Tip for doing this kind of thing, that works in any language:
In some cases, using more that one type of data structure at the same time will allow you to get the best of both worlds in terms of performance. Ie, a spatial index plus a hash table as an index, over the same data, would allow efficient lookups on spatial predicates as well as attributes, over the same set of nodes.
A simple insight maybe, but not one I discovered without a lot of thought. Typically in university they teach you to use a single data structure type at a time, but really that's like teaching kids the alphabet, without the concept of combining the letters into meaningful words.
Depends how often you are getting new data. If its not too often, you could write directly to SQL. Otherwise writing in batches like you suggest could work too.
After looking at your post again, and depending on data volume, you might be ok with a purely SQL solution too (ie, finding nodes 1 or 2 steps away on a graph is easy in SQL if your adjacency data is all in the db).
By schema-less, do you mean that it's possible that every new record in the database could have a few attribute-values that no other record has, or that you simply don't know what your attributes are at this time but at some point you will know them all?
If by schemaless you mean the arbitrary name/attribute pairs, you could simply use SQL, with an "attribute" table with "name" and "value", and "nodeid" columns.
In some cases, using more that one type of data structure at the same time will allow you to get the best of both worlds in terms of performance. Ie, a spatial index plus a hash table as an index, over the same data, would allow efficient lookups on spatial predicates as well as attributes, over the same set of nodes.
A simple insight maybe, but not one I discovered without a lot of thought. Typically in university they teach you to use a single data structure type at a time, but really that's like teaching kids the alphabet, without the concept of combining the letters into meaningful words.