Pushing up...

On Thursday, April 10, 2014 8:13:33 AM UTC+3, Yitzhak Kesselman wrote:
>
> I am using logstash 1.4.0, if I had understand correctly it uses 
> automatically the Bulk API. Do I miss something ?
> Is there a limit on the size of an Index (on single node machine)? 
>
>
> (BTW Itamar thanks for the help!)
>
>
> On Wednesday, April 9, 2014 10:39:19 PM UTC+3, Itamar Syn-Hershko wrote:
>
>> Can you try batching writes to Elasticsearch? see 
>> http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-bulk.html
>>
>> --
>>
>> Itamar Syn-Hershko
>> http://code972.com | @synhershko <https://twitter.com/synhershko>
>> Freelance Developer & Consultant
>> Author of RavenDB in Action <http://manning.com/synhershko/>
>>
>>
>> On Wed, Apr 9, 2014 at 10:33 PM, Yitzhak Kesselman <[email protected]>wrote:
>>
>>> Attached the index rate (using bigdesk):
>>> <https://lh5.googleusercontent.com/-Jve-j75qB9o/U0WgK5ZMvMI/AAAAAAAAAFo/5_WZuCryeRw/s1600/bigdesk.png>
>>>
>>> The indexing requests per second is around 2K and the Indexing time per 
>>> second is around 3K
>>>
>>>
>>> On Wednesday, April 9, 2014 9:36:12 PM UTC+3, Yitzhak Kesselman wrote:
>>>
>>>> Answers inline.
>>>>
>>>>  Regarding the slow I/O. When I analyzed the creation of the Lucene 
>>>> index files I see that they are created without any special flags (such as 
>>>> no buffering or write through). This means that we’re paying costs twice – 
>>>> when we write the file we’re going cache data in Windows’ Cache Manager, 
>>>> which takes a lot of memory (which is then not available to the 
>>>> application 
>>>> itself) but when we read the file we don’t actually read it using the 
>>>> cache, which makes the operation slow. *Any ideas?*
>>>>
>>>>
>>>> On Wednesday, April 9, 2014 5:28:11 PM UTC+3, Itamar Syn-Hershko wrote:
>>>>
>>>>> Shooting in the dark here, but here it goes:
>>>>>
>>>>> 1. Do you have anything else running on the system? for example AVs 
>>>>> are known to cause slow-downs for such services, and other I/O or memory 
>>>>> heavy services could cause thrashing or just general slowdown
>>>>>
>>>> No, nothing else is running on that machine. Initially it was working 
>>>> fast it got slower with that amount of data that in index. Moreover is 
>>>> there a way to increase buffer size for the Lucene index files (.tim, 
>>>> .doc, 
>>>> and .pos) from 8K to something much bigger.
>>>>
>>>>>
>>>>> 2. What JVM version are you running this with?
>>>>>
>>>>  java version "1.7.0_51"
>>>>
>>>>  Java(TM) SE Runtime Environment (build 1.7.0_51-b13)
>>>>
>>>>  Java HotSpot(TM) 64-Bit Server VM (build 24.51-b03, mixed mode)
>>>>
>>>>  OS_NAME="Windows"
>>>>
>>>> OS_VERSION="5.2"
>>>>
>>>> OS_ARCH="amd64"
>>>>
>>>> 3. If you changed any of the default settings for merge factors etc - 
>>>>> can you revert that and try again?
>>>>>
>>>>  Tried before was same behavior.
>>>>
>>>>>
>>>>> 4. Can you try with embedded=false and see if it makes a difference?
>>>>>
>>>>  Tried before was same behavior.
>>>>
>>>>>
>>>>> --
>>>>>
>>>>> Itamar Syn-Hershko
>>>>> http://code972.com | @synhershko <https://twitter.com/synhershko>
>>>>> Freelance Developer & Consultant
>>>>> Author of RavenDB in Action <http://manning.com/synhershko/>
>>>>>
>>>>>
>>>>> On Wed, Apr 9, 2014 at 4:11 PM, Yitzhak Kesselman 
>>>>> <[email protected]>wrote:
>>>>>
>>>>>>  Hi,
>>>>>>
>>>>>>  
>>>>>>
>>>>>> I have configured a single node ES with logstash 1.4.0 
>>>>>> (8GB memory) with the following configuration:
>>>>>>
>>>>>>    - 
>>>>>>    
>>>>>>    index.number_of_shards: 7
>>>>>>    - 
>>>>>>    
>>>>>>    number_of_replicas: 0
>>>>>>    - 
>>>>>>    
>>>>>>    refresh_interval: -1
>>>>>>    - 
>>>>>>    
>>>>>>    translog.flush_threshold_ops: 100000
>>>>>>    - 
>>>>>>    
>>>>>>    merge.policy.merge_factor: 30
>>>>>>    - 
>>>>>>    
>>>>>>    codec.bloom.load: false
>>>>>>    - 
>>>>>>    
>>>>>>    min_shard_index_buffer_size: 12m
>>>>>>    - 
>>>>>>    
>>>>>>    compound_format : true
>>>>>>    - 
>>>>>>    
>>>>>>    indices.fielddata.cache.size: 15%
>>>>>>    - 
>>>>>>    
>>>>>>    indices.fielddata.cache.expire: 5m
>>>>>>    - 
>>>>>>    
>>>>>>    indices.cache.filter.size: 15%
>>>>>>    - 
>>>>>>    
>>>>>>    indices.cache.filter.expire: 5m
>>>>>>    
>>>>>> Machine : 16GB RAM, Intel I&-2600 CPU @ 3.4GHZ.
>>>>>> OS: 64bit WindowsServer 2012 R2
>>>>>>
>>>>>> My raw data is CSV file and I use grok as a filter to parse it with 
>>>>>> output configuration (elasticsearch {  embedded => true flush_size => 
>>>>>> 100000  idle_flush_time => 30 }).
>>>>>> Row data size is about 100GB events per day which ES tries to input 
>>>>>> into one index (with 7 shards).
>>>>>>
>>>>>> At the beginning the insert was fast however after a while 
>>>>>> it's got extremely slow,  1.5K doc in 8K seconds :(
>>>>>>
>>>>>> Currently the index has around 140Million docs with size of 55GB.
>>>>>>
>>>>>>  
>>>>>>
>>>>>> When I have analyzed the write to the disk with ProcMon I have seen 
>>>>>> that the process is writing in an interleaved manner to three kinds of 
>>>>>> files (.tim, .doc, and .pos) in 4K and 8K segments, instead of batching 
>>>>>> writes to some reasonable number.
>>>>>>
>>>>>>  
>>>>>>
>>>>>> Appreciate the help.
>>>>>>  
>>>>>>  
>>>>>>
>>>>>> All the best,
>>>>>>
>>>>>> Yitzhak
>>>>>>  
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>>>>>> msgid/elasticsearch/aa0193d2-cb6a-49e4-b4a3-a2f821f732f8%
>>>>>> 40googlegroups.com<https://groups.google.com/d/msgid/elasticsearch/aa0193d2-cb6a-49e4-b4a3-a2f821f732f8%40googlegroups.com?utm_medium=email&utm_source=footer>
>>>>>> .
>>>>>> For more options, visit https://groups.google.com/d/optout.
>>>>>>
>>>>>
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>>
>>

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