graphile/crystal
🔮 Graphile's Crystal Monorepo; home to Grafast, PostGraphile, pg-introspection, pg-sql2 and much more!
Builds high-performance GraphQL APIs from PostgreSQL databases using declarative execution planning
The system starts with PostgreSQL database introspection to extract schema metadata, uses this to generate GraphQL schemas with plan resolvers, then executes GraphQL operations through declarative planning. When a query arrives, Grafast analyzes the operation to build an execution plan that batches database operations, eliminates redundant queries, and executes steps in optimal order. The plan resolvers declare what data they need rather than how to fetch it, allowing the execution engine to optimize across the entire operation.
Under the hood, the system uses 3 feedback loops, 3 data pools, 4 control points to manage its runtime behavior.
A 8-component fullstack. 913 files analyzed. Data flows through 6 distinct pipeline stages.
How Data Flows Through the System
The system starts with PostgreSQL database introspection to extract schema metadata, uses this to generate GraphQL schemas with plan resolvers, then executes GraphQL operations through declarative planning. When a query arrives, Grafast analyzes the operation to build an execution plan that batches database operations, eliminates redundant queries, and executes steps in optimal order. The plan resolvers declare what data they need rather than how to fetch it, allowing the execution engine to optimize across the entire operation.
- Database introspection — The pg-introspection library queries PostgreSQL system catalogs (pg_class, pg_attribute, pg_constraint) to extract complete schema metadata including tables, columns, relationships, functions, and permissions
- Schema generation — SchemaBuilder processes introspection results through plugin hooks, with graphile-build-pg plugins converting PostgreSQL entities to GraphQL types, fields, and plan resolvers using event-driven architecture [IntrospectionResults → GraphQLSchema]
- Operation parsing — Incoming GraphQL operations are parsed by GraphQL.js parser and validated against the schema, extracting field selections, variables, and operation type (query/mutation/subscription) [GraphQL operation string → DocumentNode]
- Execution planning — OperationPlan analyzes the parsed operation by walking the selection set, invoking plan resolvers for each field, and building a dependency graph of ExecutionStep instances that represent data fetching operations [DocumentNode → ExecutionPlan]
- Step optimization — The execution engine batches compatible steps (like database queries to the same table), eliminates redundant operations, and organizes steps into layers that can execute in parallel while respecting dependencies [ExecutionPlan → Optimized ExecutionPlan]
- Batch execution — Steps are executed layer by layer, with database operations batched into efficient SQL queries using PgSelectStep and other PostgreSQL-specific steps, minimizing round trips and eliminating N+1 problems [Optimized ExecutionPlan → ExecutionResult]
Data Models
The data structures that flow between stages — the contracts that hold the system together.
grafast/grafast/src/engine/OperationPlan.tsClass containing stepMap: Map<number, ExecutionStep>, rootStep: ExecutionStep, and layeredPlans: LayerPlan[] representing the optimized execution strategy for a GraphQL operation
Created during GraphQL operation planning by analyzing field dependencies, then executed to fetch data with minimal database queries
grafast/grafast/src/step.tsAbstract class with id: number, dependencies: ExecutionStep[], and execute(): Promise<any> method representing a single data fetching operation in the execution plan
Instantiated during plan construction, organized into batches by dependency analysis, then executed in optimized order
grafast/dataplan-pg/src/steps/pgSelect.tsExecutionStep subclass with sqlQuery: SQL, from: PgResource, where: PgConditionStep[], and orderBy: PgOrderByStep[] for database operations
Created by PostgreSQL plan resolvers, batched with other database operations, then compiled to optimized SQL queries
utils/pg-introspection/src/interfaces.tsObject with namespaces: PgNamespace[], classes: PgClass[], attributes: PgAttribute[], constraints: PgConstraint[], functions: PgProc[] containing complete PostgreSQL schema metadata
Generated by querying PostgreSQL system catalogs, cached during schema building, used to construct GraphQL types and resolvers
graphqlGraphQL.js schema object with Query/Mutation/Subscription types, each containing field definitions with plan resolvers instead of traditional resolvers
Built by graphile-build plugins from database introspection, enhanced with Grafast plan resolvers, then used for GraphQL execution
grafast/grafast/src/makeGrafastSchema.tsObject with plan: PlanResolver function, subscribe?: SubscriptionPlanResolver, args?: Record<string, ArgumentPlanResolver> defining how to resolve a GraphQL field
Registered during schema building by plugins, invoked during execution planning to create ExecutionStep instances
Hidden Assumptions
Things this code relies on but never validates. These are the things that cause silent failures when the system changes.
