sql_fusion: Python SQL Query Builder

sql_fusion is a lightweight Python SQL query builder with a fluent, composable API and zero runtime dependencies. It helps you build parameterized SQL queries in Python without an ORM, then returns the SQL string and parameter tuple for your own database connection layer.

Use sql_fusion when you want a Python query builder for SQLite, DuckDB, PostgreSQL-style DB-API drivers, or other backends where generated SQL and placeholder adaptation fit your execution layer.

• PyPI package: sql_fusion
• GitHub repository: Mastermind-U/sql_fusion

It focuses on one job: building SQL expressions and statements while keeping execution outside the library.

• build parameterized SQL with a chainable Python API
• keep the query syntax readable
• stay flexible enough for SQLite3, DuckDB, psycopg3, and other DB-API style backends

The library does not execute SQL itself. It returns:

• the SQL string
• the parameter tuple

That makes it easy to plug into your own connection layer.

Table of Contents

• Motivation
• Links
• Python SQL Query Builder Features
• Installation
• Public API
• Quick Start: Python Query Builder for SQLite
• Quick Start: Python Query Builder for DuckDB
• PostgreSQL / psycopg3 Placeholder Example
• Fluent SQL Builder API Basics
• Subquery Example
• Set Operations
• Method Reference
• Functions
• CTEs
• Backend-Specific SQL Output with Compile Expressions
• What To Remember
• PyPika and SQLAlchemy Alternatives
• FAQ
• Python SQL Query Builder Comparison

Motivation

SQL builders often look similar from the outside, but they make very different trade-offs in practice:

• some are template-driven and mainly render filter fragments
• some are lightweight CRUD helpers with a small API surface
• some are broad SQL toolkits with dialect systems and advanced composition features
• some keep SQL parameterized, while others render a finished SQL string directly

This README compares sql_fusion with several other Python query builders so it is easier to see where the library fits and what it is intentionally optimized for.

Links

• PyPI: https://pypi.org/project/sql_fusion/
• GitHub: https://github.com/Mastermind-U/sql_fusion
• Documentation: https://github.com/Mastermind-U/sql_fusion/blob/main/README.md

Why sql_fusion?

sql_fusion is built for the middle ground: a lightweight Python SQL builder for people who want to compose SQL in Python without adopting an ORM or a large database toolkit.

• it has zero runtime dependencies
• it exposes a chainable, fluent SQL builder API
• it returns (sql, params) and leaves execution to the caller
• it is not an ORM and does not manage sessions, models, migrations, or schemas
• it provides type hints across the public API for editor and static-analysis support
• it builds SELECT, INSERT, UPDATE, and DELETE statements
• it supports joins, subqueries, CTEs, set operations, grouping helpers, functions, and window expressions
• it adds automatic table alias management for composed queries
• it exposes compile_expression() for placeholder rewrites and backend-specific SQL output

In short, the goal is to keep the ergonomics of a composable SQL query builder for Python while still covering the SQL building blocks that matter in application code.

Why not SQLAlchemy ORM or Core?

SQLAlchemy is an excellent tool, but it is also a much heavier and more universal system:

• it brings a larger abstraction surface than this project needs
• it is optimized for a broad ORM and Core ecosystem, not only for a small SQL builder
• some connectors and databases still do not have first-class SQLAlchemy integrations, which can make adoption less straightforward in mixed environments

SQLAlchemy Core is closer to sql_fusion than the full ORM, but it still carries more machinery than this project is meant to expose:

• it is part of a broader ecosystem with dialects, compilation layers, and extra conventions
• it can feel more verbose when you only want a small chainable builder
• some connectors and databases still do not have smooth SQLAlchemy Core support, so portability can depend on the backend

sql_fusion is intentionally narrower so it can stay lightweight, easy to embed, and practical for DB-API style backends without extra complexity.

