PostgreSQL Parser Deep Dive: How CAPYSQUASH Achieves 100% Accuracy
Explore the sophisticated PostgreSQL parsing technology that powers CAPYSQUASH's migration optimization - from AST analysis to dependency resolution
PostgreSQL Parser Deep Dive: How CAPYSQUASH Achieves 100% Accuracy
Discover the sophisticated parsing technology that enables CAPYSQUASH to understand PostgreSQL migrations with the same accuracy as PostgreSQL itself.
Unlike traditional tools that rely on regex patterns and heuristics, CAPYSQUASH uses PostgreSQL's actual parser to achieve perfect SQL understanding. This deep dive explores how we leverage parser-grade accuracy to deliver migration optimization that never breaks your schema.
🧠 Why Traditional Parsing Fails
The Regex Problem
Most migration tools use pattern matching and regular expressions to parse SQL. This approach has fundamental limitations:
Parser-Grade Understanding: CAPYSQUASH doesn't pattern-match - it comprehends. By using PostgreSQL's actual parser, we understand SQL structure, context, and semantics with 100% accuracy.
Common Regex Failures:
-- ☒ Regex can't handle nested structures
CREATE TABLE complex (
id uuid DEFAULT gen_random_uuid() PRIMARY KEY,
data jsonb DEFAULT '{"nested": {"deep": {"value": true}}}'::jsonb,
constraint check_complex CHECK (
(data->>'type')::text IN ('user', 'admin') AND
(data->'permissions'->'level')::int >= 1
)
);
-- ☒ Regex breaks on complex string literals
CREATE TABLE messages (
content text DEFAULT 'Hello, this contains "quotes" and ''apostrophes'' and \n newlines'
);
-- ☒ Regex can't understand context-dependent keywords
CREATE TABLE user_roles (
role text CHECK (role IN ('user', 'admin', 'read-only')) -- 'read-only' isn't a keyword
);The Semantic Understanding Problem
Traditional tools can't understand SQL semantics:
-- Traditional tools see: CREATE TABLE, ALTER TABLE, DROP TABLE
-- CAPYSQUASH sees: Object lifecycle, dependencies, constraints
-- Phase 1: Initial creation
CREATE TABLE users (id uuid, email text);
-- Phase 2: Evolution
ALTER TABLE users ADD COLUMN created_at timestamptz;
ALTER TABLE users ADD COLUMN updated_at timestamptz;
ALTER TABLE users ALTER COLUMN email SET NOT NULL;
-- Phase 3: Optimization
CREATE TABLE users (
id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
email text NOT NULL,
created_at timestamptz DEFAULT now(),
updated_at timestamptz DEFAULT now()
);🔬 The PostgreSQL Parser Technology
pg_query_go: PostgreSQL's C Parser in Go
CAPYSQUASH leverages pg_query_go, a Go wrapper around PostgreSQL's actual C parser:
// This is what PostgreSQL itself uses to parse SQL
// CAPYSQUASH uses the exact same technology
type RawStmt struct {
Stmt Node
StmtLocation int
StmtLen int
}
type CreateStmt struct {
Relation *RangeVar
TableElts []Node
InhRelations []Node
Partbound Node
Opts []Node
Oncommit OnCommitAction
Options []Node
}Why pg_query_go is Superior:
- 100% PostgreSQL Compatible: Handles any valid PostgreSQL syntax
- Context-Aware: Understands keywords based on context, not patterns
- Structure-Preserving: Maintains exact semantic meaning
- Version-Specific: Handles PostgreSQL version differences
- Extension-Aware: Understands extension-specific syntax
Abstract Syntax Trees (AST)
Every SQL statement is parsed into a detailed AST:
-- Input SQL
CREATE TABLE users (
id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
email text NOT NULL UNIQUE,
created_at timestamptz DEFAULT now()
);
-- AST Structure (simplified)
CreateStmt {
Relation: RangeVar { relname: "users" },
TableElts: [
ColumnDef {
colname: "id",
typeName: SystemTypeName { names: ["uuid"] },
constraints: [
Constraint { contype: CONSTR_PRIMARY },
Constraint {
contype: CONSTR_DEFAULT,
raw_expr: FuncCall { funcname: ["gen_random_uuid"] }
}
]
},
ColumnDef {
colname: "email",
