A command-line tool written in Rust that infers SQL data types from a CSV file and generates a CREATE TABLE SQL DDL statement. It aims to determine the strictest possible SQL type for each column that can accommodate all its values.

• infers sql types for columns: integer, bigint, float, char(n), varchar(n), date, datetime, boolean.
• Determines the strictest type that fits all values in a column.
  • e.g., a column with 1, 2, 3000000000 will be BIGINT.
  • e.g., a column with 1, 2.0 will be FLOAT.
  • e.g., a column with apple, grape (both length 5, and not a more specific type like boolean/date etc.) will be CHAR(5).
  • e.g., a column with apple, banana (lengths 5 and 6) will be VARCHAR(6).
  • e.g., a column with 1, text (different types, varying lengths if applicable) will be VARCHAR.
• handles empty strings: empty strings are treated as null and do not influence the type inference for the non-empty values in the column. if all values in a column are empty, it defaults to varchar(0) (which becomes varchar(1) in sql due to max(1)).
• Generates CREATE TABLE SQL DDL statements.
• Table name is derived from the input CSV filename (e.g., my_data.csv becomes table my_data).
• Column names are taken directly from the CSV header.
• Utilizes parallel processing for type inference across columns using Rayon for improved performance on multi-core CPUs.

1. Ensure you have Rust and Cargo installed. If not, follow the instructions at rust-lang.org.

2. Clone the repository:

   git clone <your-repository-url>
   cd csv-sql-inference

3. Build the project:

   cargo build --release

   The executable will be located at target/release/csv_sql_inference.

Run the tool from the command line, providing the path to your CSV file:

./target/release/csv_sql_inference <path_to_csv_file>

or, if you've added the target/release directory to your path:

csv_sql_inference <path_to_csv_file>

the generated create table sql statement will be printed to standard output.

given a csv file named products.csv:

id,product_name,quantity,price,entry_date,last_updated
1,apple,10,0.50,2023-01-01,2024-05-01 10:00:00
2,banana,,1.20,2023-01-02,2024-05-02 11:30:15
3,orange,5,0.75,invalid-date,2024-05-03 09:00:00
4,grape,20000000000,0.05,2023-03-10,2024-05-04 14:20:05

running the command:

./target/release/csv_sql_inference products.csv

would produce output similar to this (exact varchar lengths depend on the longest string in each respective column):

CREATE TABLE "products" (
  "id" integer,
  "product_name" varchar(6),
  "quantity" bigint,
  "price" float,
  "entry_date" varchar(12),
  "last_updated" datetime
);

explanation of example output:

• "id": all are integers.
• "product_name": mixed strings, so varchar. length determined by "orange" (6).
• "quantity": contains an empty string and a very large number. the empty string forces varchar. "20000000000" is 11 chars.
• "price": all are valid floats.
• "entry_date": contains "invalid-date", so it becomes varchar. length determined by "invalid-date" (12).
• "last_updated": all are valid datetime strings.

to run the unit and integration tests:

cargo test