Python SQL Query Builder Features

• SELECT, INSERT, UPDATE, and DELETE builders
• automatic table aliases
• composable conditions with AND, OR, and NOT
• joins, subqueries, and CTEs
• set operations with UNION, INTERSECT, and EXCEPT
• ordering and grouping with GROUP BY, ROLLUP, CUBE, and GROUPING SETS
• aggregate and custom SQL functions through func
• backend-specific SQL rewrites through compile expressions

Installation

sql_fusion targets Python 3.11 or newer. Install the Python SQL query builder from PyPI:

pip install sql_fusion

uv add sql_fusion

For local development:

uv sync

Or install it in editable mode:

pip install -e .

Public API

from sql_fusion import (
    Alias,
    Column,
    Table,
    delete,
    except_,
    func,
    insert,
    intersect,
    select,
    union,
    text_op,
    update,
)

Core Objects

• Table represents a real table or a subquery.
• Column is the reusable column object used by Table when you want to predeclare columns.
• select creates a SELECT builder.
• insert creates an INSERT builder.
• update creates an UPDATE builder.
• delete creates a DELETE builder.
• func is a dynamic SQL function registry.
• text_op builds a condition with a raw SQL operator such as @>.
• Alias represents a reusable SQL alias for aggregate expressions and HAVING conditions.

Quick Start: Python Query Builder for SQLite

SQLite3 is the easiest way to start because it accepts the default ? placeholders directly. This example shows how to build SQL queries in Python without an ORM and execute the generated (sql, params) pair with sqlite3.

import sqlite3

from sql_fusion import Table, insert, select, update

users = Table("users")

conn = sqlite3.connect(":memory:")
conn.execute(
    """
    CREATE TABLE users (
        id INTEGER PRIMARY KEY,
        name TEXT NOT NULL,
        status TEXT NOT NULL
    )
    """,
)

insert_query, insert_params = (
    insert(users)
    .values(id=1, name="Alice", status="active")
    .compile()
)
conn.execute(insert_query, insert_params)

select_query, select_params = (
    select(users.id, users.name)
    .from_(users)
    .where_by(status="active")
    .compile()
)
rows = conn.execute(select_query, select_params).fetchall()

update_query, update_params = (
    update(users)
    .set(status="inactive")
    .where(users.id == 1)
    .compile()
)
conn.execute(update_query, update_params)

Expected style of generated SQL:

SELECT "a"."id", "a"."name" FROM "users" AS "a" WHERE "a"."status" = ?

Quick Start: Python Query Builder for DuckDB

DuckDB works with the default ? placeholders directly, so you can execute queries without any SQL rewriting. The same fluent SQL builder API composes the query; DuckDB handles execution.

import duckdb
from sql_fusion import Table, select


users = Table("users")

query = (
    select(users.id, users.name)
    .from_(users)
    .where(users.status == "active")
)

duck_sql, duck_params = query.compile()
duck_conn = duckdb.connect(":memory:")
duck_conn.execute("CREATE TABLE users (id INTEGER, name TEXT, status TEXT)")
duck_conn.execute(duck_sql, duck_params).fetchall()

PostgreSQL / psycopg3 Placeholder Example

psycopg3 usually expects %s placeholders instead of ?. For a Python query builder targeting Postgres through psycopg3, the simplest approach is to add a compile expression that rewrites placeholders at the very end.

from typing import Any

import psycopg

from sql_fusion import Table, select


def to_psycopg3(sql: str, params: tuple[Any, ...]) -> tuple[str, tuple[Any, ...]]:
    return sql.replace("?", "%s"), params


users = Table("users")

query = (
    select(users.id, users.name)
    .from_(users)
    .where(users.status == "active")
)

pg_sql, pg_params = query.compile_expression(to_psycopg3).compile()
pg_conn = psycopg.connect("dbname=example user=example password=example")
pg_conn.execute(pg_sql, pg_params).fetchall()

If you only target DuckDB, no rewrite is needed. If you target psycopg3, the compile expression keeps the query builder backend-agnostic while still producing driver-friendly SQL.

Fluent SQL Builder API Basics

sql_fusion acts as a Python SQL expression builder: tables expose columns, columns produce conditions, and query objects compose clauses before compiling to SQL and parameters.