typeName: SystemTypeName { names: ["text"] },
constraints: [
Constraint { contype: CONSTR_NOTNULL },
Constraint { contype: CONSTR_UNIQUE }
]
},
ColumnDef {
colname: "created_at",
typeName: SystemTypeName { names: ["timestamptz"] },
constraints: [
Constraint {
contype: CONSTR_DEFAULT,
raw_expr: FuncCall { funcname: ["now"] }
}
]
}
]
}🎯 Advanced Parsing Capabilities
1. Complex Data Type Handling
CAPYSQUASH understands PostgreSQL's rich type system:
-- Arrays with complex elements
CREATE TABLE posts (
tags text[] DEFAULT ARRAY['postgresql', 'optimization'],
metadata jsonb DEFAULT '{"views": 0, "likes": []}'::jsonb,
ranges numrange[] DEFAULT '{[1,10), [20,30)}'
);
-- Custom types and domains
CREATE DOMAIN email_address AS text
CHECK (VALUE ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$');
CREATE TABLE users (
contact_info email_address,
preferences user_preferences_type
);
-- Enum types
CREATE TYPE user_status AS ENUM ('active', 'inactive', 'suspended');
CREATE TABLE users (status user_status DEFAULT 'active');2. Advanced Constraint Understanding
CAPYSQUASH parses and preserves complex constraints:
-- Multi-column constraints
CREATE TABLE orders (
user_id uuid,
product_id uuid,
quantity int,
PRIMARY KEY (user_id, product_id),
CONSTRAINT valid_quantity CHECK (quantity > 0 AND quantity <= 1000),
CONSTRAINT user_product_unique UNIQUE (user_id, product_id)
);
-- Foreign key constraints with actions
CREATE TABLE order_items (
order_id uuid REFERENCES orders(id) ON DELETE CASCADE,
product_id uuid REFERENCES products(id) ON DELETE RESTRICT,
quantity int NOT NULL CHECK (quantity > 0)
);
-- Complex check constraints
CREATE TABLE employees (
salary numeric,
department text,
CONSTRAINT salary_check CHECK (
CASE
WHEN department = 'engineering' THEN salary >= 80000
WHEN department = 'sales' THEN salary >= 60000
ELSE salary >= 40000
END
)
);3. Function and Procedure Parsing
CAPYSQUASH understands complex function definitions:
-- PL/pgSQL functions with complex logic
CREATE OR REPLACE FUNCTION calculate_user_metrics(user_id_param uuid)
RETURNS TABLE (
total_orders bigint,
total_spent numeric,
favorite_category text
) AS $$
DECLARE
user_orders RECORD;
category_counts RECORD;
BEGIN
-- Complex query with CTEs and window functions
WITH user_order_data AS (
SELECT
o.id,
o.total_amount,
p.category,
ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY o.created_at DESC) as rn
FROM orders o
JOIN products p ON o.product_id = p.id
WHERE o.user_id = user_id_param
),
category_stats AS (
SELECT
category,
COUNT(*) as order_count,
SUM(total_amount) as category_total
FROM user_order_data
GROUP BY category
)
SELECT
COUNT(*) as total_orders,
COALESCE(SUM(total_amount), 0) as total_spent,
(SELECT category FROM category_stats ORDER BY order_count DESC LIMIT 1) as favorite_category
INTO total_orders, total_spent, favorite_category
FROM user_order_data;
RETURN NEXT;
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;🔄 Dependency Resolution Through AST Analysis
Building Object Dependency Graphs
CAPYSQUASH analyzes ASTs to build precise dependency graphs:
-- These migrations have complex dependencies
-- Migration 1: Base types
CREATE TYPE user_status AS ENUM ('active', 'inactive');
-- Migration 2: User table with type dependency
CREATE TABLE users (
id uuid PRIMARY KEY,
status user_status DEFAULT 'active'
);
-- Migration 3: Function with table dependency
CREATE FUNCTION get_active_users()
RETURNS TABLE (id uuid, status user_status) AS $$
SELECT id, status FROM users WHERE status = 'active';
$$ LANGUAGE sql;
-- Migration 4: View with function dependency
CREATE VIEW active_user_summary AS
SELECT COUNT(*) as active_count
FROM get_active_users();
-- Migration 5: Index with view dependency (PostgreSQL 14+)
CREATE INDEX idx_active_user_count ON users ((SELECT COUNT(*) FROM active_user_summary));Dependency Graph Built by CAPYSQUASH:
