Sep 6, 2021
Hi all, I am trying to decide on a database for time-series data. The data could be tracking some simple series like statistics over time or could be a nested JSON (multi-level nested). I have been experimenting with InfluxDB for the former case of a simple list of variables over time. The continuous queries are powerful too. But for the latter case, where InfluxDB requires to flatten out a nested JSON before saving it into the database the complexity arises. The nested JSON could be objects or a list of objects and objects under objects in which a complete flattening doesn't leave the data in a state for the queries I'm thinking.
[
{ "timestamp": "2021-09-06T12:51:00Z",
"name": "Name1",
"books": [
{ "title": "Book1", "page": 100 },
{ "title": "Book2", "page": 280 },
]
},
{ "timestamp": "2021-09-06T12:52:00Z",
"name": "Name2",
"books": [
{ "title": "Book1", "page": 320},
{ "title": "Book2", "page": 530 },
{ "title": "Book3", "page": 150 },
]
}
]
Sample query: With a time range, for name xyz, find all the book title for which # of page < 400.
If I flatten it completely, it will result in fields like books_0_title, books_0_page, books_1_title, books_1_page, ... And by losing the nested context it will be hard to return one field (title) where some condition for another field (page) satisfies.
Appreciate any suggestions. Even a piece of generic advice on handling the time-series and choosing the database is welcome!
