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sqlgg: SQL Guided (code) Generator
==================================

Problem
-------

Writing database layer code is usually tedious and error-prone, due to the mix of different
languages. SQL queries constructed dynamically need to bind external data (from application), and
the resulting rowset must be decomposed into application native data. Data crossing these
application-to-database boundaries is what causes troubles. One can factor out all common database
communication code, hide the database under some application-specific abstraction, but one always
needs to manually specify correspondence between SQL query binding slots (or resulting rowset
columns) and code variables. This mapping should be updated manually every time SQL query is
modified.

Solution
--------

SQL parser and code generator which ensures that application code and database queries are in sync.
It analyzes SQL query and determines the set of input parameters (values for INSERT, run-time
substitution parameters) and the set of resulting columns (for SELECT). Then it generates the
code in host language, matching query input and output to function parameters and return values with
corresponding native data types. So basically you provide an SQL query and generator creates a
function which takes the set of typed parameters as required to fill slots in a query. Generated
code binds provided parameters into query and executes it. SELECT statements additionally return the
collection of structures with fields representing columns of resulting rowset (or pass those
structures to callback-function). The most fruitful consequence of such approach is that
the host language compiler will itself check that functions generated from SQL queries will be
called correctly (i.e. all parameters bound with correct types). So if you modify the query and
forget to update the code -- the compiler will point on erroneous parts.

Complete example
----------------

Suppose we have the database of some money relationships for the group of people. We use the
following SQL tables and queries:

        -- @create_person
        CREATE TABLE person (id INTEGER PRIMARY KEY AUTOINCREMENT,name TEXT,surname TEXT);
        -- @add_person
        INSERT INTO person (name,surname) VALUES;

        -- @create_money
        CREATE TABLE money (src INTEGER, dst INTEGER, amount INTEGER);
        -- @add_money
        INSERT INTO money VALUES;

        -- @calc_total
        SELECT name || ' ' || surname AS fullname, SUM(amount) as total FROM person JOIN money ON src = id GROUP BY id;
        -- @list_donors
        SELECT DISTINCT surname FROM person JOIN money ON src = id AND dst = (SELECT id FROM person WHERE surname LIKE @surname) LIMIT @limit;

Generate the binding C++ code (boilerplate omitted, only function prototypes shown):

        // DO NOT EDIT MANUALLY

        // generated by sqlgg 0.2.0 (735e2815)

        #pragma once

        template <class Traits>
        struct sqlgg
        {
                // CREATE TABLE person (id INTEGER PRIMARY KEY AUTOINCREMENT,name TEXT,surname TEXT)
                static bool create_person(typename Traits::connection db);

                // INSERT INTO person (name,surname) VALUES
                static bool add_person(typename Traits::connection db, typename Traits::Text const& name,       typename Traits::Text const& surname);

                // CREATE TABLE money (src INTEGER, dst INTEGER, amount INTEGER)
                static bool create_money(typename Traits::connection db);

                // INSERT INTO money VALUES
                static bool add_money(typename Traits::connection db, typename Traits::Int const& src, typename Traits::Int const& dst, typename Traits::Int const& amount);

                // SELECT name || ' ' || surname AS fullname, SUM(amount) as total FROM person JOIN money ON src = id GROUP BY id
                struct data_4
                {
                        typename Traits::Text fullname;
                        typename Traits::Int total;
                }; // struct data_4

                template<class T>
                static bool calc_total(typename Traits::connection db, T& result);

                // SELECT DISTINCT surname FROM person JOIN money ON src = id AND dst = (SELECT id FROM person WHERE surname LIKE @surname)
                struct data_5
                {
                        typename Traits::Text surname;
                }; // struct data_5

                template<class T>
                static bool list_donors(typename Traits::connection db, T& result, typename Traits::Text const& surname, typename Traits::Int const& limit);

        }; // struct sqlgg

Things to note above:

2. Syntax. All queries are written in plain SQL. Every statement should be terminated with
    semicolon. There is no need to write (?,?) after VALUES in INSERT statement. The annotation
    `[sqlgg] name=function_name` (or simply `@function_name`) before the query specifies the name
    of the generated function (this annotation optional but very convenient).
1. The generated code is parametrized by database-specific class `Traits`. It specifies the
                correspondence between SQL and native types, provides types for database connection and
    implements actual code to execute statements. `Traits` should be implemented
                once for every specific database API.
1. `add_money()` function takes three data parameters -- the values to INSERT into table (note the
    names and types).
3. `calc_total()` returns data via `result` parameter. Under the scenes it will
    bind columns of resulting rowset to the fields of `T::value_type`, which should provide fields
    `fullname` of type `Traits::Text` and 
    `total` of type `Traits::Int` (otherwise it will fail to compile). For convenience a structure
    satisfying the requirements for output type is generated alongside the function, `data_4` in
    this particular case, so `std::vector<data_4>` for `result` is fine.
4. The types of parameters for `list_donors` were inferred correctly (`limit` is `Int` and `surname`
    is `Text`. SQL is not a statically-typed language so the inferred types are based on some
    reasonable assumptions.
5. Statements of arbitrary depth across many tables should be supported.
6. Statements are checked for correctness as far as generator is concerned, so it will detect
                syntax errors, non-existent columns in expressions, mismatched columns in compound statements,
                ambiguous column names etc.