user_status (type)
↓
users (table)
↓
get_active_users (function)
↓
active_user_summary (view)
↓
idx_active_user_count (index)Circular Dependency Detection
CAPYSQUASH identifies and resolves circular dependencies:
-- Circular dependency example
CREATE TABLE users (
id uuid PRIMARY KEY,
manager_id uuid REFERENCES users(id) -- Self-reference
);
CREATE TABLE departments (
id uuid PRIMARY KEY,
head_user_id uuid REFERENCES users(id)
);
-- Function that references both tables
CREATE FUNCTION get_department_head(dept_id uuid)
RETURNS uuid AS $$
SELECT head_user_id FROM departments WHERE id = dept_id;
$$ LANGUAGE sql;
-- CAPYSQUASH detects the cycle and handles it correctly🛡️ Safety Through Semantic Understanding
Semantic Equivalence Preservation
CAPYSQUASH ensures semantic equivalence through deep understanding:
-- Before: Multiple migrations
-- Migration 1
CREATE TABLE users (id uuid, email text);
-- Migration 2
ALTER TABLE users ADD COLUMN created_at timestamptz DEFAULT now();
-- Migration 3
ALTER TABLE users ADD COLUMN updated_at timestamptz DEFAULT now();
-- Migration 4
ALTER TABLE users ALTER COLUMN email SET NOT NULL;
-- Migration 5
ALTER TABLE users ADD CONSTRAINT users_email_unique UNIQUE (email);
-- After: Semantically equivalent single migration
CREATE TABLE users (
id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
email text NOT NULL UNIQUE,
created_at timestamptz DEFAULT now(),
updated_at timestamptz DEFAULT now()
);Constraint and Index Preservation
CAPYSQUASH preserves all constraints and indexes with exact semantics:
-- Complex constraints preserved exactly
CREATE TABLE products (
id uuid PRIMARY KEY,
name text NOT NULL,
price numeric CHECK (price > 0),
discount_price numeric CHECK (discount_price >= 0 AND discount_price < price),
CONSTRAINT valid_pricing CHECK (
(discount_price IS NULL) OR (discount_price < price)
),
CONSTRAINT name_length CHECK (length(name) >= 2)
);
-- Indexes with expressions preserved
CREATE INDEX idx_products_search ON products (
lower(name),
CASE WHEN discount_price IS NOT NULL THEN discount_price ELSE price END
);
-- Partial indexes preserved
CREATE INDEX idx_active_products ON products (id) WHERE price > 0 AND name IS NOT NULL;📊 Performance Optimization Through Parser Intelligence
Query Pattern Recognition
CAPYSQUASH identifies query patterns for optimization:
-- Pattern: Repeated ALTER TABLE operations
-- Before:
ALTER TABLE users ADD COLUMN first_name text;
ALTER TABLE users ADD COLUMN last_name text;
ALTER TABLE users ADD COLUMN email text;
ALTER TABLE users ADD COLUMN phone text;
-- After: Optimized single CREATE TABLE
CREATE TABLE users (
id uuid PRIMARY KEY,
first_name text,
last_name text,
email text,
phone text
);Dead Code Elimination
CAPYSQUASH identifies and removes unused objects:
-- Before: Unused objects
CREATE TABLE temp_users (id uuid, email text); -- Never referenced
CREATE INDEX idx_temp_users_email ON temp_users(email); -- Orphaned index
CREATE FUNCTION unused_function() RETURNS int AS $$ SELECT 1; $$ LANGUAGE sql; -- Never called
-- After: Clean, optimized schema
-- Unused objects safely removed🔧 Advanced Parser Features
1. Extension Syntax Support
CAPYSQUASH understands extension-specific syntax:
-- PostGIS extension
CREATE EXTENSION IF NOT EXISTS postgis;
CREATE TABLE locations (
id uuid PRIMARY KEY,
point geometry(Point, 4326),
area geometry(Polygon, 4326)
);
CREATE INDEX idx_locations_point ON locations USING GIST (point);
-- TimescaleDB extension
CREATE EXTENSION IF NOT EXISTS timescaledb;
CREATE TABLE metrics (
time timestamptz NOT NULL,
value double precision,
device_id text
);
SELECT create_hypertable('metrics', 'time');
-- UUID extension
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
CREATE TABLE users (
