Skip to content
This repository has been archived by the owner on Dec 18, 2024. It is now read-only.

Latest commit

 

History

History
368 lines (287 loc) · 18 KB

README.md

File metadata and controls

368 lines (287 loc) · 18 KB
Raw

Language-Integrated SQL Queries in Python

pylinsql helps you write SQL queries in Python that integrate with the type checker and produce standard SQL query strings as an end result. The main idea is to take a Python generator expression such as

select(
    asc(p.given_name)
    for p, a in entity(Person, Address)
    if inner_join(p.address_id, a.id)
    and (
        (p.given_name == "John" and p.family_name != "Doe")
        or (a.city != "London")
    )
)

and transform it into a SQL query such as

SELECT p.given_name
FROM "Person" AS p INNER JOIN "Address" AS a ON p.address_id = a.id
WHERE p.given_name = 'John' AND p.family_name <> 'Doe' OR a.city <> 'London'
ORDER BY p.given_name ASC

Using a language-integrated query formalism (analogous to LINQ in C#), users can write queries in a format that is transparent to lint tools, and identify errors early. The query expressions map to SQL statement strings, which allows for constant-time look-up, making pylinsql incur almost zero additional run-time cost over writing raw SQL statements while providing type safety.

Objectives

The inspiration for pylinsql has been to employ efficient asynchronous communication with the database engine (such as in asyncpg) while providing a type-safe means to formulate SELECT and INSERT queries (as in PonyORM).

This work is no substitute for an all-in-one boxed solution that handles database connections, performs pooling, caching, manages entity relationships, etc. (such as SQLAlchemy). Its purpose is to help write a SQL query in the style of C# language-integrated queries that you can then execute with a(n asynchronous) SQL engine client (e.g. asyncpg in Python).

Usage

Expressions preceding for in a Python generator expression go into SELECT in SQL:

select((p.family_name, p.given_name) for p in entity(Person))
SELECT p.family_name, p.given_name
FROM "Person" AS p

If you have an entity variable preceding for, it will expand into all properties of that entity:

select(p for p in entity(Person))
SELECT *
FROM "Person" AS p

Boolean expressions in the condition part of a Python generator expression (i.e. following if) normally go into the WHERE clause:

select(
    p
    for p in entity(Person)
    if p.given_name == "John"
    and p.family_name != "Doe"
    or year(p.birth_date) >= 1982
)
SELECT *
FROM "Person" AS p
WHERE p.given_name = 'John' AND p.family_name <> 'Doe' OR EXTRACT(YEAR FROM p.birth_date) >= 1982

The conditional part also accepts special functions inner_join, left_join, right_join, etc. to create join expressions in SQL. These special functions are only allowed in the condition part of the generator expression but not elsewhere. You can combine several join conditions with Python's and.

select(
    p
    for p, a1, a2 in entity(Person, Address, Address)
    if inner_join(p.perm_address_id, a1.id)
    and left_join(p.temp_address_id, a2.id)
)
SELECT *
FROM "Person" AS p
    INNER JOIN "Address" AS a1 ON p.perm_address_id = a1.id
    LEFT JOIN "Address" AS a2 ON p.temp_address_id = a2.id

You can also use aggregation functions. Expressions that are not aggregated automatically go into the GROUP BY clause. If you have a condition that involves an aggregated expression, it becomes part of the HAVING clause.

select(
    (a.city, min(p.birth_date))
    for p, a in entity(Person, Address)
    if inner_join(p.perm_address_id, a.id) and min(p.birth_date) >= date(1989, 10, 23)
)
SELECT a.city, MIN(p.birth_date)
FROM "Person" AS p INNER JOIN "Address" AS a ON p.perm_address_id = a.id
GROUP BY a.city
HAVING MIN(p.birth_date) >= MAKE_DATE(1989, 10, 23)

Join expressions

pylinsql supports (inner) join, left join, right join and full (outer) join via the Python functions inner_join, left_join, right_join and full_join. These go into condition part of the generator expression. They take two parameters, both of which must be table attribute references, e.g. p.perm_address_id or a1.id:

select(
    p
    for p, a1, a2 in entity(Person, Address, Address)
    if inner_join(p.perm_address_id, a1.id)
    and left_join(p.temp_address_id, a2.id)
    and ((a1.city != "London") or (a2.city != "Zürich"))
)

If two tables are listed as entities but not referenced by a join in the condition part, they are assumed to expand to a cross product, as in SQL.

