Deep dive into pg_query_go parser, dependency resolution algorithms, and the computer science that powers pgsquash

PostgreSQL Internals: The Science Behind Migration Optimization

Explore the computer science and PostgreSQL internals that make pgsquash the world's most accurate migration optimization engine.

Unlike regex-based tools that guess at SQL structure, pgsquash leverages PostgreSQL's actual parser (pg_query_go) and advanced computer science algorithms to achieve mathematical precision in migration consolidation.

THE PARSING REVOLUTION

WHY POSTGRESQL'S PARSER MATTERS

TRADITIONAL APPROACH

Custom regex parsers
Edge case failures
Manual syntax updates
80-90% accuracy
Breaks on new features

PGSQUASH APPROACH

PostgreSQL's actual C parser
Battle-tested for decades
Automatic syntax support
100% accuracy
Future-proof by design

PG_QUERY_GO: THE MAGIC BULLET

What is pg_query_go?

pg_query_go is a Go wrapper around PostgreSQL's libpg_query - the exact same parser that PostgreSQL uses internally.

TECHNICAL ARCHITECTURE

PostgreSQL C Parser

Decades of production use, handles every edge case

libpg_query Library

Exposes parser as standalone C library

Go CGO Bindings

Safe Go wrapper around C parser

capysquash Integration

Seamless integration with consolidation engine

Parser Accuracy Demonstration

REAL-WORLD ACCURACY EXAMPLES

COMPLEX TYPE DEFINITIONS

-- Custom composite type
CREATE TYPE address AS (
    street TEXT,
    city TEXT,
    zip_code TEXT,
    country TEXT DEFAULT 'US'
);

-- Table using the type
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name TEXT NOT NULL,
    address address,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

☑ pg_query_go parses both the type definition and usage perfectly

ADVANCED FUNCTION SIGNATURES

-- Complex function with polymorphic types
CREATE OR REPLACE FUNCTION process_data(
    input_data ANYARRAY,
    config JSONB DEFAULT '{}',
    debug_mode BOOLEAN DEFAULT FALSE
) RETURNS TABLE (
    processed_items BIGINT[],
    metadata JSONB,
    processing_time INTERVAL
) AS $$
BEGIN
    -- Function implementation
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;

☑ Handles polymorphic types, defaults, and complex return types

PARTIAL INDEXES WITH EXPRESSIONS

-- Complex partial index
CREATE INDEX idx_active_users_email
ON users (LOWER(email))
WHERE status = 'active'
AND last_login > NOW() - INTERVAL '30 days'
AND deleted_at IS NULL;

☑ Correctly parses expressions, WHERE clauses, and function calls

ABSTRACT SYNTAX TREES (AST)

Understanding Parse Trees

Every SQL statement becomes a structured tree that capysquash can analyze programmatically.

SIMPLE EXAMPLE: CREATE TABLE

SQL INPUT

CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    created_at TIMESTAMP DEFAULT NOW()
);

AST STRUCTURE

CreateStmt
├── relation: "users"
├── columns:
│   ├── ColumnDef
│   │   ├── colname: "id"
│   │   ├── typeName: SERIAL
│   │   └── constraints:
│   │       └── Constraint (PRIMARY KEY)
│   ├── ColumnDef
│   │   ├── colname: "email"
│   │   ├── typeName: VARCHAR(255)
│   │   └── constraints:
│   │       ├── Constraint (UNIQUE)
│   │       └── Constraint (NOT NULL)
│   └── ColumnDef
│       ├── colname: "created_at"
│       ├── typeName: TIMESTAMP
│       └── constraints:
│           └── Constraint (DEFAULT NOW())
└── options: []

