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Lightweight and intuitive Python SQL query builder.

Project description

Status

FLUENTQL IS WORK IN PROGRESS. WHILE IT IS AVAILABLE ON PYPI, USE IT WITH CAUTION. PUBLIC APIS MAY CHANGE UNEXPECTEDLY WHILE FEATURES ARE ADDED AT PACE.

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Introduction

FluentQL is a lightweight, fast and intuitive Python SQL query builder. It helps you build cross-dialect queries straight in your code, as well as test and validate your queries.

Using SqlAlchemy and Pandas? FluentQL seamlessly integrates with your existing codebase, providing type checking capabilities out of the box.

Getting Started

Fluentql requires Python 3.5 and up.

pip install fluentql

Quick overview

from fluentql import GenericSQLDialect, Q, Table

table1 = Table("table1")
table2 = Table("table2")

query = (
    Q.select(table1.all(), table2["col_x"])
    .set_from(table1)
    .left_join(table2, lambda q: q.on(table1["id"] == table2["id"]))
    .order_by(table1["col_y"])
    .fetch(10)
    .distinct()
)

compiled_query = query.compile(GenericSQLDialect)

print(compiled_query)

will output

select distinct table1.*, table2.col_x from table1 left join table2 on table1.id = table2.id order by col_y limit 10;

Supported SQL Dialects

There are many varieties of SQL out there, and while FluentQL comes with popular dialects implemented out of the box, you might need additional functionality for your use case. This library was built with extensibility in mind, so you can easily extend an existing Dialect implementation or implement your own. No only that, but you can roll out your custom functions with ease.

FluentQL includes the following dialect implementations:

  • Generic SQL - should provide compatibility with a variety of dialects for basic queries
  • Microsoft SQL (coming soon)
  • PosgreSQL (coming soon)
  • Teradata (coming soon)

Usage

Overview

Fluentql comes with a built-in runtime type checking system based on Python's typing module and PEP484 type annotations. However, by default, all types are Any, thus giving users control over how much they want type checking to be used.

If using type checking, errors are thrown as queries are constructed if types don't match, such as when adding a string column to a number column, or when using SUM on a Date column.

The core logical units of fluentql are:

  • Column
  • Table
  • Function
  • Query
  • Dialect

Column

A column has a name and a type and it is bound to a table. The base class Column should never be instantiated on its own; the correct way to create a column that holds a numeric type is as follows:

from fluentql.base_types import Collection
from fluentql.types import Column, NumberType


class NumberColumn(Collection[NumberType], Column):
    pass

Or just use fluentql.types.NumberColumn.

The Generic typeCollection provides the functionality required by the type checking engine and it is simply a subclass of typing.Generic.

Fluentql comes with a number of concrete column types in fluentql.types:

  • AnyColumn
  • BooleanColumn
  • DateColumn
  • DateTimeColumn
  • NumberColumn
  • StringColumn
  • TimeColumn

Those implementations are based on their base type counterparts found in fluentql.base_types:

  • BooleanType
  • DateTime
  • DateTimeType
  • NumberType
  • StringType
  • TimeType

Table

Tables are used as targets for Queries and as containers for columns. That does not mean you need to define a schema, as you will see below. Tables can have schemas, but by default they hold any column:

from fluentql import Table

books = Table("books")

In this case, books is a table that will pass AnyColumn columns of a given name when asked, like so:

book_id = books["id"] # Instance of AnyColumn
title = books["title"] # Instance of AnyColumn

However, tables can have schemas. Schemas can be defined using PEP484 type hints, like so:

from fluentql import Table
from fluentql.types import BooleanColumn, NumberColumn, StringColumn


class Books(Table):
    id: NumberColumn
    title: StringColumn
    is_available: BooleanColumn

    def __init__(self, db=None):
        super().__init__("books", db)

books = Books()

Then, reference the columns as normal:

title = books["title"] # Instance of StringColumn

However, now, this raises a KeyError:

release_date = books["release_date"]

Using SqlAlchemy Models [TODO]

from fluentql import Table

from project.models import MySqlAlchemyModel

# MySqlAlchemyModel is a subclass of DeclarativeBase
table = Table.from_model(MySqlAlchemyModel)

The table name and schema are automatically copied from the model class.

Functions

Functions are the most important units in fluentql. They implement (well, semantically only) various functionalities of SQL and are the main interface between queries and the type checking system.