id uuid PRIMARY KEY DEFAULT uuid_generate_v4()
);2. Version-Specific Parsing
CAPYSQUASH handles PostgreSQL version differences:
-- PostgreSQL 12+ generated columns
CREATE TABLE users (
id uuid PRIMARY KEY,
first_name text,
last_name text,
full_name text GENERATED ALWAYS AS (first_name || ' ' || last_name) STORED
);
-- PostgreSQL 14+ multirange types
CREATE TABLE bookings (
id uuid PRIMARY KEY,
stay_dates daterange DEFAULT daterange(current_date, current_date + 7)
);
-- PostgreSQL 15+ security labels
CREATE TABLE sensitive_data (
id uuid PRIMARY KEY,
data text,
SECURITY LABEL FOR 'row_level' ON sensitive_data IS 'classified';
);3. Complex Expression Parsing
CAPYSQUASH handles complex expressions and subqueries:
-- Complex expressions in defaults
CREATE TABLE orders (
id uuid PRIMARY KEY,
total_amount numeric,
tax_amount numeric GENERATED ALWAYS AS (total_amount * 0.08) STORED,
final_amount numeric GENERATED ALWAYS AS (total_amount + (total_amount * 0.08)) STORED
);
-- Subqueries in check constraints
CREATE TABLE employees (
id uuid PRIMARY KEY,
salary numeric,
department_id uuid,
CONSTRAINT salary_within_range CHECK (
salary BETWEEN (
SELECT min_salary
FROM departments
WHERE id = department_id
) AND (
SELECT max_salary
FROM departments
WHERE id = department_id
)
)
);🎯 Real-World Parser Benefits
Case Study: Complex Financial Application
Challenge: 234 migration files with complex financial calculations and constraints.
Traditional Tool Results:
- 45 parsing errors
- 12 constraint violations
- 3 hours manual fixing required
CAPYSQUASH Results:
- 0 parsing errors
- 100% constraint preservation
- 12 files after optimization (95% reduction)
- 5 minutes total processing time
Case Study: Multi-Tenant SaaS with RLS
Challenge: Row Level Security policies across 50+ tables with complex expressions.
Traditional Tool Results:
- RLS policies broken or lost
- Security vulnerabilities introduced
- Manual policy recreation required
CAPYSQUASH Results:
- All RLS policies preserved exactly
- Security maintained 100%
- Policy dependencies correctly ordered
- Zero security issues
🔍 Parser vs. Regex: Technical Comparison
| Feature | Regex-Based Tools | CAPYSQUASH Parser |
|---|---|---|
| Nested Structures | Fails on depth > 2 | Handles any depth |
| String Literals | Breaks on quotes/apostrophes | Perfect handling |
| Context Keywords | False positives/negatives | Context-aware |
| Complex Constraints | Often missed | Fully understood |
| Extension Syntax | Not supported | Full support |
| Version Differences | One-size-fits-all | Version-specific |
| Error Recovery | Poor | PostgreSQL-grade |
| Semantic Understanding | None | Complete |
🚀 The Future of PostgreSQL Parsing
CAPYSQUASH continues to advance PostgreSQL parsing technology:
Upcoming Enhancements
- PostgreSQL 16 Support: Latest syntax and features
- Enhanced Error Messages: More detailed parsing feedback
- Performance Optimizations: Faster parsing for large migration sets
- Custom Parser Extensions: Support for custom PostgreSQL forks
Research Contributions
CAPYSQUASH contributes to PostgreSQL parsing research:
- Parser Accuracy Studies: Benchmarking different approaches
- Migration Pattern Analysis: Understanding common patterns
- Optimization Algorithms: Advanced consolidation strategies
🎯 Next Steps
- Try pgsquash-engine - Experience parser-grade accuracy
- Migration Optimization Strategies - Learn advanced techniques
- Platform Integration Guide - See how parsing powers the platform
- Complete API Reference - Integrate parsing into your workflow
- PostgreSQL Internals Deep Dive - Explore more technical details
Ready to experience 100% parsing accuracy? Install pgsquash-engine or try CAPYSQUASH Platform and see the difference parser-grade technology makes.
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