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When doing database design, I often use reference/support tables, as we all do. Each time I start up a new project there are inevitably tables which:

  • Have a predefined set of values.
  • Will never change, and likely never encounter new records (or very infrequently).

A perfect example of this might be a Country table,.

create table Country
(
  CountryKey  int         not null identity
 ,CountryName varchar(64) not null
 ,IsoNumber   int         not null
 ,Iso3        varchar(8)  not null
 ,constraint pk_country primary key clustered (CountryKey)
);

I have used a number of different approaches to "hydrate" these kinds of tables, primarily:

  • A hydration SQL script with insert statements.
    • insert into Country (CountryName, IsoNumber ...) values ('Canada', ...)
  • A hydration SQL script, reading from disk, using bulk insert.

    • bulk insert MyDatabase.dbo.Country from ...
  • And in extreme cases a programmatic script (f#, python).

My preferred approach is the first if all tables only contain a few records. If the tables are beyond this limit, I typically like a script reading from CSV files. I elect to use CSV in that it's both compact and human-readable.

How is everyone else handling this situation?

I realize that this is a somewhat opinionated question. But I figured it was worth asking, since it will inevitably have concrete answers with technical rationale.

3

Separate Files

Keep CREATE TABLE, INSERT, GRANT scripts separately. Data MIGHT change "per project" (eg 'Oui' vs 'YES'). Additionally, this seems to be a common design for most projects I've seen/worked on.

INSERT vs Other

Bulk operations (f#,python,CSV) are faster than multiple single-row INSERT statements.

I would definitely use INSERT for a few rows. The "cutoff point" between INSERT and Code/CSV would be a personal decision.

Usage of Code

I would only consider using code (f#,python) if and only if the data is Calculated based on input parameter(s). (eg a DAY_DIMENSION table that loads n days at a time)

CSV Scripts

For sizable data, you'd want to use a CSV Script for speed

The script that loads the CSV (or other format) file should use the Filename as a parameter. All utilities (eg SQL*Loader) do this; you just need to filter the parameter up to the Shell Script level. This allows you to load a different (project specific/updated revision) file at-will.

Make sure you note if globbing (eg LoadMyData.sh data_0[1-3]*.csv) is allowed.

Dumps

One of the easiest ways to port tables is to use Dumps (as Dave has suggested). (eg Oracle's expdp/impdp)

You should still have the DDL statements available so that you can recreate the tables (and dump) as necessary.

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