Question

I need to work with several data samples, to say, N. The samples represent similar data but from different origins. For example, history of order in different shops. So the structure of all the samples is the same. To operate with the data I have several possibilities:

1. Use N databases with identical
   schema, one for each sample

2. Use one database, but N sets of tables. For example, User_1,…, User_N; Product_1, …, Product_N, Order_1, …, Order_N and so on.

3. Use one database with one set of tables User, Product, Order, but add to each table a helper column which represents a sample index. Clearly, this column should be an index.

The last variant seems to be the most convenient for use because all queries become simple. In the second case I need to send a table name to a query (stored procedure) as a parameter (is it possible?).

So which way would you advise? The performance is very important.

Answer 1

Step 1. Get a book on data warehousing — since that’s what you’re doing.

Step 2. Partition your data into facts (measurable things like $’s, weights, etc.) and dimensions (non-measurable attributes like Product Name, Order Number, User Names, etc.)

Step 3. Build a fact table (e.g., order items) surrounded by dimensions of that fact. The order item’s product, the order item’s customer, the order item’s order number, the order item’s date, etc., etc. This will be one fact table and several dimension tables in a single database. Each “origin” or “source” is just a dimension of the basic fact.

Step 4. Use very simple “SELECT SUM() GROUP BY” queries to summarize and analyze your data.

This is the highest performance, most scalable way to do business. Buy Ralph Kimball’s Data Warehouse Toolkit books for more details.

Do not build N databases with identical structure. Build one for TEST, and one for PRODUCTION, but don’t build N.

Do not build N tables with identical structure. That’s what keys are for.