We are indexing all sort of events (Windows, Linux, Apache, Netflow and so 
on...) and impact is defined in speed of the Kibana GUI / how long it takes 
to load 7 or 14 days of data. Thats what is important for my colleagues.


Am Montag, 4. August 2014 10:52:25 UTC+2 schrieb Mark Walkom:
>
> What sort of data are you indexing? When you said performance impact was 
> minimal, how minimal and at what points are you seeing it?
>
> Regards,
> Mark Walkom
>
> Infrastructure Engineer
> Campaign Monitor
> email: [email protected] <javascript:>
> web: www.campaignmonitor.com
>  
>
> On 4 August 2014 16:43, horst knete <[email protected] <javascript:>> 
> wrote:
>
>> Hi again,
>>
>> a quick report regarding compression:
>>
>> we are using a 3-TB btrfs-volume with 32k block size now which reduced 
>> the amount of data from 3,2 TB to 1,1TB without any segnificant performance 
>> losses ( we are using a 8 CPU, 20 GB Memory machine with an iSCSI.Link to 
>> the volume ).
>>
>> So for us i can only suggest using the btrfs-volume for long term storage.
>>
>> Am Montag, 21. Juli 2014 08:48:12 UTC+2 schrieb Patrick Proniewski:
>>>
>>> Hi, 
>>>
>>> gzip/zlib compression is very bad for performance, so it can be 
>>> interesting for closed indices, but for live data I would not recommend it. 
>>> Also, you must know that: 
>>>
>>> Compression using lz4 is already enabled into indices, 
>>> ES/Lucene/Java usually read&write 4k blocks, 
>>>
>>> -> hence, compression is achieved on 4k blocks. If your filesystem uses 
>>> 4k blocks and you add FS compression, you will probably have a very small 
>>> gain, if any. I've tried on ZFS: 
>>>
>>> Filesystem             Size    Used   Avail Capacity  Mounted on 
>>> zdata/ES-lz4           1.1T    1.9G    1.1T     0%    /zdata/ES-lz4 
>>> zdata/ES               1.1T    1.9G    1.1T     0%    /zdata/ES 
>>>
>>> If you are using a larger block size, like 128k, a compressed filesystem 
>>> does show some benefit: 
>>>
>>> Filesystem             Size    Used   Avail Capacity  Mounted on 
>>> zdata/ES-lz4           1.1T    1.1G    1.1T     0%   
>>>  /zdata/ES-lz4        -> compressratio  1.73x 
>>> zdata/ES-gzip          1.1T    901M    1.1T     0%   
>>>  /zdata/ES-gzip        -> compressratio  2.27x 
>>> zdata/ES               1.1T    1.9G    1.1T     0%    /zdata/ES 
>>>
>>> But a file system block larger than 4k is very suboptimal for IO (ES 
>>> read or write one 4k block -> your FS must read or write a 128k block). 
>>>
>>> On 21 juil. 2014, at 07:58, horst knete <[email protected]> wrote: 
>>>
>>> > Hey guys, 
>>> > 
>>> > we have mounted an btrfs file system with the compression method 
>>> "zlib" for 
>>> > testing purposes on our elasticsearchserver and copied one of the 
>>> indices 
>>> > on the btrfs volume, unfortunately it had no success and still got the 
>>> size 
>>> > of 50gb :/ 
>>> > 
>>> > I will further try it with other compression methods and will report 
>>> here 
>>> > 
>>> > Am Samstag, 19. Juli 2014 07:21:20 UTC+2 schrieb Otis Gospodnetic: 
>>> >> 
>>> >> Hi Horst, 
>>> >> 
>>> >> I wouldn't bother with this for the reasons Joerg mentioned, but 
>>> should 
>>> >> you try it anyway, I'd love to hear your findings/observations. 
>>> >> 
>>> >> Otis 
>>> >> -- 
>>> >> Performance Monitoring * Log Analytics * Search Analytics 
>>> >> Solr & Elasticsearch Support * http://sematext.com/ 
>>> >> 
>>> >> 
>>> >> 
>>> >> On Wednesday, July 16, 2014 6:56:36 AM UTC-4, horst knete wrote: 
>>> >>> 
>>> >>> Hey Guys, 
>>> >>> 
>>> >>> to save a lot of hard disk space, we are going to use an compression 
>>> file 
>>> >>> system, which allows us transparent compression for the es-indices. 
>>> (It 
>>> >>> seems like es-indices are very good compressable, got up to 65% 
>>> >>> compression-rate in some tests). 
>>> >>> 
>>> >>> Currently the indices are laying at a ext4-Linux Filesystem which 
>>> >>> unfortunately dont have the transparent compression ability. 
>>> >>> 
>>> >>> Anyone of you got experience with compression file systems like 
>>> BTRFS or 
>>> >>> ZFS/OpenZFS and can tell us if this led to big performance losses? 
>>> >>> 
>>> >>> Thanks for responding 
>>>
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