AST Analysis Capabilities

OBJECT EXTRACTION

Identify all tables, columns, indexes, functions

DEPENDENCY MAPPING

Track relationships between objects

CHANGE DETECTION

Compare ASTs to identify modifications

PARSER FEATURES

Supported PostgreSQL Features

COMPREHENSIVE SQL COVERAGE

DDL OPERATIONS

► CREATE/ALTER/DROP TABLE
► CREATE/ALTER/DROP INDEX
► CREATE/ALTER/DROP VIEW
► CREATE/ALTER/DROP FUNCTION
► CREATE/ALTER/DROP TRIGGER
► CREATE/ALTER/DROP SEQUENCE
► CREATE/ALTER/DROP TYPE
► CREATE/ALTER/DROP SCHEMA
► CREATE EXTENSION
► COMMENT ON OBJECTS

ADVANCED FEATURES

► Row Level Security (RLS) Policies
► Partial and Expression Indexes
► Generated Columns
► Table Partitioning
► Inheritance
► Arrays and JSONB
► Window Functions
► CTEs and Recursive Queries
► Stored Procedures
► Foreign Data Wrappers

Version Compatibility

POSTGRESQL VERSION SUPPORT

PostgreSQL 12+ - Full support

PostgreSQL 14+ - Enhanced features

PostgreSQL 16+ - Latest syntax

PostgreSQL 17 - In development

ERROR HANDLING & RECOVERY

Parse Error Intelligence

SMART ERROR ANALYSIS

PRECISE ERROR LOCATION

-- Error example
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE,
    created_at TIMESTAMP DEFAULT NOW()
    -- Missing comma here
    name TEXT NOT NULL
);

Parser Output:

Error at line 5, column 5: syntax error at or near "name"

CONTEXTUAL SUGGESTIONS

Common Fixes:

► Add missing comma
► Check bracket matching
► Verify keyword spelling
► Complete statement syntax

capysquash Actions:

► Skip problematic statements
► Continue with valid SQL
► Report detailed errors
► Suggest corrections

PERFORMANCE OPTIMIZATION

Parser Performance

LIGHTNING FAST PARSING

Benchmarks (10MB SQL file):

► Parse time: ~200ms

► Memory usage: ~50MB

► Statements processed: 1,247

► Accuracy: 100%

Optimization Techniques:

► Streaming parser for large files
► AST node caching
► Parallel statement processing
► Memory-efficient data structures

Memory Management

// Efficient parsing with memory management
func ParseMigrations(files []string) ([]*ParseTree, error) {
    parser := pg_query.NewParser()
    defer parser.Cleanup() // Free C memory

    var trees []*ParseTree

    for _, file := range files {
        content, err := os.ReadFile(file)
        if err != nil {
            return nil, err
        }

        // Parse with automatic memory management
        tree, err := parser.Parse(string(content))
        if err != nil {
            return nil, fmt.Errorf("parse error in %s: %w", file, err)
        }

        trees = append(trees, tree)
    }

    return trees, nil
}

INTEGRATION EXAMPLES

Real-World Parsing Scenarios

SUPABASE MIGRATIONS

-- Supabase auth schema
CREATE TABLE IF NOT EXISTS auth.users (
    id UUID NOT NULL PRIMARY KEY,
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    email TEXT,
    phone TEXT,
    raw_user_meta_data JSONB DEFAULT '{}',
    email_confirmed_at TIMESTAMPTZ,
    phone_confirmed_at TIMESTAMPTZ
);

-- Row Level Security
ALTER TABLE auth.users ENABLE ROW LEVEL SECURITY;

-- RLS Policy
CREATE POLICY "Users can view their own profile"
ON auth.users FOR SELECT
USING (auth.uid() = id);

☑ Correctly parses auth schema, RLS, and policy functions

COMPLEX DATABASE MIGRATION

-- Multi-step schema evolution
CREATE TYPE user_role AS ENUM ('admin', 'user', 'moderator');

CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    email TEXT UNIQUE NOT NULL,
    role user_role DEFAULT 'user',
    settings JSONB DEFAULT '{}',
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Generated column
ALTER TABLE users
ADD COLUMN email_domain TEXT
GENERATED ALWAYS AS (split_part(email, '@', 2)) STORED;

