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From iain wright <iainw...@gmail.com>
Subject Re: File Channel Best Practice
Date Wed, 18 Dec 2013 18:23:22 GMT
Hi Brock,

Just curious here and please forgive my ignorance :)

In terms of batching at the source for a file channel, is there a
combination of time and quota for the source polling?

For instance does there have to be 1000 new events to load anything into
the channel when using a 1k batch size, or if say 5 seconds passes and only
250 new events are available in the source will it grab those on some time
based interval?

Thank you,

-- 
Iain Wright
Cell: (562) 852-5916

<http://www.labctsi.org/>
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On Wed, Dec 18, 2013 at 9:51 AM, Brock Noland <brock@cloudera.com> wrote:

> FYI I am trying to capture some of the best practices in the Flume doc
> itself:
>
> https://issues.apache.org/jira/browse/FLUME-2277
>
>
> On Tue, Dec 17, 2013 at 12:17 PM, Brock Noland <brock@cloudera.com> wrote:
>
>> Hi,
>>
>> I'd also add the biggest issue I see with the file channel is batch size
>> at the source. Long story short is that file channel was written to
>> guarantee no data loss. In order to do that when a transaction is committed
>> we need to perform a "fsync" on the disk the transaction was written to.
>> fsync's are very expensive so in order to obtain good performance, the
>> source must have written a large batch of data. Here is some more
>> information on this topic:
>>
>> http://blog.cloudera.com/blog/2012/09/about-apache-flume-filechannel/
>>
>> http://blog.cloudera.com/blog/2013/01/how-to-do-apache-flume-performance-tuning-part-1/
>>
>> Brock
>>
>>
>> On Tue, Dec 17, 2013 at 11:50 AM, iain wright <iainwrig@gmail.com> wrote:
>>
>>> Ive been meaning to try ZFS with an SSD based SLOG/ZIL (intent log) for
>>> this as it seems like a good use case.
>>>
>>> something like:
>>>
>>> pool
>>>   sdaN - ZIL (enterprise grade ssd with capacitor/battery for persisting
>>> buffers in event of sudden power loss)
>>>   mirror
>>>     sda1
>>>     sda2
>>>   mirror
>>>     sda3
>>>     sda4
>>>
>>> theres probably further tuning that can be done as well within ZFS, but
>>> i believe the ZIL will allow for immediate responses to flumes
>>> checkpoint/data fsync's while the "actual data" is flushed asynchronously
>>> to the spindles.
>>>
>>> Haven't tried this and YMMV. Some good reading available here:
>>> https://pthree.org/2013/04/19/zfs-administration-appendix-a-visualizing-the-zfs-intent-log/
>>>
>>> Cheers
>>>
>>>
>>> On Dec 17, 2013 8:30 AM, "Devin Suiter RDX" <dsuiter@rdx.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> There has been a lot of discussion about file channel speed today, and
>>>> I have had a dilemma I was hoping for some feedback on, since the topic is
>>>> hot.
>>>>
>>>>  Regarding this:
>>>> "Hi,
>>>>
>>>> 1) You are only using a single disk for file channel and it looks like
>>>> a single disk for both checkpoint and data directories therefore throughput
>>>> is going to be extremely slow."
>>>>
>>>> How do you solve in a practical sense the requirement for file channel
>>>> to have a range of disks for best R/W speed, yet still have network
>>>> visibility to source data sources and the Hadoop cluster at the same time?
>>>>
>>>> It seems like for production file channel implementation, the best
>>>> solution is to give Flume a dedicated server somewhere near the edge with
a
>>>> JBOD pile properly mounted and partitioned. But that adds to implementation
>>>> cost.
>>>>
>>>> The alternative seems to be to run Flume on a  physical Cloudera
>>>> Manager SCM server that has some extra disks, or run Flume agents
>>>> concurrent with datanode processes on worker nodes, but those don't seem
>>>> good to do, especially piggybacking on worker nodes, and file channel >
>>>> HDFS will compound the issue...
>>>>
>>>> I know the namenode should definitely not be involved.
>>>>
>>>> I suppose you could virtualize a few servers on a properly networked
>>>> host and a fast SANS/NAS connection and get by ok, but that will merge your
>>>> parallelization at some point...
>>>>
>>>> Any ideas on the subject?
>>>>
>>>> *Devin Suiter*
>>>> Jr. Data Solutions Software Engineer
>>>> 100 Sandusky Street | 2nd Floor | Pittsburgh, PA 15212
>>>> Google Voice: 412-256-8556 | www.rdx.com
>>>>
>>>
>>
>>
>> --
>> Apache MRUnit - Unit testing MapReduce - http://mrunit.apache.org
>>
>
>
>
> --
> Apache MRUnit - Unit testing MapReduce - http://mrunit.apache.org
>

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