Then manually-written C++ code boils down to:

    #include "../sqlite3_helper.hpp" // sqlite3 traits
    #include "demo_cxx_gen.hpp" // generated
    #include <iostream>
    #include <vector>

    using namespace std;

    typedef sqlgg<sqlite3_traits> gen;
    typedef long long int64;

    int main()
    {
      sqlite3* db = NULL;
      sqlite3_open(":memory:", &db);

      // create tables
      gen::create_person(db);
      gen::create_money(db);

      // add all person records
      gen::add_person(db,"John","Black");
      int64 john = sqlite3_last_insert_rowid(db);
      gen::add_person(db,"Ivan","Petrov");
      int64 ivan = sqlite3_last_insert_rowid(db);
      gen::add_person(db,"Sancho","Alvares");
      int64 sancho = sqlite3_last_insert_rowid(db);

      // add money relations
      gen::add_money(db,john,ivan,200);
      gen::add_money(db,john,sancho,100);
      gen::add_money(db,john,sancho,250);
      gen::add_money(db,sancho,ivan,300);

      // summarize by person
      typedef vector<gen::data_4> collection;
      collection all;
      gen::calc_total(db,all);

      // output
      cout << "Total transfers:" << endl;
      for (collection::const_iterator i = all.begin(), end = all.end(); i != end; ++i)
      {
         cout << i->fullname << " = " << i->total << endl;
      }

      // list donors
      typedef vector<gen::data_5> people;
      people p;
      gen::list_donors(db,p,"petrov",100);

      cout << "Donors:" << endl;
      for (people::const_iterator i = p.begin(), end = p.end(); i != end; ++i)
      {
        cout << i->surname << endl;
      }

      // properly close database
      sqlite3_close(db);

      return 0;
    }

The code is straightforward and free of any SQL-specific details. More importantly it is statically
checked by the compiler -- suppose we change the database scheme and add one more field to `money`
table (e.g. timestamp of transfer). Compilation rightfully fails:

    demo_cxx.cpp: In function `int main()':
    demo_cxx.cpp:29: error: no matching function for call to
    `sqlgg<sqlite3_traits>::add_money(sqlite3*&, int64&, int64&, int)'
    demo_cxx_gen.hpp:75: note: candidates are: static bool sqlgg<Traits>::add_money(typename
    Traits::connection, const typename Traits::Int&, const typename Traits::Int&, const typename
    Traits::Int&, const typename Traits::Int&) [with Traits = sqlite3_traits]

Details
-------

The main idea is that the generator should take care only of semantic binding between SQL and code
sides, being as unobtrusive as possible. So the choice of the specific database and API is a
programmer's choice. Similarly, queries to the database are expressed in plain SQL, so that the
generator can be easily plugged in any existing project -- just move all SQL statements used in the
code to separate file and feed it to generator.

Distinguishing feature of **sqlgg** is that it starts off with actual SQL queries, not object models
or SQL table descriptions.

This is work in progress and there is plenty of room for improvement. For now the status of this
project is **works for me** .  I use it for some simple database-access code with
[sqlite3](http://sqlite.org) engine (using suitable [sqlite3_traits](sqlite3_helper.hpp) helper).
This project was started when I found myself editing existing code with tons of C++ wrappers for SQL
queries, each binding several parameters and decomposing results.

For now it can generate C++ and OCaml code. Generated C++ code is parametrized with template class
for database specific code. Generated OCaml code is a functor
`module Sqlgg (T:`[Sqlgg\_traits.M](sqlgg_traits.ml)`)` (sample [sqlgg\_sqlite3](sqlgg_sqlite3.ml) 
for [OCaml-SQLite3][]).

[OCaml-SQLite3]:        http://caml.inria.fr/cgi-bin/hump.en.cgi?contrib=471

Try it [online](sql.cgi).

TODO
----

* distinguish predicates and expressions (research)
* choose better names for some common cases (WHERE id = ? etc)
* fix line numbers in error output
* resolve conflicts in grammar, check precedences
* type-inference is too primitive
* detect statements on single tables and group the corresponding generated code in one class
* check names (functions and bindings) for uniqueness
* support/test other SQL engines
* generate code for more languages
* read SQL spec
* type check expressions

----
2009-05-17

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