Select expressions

In addition to a scalar expression (single column per row) and a tuple expression (for multiple columns per row), pylinsql offers a convenience syntax with a @dataclass annotated type acting as the output.

Assume that you have a custom @dataclass type called PersonCity:

@dataclass
class PersonCity:
    family_name: str
    given_name: str
    city: str

When executed against a database engine, the following query will produce a list of PersonCity instances:

select(
    PersonCity(p.family_name, p.given_name, a.city)
    for p, a in entity(Person, Address)
    if inner_join(p.perm_address_id, a.id)
)

Positional and keyword arguments in the @dataclass initializer are both supported.

Sort order

If you specify the sort order of a column with special Python functions asc(column) and desc(column), pylinsql will append the appropriate ORDER BY clause at the end of the SQL query:

select(
    (asc(p.family_name), desc(p.given_name), p.birth_date)
    for p in entity(Person)
)
SELECT p.family_name, p.given_name, p.birth_date
FROM "Person" AS p
ORDER BY p.family_name ASC, p.given_name DESC

Aggregation functions

Several aggregation functions are available, including avg, count, max, min, sum, avg_if, count_if, max_if, min_if and sum_if.

The following example illustrates how to use simple aggregation functions:

select(
    (count(p.birth_date), min(p.birth_date), max(p.birth_date))
    for p in entity(Person)
)

Conditional aggregation functions take a Boolean filter expression as a second parameter:

select(
    (
        count_if(p.birth_date, p.given_name != "John")
    )
    for p in entity(Person)
)
SELECT COUNT(p.birth_date) FILTER (WHERE p.given_name <> 'John')
FROM "Person" AS p

Date and time

A date constructed with datetime.date(y, m, d) in Python is translated to MAKE_DATE(y, m, d) in PostgreSQL. Likewise, datetime.time(h, m, s) is translated to MAKE_TIME(h, m, s). Parts of a date or time can be extracted with functions like year(dt) or hour(dt), which map to the appropriate EXTRACT clause in SQL. Date and time differences are also supported.

select(p for p in entity(Person) if year(now() - p.birth_date) >= 18)
SELECT * FROM "Person" AS p WHERE EXTRACT(YEAR FROM (CURRENT_TIMESTAMP - p.birth_date)) >= 18

String matching

String matching with the SQL-standard LIKE operator and PostgreSQL's regular expression match operators ~ (case sensitive) and ~* (case insensitive match) are both supported, use Python functions like, ilike, match and imatch. The following example matches all people records whose family name ends in can (with a case sensitive match):

select(p for p in entity(Person) if matches(p.family_name, r"can$"))
SELECT * FROM "Person" AS p WHERE p.family_name ~ 'can$'

Executing a query

The package async_database contains functions to create a database connection, acquire a connection from a connection pool, and run queries in a transaction. Member functions of the class DatabaseConnection accept Python generator expressions the same way that select does in the examples above.

async with async_database.connection() as conn:
    results = await conn.select(p for p in entity(Person))

    result = await conn.select_first(p for p in entity(Person))

Code generator for data classes

pylinsql depends on Python data classes for its language-integrated query mechanism. For example, in order to execute

select(
    asc(p.given_name)
    for p, a in entity(Person, Address)
    if inner_join(p.address_id, a.id)
    and (
        (p.given_name == "John" and p.family_name != "Doe")
        or (a.city != "London")
    )
)

one has to define data classes corresponding to entities Address and Person:

@dataclass
class Address:
    id: int
    city: str

@dataclass
class Person:
    id: int
    family_name: str
    given_name: str
    birth_date: datetime
    perm_address_id: int = field(default=...)
    temp_address_id: Optional[int] = field(default=...)

