graphile/crystal

🔮 Graphile's Crystal Monorepo; home to Grafast, PostGraphile, pg-introspection, pg-sql2 and much more!

12,916 stars TypeScript 10 components 7 connections

Graphile's monorepo providing Grafast GraphQL execution engine and PostGraphile PostgreSQL API generator

GraphQL queries flow through Grafast's plan-based execution engine, where operations are parsed into execution plans with optimized database access patterns, then executed in batches for maximum efficiency.

Under the hood, the system uses 3 feedback loops, 3 data pools, 5 control points to manage its runtime behavior.

Structural Verdict

A 10-component fullstack with 7 connections. 909 files analyzed. Well-connected — clear data flow between components.

How Data Flows Through the System

Query Parsing — GraphQL operations are parsed and validated against the schema
Plan Construction — OperationPlan builds execution steps with dependency tracking
Step Optimization — Related database operations are batched together for efficiency
Execution — Plans execute steps in dependency order with parallel processing
Result Assembly — Individual step results are assembled into final GraphQL response

System Behavior

How the system actually operates at runtime — where data accumulates, what loops, what waits, and what controls what.

Data Pools

LRU Cache (cache)
In-memory LRU cache for query plans and database metadata

PostgreSQL Connection Pool (database)
Database connections managed for query execution

Operation Plan Cache (cache)
Cached execution plans indexed by operation hash

Feedback Loops

Query Plan Optimization (training-loop, reinforcing) — Trigger: Repeated query patterns. Action: Cache and reuse optimized execution plans. Exit: Plan cache hit.
Connection Pool Scaling (auto-scale, balancing) — Trigger: Database load metrics. Action: Adjust pool size based on demand. Exit: Optimal pool size reached.
Benchmark Iteration (convergence, reinforcing) — Trigger: bench() function execution. Action: Run operation multiple times collecting metrics. Exit: 200 iterations completed.

Delays & Async Processing

Database Query Batching (batch-window, ~next tick) — Multiple database queries are batched together for efficiency
Schema Introspection (async-processing, ~connection dependent) — PostgreSQL schema metadata is loaded asynchronously during startup
TypeScript Compilation (batch-window, ~build dependent) — All packages compile in topological dependency order

Control Points

NODE_ENV (env-var) — Controls: Test execution mode and development features. Default: test
BUILD_MODE (env-var) — Controls: Development vs production build optimization. Default: development
Debug Tools (feature-flag) — Controls: Which debugging tools are available in Ruru IDE. Default: explain, plan
Cache Size (threshold) — Controls: Maximum number of cached items
Batch Size (threshold) — Controls: Database query batching limits

Package Structure

This monorepo contains 8 packages:

grafast (library)
Core GraphQL execution engine with plan-based resolvers for high performance query execution.

postgraphile (library)
Auto-generates high-performance GraphQL APIs from PostgreSQL databases with extensive customization options.

pg (library)
PostgreSQL-specific plan classes for integrating database operations with Grafast execution engine.

graphile-build (library)
Schema building system for creating GraphQL schemas from various data sources with plugin architecture.

ruru (app)
GraphQL IDE and query interface with subscription support and debugging tools.

grafserv (library)
HTTP server implementation for serving Grafast-powered GraphQL APIs.

pg-introspection (library)
PostgreSQL schema introspection utilities for analyzing database structure programmatically.

graphile-export (utility)
Exports dynamically constructed GraphQL schemas to static JavaScript source code.