Tables and Aliases

Table automatically assigns aliases in creation order:

users = Table("users")   # alias "a"
orders = Table("orders") # alias "b"

Table can also wrap a subquery. In practice, you usually pass a query builder directly to from_() or join(), and the library wraps it for you.

If you want explicit, hint-friendly columns on a table instance, pass them when you create it:

from sql_fusion import Column, Table, select


users = Table(
    "users",
    Column("id"),
    Column("name"),
)


query = select(users.id, users.name).from_(users)

This style keeps the column list declared in one place and is verified at runtime when you access users.id / users.name.

Conditions

Columns support the usual comparison operators:

• ==
• !=
• <
• <=
• >
• >=

They also support SQL helpers:

• .like(pattern)
• .ilike(pattern)
• .in_(values)
• .not_in(values)
• text_op(column, operator, value) for backend-specific operators such as PostgreSQL array containment (@>).

Use | for SQL OR. Python's or cannot be overloaded for SQL expressions.

Conditions can be combined with:

• & for AND
• | for OR
• ~ for NOT

Example:

query = (
    select(users.id, users.name)
    .from_(users)
    .where(
        (users.age >= 18)
        & ((users.status == "active") | (users.status == "pending"))
        & users.country.not_in(["DE", "FR"])
    )
)

For PostgreSQL-style array containment, text_op() lets you pass the operator symbol directly:

users = Table("users", Column("name"), Column("tags"))

query = (
    select(users.name)
    .from_(users)
    .where((users.name == "bob") | text_op(users.tags, "@>", ["coffee"]))
)

Join Example

users = Table("users")
orders = Table("orders")

query = (
    select(users.id, users.name, orders.total)
    .from_(users)
    .join(orders, users.id == orders.user_id)
    .where_by(status="active")
)

This produces a standard INNER JOIN. If you need a different join type, use:

• left_join()
• right_join()
• full_join()
• cross_join()
• semi_join()
• anti_join()

Subquery Example

Subqueries work both as a source table and inside conditions.

orders = Table("orders")
users = Table("users")

paid_order_user_ids = (
    select(orders.user_id)
    .from_(orders)
    .where_by(status="paid")
)

query, params = (
    select(users.id, users.name)
    .from_(users)
    .where(users.id.in_(paid_order_user_ids))
    .compile()
)

The same idea also works in FROM:

orders = Table("orders")

paid_orders = (
    select(orders.user_id, orders.total)
    .from_(orders)
    .where_by(status="paid")
)

query, params = select().from_(paid_orders).compile()

Set Operations

sql_fusion supports compound queries through three small wrapper classes:

• union(query1, query2, all_=False, by_name=False)
• intersect(query1, query2, all_=False)
• except_(query1, query2, all_=False)

Each builder accepts two query objects and returns a new query that compiles to the matching SQL set operation.

union(...)

users = Table("users")
archived_users = Table("archived_users")

active_users = select(users.id, users.name).from_(users).where_by(status="active")
archived_active_users = (
    select(archived_users.id, archived_users.name)
    .from_(archived_users)
    .where_by(status="active")
)

query, params = union(active_users, archived_active_users).compile()

Use all=True for UNION ALL:

query, params = union(active_users, archived_active_users, all_=True).compile()

Use by_name=True when the two result sets expose the same logical columns in different orders:

left = select(users.id, users.name).from_(users)
right = select(archived_users.name, archived_users.id).from_(archived_users)

query, params = union(left, right, all_=True, by_name=True).compile()

intersect(...)

users = Table("users")
premium_users = Table("premium_users")

active_users = select(users.id).from_(users).where_by(status="active")
premium_active_users = (
    select(premium_users.id).from_(premium_users).where_by(status="active")
)

query, params = intersect(active_users, premium_active_users).compile()

Use all_=True for INTERSECT ALL:

query, params = intersect(
    active_users,
    premium_active_users,
    all_=True,
).compile()

except_(...)

users = Table("users")
banned_users = Table("banned_users")

all_users = select(users.id).from_(users)
banned_user_ids = select(banned_users.id).from_(banned_users)

query, params = except_(all_users, banned_user_ids).compile()

Use all_=True for EXCEPT ALL:

query, params = except_(all_users, banned_user_ids, all_=True).compile()

These builders preserve the parameter order from left to right, so the returned params tuple can be passed directly to DB-API drivers.

