarity.
1. We were aware of the migration tool but since we haven't used Kafka for production yet we just started using the 0.8 version directly.
2. I hadn't seen those particular slides, very interesting. I'm not sure we're testing the same thing though. In our case we vary the number of physical machines, but each one has 10 threads accessing a pool of Kafka producer objects and in theory a single machine is enough to saturate the brokers (which our test mostly confirms). Also, assuming that the slides are based on the built-in producer performance tool, I know that we started getting very different numbers once we switched to use "real" (actual production log) messages. Compression may also be a factor in case it wasn't configured the same way in those tests.
3. In the latency section, there are two tests, one for average and another for maximum latency. Each one has two graphs presenting the exact same data but at different levels of zoom. The first one is to observe small variations of latency when target throughput <= actual throughput. The second is to observe the overall shape of the graph once latency starts growing when target throughput > actual throughput. I hope that makes sense.
4. That sounds great, looking forward to it.
On Mon, Apr 8, 2013 at 9:48 PM, Jun Rao <[EMAIL PROTECTED]> wrote:
"Trying to push more data than the brokers can handle for any sustained period of time has catastrophic consequences, regardless of what timeout settings are used. In our use case this means that we need to either ensure we have spare capacity for spikes, or use something on top of Kafka to absorb spikes."
That's actually a question we are trying to answer. In our current production system, Scribe does spooling to local disk, so each producer node becomes a local broker until the actual brokers are able to receive all messages again. It looks like unless a similar feature is added to Kafka we will have to come up with our own spooling system.
On Wed, Apr 10, 2013 at 12:04 PM, Otis Gospodnetic < [EMAIL PROTECTED]> wrote:
When talking about "catastrophic consequences" I was actually only referring to the producer side. in our use case (logging requests from webapp servers), a spike in traffic would force us to either tolerate a dramatic increase in the response time, or drop messages, both of which are really undesirable. Hence the need to absorb spikes with some system on top of Kafka, unless the spooling feature mentioned by Wing ( https://issues.apache.org/jira/browse/KAFKA-156) is implemented. This is assuming there are a lot more producer machines than broker nodes, so each producer would absorb a small part of the extra load from the spike.
On Wed, Apr 10, 2013 at 10:17 PM, Jun Rao <[EMAIL PROTECTED]> wrote:
I posted an update on the post ( https://blog.liveramp.com/2013/04/08/kafka-0-8-producer-performance-2/) to test the effect of disabling ack messages from brokers. It appears this only makes a big difference (~2x improvement ) when using synthetic log messages, but only a modest 12% improvement when using real production messages. This is using GZIP compression. The way I interpret this is that just turning acks off is not enough to mimic the 0.7 behavior because GZIP consumes significant CPU time and since the brokers now need to decompress data, there is a hit on throughput even without acks. Does this sound reasonable?
On Mon, Apr 8, 2013 at 4:42 PM, Piotr Kozikowski <[EMAIL PROTECTED]> wrote:
Not sure where the updated numbers are. but what you described may make sense. In the no ack mode, if the broker is saturated, it will put back pressure to the producer. Eventually, the producer will slow down because the socket buffer is full. One big difference btw 0.8 and 0.7 is that the 0.8 broker has the overhead of recompressing compressed messages. If you only have one partition in your test, all producers have to synchronize on the log when doing recompressing, which could limit the throughput. To improve the throughput, you can try using more partitions, turning off compression or using a cheaper compression codec like snappy.
Jun On Fri, Apr 12, 2013 at 4:08 PM, Piotr Kozikowski <[EMAIL PROTECTED]>wrote:
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