Defining these classes manually would be tedious work. Fortunately, pylinsql comes with a code generator utility that scans table schema definitions in a database, and writes corresponding Python code:

$ python3 -m pylinsql.generator.code_generator example.py --schema public

The generated code takes into account type mappings, nullable types, table references and even table and column comments.

Use the switch --help to learn more:

$ python3 -m pylinsql.generator.code_generator --help

Background and related work

psycopg2 provides a way to piece together SQL queries using composable primitive objects like Identifier (e.g. a table name), Literal (e.g. an integer or string value), Placeholder (in a prepared statement) and SQL (represents a SQL statement segment). It also provides a mechanism to establish a synchronous connection to a PostgreSQL server.

asyncpg is a library that exposes an asynchronous connection to a PostgreSQL server utilizing Python's asyncio services. If queries or parameterized queries are available as a string, asyncpg can execute them efficiently.

PonyORM is an object-relational mapping (ORM) library that uses a similar syntax based on Python generator expressions. It is a full-fledged ORM solution that uses a synchronous connection to a SQL server.

SQLAlchemy is the most widely-used object-relational mapping with a rich set of features (organized in a hierarchy), and an ability to use asynchronous database connections. Unfortunately, the query syntax is rather verbose and does not look like a neat Python expression.

The disassembling approach to reverse-engineer the abstract syntax tree (AST) from the control flow graph (CFG) is similar to that used in PonyORM.

The consistent coloring of incoming green/red edges of nodes in the abstract node graph is discussed in detail in Decompiling Boolean Expressions from Java Bytecode, specifically Algorithm 2.

For further reading, check out No More Gotos: Decompilation Using Pattern-Independent Control-Flow Structuring and Semantics-Preserving Transformations. Also, Solving the structured control flow problem once and for all might be of interest.

Implementation details

pylinsql utilizes some more advanced features and programming language concepts such as Python intermediate language, low-level code analysis, graph theory and parsers/generators.

As an example, let's consider the following Python generator expression:

((p.family_name, p.given_name) for p in entity(Person) if p.given_name == "John" and p.family_name != "Doe")

This has a conditional part:

p.given_name == "John" and p.family_name != "Doe"

and a yield part:

(p.family_name, p.given_name)

pylinsql performs several steps to construct a SQL query string from a Python generator expression:

  1. Disassembly.

    pylinsql uses the Python module dis to retrieve a Python generator expression as a series of instructions, which are low-level intermediate language statements such as BINARY_ADD (to add two numbers on the top of the stack), CALL_FUNCTION (to call a function with arguments on the stack), LOAD_GLOBAL (push a global variable to the top of the stack), or POP_JUMP_IF_TRUE (jump to a label if the value on the top of the stack is true).

  2. Extract basic blocks.

    A basic block is a series of instructions that starts with a label (that jump instructions point to) and/or ends with a (conditional or unconditional) jump statement (e.g. POP_JUMP_IF_TRUE). For example, the following snippet shows the disassembly of our sample Python generator expression (including both the conditional and the yield part), with horizontal bars separating basic blocks. Target labels for jump instructions are shown with >>. The number in the first column is the instruction address.

