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"You need to understand what situations it works best in" I always hear this, but with no "such as" examples given. Could you please provide some?
by kentosi 9y ago
"You need to understand what situations it works best in"
I always hear this, but with no "such as" examples given. Could you please provide some?
- nevi-me 9y agoThe same example I provide everywhere. I have successfully used Mongo as a geospatial data store, where I have a public transit app/website, and most objects/documents have geospatial properties. Sure, there's PostGIS, but saving a GeoJSON multiline which I can slap an index on works for me. I have grown fond of Mongo because I've been using it for almost 5 years now. I know its limitations, and also what I can do in RDBMS that I can also do in it. I sometimes build OLAP logical models at work for clients, and most of what I touch at work is SQL, but sometimes when I start a project I use Mongo instead of SQL. It's only one use-case, but I hope it is something.
- kentosi 9y agoInteresting. I haven't used JSON-stores because in my mind it always boils down to the question of how you would link entities together. I can't seem to get a good answer... so let me put this to you: In a standard RDBMS I would probably have a custom datatype for LatLon (or MGRS): create custom type LatLon(...) create table Person(id as int, name as string, location as LatLon) create table Shop(id as int, name as string, location as LatLon) Find everyone 10k around Macy's: select p.* from Person p inner join Shop s where s.name="Macy's" and with_distance(p.location, s.location, 10km) How would a JSON-store be better at managing this, given that with RDBMS systems you have indexes to help speed such cross-table looks?