The base class for all functions is fluentql.F. The theory is:

  • a function takes 0 or more arguments of a given type
  • a function returns a typed value

Let's look at a couple of examples. First, let's imagine a function CoolFunction that takes two Boolean columns as arguments and returns a Boolean column:

from fluentql import F
from fluentql.base_types import BooleanType, Collection


class CoolFunction(F):
    a: Collection[BooleanType]
    b: Collection[BooleanType]
    returns: Collection[BooleanType]

Note: the arguments a and b must be provided in order, as you expect the function to be called; however, their name is irrelevant. The only name that matters here is returns, that will always be used as the return type

Remember, there is no functionality associated to CoolFunction, meaning that it's purpose is not to be called with two booleans and return a value after applying some logic - the snippet above is all there is to it. However, let's assume that our hypothetical SQL dialect actually has this function, and we'd call it like so:

select * from my_table where CoolFunction(boolcol1, boolcol2) = True;

Fluentql wants to tell you when the query you're building doesn't really make sense (meaning that it's not type safe).

class Books(Table):
    id: NumberColumn
    title: StringColumn
    is_on_sale: BooleanColumn
    is_sold_out: BooleanColumn

books = Books()

So, while this is OK:

CoolFunction(books["is_on_sale"], books["is_sold_out"])

this is not:

CoolFunction(books["is_on_sale"], books["title"]) # raises TypeError

and neither is this:

CoolFunction(books["is_on_sale"]) # raises TypeError

Let's take this one step further and look at a function Add that takes either scalars or columns of type String and Number:

from typing import TypeVar

T = TypeVar("T", NumberType, StringType)


class Add(F):
    arg1: Union[T, Collection[T]] # scalar or collection
    arg2: Union[T, Collection[T]] # scalar or collection
    returns: Union[T, Collection[T]] # scalar or collection

This behaves as you'd expect a TypeVar to behave. You can pass it two Numbers, two NumberColumns, or one of each (same for Strings and StringColumn). However, the return type is quite vague, so let's enhance this.

Note: When a F is instantiated, all the types are checked and ran through the typing engine. Each instance of F will have a type_checker object which holds details about the types that were matched, including a mapping from all the TypeVars found in the expected arg types list to concrete types that were matched against the given values. Explore fluentql.type_checking.TypeChecker to get a better feel of it.

Instead of passing a type annotation for returns, we can create a function. This function must take matched_types and type_var_mapping as its only arguments.

matched_types is a list of types that the given arguments matched against the expected type definition. Effectively, if our arg of type A is matched against Union[A, B], the matched type for it will be A. The reference here is against the expected type, not the given type, so if we instead saw Union[AS, B], where AS is a superclass of A, the matched type would've been AS. There's obviously a bit more to it, but it's easy to think of it as a Union unpacker.

type_var_mapping is a dict, where keys are TypeVars and the value is a tuple of two elements: the first is a list of argument indices, showing which arguments in our list were actually matched against that TypeVar; the second is the concrete type that resolves the TypeVar for that specific function call.

So, back to our example, we can be a bit smarter with our Add function and determine exactly what it will return, like so:

class Add(F):
    arg1: Union[T, Collection[T]] # scalar or collection
    arg2: Union[T, Collection[T]] # scalar or collection

    def returns(self, matched_types, type_var_mapping):
        # Get the type that resolved T
        t_type = type_var_mapping[T][1]

        # If any of the arguments is an instance of Collection
        # Please note that isinstance behaviour is inconsistent with tying.Generic subtypes, hence this unusual check
        if any(Collection in t.__mro__ for t in matched_types if hasattr(t, "__mro__")):
            return Collection[t_type]

        return t_type

Now, we can observe:

nc1 = NumberColumn("nc1")
nc2 = NumberColumn("nc2")

a1 = Add(nc1, nc2) # a1.__returns__ is Collection[NumberType]
a2 = Add(nc1, 100) # a2.__returns__ is Collection[NumberType]
a3 = Add(200, 300) # a3.__returns__ is NumberType
a4 = Add(nc1, "abc") # TypeError: T was not matched

Where are functions used? All operators are implemented as functions (Add, Subtract, Multiply, Divide, Modulo, BitwiseAnd etc). As well as that, any SQL functions (such as Max, Min, Count) should be implemented as subclasses of F.

