> Just a note: Bigdesk does not show breakdown of GC between old and new
> generation. So currently one needs to be careful when interpreting this
> chart. Better chart is coming. There are other tools that will show you the
> breakdown of GC (Sematext SPM, Marvel, HQ might as well, you can use
> visualVM ...)
>

Thanks for that Lukas. Awaiting the next version. :)


>
> Lukáš
>
>
> On Thu, Jan 30, 2014 at 10:14 AM, Mark Walkom 
> <[email protected]>wrote:
>
>> Facets use a fair bit of memory and in general ES will cache a much as it
>> can to speed up query times.
>>
>> Your graphs look pretty reasonable to me, lots of little GC's rather than
>> big ones rarely is what you want.
>> What are you expecting?
>>
>> Regards,
>> Mark Walkom
>>
>> Infrastructure Engineer
>> Campaign Monitor
>> email: [email protected]
>> web: www.campaignmonitor.com
>>
>>
>> On 30 January 2014 18:43, Vaidik Kapoor <[email protected]> wrote:
>>
>>> Hi Guys,
>>>
>>> I have the following situation. I have 3 nodes cluster, each node has 32
>>> GB DDR3 RAM and 2 Octocore Processors (16 cores), with HT. Allocated 50% of
>>> the RAM to Elasticsearch. We have only 1 index with 32 shards and 1 replica
>>> each, making it 64 shards in the cluster. We have a lot of mappings in the
>>> index (more than 3000 ~ no. of users).
>>>
>>> We have a lot of updates queries. In fact, we have only update queries
>>> with upserts. We are using routing to put similar data in the same shard.
>>> We see the rate of update queries ranging between 20/sec to 60/sec. This is
>>> going to increase to about 130-140/sec when we go live. Our queries are
>>> mostly filtered queries with a lot of use of term faceting (now we are
>>> using the Aggregations module).
>>>
>>> We are using doc_values as much as possible to reduce bringing of field
>>> data in the field data cache.
>>>
>>> For the last 2 weeks, I have been noticing that the memory usage would
>>> never drop. Every node in the cluster is using about 90% of the memory all
>>> the time. This triggers the GC a lot (sometimes more than 5 times in a
>>> minute). We are using G1GC instead of the default CMS.
>>>
>>> I then decided to stop querying and indexing both. The memory usage
>>> still does not drop and there are still a lot of GCs in a given minute. The
>>> effect of this is visible in the queue we are using to update documents. I
>>> can clearly relate the spikes in the queue with the GC activity i.e.
>>> whenever GC happens, indexing seems to become slow. This is sort of
>>> worrying me.
>>>
>>> I am attaching screenshots of BigDesk of two of our nodes in the cluster
>>> (1 master data node and 1 non-master data node).
>>>
>>> What could be the reason? How should I debug this issue? Will be happy
>>> to share more details if needed.
>>>
>>> Vaidik Kapoor
>>> vaidikkapoor.info
>>>
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