The random extension provides a collection of functions to generate random values for various data types in PostgreSQL. It’s particularly useful for testing, data generation, and creating reproducible datasets. Your Nile database arrives with the random extension already enabled.

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Understanding Reproducible Output

The extension is designed to generate repeatable data sets. Each function takes seed and nvalues parameters:
  • seed: Determines a subset of possible values to generate
  • nvalues: Determines the number of distinct values
This allows you to generate the same set of values consistently when needed, though the order of values may vary.

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Examples

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String Generation

-- Generate random strings with specified length range
-- This will generate 10 random strings between 5 and 10 characters long
SELECT random_string(
    42,           -- seed for reproducibility
    1000,        -- size of pool of possible values
    5,      -- minimum string length
    10      -- maximum string length
) from generate_series(1, 10);

-- If we set a small pool size, we will get less unique values
-- This will return the same two strings 10 times
SELECT random_string(42,2,5,10) from generate_series(1, 10);

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Numeric Types

-- Generate 10 random 32-bit integers between 1 and 100
SELECT random_int(42,1000,1,100) from generate_series(1, 10);

-- Generate 10 random double precision numbers between 0 and 1
SELECT random_double_precision(42,1000,0.0,1.0) from generate_series(1, 10);

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Network Types

-- Generate 10 random IP addresses
SELECT random_inet(42,1000) from generate_series(1, 10);

-- Generate 10 random CIDR addresses
SELECT random_cidr(42,1000) from generate_series(1, 10);

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Common Use Cases

The random extension is invaluable for creating reproducible test datasets, populating development databases, generating real-looking data for demos and conducting performance testing with controlled data.

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Best Practices

  1. Seed Management
    • Use consistent seeds for reproducible results
    • Document seeds used in test scenarios
    • Vary seeds systematically for different test cases
  2. Value Distribution
    • Consider the actual number of distinct values needed
    • Account for potential PRNG collisions
    • Use appropriate ranges for your use case

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Additional Resources

The random extension is a powerful tool for generating reproducible data. It contains many more functions than what is shown here. To see the full list of functions, please refer to the Random Extension Repository.