Technology Stack

TypeScript (framework)
Primary development language with strict type checking

GraphQL (framework)
Query language and schema definition

PostgreSQL (database)
Primary database integration target

React (framework)
UI framework for Ruru IDE components

Jest (testing)
Testing framework for all packages

ESLint (build)
Code linting and style enforcement

Yarn (build)
Package management and workspace orchestration

Babel (build)
JavaScript compilation and transformation

Changesets (build)
Version management and changelog generation

Docker (infra)
Development environment and testing infrastructure

Key Components

execute (function) — Core GraphQL execution function that replaces GraphQL.js execute with plan-based resolution grafast/grafast/src/execute.ts
makeGrafastSchema (function) — Creates GraphQL schemas optimized for Grafast execution with plan resolvers grafast/grafast/src/makeGrafastSchema.ts
OperationPlan (class) — Represents the execution plan for a GraphQL operation with step dependencies grafast/grafast/src/engine/OperationPlan.ts
PgSelectStep (class) — PostgreSQL SELECT query planning step for efficient database access grafast/dataplan-pg/src/steps/pgSelect.ts
Ruru (component) — React component providing the main GraphQL IDE interface with query editor and results grafast/ruru-components/src/ruru.tsx
bench (function) — Benchmarking framework for measuring GraphQL operation performance across multiple runs grafast/bench/src/index.ts
jsonParse (function) — Step function for parsing JSON data within Grafast execution plans grafast/dataplan-json/src/steps/jsonParse.ts
ruruHTML (function) — Generates HTML for serving the Ruru GraphQL IDE interface grafast/ruru/src/server.ts
PersistedPlugin (plugin) — Grafserv middleware plugin enabling persisted GraphQL operations support grafast/grafserv-persisted/src/index.ts
useStorage (hook) — React hook managing localStorage for Ruru IDE preferences and settings grafast/ruru-components/src/hooks/useStorage.ts

Sub-Modules

Grafast Ecosystem (independence: medium)
High-performance GraphQL execution engine with PostgreSQL optimization and development tools

PostGraphile Suite (independence: low)
Auto-generates GraphQL APIs from PostgreSQL databases with schema building system

Shared Utilities (independence: high)
Common libraries and tools used across the GraphQL ecosystem packages

Configuration

tsdoc.json (json)

$schema (string, unknown) — default: https://developer.microsoft.com/json-schemas/tsdoc/v0/tsdoc.schema.json
tagDefinitions (array, unknown) — default: [object Object],[object Object],[object Object]
supportForTags.@rc (boolean, unknown) — default: true
supportForTags.@defaultValue (boolean, unknown) — default: true
supportForTags.@decorator (boolean, unknown) — default: true
supportForTags.@deprecated (boolean, unknown) — default: true
supportForTags.@eventProperty (boolean, unknown) — default: true
supportForTags.@example (boolean, unknown) — default: true
+20 more parameters

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Frequently Asked Questions

What is crystal used for?

Graphile's monorepo providing Grafast GraphQL execution engine and PostGraphile PostgreSQL API generator graphile/crystal is a 10-component fullstack written in TypeScript. Well-connected — clear data flow between components. The codebase contains 909 files.

How is crystal architected?

crystal is organized into 5 architecture layers: Core Engine, Schema Building, PostgreSQL Integration, Developer Interface, and 1 more. Well-connected — clear data flow between components. This layered structure enables tight integration between components.

How does data flow through crystal?

Data moves through 5 stages: Query Parsing → Plan Construction → Step Optimization → Execution → Result Assembly. GraphQL queries flow through Grafast's plan-based execution engine, where operations are parsed into execution plans with optimized database access patterns, then executed in batches for maximum efficiency. This pipeline design reflects a complex multi-stage processing system.

What technologies does crystal use?

The core stack includes TypeScript (Primary development language with strict type checking), GraphQL (Query language and schema definition), PostgreSQL (Primary database integration target), React (UI framework for Ruru IDE components), Jest (Testing framework for all packages), ESLint (Code linting and style enforcement), and 4 more. This broad technology surface reflects a mature project with many integration points.

What system dynamics does crystal have?

crystal exhibits 3 data pools (LRU Cache, PostgreSQL Connection Pool), 3 feedback loops, 5 control points, 3 delays. The feedback loops handle training-loop and auto-scale. These runtime behaviors shape how the system responds to load, failures, and configuration changes.

What design patterns does crystal use?

5 design patterns detected: Plan-based Execution, Step Dependencies, Plugin Architecture, Forward Declarations, Monorepo Package Sharing.

Analyzed on March 31, 2026 by CodeSea. Written by Karolina Sarna.