So I've collected about 100GB of logstash logs over 3 months.

So there are roughly 100 Indexes such as logstash-2014.07.01 and so forth.

I have a cluster of 3 EC2 Instances, 1 CPU w/ 4GB RAM Each.  Granted it's 
not much.

When I do queries, it's usually fast, until I run a large timespan that 
span say 10-30 indexes.  At that point, I'm guessing each node has loaded 
so much index and field data that it was nearly impossible to avoid 
overrunning the heap or RAM on the cluster.  I end up with nodes at 100% 
CPU, and 75% RAM usage.  

I just wanted to check what was possible with tuning:

1) Given limited RAM, is it possible somehow tune my nodes such that in 
event of a large query requiring too much RAM:
   1a) The job gets killed due to timeout
   1b) Something else saves my node from becoming non-responsive?

2) Is it possible to make some indexes work fast, while others slow?
  2a) When I query historical data, I don't need an answer quickly.  Just 
eventually.
  2b) When I query the last 72 hours, I really want an answer quickly, even 
if that means killing other jobs

3) Is it an unavoidable fact that as my data increases, I have no choice 
but to either:
  3a) Increase cluster RAM to hold every index/field at the same time?
  3b) Delete indexes until everything fits in RAM?

As I attempt to open opensearch to more people, they are running queries in 
Kibana that span a larger and larger timeframe.  Thus leading to random 
frozen nodes.

If there was just some way to prevent frozen nodes (Maxed out CPU @ 100% 
despite ram usage at say 3gb out of 4gb) then I would have a more stable 
cluster.

As EC2 does carry a noticable cost, I was trying to minimize my EC2 
requirement.  So I'm trying to find ways to selectively reduce performance 
where I don't need it.

Any ideas?

Jeff

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