SQL to MATLAB Converter

About SQL to MATLAB Converter

Convert SQL database dumps (CREATE TABLE and INSERT statements) to MATLAB-compatible formats. Supports matrices, cell arrays, struct arrays, and table objects for seamless integration into MATLAB data analysis, scientific computing, and visualization workflows.

Key Features

• Multiple Formats: Matrix, cell array, struct array, and table (R2013b+)
• Automatic Type Detection: Identifies numeric columns for optimal format
• Variable Name Sanitization: Ensures MATLAB-compatible variable names
• NaN Handling: Proper handling of NULL and missing values
• Usage Examples: Optional comments with data access patterns
• File Download: Save as .m file for direct use in MATLAB

How to Use

1. Input SQL Data: Paste your SQL CREATE TABLE and INSERT statements or upload a .sql file
2. Choose Format: Select matrix, cell array, struct array, or table format
3. Configure Options: Toggle comments and usage examples
4. Copy or Download: Use the Copy or Download button to save your MATLAB code
5. Run in MATLAB: Execute the code in MATLAB or save as .m file

Output Formats

Table (Recommended, R2013b+):

• Modern MATLAB table object with named variables
• Supports mixed data types (numeric and string)
• Easy variable access by name
• Compatible with MATLAB's data analysis functions
• Best for most use cases

Matrix (Numeric Only):

• 2D numeric array format
• Only includes numeric columns
• Non-numeric columns replaced with NaN
• Fast mathematical operations
• Ideal for pure numeric data

Cell Array (Mixed Types):

• Cell array with header row
• Supports mixed data types
• Access by row and column index
• Compatible with all MATLAB versions
• Good for heterogeneous data

Struct Array (Field-Based):

• Array of structures with named fields
• Record-oriented data access
• Easy to work with individual records
• Compatible with all MATLAB versions
• Best for database-like operations

Example Conversion

SQL Input:

CREATE TABLE experiments (
  id INT,
  sample_name VARCHAR(50),
  temperature DECIMAL(5,2),
  pressure DECIMAL(6,2)
);

INSERT INTO experiments VALUES (1, 'Sample_A', 25.5, 101.3);
INSERT INTO experiments VALUES (2, 'Sample_B', 30.0, 98.7);

MATLAB Output (Table format):

% Table format (MATLAB R2013b and later)
% Rows: 2, Variables: 4

id = [1; 2];
sample_name = {'Sample_A'; 'Sample_B'};
temperature = [25.5; 30.0];
pressure = [101.3; 98.7];

experiments = table(id, sample_name, temperature, pressure);

% Access data:
% By variable name: experiments.id
% By row and column: experiments(1, :)
% By row and variable: experiments(1, 'id')

MATLAB Output (Matrix format):

% Numeric matrix format (only numeric columns)
% Rows: 2, Columns: 4

experiments = [
    1, NaN, 25.5, 101.3;
    2, NaN, 30.0, 98.7
];

% Column names:
% Column 1: id (numeric)
% Column 2: sample_name (non-numeric, replaced with NaN)
% Column 3: temperature (numeric)
% Column 4: pressure (numeric)

MATLAB Output (Struct format):

% Struct array format (field-based access)
% Records: 2

experiments(1).id = 1;
experiments(1).sample_name = 'Sample_A';
experiments(1).temperature = 25.5;
experiments(1).pressure = 101.3;

experiments(2).id = 2;
experiments(2).sample_name = 'Sample_B';
experiments(2).temperature = 30.0;
experiments(2).pressure = 98.7;

% Access data: experiments(index).fieldname
% Example: experiments(1).id
% Get all values for a field: [experiments.id]

Common Use Cases

• Scientific Data Analysis: Import experimental data from databases
• Machine Learning: Prepare datasets for MATLAB ML toolboxes
• Signal Processing: Load time-series data for analysis
• Statistics: Import data for statistical analysis
• Visualization: Create plots and charts from database data
• Simulation: Use database parameters in simulations

Data Type Handling

• Numeric Values: Converted to MATLAB numeric types (integers, decimals)
• Strings: Converted to MATLAB strings or cell arrays of strings
• NULL Values: Converted to NaN (numeric) or empty strings (text)
• Mixed Columns: Handled appropriately based on format choice

MATLAB Integration Examples

Table Operations:

% Filter rows
highTemp = experiments(experiments.temperature > 27, :);

% Calculate statistics
meanTemp = mean(experiments.temperature);

% Sort by column
sortedData = sortrows(experiments, 'temperature');

% Plot data
plot(experiments.id, experiments.temperature);

Matrix Operations:

% Extract numeric columns (skip NaN columns)
numericData = experiments(:, ~any(isnan(experiments)));

% Calculate correlation
corrMatrix = corrcoef(numericData);

% Perform linear regression
X = numericData(:, 1);
Y = numericData(:, 2);
coeffs = polyfit(X, Y, 1);

Struct Operations:

% Loop through records
for i = 1:length(experiments)
    fprintf('Sample: %s, Temp: %.1f\n', ...
        experiments(i).sample_name, experiments(i).temperature);
end

% Extract all temperatures
temps = [experiments.temperature];

% Filter by condition
highPressure = experiments([experiments.pressure] > 100);

Variable Name Sanitization

The converter automatically sanitizes column names to be MATLAB-compatible:

• Replaces special characters with underscores
• Ensures names start with a letter
• Removes leading underscores
• Handles numeric prefixes (e.g., "2nd_column" → "col_2nd_column")

MATLAB Version Compatibility

• Table Format: Requires MATLAB R2013b or later
• Matrix Format: Compatible with all MATLAB versions
• Cell Array: Compatible with all MATLAB versions
• Struct Array: Compatible with all MATLAB versions

Privacy & Security

All conversions happen locally in your browser. Your SQL data is never uploaded to any server, ensuring complete privacy and security.