My primary concern was how it would cope when data exceeded RAM (I like to call this the MongoDB fallacy). However, based on this post it looks like the ElasticSearch setup works just fine.
First of, you need to make sure Elasticsearch can lock a chunk of memory (using mlock). About half of the available RAM is a good size, as other system processes need some memory, too, and not everything is on the heap.
You want to look for how many items you are indexing and how much of the heap the field data cache is using while doing queries. By default ES tries to keep the total heap size at about 3/4 of the allocated memory. The types of queries are important, too. E.g. if you do faceting or sorting on fields that have many different values, this will fill up the field data cache in no time.
Is that the kind of information you're after? I can go into in more detail if you have more specific question.
After that, you'll have to tune merging. The underlying Lucene storage engine chokes when it tries to merge segments that are large, so you have to tune the max segment sizes, etc.
Then, you'll have to tune your queries -- it's not feasible to do a full index scan on a large index so you have to get clever about how you pull data in chunks. If your data is timestamped and that timestamp is indexed, you can pull data for smaller time ranges which will be faster than pulling all data for an index at once.
It's a bit introductory on the memory-parts. One that goes in more detail on memory- and JVM-tuning is planned.
(Full disclosure: I work for Found)