Having Example

orders = Table("orders")
count_orders = Alias("count_orders")

query = (
    select(
        orders.status,
        func.count(orders.id).as_(count_orders),
        func.sum(orders.total),
    )
    .from_(orders)
    .group_by(orders.status)
    .having(count_orders >= 3)
)

HAVING works after grouping and is ideal for filtering aggregates, for example "only statuses with at least 3 orders".

Because as is a reserved Python keyword, the method is exposed as as_().

Method Reference

Shared Query Methods

These methods are available on the shared query builders.

Method	Purpose	Notes
where(*conditions)	Add explicit conditions	Multiple conditions are combined with AND. Repeated calls merge safely.
where_by(**kwargs)	Build equality filters from keyword arguments	Uses the current FROM table alias. where_by(status="active") becomes status = ?.
with_(recursive=False, **ctes)	Add one or more CTEs	Repeated calls merge CTEs. recursive=True emits WITH RECURSIVE.
compile_expression(fn)	Add a final SQL transformation step	fn receives (sql, params) and must return (sql, params).
comment(text, hint=False)	Prefix the query with a SQL comment	hint=True renders optimizer-style comments like /*+ ... */.
before_clause(clause, text, hint=False)	Insert a comment before a clause	clause is case-insensitive, such as "FROM" or "UPDATE".
after_clause(clause, text, hint=False)	Insert a comment after a clause keyword	Useful for hints and debug annotations.
explain(analyze=False, verbose=False)	Wrap the query in EXPLAIN	Can be chained with other compile expressions.
analyze(verbose=False)	Shortcut for EXPLAIN ANALYZE	Equivalent to explain(analyze=True, verbose=verbose).
compile()	Build the final SQL and parameters	Returns (sql, params).

select(...)

query = select(users.id, users.name)

Constructor:

• select(*columns)

If no columns are provided, the builder emits SELECT *.

select Methods

Method	Purpose	Notes
from_(table)	Set the source table or subquery	Accepts a Table or another query builder.
join(table, condition)	Add an INNER JOIN	The default join type.
left_join(table, condition, *, is_outer=False)	Add a LEFT JOIN	Set is_outer=True for LEFT OUTER JOIN.
right_join(table, condition, *, is_outer=False)	Add a RIGHT JOIN	Set is_outer=True for RIGHT OUTER JOIN.
full_join(table, condition, *, is_outer=True)	Add a FULL JOIN	Set is_outer=False for FULL JOIN; defaults to FULL OUTER JOIN.
cross_join(table)	Add a CROSS JOIN	No ON clause.
semi_join(table, condition)	Add a SEMI JOIN	Backend support depends on the database.
anti_join(table, condition)	Add an ANTI JOIN	Backend support depends on the database.
limit(n)	Limit the number of rows	n must be non-negative.
offset(n)	Skip the first n rows	n must be non-negative.
distinct()	Add DISTINCT	Safe to chain more than once.
group_by(*columns)	Add a standard GROUP BY	With no columns, emits GROUP BY ALL.
group_by_rollup(*columns)	Add GROUP BY ROLLUP (...)	Requires at least one column.
group_by_cube(*columns)	Add GROUP BY CUBE (...)	Requires at least one column.
group_by_grouping_sets(*column_sets)	Add GROUPING SETS	Requires at least one set. Empty tuples become ().
having(*conditions)	Add a HAVING clause	Requires grouping.
having_by(**kwargs)	Add equality-based HAVING filters	Requires grouping.
window(name, ...)	Add a named WINDOW clause	Supports base, partition_by, order_by, descending, rows, range_, groups, and exclude.
order_by(*columns, descending=False)	Add ORDER BY	Repeated calls merge columns. descending=True applies DESC.

insert(table, or_replace=False, or_ignore=False)

query = insert(users).values(id=1, name="Alice")

insert Methods

Method	Purpose	Notes
values(**kwargs)	Add column values	Multiple calls merge into one row payload.
compile()	Build INSERT SQL	Raises if no values were provided.