             0 LOAD_FAST                0 (.0)
------------------------------------------------------------
>>       2 FOR_ITER                38 (to 42)
         4 STORE_FAST               1 (p)
         6 LOAD_FAST                1 (p)
         8 LOAD_ATTR                0 (given_name)
        10 LOAD_CONST               0 ('John')
        12 COMPARE_OP               2 (==)
        14 POP_JUMP_IF_FALSE        2
------------------------------------------------------------
        16 LOAD_FAST                1 (p)
        18 LOAD_ATTR                1 (family_name)
        20 LOAD_CONST               1 ('Doe')
        22 COMPARE_OP               3 (!=)
        24 POP_JUMP_IF_FALSE        2
------------------------------------------------------------
        26 LOAD_FAST                1 (p)
        28 LOAD_ATTR                1 (family_name)
        30 LOAD_FAST                1 (p)
        32 LOAD_ATTR                0 (given_name)
        34 BUILD_TUPLE              2
        36 YIELD_VALUE
        38 POP_TOP
        40 JUMP_ABSOLUTE            2
------------------------------------------------------------
>>      42 LOAD_CONST               2 (None)
        44 RETURN_VALUE

  3. Create control flow graph (CFG).

    The control flow graph has basic blocks as nodes, and jump instruction targets as edges. For example, a basic block that ends with POP_JUMP_IF_TRUE has two outgoing edges: one points to the basic block targeted when the condition is true, and the other points to the next basic block (i.e. the next statement in the program).

    pylinsql uses a jump resolver to translate numeric instruction addresses into control flow graph edges.

  4. Merge nodes that correspond to conditional expressions and loop conditions.

    When you have a conditional expression such as

    p.given_name == "John" and p.family_name != "Doe"

    then the expression is represented in low-level instructions as a series of basic blocks, interconnected with jump instructions. pylinsql merges nodes corresponding to these basic blocks into a single compound node. For example, the above expression would become NodeConjunction(a, b) where a stands for the node representing the equality test and b stands for that of the inequality test, and NodeConjunction captures the intent of the Python keyword and.

    pylinsql merges nodes in a well-defined order. First, it merges all conditions that act like expressions (e.g. they are part of a function call). Second, it merges the condition that constitutes the loop condition of the Python generator expression. The end result is a chain of nodes, where each node is a simple node (a single basic block), or a composite node: a sequence, a conjunction or a disjunction. All composite nodes are also chains inside, with no branches.

  5. Create an abstract syntax tree (AST).

    pylinsql converts the node chains into an abstract representation with the help of an evaluator. The evaluator builds a symbolic expression (e.g. 0 * 1 + 2 or a and b and c) from a chain of nodes and the low-level instructions stored in them.

    The evaluator maintains a stack, mimicking how the Python interpreter works. It goes through the instructions of a basic block, and manipulates the stack following the instructions. Whenever global or local symbols are referenced (e.g. constants or variable names), the evaluator pushes their symbolic representation. Any further operations are performed with this symbolic representation. For example, when encountering the instruction BINARY_ADD, which adds two numbers popping off items from the top of the stack and pushing the result, the evaluator will pop off two symbolic expressions (e.g. Variable(a) and Constant(2)), and push a new symbolic expression (e.g. Addition(Variable(a), Constant(2))).

    Conjunctions and disjunctions are handled in a special way. These are represented by multiple basic blocks, interconnected by conditional jump instructions.

    Whenever the AST builder encounters a conjunction, it tells the evaluator to process jump instructions as if the condition evaluated to true. In a structured expression such as a and b and c, this would force evaluating the rest of the expression, and not short-circuit at a or a and b. Likewise, jump instructions in blocks of a disjunction are processed as if conditions evaluated to false. In either case, the top of the stack is going to contain a symbolic expression for all the sub-expressions combined into a conjunction or disjunction, respectively, as the evaluator jumps through all basic blocks that comprise them.

  6. Analyze the abstract syntax tree.

    pylinsql checks if the expression is well-formed, e.g. whether you join objects along existing properties (e.g. Person has given_name).

  7. Emit an SQL statement.

    pylinsql maps Python function calls into SQL statement equivalents, e.g. asc() becomes ORDER BY, inner_join() maps to an INNER JOIN in a FROM clause, a condition on a min() becomes part of HAVING, GROUP BY is generated based on the result expressions in the original Python generator expression, etc.