On 2/14/25 11:34, Allan Kamau wrote:
On Thu, Feb 13, 2025 at 9:44 PM Thiemo Kellner <thi...@gelassene-pferde.biz> wrote:13.02.2025 10:54:05 Achilleas Mantzios - cloud <a.mantz...@cloud.gatewaynet.com>: > If we followed a strict normalized approach then we would create additionally 11 tables each tag of type c) . And we are not guaranteed that the same tags would have the same structure over the whole fleet/manufacturers. So we are thinking of putting all semi-structured data of tags of type c) into one table with a single col of type jsonb . From what I read timescaledb plays nice with jsonb (or at least not bad). > > Do you ppl see any gotcha with this approach ? > > For starters we will not convert yet to timescaledb, but store them and handle them like normal tables. At least until we grasp the ins and outs of this. I have not come to see the real advantage of putting data into something like Jason or XML columns. Sue, you make life easier initially saving the time to put them into columns one by one, on the other end you have the hassle of dissecting the JSON, XML you name it when you retrieve/select the data, every query. While the query might stay stable the computational effort dissection will have to be done with every query execution. For c) you could go to 6th normal form, or what number that is, by a table like TAG(id, tag_name, tag_value). You would have to convert the values to string to enter them. It is a pain in the butt, imho, retrieving them again. You can alleviate by having a specific column/table per data type, e.g. TAG(id, tag_name,tag_value_varray,tag_value_date,...). What is the downside of having each a table for the special tags? More effort in setting up. The upside is less effort in retrieval, and a much more understandable model. If your tag structure is volatile, you might have generic column names on a table mapping them in a view to speaking names. Taking this further does anchor modelling https://www.anchormodeling.com/ .Though you haven't asked for comments on database design, I have a suggestion, have you considered using table partitioning based on a well defined date interval, maybe monthly? Here your DB data population application will determine the name of the table based on the current date then check for the existence of the table and then construct the inherited table if one does not exist and proceed to populate the child table accordingly.
Absolutely, this will be a necessity sooner or later, as I can see the future coming it will be a new timescaledb functioning at the start as a logical replica of our maindb, and timescaledb will handle all the partitioning (i hope). At some point switch the app (both the data loading (from the vessels) and the queries ) to the timescaledb, and free the space in the maindb or smth along those line.
This may prove useful in future. -Allan.
