Hi, this Frank from ArangoDB again. We've not added absolute times because it strongly depends of your machines, network. For the setup we have used, the baseline (ArangoDB) is as follows: shortest path 0.5 sec, neighbors 0.15 sec, single read 26 sec, single write 27 sec, aggregations 1.4 sec, memory 12.5 GByte. Therefore a 16 GByte machine should have been enough. But we did not know beforehand, therefore we selected the biggest machine from GCE available to us (thanks to Google for giving us credits to use the machines for free).
I agree never trust a benchmark. It really all depends on your use case. If you have ideas for improvements, we would love to hear about them. Also if you have any idea how to improve the mongodb or neo4j queries, please check-out github and let us know.
I don't understand the % either. They state it is graph of _throughput_, with higher percentages from baseline being less throughput? If I hadn't read the backwards description I would've concluded that their DB is really slow on most fronts.
>>The uncompressed JSON data for the vertices need around 600 MB and the uncompressed JSON data for the edges requires around 1.832 GB.
So why use a 60GB RAM machine for so little data?
Can we get some raw numbers instead of %?