Behavior notes:

• or_replace=True emits INSERT OR REPLACE
• or_ignore=True emits INSERT OR IGNORE
• both flags together raise an error

update(table)

query = update(users).set(status="inactive")

update Methods

Method	Purpose	Notes
set(**kwargs)	Add assignments for the SET clause	Multiple calls merge assignments.
where(...) / where_by(...)	Restrict the rows to update	Works like the shared query methods.
compile()	Build UPDATE SQL	Raises if no values were provided.

Behavior notes:

• column references in SET are table-qualified by default
• if a backend needs a different style, use compile_expression(...)

delete(table=None)

query = delete().from_(users).where(users.id == 1)

delete Methods

Method	Purpose	Notes
from_(table)	Set the target table	Required before compiling.
returning(*columns)	Add a RETURNING clause	With no arguments, emits RETURNING *. Multiple calls merge columns.
where(...) / where_by(...)	Restrict the rows to delete	Works like the shared query methods.
compile()	Build DELETE SQL	Returns (sql, params).

Functions

func is a dynamic SQL function registry. It converts attribute access into an uppercased SQL function name.

from sql_fusion import Alias, Table, Window, func, select

orders = Table("orders")
count_orders = Alias("count_orders")

query = select(
    func.count("*"),
    func.count(orders.id).as_(count_orders),
    func.sum(orders.total),
    func.coalesce(orders.status, "unknown"),
).from_(orders)

Examples:

• func.count("*") -> COUNT(*)
• func.sum(table.total) -> SUM("a"."total")
• func.my_custom_func(table.name) -> MY_CUSTOM_FUNC("a"."name")
• nested calls are supported, for example func.round(func.avg(...), 2)
• func.count(table.id).as_(Alias("count_orders")) -> COUNT("a"."id") AS "count_orders"

String and numeric literals are parameterized automatically.

Window Functions

Call .over() on any func expression to render an OVER clause.

orders = Table("orders")

query, params = (
    select(
        orders.user_id,
        orders.id,
        func.rank()
        .over(
            partition_by=orders.user_id,
            order_by=orders.total,
            descending=True,
        )
        .as_("total_rank"),
        func.sum(orders.total)
        .over(
            partition_by=orders.user_id,
            order_by=orders.id,
            rows=Window.Rows.between(
                Window.Rows.unbounded_preceding(),
                Window.Rows.current_row(),
            ),
        )
        .as_("running_total"),
    )
    .from_(orders)
    .compile()
)

Reusable named windows are supported with select.window().

query, params = (
    select(
        orders.user_id,
        orders.id,
        func.sum(orders.total).over("by_user").as_("running_total"),
    )
    .from_(orders)
    .window(
        "by_user",
        partition_by=orders.user_id,
        order_by=orders.id,
        rows=Window.Rows.between(Window.Rows.unbounded_preceding(), Window.Rows.current_row()),
    )
    .compile()
)

Examples:

• func.row_number().over(order_by=orders.id) -> ROW_NUMBER() OVER (ORDER BY "a"."id")
• func.sum(orders.total).over(partition_by=orders.user_id) -> SUM("a"."total") OVER (PARTITION BY "a"."user_id")
• func.rank().over("ranked") -> RANK() OVER "ranked"
• .window("ranked", partition_by=..., order_by=...) -> WINDOW "ranked" AS (...)
• rows=Window.Rows.between(Window.Rows.unbounded_preceding(), Window.Rows.current_row()) -> ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
• range_=Window.Range.between(Window.Range.interval_preceding(3, "DAYS"), Window.Range.interval_following(3, "DAYS")) -> RANGE BETWEEN INTERVAL 3 DAYS PRECEDING AND INTERVAL 3 DAYS FOLLOWING
• exclude="CURRENT ROW" appends EXCLUDE CURRENT ROW to the window specification
• func.sum(table.amount).filter(table.kind != "x").over(order_by=table.id) -> SUM("a"."amount") FILTER (WHERE "a"."kind" != ?) OVER (...)
• Window(base="ranked", rows=Window.Rows.between(Window.Rows.unbounded_preceding(), Window.Rows.current_row())) renders a chained window specification

