Hi, 

Thank you for the response. However, in our scenario, both the nodes are on 
the same machine. Our setup doesn't allow us to have two separate machines 
for each node. Also, we're indexing logs using logstash. Sometimes, we have 
to query data from the logs over a period of two or three months and then, 
we're thrown an out of memory error. This affects the indexing that is 
simultaneously going on and we lose events. 

I'm not sure what configuration of elasticsearch will help achieve this.

Thanks,
Rujuta

On Friday, March 21, 2014 10:36:51 PM UTC+5:30, Ivan Brusic wrote:
>
> One of the main usage of having a data-less node is that it would act as a 
> coordinator between the other nodes. It will gather all the responses from 
> the other nodes/shards and reduce them into one.
>
> In your case, the data-less node is gathering all the data from just one 
> node. In other words, it is not doing much since the reduce phase is 
> basically a pass-thru operation. With a two node cluster, I would say you 
> are better off having both machines act as full nodes.
>
> Cheers,
>
> Ivan
>
>
>
> On Fri, Mar 21, 2014 at 5:04 AM, Rujuta Deshpande 
> <ruj...@gmail.com<javascript:>
> > wrote:
>
>> Hi, 
>>
>> I am setting up a system consisting of elasticsearch-logstash-kibana for 
>> log analysis. I am using one machine (2 GB RAM, 2 CPUs) running logstash, 
>> kibana and  two instances of elasticsearch. Two other machines, each 
>> running  logstash-forwarder are pumping logs into the ELK system. 
>>
>> The reasoning behind using two ES instances was this - I needed one 
>> uninterrupted instance to index the incoming logs and I also needed to 
>> query the currently existing indices. However, I didn't want any complex 
>> querying to result in loss of events owing to Out of Memory Errors because 
>> of excessive querying. 
>>
>> So, one elasticsearch node was master = true  and data = true which did 
>> the indexing (called the writer node) and the other node, was master = 
>> false and data = false (this was the workhorse or reader node) .
>>
>> I assumed that, in cases of excessive querying, although the data is 
>> stored on the writer node, the reader node will query the data and all the 
>> processing will take place on the reader as a result of which issues like 
>> out of memory error etc will be avoided and uninterrupted indexing will 
>> take place. 
>>
>> However, while testing this, I realized that the reader hardly uses the 
>> heap memory ( Checked this in Marvel )  and when I fire a complex search 
>> query - which was a search request using the python API where the 'size' 
>> parameter was set to 10000, the writer node throws an out of memory error, 
>> indicating that the processing also takes place on the writer node only. My 
>> min and max heap size was set to 256m  for this test. I also ensured that I 
>> was firing the search query to the port on which the reader node was 
>> listening (Port 9200). The writer node was running on Port 9201.  
>>
>> Was my previous understanding of the problem incorrect - i.e. having one 
>> reader and one writer node, doesn't help in uninterrupted indexing of 
>> documents? If this is so, what is the use of having a separate workhorse or 
>> reader node? 
>>
>> My eventual aim is to be able to query elasticsearch and fetch large 
>> amounts of data at a time without interrupting/slowing down the indexing of 
>> documents. 
>>
>> Thank you. 
>>
>> Rujuta 
>>
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