jinjasql2
Generate SQL Queries and Corresponding Bind Parameters using a Jinja2 Template
Decision gist · record as of 2026-08-14
Yes, if you need to generate dynamic SQL queries safely in Python and your use case genuinely requires raw SQL (reporting, complex aggregations, bulk operations). The package is lightweight, actively maintained, has no known vulnerabilities, and supports modern Python versions. Not necessary if an ORM handles your queries adequately.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with only Jinja2 as a runtime dependency.
- Actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and proprietary projects with minimal obligations.
last release 2026-07-07 (38 days) · last repo commit 2026-07-07 · 45 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 142,297 downloads/mo, #11,214 on PyPI
Alternatives
Verify before relying
from jinjasql import JinjaSql
j = JinjaSql()
template = "SELECT * FROM users WHERE id = {{ user_id }}"
query, bind_params = j.prepare_query(template, {"user_id": 123})
# query: "SELECT * FROM users WHERE id = :user_id_1"
# bind_params: {"user_id_1": 123}- Performance characteristics when handling large or deeply nested template contexts
- Compatibility with specific database drivers beyond the examples given (Django, asyncpg)
What it is and what it does
JinjaSQL is a template engine that generates parameterized SQL queries from Jinja2 templates. It processes template variables and conditional logic, then outputs a query with placeholders and a separate dictionary or tuple of bind parameters, ensuring values are never inlined into the SQL string itself. The package supports multiple parameter styles (named, format, qmark, numeric, pyformat, asyncpg) to match different database drivers' conventions.
The package is designed for scenarios where raw SQL is necessary—reporting, aggregation, bulk migrations, and multi-table queries—rather than as an ORM replacement. It protects against SQL injection by binding all template variables as parameters, though templates themselves must be trusted code (the sqlsafe and identifier filters allow deliberate inlining when needed). You prepare the query and parameters, then execute them using your database driver of choice.
Use it for
- Build dynamic reporting queries with conditional WHERE clauses, GROUP BY, and aggregations that an ORM cannot express cleanly
- Generate parameterized bulk update or migration scripts using Jinja macros and loops while maintaining SQL injection safety
- Construct multi-table SELECT queries with optional joins and filters based on runtime conditions
- Support IN clauses with the inclause filter to bind list elements as separate parameters for database drivers
- Escape table and column names safely using the identifier filter when dynamic schema references are required
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to generate dynamic SQL queries safely in Python and your use case genuinely requires raw SQL (reporting, complex aggregations, bulk operations).
The package is lightweight, actively maintained, has no known vulnerabilities, and supports modern Python versions. Not necessary if an ORM handles your queries adequately.
Install
jinjasql2 on PyPI
Before you install
Low friction: pure Python wheel with only Jinja2 as a runtime dependency. Actively maintained with a recent release and no known vulnerabilities.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and proprietary projects with minimal obligations.
Quickstart
from jinjasql import JinjaSql
j = JinjaSql()
template = "SELECT * FROM users WHERE id = {{ user_id }}"
query, bind_params = j.prepare_query(template, {"user_id": 123})
# query: "SELECT * FROM users WHERE id = :user_id_1"
# bind_params: {"user_id_1": 123}Verify before relying
- Performance characteristics when handling large or deeply nested template contexts
- Compatibility with specific database drivers beyond the examples given (Django, asyncpg)
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageJinja2 |
| Maintenance | Actively maintained 38 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 142,297 / month, #11,214 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: jinjasql2-0.1.13-py3-none-any.whl
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See also jinjasql · Jinja2 · sqlvalidator · sqlparams · sqlfluff · buildpg · sqlfluff-templater-dbt · jinja_partials · mkdocs-macros-plugin · python-sql