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https://issues.apache.org/jira/browse/KAFKA-1955?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16015046#comment-16015046
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ASF GitHub Bot commented on KAFKA-1955:
---------------------------------------
GitHub user blbradley opened a pull request:
https://github.com/apache/kafka/pull/3083
KAFKA-1955: [WIP] Disk based buffer in Producer
Based on patch from @jkreps in [this JIRA
ticket](https://issues.apache.org/jira/browse/KAFKA-1955).
- [ ] Get some unit tests that would cover disk-backed usage
- [ ] Do some manual performance testing of this usage and understand the
impact on throughput.
- [ ] 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.
- [ ] Add a new configuration for the producer to enable this, something
like use.file.buffers=true/false.
- [ ] Add documentation that covers these new options.
I've brought the patch into sync with trunk. Testing is next, which I've
started on. I am flexible on how this can be implemented.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/blbradley/kafka kafka-disk-buffer
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/kafka/pull/3083.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #3083
----
commit 6b29fc95c394283ff4f2410ad37f7c8fcbd0d8d7
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-17T17:12:53Z
WIP: KAFKA-1955 August 8th 2015 rebase
commit 75d2af1d7f8dda4e2fe41da60455d813d655edd0
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-17T22:43:47Z
Merge branch 'trunk' into kafka-disk-buffer
patch works against trunk test suite
commit d3c765db789eef2fe71eca7a45dbca72e356f346
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-17T23:14:34Z
fix imports, add whitespace from diff
commit b58118c6413a5e900f5c1ebee112bd24e8d4b119
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-17T23:34:04Z
simple file buffer test
commit cd389f073eca18effa6449d9934aea0f90e84139
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-17T23:35:21Z
failing unallocated memory check
commit 49b6860e6c3be4bac62937dc835d5b6f97c7ff11
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-18T00:35:29Z
allocate buffer dynamically, passing tests
commit ed7aab5357fe9d7805dcb305d0318fb4ea770550
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-18T00:46:10Z
failing allocated memory check
commit 875ac83096199e35307a7ef47772907607aba1f1
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-18T00:56:47Z
do not add to free list during allocation
commit 4223e14896f4609d5bef80e97ee6d9982d2127a5
Author: Brandon Bradley <bradleytas...@gmail.com>
Date: 2017-05-18T01:20:46Z
add license
----
> Explore disk-based buffering in new Kafka Producer
> --------------------------------------------------
>
> Key: KAFKA-1955
> URL: https://issues.apache.org/jira/browse/KAFKA-1955
> Project: Kafka
> Issue Type: Improvement
> Components: producer
> Affects Versions: 0.8.2.0
> Reporter: Jay Kreps
> Assignee: Jay Kreps
> Attachments: KAFKA-1955.patch,
> KAFKA-1955-RABASED-TO-8th-AUG-2015.patch
>
>
> 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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