CTEs

CTEs are supported through with_().

orders = Table("orders")
users = Table("users")
paid_orders = Table("paid_orders")

paid_orders_cte = (
    select(orders.user_id, orders.total)
    .from_(orders)
    .where_by(status="paid")
)

query, params = (
    select(users.name, func.sum(paid_orders.total))
    .with_(paid_orders=paid_orders_cte)
    .from_(paid_orders)
    .join(users, paid_orders.user_id == users.id)
    .group_by(users.name)
    .compile()
)

CTE Rules

• with_() accepts query-like objects only
• repeated with_() calls merge CTEs
• recursive=True emits WITH RECURSIVE
• parameter order is preserved across all nested queries
• CTE names are quoted automatically

Recursive CTE Example

nodes = Table("nodes")

tree = select(nodes.id, nodes.parent_id).from_(nodes).where_by(active=True)

query, params = (
    select()
    .with_(recursive=True, tree=tree)
    .from_(Table("tree"))
    .compile()
)

Backend-Specific SQL Output with Compile Expressions

compile_expression() is the escape hatch for backend-specific SQL tweaks. It receives the final SQL string and parameter tuple, then returns a modified pair.

This is useful for:

• placeholder rewrites
• backend-specific syntax adjustments
• adding ORDER BY, LIMIT, or other final SQL fragments

Example: psycopg3 Placeholder Rewrite

from typing import Any


def to_psycopg3(sql: str, params: tuple[Any, ...]) -> tuple[str, tuple[Any, ...]]:
    return sql.replace("?", "%s"), params

Example: Append Sorting and Pagination

def order_by_second_column_desc_limit_two(
    sql: str,
    params: tuple[Any, ...],
) -> tuple[str, tuple[Any, ...]]:
    return f"{sql} ORDER BY 2 DESC LIMIT 2", params

Then attach it to any query:

query, params = (
    select(users.id, users.name)
    .from_(users)
    .compile_expression(order_by_second_column_desc_limit_two)
    .compile()
)

Built-in Compile Helpers

The library also exposes a few built-in compile-time helpers:

• comment(text, hint=False) prefixes the query with a comment
• before_clause(clause, text, hint=False) injects a comment before a clause
• after_clause(clause, text, hint=False) injects a comment after a clause
• explain() wraps the query in EXPLAIN
• analyze() wraps the query in EXPLAIN ANALYZE

What To Remember

• compile() returns (sql, params)
• SQL identifiers are quoted with double quotes
• values are parameterized with placeholders
• query builders are chainable
• repeated calls to many methods merge rather than overwrite
• backend support still depends on the database you execute against

PyPika and SQLAlchemy Alternatives

sql_fusion may fit when you are looking for a PyPika alternative or a SQLAlchemy query builder alternative for a narrower job: build parameterized SQL in Python, then execute it through your own connection layer.

SQLAlchemy is a broad database toolkit with ORM, Core, dialect, engine, and connection-management layers. sql_fusion does not try to replace that full ecosystem. It is a lightweight Python SQL builder for projects that want a small fluent API and no execution layer.

PyPika is a mature fluent SQL builder with broad dialect features. sql_fusion is positioned differently: it returns (sql, params) by default, keeps runtime dependencies at zero, and uses compile hooks when the final SQL needs a backend-specific placeholder or syntax rewrite.

FAQ

What is sql_fusion?

sql_fusion is a Python SQL query builder. It provides a fluent, composable API for building SQL statements and expressions in Python, then compiling them to a SQL string and parameter tuple.

