Hi list,
In France, the total number of cadastral parcels is around 10 000 000
The data can be heavy, because each parcel stores a geometry (PostGIS geometry data type inside a geom column).
Indexes must be created to increase performance of day-to-day requests:
* GIST index on geom for spatial filtering and intersection with other geometries (other tables)
* Primary key and probably another unique code to index
* one index on the "department" field. There are around 100 "departments" (admin boundaries) in France, and the parcels are homogeneously distributed (~ 1M parcel per "department")
The initial import of this data is made one department by one department (the data source is distributed by department by French authorities). And each year, data must be deleted and reimported (data change each year), and this is also often done one department at a time.
* Sometimes requests are made with a department filter (for example WHERE department IN ('2A', '34', '30', '48') )
* Sometimes other client database clients must be able to get data from the whole dataset ( for example get the parcels for a list of known IDs)
I would like to question the list about the following 2 strategies to maintain such data:
1/ Put the whole dataset into one big table
2/ Create one table per department, and create a VIEW with 100 UNION ALL to gather all the parcels
1/ Seems simpler for the database clients, but it seems to me this can be a pain to maintain. For example, each time we will need to replace last year data for one department with the upcoming new data, we will need to delete 1M lines, reimport the new 1M lines and VACUUM FULL to regain space. Indexes will be huge, and I can suffer questions like :
I often need to use pg_repack to regain spaces on this kind of table. VACUUM FULL cannot be used because it locks the table, and it takes times (!)
2/ Seems more kiss, but only if queries on the UNION VIEW will be able to use the tables indexes (geom, department) and perform as well as the big table.
Any hint appreciated !
Regards
Kimaidou
