Simple Random Samples from a MySQL Sql database

Quick sampling with WHERE clause

For a larger dataset, use RAND() in a WHERE clause. It achieves better performance as it reduces the time complexity of random sorting.

/* 
    Whether you think you can or you can't, you're right.
    ...But can you speed up your SQL queries?
*/
SELECT * FROM my_table WHERE rand() <= .3;
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Here, .3 indicates a 30% chance for each record to be selected.

Index-smart random sampling

If your table has a numeric, unique, indexed primary key (we'll call it id), consider a different strategy to select random IDs first, then fetch their rows.

1. Determine the range of your primary key (MIN(id) to MAX(id)).
2. Generate unique random integers within this range (ids).
3. Fetch rows matching these random ids.

Advanced approach: Pre-sampling using RAND()

This approach uses RAND() efficiently by pre-filtering rows. We execute a pre-sample query, expanding our desired sample size by a multiplication factor (e.g., 2). Then, we'd store these in an indexed column, accelerating the final sample selection.

Not just random, but relevant

Switching directions, also consider the frequency of running these random samples and the size of your database. Your approach should fit naturally with the current state of your database without requiring massive changes to the structure or indexes.

Potential pitfalls and tips for success

• Uniform Distribution: Ensure RAND() generates numbers uniformly, keeping your sample random.
• Sampling bias: Beware of bias while ensuring randomness in your selection methodology.
• Performance Testing: Execute performance tests to certify your method scales well with the table's growth.

Possible issues include:

• Query Costs: Balance query costs and possible performance bottlenecks when frequently sampling large datasets.
• Data Skew: Be watchful of data skews as they may interfere with the forecasting accuracy of your sampling.