Is sql_fusion an ORM?

No. sql_fusion is not an ORM. It does not define models, track sessions, execute queries, manage relationships, run migrations, or own your connection layer. It is for building SQL queries without an ORM.

What databases does sql_fusion support?

sql_fusion generates parameterized SQL with ? placeholders and quoted identifiers. The test suite verifies execution with SQLite and DuckDB. The README also shows how to adapt placeholders for PostgreSQL through psycopg3. Other DB-API style backends, including MySQL drivers, may work when the generated SQL and any placeholder rewrites match the database, but they are not claimed as fully tested dialects.

How is sql_fusion different from SQLAlchemy?

SQLAlchemy is a comprehensive toolkit with ORM, Core, dialect, engine, and connection abstractions. sql_fusion is intentionally smaller: it builds SQL and parameters, then leaves execution and database integration to your own code. That makes it useful when you want a SQLAlchemy query builder alternative for a small, composable query-building layer.

Is sql_fusion an alternative to PyPika?

sql_fusion can be considered a PyPika alternative when you want a lightweight, typed SQL query builder for Python that returns (sql, params) instead of only a rendered SQL string. PyPika remains a mature project with broad dialect and SQL-generation features, so the right choice depends on the API and output model you need.

Does sql_fusion support type hints / static typing?

Yes. sql_fusion is implemented with type hints and exposes typed public APIs, so editors and static analysis tools can reason about the builder methods and return values. It can fit searches for a typed or type-safe SQL builder in Python at the API level, but it should not be described as a full compile-time schema validator.

Python SQL Query Builder Comparison

Project	Focus	SQL coverage	SQL injection protected	Automatic alias management	Window functions	Advanced features	Dialect/output model	Takeaway
Py-QueryBuilder	Template-driven filter rendering	No direct CRUD builder; it renders a WHERE fragment into a Jinja template	Yes, via JinjaSQL qmark placeholders and a separate params list	No, subquery and join aliases are template-defined rather than auto-managed by the builder	No dedicated API; possible only by writing window SQL in templates	Nested rule groups, operator mapping, field pruning	Jinja2 + JinjaSQL, SQL formatting	Fits UI-driven search forms, not full-statement composition
simple-query-builder-python	Small mutable CRUD helper	SELECT, INSERT, UPDATE, DELETE	Mostly yes, because execution uses ? placeholders and a params tuple; get_sql(with_values=True) can inline values for display	No, subquery and join aliases are supplied manually in the input data	No dedicated API	JOIN, GROUP BY, HAVING, UNION, EXCEPT, INTERSECT, LIMIT, OFFSET	SQLite-first, raw SQL string builder	Simple and approachable, but the SQL surface is modest
sqlquerybuilder	Django-ORM-style queryset wrapper	Basic read/write queries	No, it renders a ready SQL string with values embedded into the query text	No, subquery and join aliases are handled manually in query strings	Not general-purpose; uses ROW_NUMBER() OVER (...) internally for one SQL Server pagination path	Filters and excludes, joins, grouping, ordering, extra(), slicing, with_nolock()	SQLite-oriented, with SQL Server pagination branches in code	Convenient for ORM-like chaining, but not aimed at deep SQL composition
python-sql	Rich Pythonic SQL builder	SELECT, INSERT, UPDATE, DELETE	Yes, it keeps placeholders separate from args and can switch param styles via flavor	Partial, it can auto-alias tables and some subqueries, while join aliases are still often explicit	Rich support: named windows, aggregate/window functions, FILTER, ROWS / RANGE / GROUPS, EXCLUDE	JOIN, subqueries, CTEs, DISTINCT ON, windows, RETURNING, MERGE, UNION / INTERSECT / EXCEPT	Dialect/flavor system with multiple param styles	Very broad SQL coverage and strong backend flexibility
PyPika	Mature fluent query builder	SELECT, INSERT, UPDATE, DELETE	No by default, it renders literal SQL strings with values injected into the output	Partial, it auto-aliases some subqueries and duplicate joins, but most table and join aliases are explicit	Broad analytics helpers: ranking/value/aggregate windows, partition/order, ROWS / RANGE, and QUALIFY	JOIN, subqueries, CTEs, set operations, analytics/window helpers, DDL support	Dialect-aware with vendor-specific extensions	One of the broadest and most extensible builders in the set
SQLFactory	General-purpose SQL builder	SELECT, INSERT, UPDATE, DELETE	Yes, it emits placeholders and keeps args separately	No, subquery and join aliases are mostly explicit and part of the statement shape	Yes: WindowableFunction.over(...), ranking/value functions, partition/order, and frame objects	JOIN, subselects, CTEs, window functions, set operations, INSERT ... SELECT, MySQL-style duplicate-key handling	MySQL / SQLite / PostgreSQL / Oracle / custom dialects, async execution helpers	Full-featured and explicit, with a heavier API than lightweight builders
sql_fusion	Lightweight chainable builder	SELECT, INSERT, UPDATE, DELETE	Yes, it returns (sql, params) and leaves binding to the caller	Yes, it auto-assigns stable table aliases and reuses them for subqueries and joins	Yes: func.*().over(...), select.window(...), FILTER, named/chained windows, ROWS / RANGE / GROUPS, EXCLUDE	JOIN variants including CROSS, SEMI, ANTI, subqueries, recursive CTEs, ROLLUP, CUBE, GROUPING SETS, functions, comments, EXPLAIN / ANALYZE, DELETE RETURNING	Backend-agnostic, compile_expression() hook for rewrites	Fits compact, composable query building with post-processing hooks and no execution layer

