Hello,
First, for those who don't know me, I'm a big fan of Arango. I've written a 
Clojure driver, travesedo <https://github.com/deusdat/travesedo>, and the 
only, as far as I know, Hadoop/Cascading taps in existence 
<https://github.com/deusdat/guacaphant>, which makes Arango a first class 
citizen in the "big data" world. So what follows comes from love.

Is there a plan to move Arango to a more efficient memory model than 
essentially mapped files? I've seen multi-Gb databases work fine in MySQL 
with just 1 GB of RAM. I don't think that Arango would do as well under 
these tight, bootstrapped requirements. I've personally watched a mere 700 
GB collection bring Arango to its knees if the whole server only has 1 GB 
of RAM.

The reason I ask is because I think there's a large market of small 
projects/bootstrapped startups out there that could really use an ArangoDB 
type store. Money is drying up in Silicon Valley. This means that single 
node, cloud-based systems of 96 GB are going away. We're going to return to 
micro systems on various cloud providers. ArangoDB is pricing itself out of 
the market.

I appreciate the work of the shapes storage for the the data. I understand 
how it can condense documents much better than MongoDB. At the same time 
the memory usage for collections having to be read entirely cached in 
memory makes the system difficult. The non-binary storage makes it terribly 
inefficient as shown by ArangoDB's own benchmarks.

Is there an effort to support partial collection loads (or other 
optimizations)? 

Postgres does really well with the inefficient JSON format used by Arango 
in the comparison tests. It could do better in a Relational v Document 
showdown too provided the proper indexes are in Postgres.

How does ArangoDB's dev team view this issue(s)? 

I ask because I'm at a cross roads now. I can only afford about $80/month 
for my data tier. I can get a MySQL or Postgres system that would hum along 
for years in single node mode. ArangoDB concerns me because I can quickly 
need multiple systems to just happily process a few GB.

Thanks,
JPD

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