List of all included functions (in fluentql.function):

  • Add
  • Subtract
  • Multiply
  • Divide
  • Modulo
  • BitwiseOr
  • BitwiseAnd
  • BitwiseXor
  • Equals
  • LessThan
  • LessThanOrEqual
  • GreaterThan
  • GreaterThanOrEqual
  • NotEqual
  • Not
  • As (used for aliases)
  • TableStar
  • Star
  • Like
  • In
  • Max
  • Min
  • Sum
  • Asc
  • Desc

Column objects also come with a comprehensive implementation of the Data Model to facilitate a simpler, more expressive syntax:

col1 = NumberColumn("col1")
col2 = NumberColumn("col2")

col1 + col2 # Returns Add(col1, col2)
col1 - col2 # Returns Subtract(col1, col2)
col1 == col2 # Returns Equals(col1, col2)
col1 < col2 # Returns LessThan(col1, col2)

# Also
col1.max() # Returns Max(col1)
col1.isin(col2) # Returns In(col1, col2)
col1.asc() # Returns Asc(col1)

and more.

Query

The fluentql.query.Query or, simpler, fluentql.Q class is what users will mostly interact with - it is the interface to the query builder itself.

Each instance of Q has a _command attribute, which is a value of QueryCommands. The possible commands are:

  • SELECT
  • INSERT
  • UPDATE
  • DELETE
  • CREATE
  • DROP
  • WHERE
  • ON
  • JOIN
  • HAVING

The first half of them are matches for actual SQL statements, while the last ones are synthetic sub-query types that are used as containers in certain circumstances.

There are two main ideas to bear in mind:

  • Each core method of Q is only set to execute for a particular set of QueryCommands (e.g. where() will only work for SELECT and DELETE statements)
  • Each statement is initialised through a classmethod which returns an instance of Q: Q.select() returns a Q with _command = QueryCommands.SELECT and so on.

SELECT

Basic use
Q.select().set_from(table) # select * from table;
Q.select(table["col1"]).from_(table) # select col1 from table; -- note that Q.set_from is an alias for Q.from_

Calling select with no arguments returns a select *.

Where
# select * from table where col1 < col2;
Q.select().set_from(table).where(table["col1"] < table["col2"])

 # select * from table where col1 > col2 or (col3 like '%abc' and col4 <> 'XYZ')
Q.select().set_from(table).where(table["col1"] > table["col2"]).or_where(lambda q: q.where(table["col3"].like("%abc")).and_where(table["col4"] != "XYZ"))

Pass a lambda function to where, and_where or or_where to nest conditions.

Join
# select * from table inner join table2 on table.col1 = table2.col3;
Q.select().set_from(table).inner_join(table2, lambda q: q.on(table["col1"] == table2["col3"]))

# select * from table left join table2 on table.col1 = table2.col3 and (table.col1 % 2 = 0);
Q.select().set_from(table).left_join(table2, lambda q: q.on(table["col1"] == table2["col3"]).and_on(table["col1"] % 2 = 0))

# select * from table right join table2 using ('id');
Q.select().set_from(table).right_join(table2, lambda q: q.using("id"))

Available join methods:

  • inner_join
  • outer_join
  • left_join
  • right_join
  • cross_join
Group By
# select col1 from table group by col1;
Q.select(table["col1"]).set_from(table).group_by(table["col1"])

# select col2, col3, max(col1) from table group by col2, col3;
Q.select(table["col2"], table["col3"], table["col1"].max()).set_from(table).group_by(table["col2"], table["col3"])
Having
# select col1 from table group by col1 having col1 < 20 and (col1 % 2 = 1);
Q.select(table["col1"]).set_from(table).group_by(table["col1"]).having(table["col1"] < 20).and_having(table["col1"] % 2 == 1)

having, and_having, or_having methods work in a similar way to where methods, in that they can also take a lambda as argument to nest conditions.

Order By
# select * from table order by col1 asc;
Q.select().set_from(table).order_by(table["col1"]) # if not specified, Ascending order is assumed
# equivalent to
Q.select().set_from(table).order_by(table["col1"].asc())

# select * from table order by col1 asc, col2 desc;
Q.select().set_from(table).order_by(table["col1"], table["col2"].desc())

Use Column.asc and Column.desc to mark ordering in an order_by clause.

Fetch and Skip
# select * from table limit 100;
Q.select().set_from(table).fetch(100)

# select * from table offset 30;
Q.select().set_from(table).skip(30)

# select * from table limit 100 offset 30;
Q.select().set_from(table).fetch(100).skip(30)

Delete

Delete queries may only have where clauses:

# delete from table;
Q.delete().set_from(table)

# delete from table where col1 = 'val';
Q.delete().set_from(table).where(table["col1"] == "val")

Insert

Coming soon

Create

Coming soon

Drop

Coming soon

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