Syntax Comparison

The examples below are representative shapes, not copy-paste snippets for every library. Where a library exposes a Table object, the snippet uses it.

Project	Typical syntax shape
sql_fusion	users = Table("users"); orders = Table("orders"); select(users.id, users.name).from_(users).join(orders, users.id == orders.user_id).where(users.active == True).compile()
PyPika	users = Table("users"); orders = Table("orders"); Query.from_(users).join(orders).on(users.id == orders.user_id).select(users.id, users.name).where(users.active == True).get_sql()
python-sql	user = Table("users"); tuple(user.select(user.name, where=user.active == True))
SQLFactory	users = Table("users"); orders = Table("orders"); Select(users.id, users.name, table=users, join=[Join(orders, Eq("users.id", "orders.user_id"))]).where(Eq("users.active", True))
simple-query-builder-python	qb.select("users").where([["active", "=", True]]).join("orders", on=[["users.id", "=", "orders.user_id"]]).all()
sqlquerybuilder	Queryset("users").filter(active=True).join("orders", on="users.id=orders.user_id")
Py-QueryBuilder	QueryBuilder("app.users", filters).render("query.sql", query)

Window Syntax Comparison

Project	Representative window syntax
sql_fusion	func.sum(orders.total).filter(orders.status != "cancelled").over(partition_by=orders.user_id, order_by=orders.created_at, rows=Window.Rows.between(Window.Rows.unbounded_preceding(), Window.Rows.current_row())).as_("running_total")
PyPika	an.Sum(t.amount).over(t.account_id).orderby(t.date).rows(an.Preceding(), an.CURRENT_ROW).as_("running_total")
python-sql	Sum(t.amount, filter_=t.status != "cancelled", window=Window([t.user_id], order_by=[t.created_at], frame="ROWS", start="UNBOUNDED PRECEDING", end="CURRENT ROW"))
SQLFactory	Sum("amount").over(partition_by=["user_id"], order=[("created_at", Direction.ASC)], frame=Frame(FrameType.ROWS, Preceding(), CurrentRow()))
simple-query-builder-python	No dedicated window API; pass a raw selected expression if needed.
sqlquerybuilder	No general-purpose window API; ROW_NUMBER() OVER (...) appears only in an internal pagination branch.
Py-QueryBuilder	No dedicated window API; write the window expression in the Jinja SQL template.