Generating Dynamic 'CREATE TABLE' Statements with psycopg2 and PostgreSQL's INFORMATION

Dynamic “CREATE TABLE” Statement in psycopg2 / Python

As a developer, we’ve all been there – trying to create a dynamic SQL statement that can handle varying numbers of parameters without resorting to string concatenation and the associated security risks. In this article, we’ll explore how to achieve this using psycopg2, a popular PostgreSQL database adapter for Python.

Introduction to Dynamic SQL

Dynamic SQL is a technique used in programming to generate SQL statements at runtime based on user input, configuration settings, or other dynamic data sources. This approach allows developers to create flexible and adaptive applications that can handle diverse use cases without requiring extensive manual coding.

However, as the question from Stack Overflow highlights, generating dynamic SQL can be challenging due to security concerns. String concatenation with user-provided inputs can lead to SQL injection attacks, which can compromise the integrity of your database and application.

Using %s and psycopg2’s Parameterized Queries

To avoid string concatenation and SQL injection risks, we’ll utilize psycopg2’s parameterized queries feature, which allows us to separate the SQL statement from its parameters. This approach enables the database driver to handle the parameters securely and prevents malicious inputs from being executed as part of the SQL code.

In Python, you can use the %s placeholder in your SQL string, followed by a list of values passed separately. The psycopg2 library will automatically escape and quote the values according to PostgreSQL’s syntax rules.

For example:

import psycopg2

conn = psycopg2.connect(
    host="localhost",
    database="mydatabase",
    user="myuser",
    password="mypassword"
)

cur = conn.cursor()

# Define a dynamic SQL statement with placeholders for parameters
sql_stmt = """
CREATE TABLE test(id SERIAL PRIMARY KEY, name VARCHAR(64), address VARCHAR(64))
"""

# Pass the actual values as a list to be used in the query
values = ["id", "name", "address"]

cur.execute(sql_stmt, values)

conn.commit()

cur.close()
conn.close()

This approach ensures that the SQL statement is not executed with malicious values, reducing the risk of SQL injection attacks.

Using PostgreSQL’s INFORMATION_SCHEMA to Dynamically Generate Columns

To address the specific requirement of generating a dynamic CREATE TABLE statement based on the CSV file’s headers, we’ll leverage PostgreSQL’s built-in INFORMATION_SCHEMA tables. These tables provide metadata about the database schema, including column names and data types.

We can query the COLUMNS table in the INFORMATION_SCHEMA to retrieve the list of columns from a specific table (in this case, our temporary table ’test’).

Here’s an example code snippet that demonstrates how to use INFORMATION_SCHEMA to generate the dynamic SQL statement:

import psycopg2

conn = psycopg2.connect(
    host="localhost",
    database="mydatabase",
    user="myuser",
    password="mypassword"
)

cur = conn.cursor()

# Define a CSV file path and read its contents
csv_file_path = "path/to/your/file.csv"
with open(csv_file_path, 'r') as csv_file:
    headers = [line.strip() for line in csv_file.readlines()]

# Construct the SQL statement using INFORMATION_SCHEMA
sql_stmt = f"""
CREATE TABLE test ({', '.join([f'{header} VARCHAR(64)' for header in headers])})
"""

# Execute the constructed SQL statement
cur.execute(sql_stmt)

conn.commit()

cur.close()
conn.close()

This code reads the CSV file, extracts its headers, and constructs a dynamic CREATE TABLE statement using the INFORMATION_SCHEMA. The resulting SQL string is then executed to create the table with the specified columns.

Handling Varying Column Counts

To handle varying column counts between CSV files, we can modify the previous approach by querying the COLUMNS table in INFORMATION_SCHEMA and dynamically constructing the SQL statement based on the retrieved metadata.

Here’s an updated code snippet that demonstrates how to achieve this:

import psycopg2

conn = psycopg2.connect(
    host="localhost",
    database="mydatabase",
    user="myuser",
    password="mypassword"
)

cur = conn.cursor()

# Define a CSV file path and read its contents
csv_file_path = "path/to/your/file.csv"
with open(csv_file_path, 'r') as csv_file:
    headers = [line.strip() for line in csv_file.readlines()]

# Query the COLUMNS table in INFORMATION_SCHEMA to retrieve column metadata
cur.execute(f"""
SELECT column_name, data_type 
FROM information_schema.columns 
WHERE table_schema = 'public' AND table_name = 'test'
ORDER BY data_type
""")
column_metadata = cur.fetchall()

# Construct the SQL statement using the retrieved column metadata
sql_stmt = f"""
CREATE TABLE test ({', '.join([f'{header} {data_type}' for header, data_type in column_metadata])})
"""

# Execute the constructed SQL statement
cur.execute(sql_stmt)

conn.commit()

cur.close()
conn.close()

This updated code snippet queries the COLUMNS table in INFORMATION_SCHEMA to retrieve metadata about the columns in the test table. It then constructs a dynamic CREATE TABLE statement using this metadata and executes it to create the table with the specified columns.

Conclusion

In this article, we explored how to generate dynamic SQL statements using psycopg2 and Python’s built-in libraries, such as INFORMATION_SCHEMA. We demonstrated two approaches: using parameterized queries with %s placeholders and querying COLUMNS table in INFORMATION_SCHEMA to dynamically construct the SQL statement.

By utilizing these techniques, you can create flexible and adaptive applications that can handle varying numbers of parameters and column counts without resorting to string concatenation and SQL injection risks.

Last modified on 2025-01-04