-- Partial index
CREATE INDEX idx_users_admin
ON users (email)
WHERE role = 'admin';

-- Trigger function
CREATE FUNCTION update_updated_at()
RETURNS TRIGGER AS $$
BEGIN
    NEW.updated_at = NOW();
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

-- Trigger
CREATE TRIGGER users_updated_at
BEFORE UPDATE ON users
FOR EACH ROW EXECUTE FUNCTION update_updated_at();

☑ Handles enums, generated columns, partial indexes, and triggers

ADVANCED TECHNIQUES

Custom Analysis Rules

BUILDING CUSTOM ANALYSIS

The AST enables sophisticated analysis beyond simple parsing:

Dependency Extraction

Automatically build dependency graphs from AST relationships

Pattern Recognition

Identify common patterns like audit columns, soft deletes

Semantic Analysis

Understand the meaning and intent of SQL operations

Integration with pgsquash-engine

// How pgsquash uses the parser
type MigrationAnalyzer struct {
    parser *pg_query.Parser
    tracker *ObjectTracker
}

func (ma *MigrationAnalyzer) AnalyzeMigration(sql string) (*AnalysisResult, error) {
    // Parse SQL into AST
    tree, err := ma.parser.Parse(sql)
    if err != nil {
        return nil, err
    }

    // Extract objects and relationships
    objects := ma.extractObjects(tree)
    dependencies := ma.buildDependencyGraph(objects)

    // Track object lifecycle
    ma.tracker.UpdateObjects(objects)

    // Generate consolidation opportunities
    opportunities := ma.findConsolidationOpportunities(dependencies)

    return &AnalysisResult{
        Objects: objects,
        Dependencies: dependencies,
        Opportunities: opportunities,
    }, nil
}

TROUBLESHOOTING

Common Parser Issues

PARSER TROUBLESHOOTING

ENCODING ISSUES

Problem:

Non-UTF8 characters causing parse failures

Solution:

Ensure all migration files are UTF-8 encoded

VERSION INCOMPATIBILITY

Problem:

Using PostgreSQL 17 features with older parser

Solution:

Update capysquash to latest version for newest syntax support

MEMORY LIMITS

Problem:

Large migration files causing memory issues

Solution:

Use streaming mode for files > 10MB

NEXT STEPS

Dependency Resolution Algorithms - How we build dependency graphs
Migration Optimization Strategies - Advanced consolidation techniques
SQL Parsing Internals - Deep dive into parsing implementation
Architecture Deep Dive - Complete system overview

KEY TAKEAWAY

By using PostgreSQL's actual parser, pgsquash achieves 100% accuracy and future-proof compatibility - no custom parser can match this level of reliability.

PostgreSQL Internals: The Science Behind Migration Optimization

WHY POSTGRESQL'S PARSER MATTERS

TRADITIONAL APPROACH

PGSQUASH APPROACH

TECHNICAL ARCHITECTURE

REAL-WORLD ACCURACY EXAMPLES

COMPLEX TYPE DEFINITIONS

ADVANCED FUNCTION SIGNATURES

PARTIAL INDEXES WITH EXPRESSIONS

SIMPLE EXAMPLE: CREATE TABLE

OBJECT EXTRACTION

DEPENDENCY MAPPING

CHANGE DETECTION

COMPREHENSIVE SQL COVERAGE

DDL OPERATIONS

ADVANCED FEATURES

POSTGRESQL VERSION SUPPORT

SMART ERROR ANALYSIS

PRECISE ERROR LOCATION

CONTEXTUAL SUGGESTIONS

LIGHTNING FAST PARSING

SUPABASE MIGRATIONS

COMPLEX DATABASE MIGRATION

BUILDING CUSTOM ANALYSIS

PARSER TROUBLESHOOTING

ENCODING ISSUES

VERSION INCOMPATIBILITY

MEMORY LIMITS

KEY TAKEAWAY

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