Details
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Improvement
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Status: Patch Available
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Major
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Resolution: Unresolved
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0.8.2.0
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None
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None
Description
There are two approaches to using Kafka for capturing event data that has no other "source of truth store":
1. Just write to Kafka and try hard to keep the Kafka cluster up as you would a database.
2. Write to some kind of local disk store and copy from that to Kafka.
The cons of the second approach are the following:
1. You end up depending on disks on all the producer machines. If you have 10000 producers, that is 10k places state is kept. These tend to fail a lot.
2. You can get data arbitrarily delayed
3. You still don't tolerate hard outages since there is no replication in the producer tier
4. This tends to make problems with duplicates more common in certain failure scenarios.
There is one big pro, though: you don't have to keep Kafka running all the time.
So far we have done nothing in Kafka to help support approach (2), but people have built a lot of buffering things. It's not clear that this is necessarily bad.
However implementing this in the new Kafka producer might actually be quite easy. Here is an idea for how to do it. Implementation of this idea is probably pretty easy but it would require some pretty thorough testing to see if it was a success.
The new producer maintains a pool of ByteBuffer instances which it attempts to recycle and uses to buffer and send messages. When unsent data is queuing waiting to be sent to the cluster it is hanging out in this pool.
One approach to implementing a disk-baked buffer would be to slightly generalize this so that the buffer pool has the option to use a mmap'd file backend for it's ByteBuffers. When the BufferPool was created with a totalMemory setting of 1GB it would preallocate a 1GB sparse file and memory map it, then chop the file into batchSize MappedByteBuffer pieces and populate it's buffer with those.
Everything else would work normally except now all the buffered data would be disk backed and in cases where there was significant backlog these would start to fill up and page out.
We currently allow messages larger than batchSize and to handle these we do a one-off allocation of the necessary size. We would have to disallow this when running in mmap mode. However since the disk buffer will be really big this should not be a significant limitation as the batch size can be pretty big.
We would want to ensure that the pooling always gives out the most recently used ByteBuffer (I think it does). This way under normal operation where requests are processed quickly a given buffer would be reused many times before any physical disk write activity occurred.
Note that although this let's the producer buffer very large amounts of data the buffer isn't really fault-tolerant, since the ordering in the file isn't known so there is no easy way to recovery the producer's buffer in a failure. So the scope of this feature would just be to provide a bigger buffer for short outages or latency spikes in the Kafka cluster during which you would hope you don't also experience failures in your producer processes.
To complete the feature we would need to:
a. Get some unit tests that would cover disk-backed usage
b. Do some manual performance testing of this usage and understand the impact on throughput.
c. Do some manual testing of failure cases (i.e. if the broker goes down for 30 seconds we should be able to keep taking writes) and observe how well the producer handles the catch up time when it has a large backlog to get rid of.
d. Add a new configuration for the producer to enable this, something like use.file.buffers=true/false.
e. Add documentation that covers these new options.
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