​Not really, the smaller server you mentioned before would be suitable.​


On 23 April 2015 at 11:04, Jack Park <jackp...@topicquests.org> wrote:

> That starts to argue for lots of smaller servers maybe even with smaller
> SSD's. Say, a low power i3 with 16 or 23gb ram, and a 128gb SSD. Is that
> right?
>
> On Wed, Apr 22, 2015 at 2:56 PM, Mark Walkom <markwal...@gmail.com> wrote:
>
>> If you are using time series data then you should be using time series
>> indices. As Fred pointed out, routing an entire month's worth of data to a
>> single shard is not going to scale.
>>
>> Also, we recommend that you keep shard size below 50GB, this helps with
>> recovery and distribution. There is also a hard 2 billion doc per shard
>> limit in the underlying lucene engine, if you hit this then you may lose
>> data.
>>
>> On 23 April 2015 at 03:12, Kimbro Staken <ksta...@kstaken.com> wrote:
>>
>>> Hello Fred,
>>>
>>> I have clusters as large as 200billion documents/130TB. Sharing
>>> experiences on that would require a book, but a couple quick things that
>>> jumped out at me.
>>>
>>> 1. do not go the huge server route. Elasticasearch works best when you
>>> scale it horizontally. The 64GB route is a much better option.
>>>
>>> 2. If I understand correctly you're routing an entire months data to a
>>> single shard? By doing that you're directing all activity on that shard to
>>> a single machine, or small set of machines if you have replicas. That has
>>> to be much slower than if you were to do something like use a monthly index
>>> with a reasonable number of shards to spread that load across the cluster.
>>> That is also creating shard sizes that are fairly large and if you have
>>> month to month variation in data rates you'll end up with "lumpy" shard
>>> sizes which will definitely cause issues if you ever run your cluster low
>>> on disk space.
>>>
>>> 3. Get off of ES 1.3 as fast as you can. 8TB spread across 37 machines
>>> is very low density, as you push more data in you don't want to be on ES
>>> 1.3.
>>>
>>> 4. If you're not already using doc_values start looking into it now.
>>> Managing heap memory is let's be nice and call it "a challenge" and
>>> fielddata can eat heap in ways that will make your head spin.
>>>
>>>
>>>
>>> Kimbro Staken
>>>
>>>
>>> On Wed, Apr 22, 2015 at 1:14 AM, <fdevilla...@synthesio.com> wrote:
>>>
>>>> Hi list,
>>>>
>>>> I've been using ES in production since 0.17.6 with clusters up to 64
>>>> virtual machines and 20T data (including 3 replica). We're now thinking
>>>> about pushing things a bit further and I wondered if people here had
>>>> similar experience / needs as we do.
>>>>
>>>> Our current index is 1.1 billion unique documents, 8Tb data (including
>>>> 1 replica) on 37 physical machines (32 data nodes, 3 master nodes and 2
>>>> nodes dedicated to http requests) with ES 1.3 (upgrade to 1.5 already
>>>> planned). We're indexing about 2500 new documents / second and everything's
>>>> fine so far.
>>>>
>>>> Our goal is to index (and search) about 30 billion more documents (the
>>>> backdata) + about 200 million new documents each month.
>>>>
>>>> Our company is providing analytics dashboards to their clients, and
>>>> they mostly browse their data on a monthly scale, so we're routing
>>>> documents monthly. Each shard makes between 200 and 250G. The index is made
>>>> of 128 shards, which makes about 10 years of data with 1 month per shard.
>>>> Considering what we already have, we should reach 240T of data (and
>>>> counting) with a single replica after we index all our backdata.
>>>>
>>>> So, my questions here:
>>>>
>>>> - Has someone here the same use / amount of data as we do?
>>>>
>>>> - Is ES the right technology to do realtime, ligthspeed queries
>>>> (filtered queries and high cardinality agregations) on such an amount of
>>>> data?
>>>>
>>>> - What were the traps to avoid? Is it better to add lots of medium
>>>> machines (12 core  Xeon E5-1650 v2, 64G RAM, 1.8T SAS 15k hard drives) or a
>>>> few huge machines with petabytes of RAM, terabytes of SSD and multiple ES
>>>> processes?
>>>>
>>>> Any feedback on similar situation is indeed appreciated.
>>>>
>>>> Have a nice day,
>>>> Fred
>>>>
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