This question is about choosing the type of database to run queries on for an application. Keeping other factors aside for the moment, and given that the choice is between mongodb and elastic, the key criterion is that the query should be resolved in near real time. The queries will be ad-hoc and as such can contain any of the fields in the JSON objects and will likely contain aggregations and subaggregations. Furthermore, there will not be nested objects and none of the fields will be containing 'descriptive' text (like movie reviews etc.), i.e., all the fields will be keyword type fields like State, Country, City, Name etc.
Now, I have read that elasticsearch performance is near real time and that elasticsearch uses inverted indices and creates them automatically for every field. Given all the above, my questions are as follows. (there is a similar question posted in stack but I do not think it answers my questions elasticsearch v.s. MongoDB for filtering application)
1) Since the fields in the use case I mentioned do not contain descriptive text and hence would not require the full-text search capability and other additional features that elastic provides (especially for text search), what would be a better choice between elastic and mongo? How would elastic search and mongo query/aggregation performance compare if I were to create single field indices on all the available fields in mongo?
2) I am not familiar with advanced indexing, so I am assuming that it would be possible to create indices on all available fields in mongo (either using multiple single field indices or maybe compound indices?). I understand that this will come with a cost for storage and write speed which is true for elastic as well.
3) Also, in elastic the user can trade off write speed (indexing rate) with the speed with which the written document becomes available (refresh_interval) for a query. Is there a similar feature in mongo?