The `setup()` function returns an object that can be cast to `any` type when no specific setup is provided, and this object will be compatible with all `contextFactory` calls
If this fails: If setup() returns undefined or null, the contextFactory will receive undefined as setupResult, potentially causing runtime errors when accessing properties
grafast/bench/src/index.ts:bench
GraphQL operation source contains regex-matchable comment patterns like `#> variableValues: {...}` for variable extraction, and these comments follow JSON5 format exactly
If this fails: If operation source contains malformed JSON5 in variableValues comment or uses different comment syntax, JSON5.parse() will throw and crash the benchmark
grafast/bench/src/index.ts:bench
The EventEmitter 'onPlan' event will always fire during operation execution, and the event payload will contain both 'elapsed' and 'laps' properties
If this fails: If the execution engine doesn't emit the expected event or emits with different payload structure, the timing object remains incomplete with undefined planning/laps data
grafast/bench/src/index.ts:bench
Schema types filtering assumes that all system types start with '__' and all built-in scalars are specified scalars, with no custom types following these naming conventions
If this fails: Custom types starting with '__' will be incorrectly filtered out, and custom scalars that happen to be specified scalars will be excluded from code generation
grafast/codegen-plugin/src/index.ts:GrafastGenerator.constructor
Type names can be safely JSON.stringify'd as object keys and will always produce valid TypeScript property access syntax
If this fails: Type names with special characters, quotes, or newlines will generate invalid TypeScript code that fails compilation
grafast/codegen-plugin/src/index.ts:_get
The `body` object passed to the middleware can be safely mutated by setting `body.query`, and downstream middleware expects this mutation
If this fails: If body is immutable or frozen, setting body.query will silently fail or throw, breaking persisted operation resolution
grafast/grafserv-persisted/src/index.ts:PersistedPlugin.middleware.processGraphQLRequestBody
When `realQuery` is a Promise, the Promise will always resolve to either a string or null/undefined, never reject or resolve to other types
If this fails: If the Promise resolves to an object, number, or other type, the typeof check fails and the code throws an incorrect 'provide document id' error instead of handling the actual data
grafast/grafserv-persisted/src/index.ts:persistedOperationFromPayload
The 'http-proxy' module, if installed, will have either `default.createProxyServer` or `createProxyServer` export, and dynamic import will either succeed completely or fail completely
If this fails: If http-proxy has different export structure or is partially corrupt, the function returns null instead of the proxy server, causing proxy features to silently fail
grafast/ruru/src/cli.ts:tryLoadHttpProxyCreateProxyServer
The `loadConfig` function can handle undefined configFileLocation and will not throw when the config file doesn't exist
If this fails: If loadConfig throws on missing files instead of returning null/undefined, the CLI crashes on startup when no config is specified
grafast/ruru/src/cli.ts:configFromArgs
ExecutionResult.errors, when defined, is always a non-empty array with each element having a 'message' property
If this fails: If errors is an empty array or contains objects without 'message' property, accessing errors[0].message will throw undefined access or property access errors
grafast/bench/src/index.ts:checkForErrors
System Behavior
How the system operates at runtime — where data accumulates, what loops, what waits, and what controls what.
Data Pools
Caches compiled GraphQL schemas and their associated plan resolvers to avoid recompilation on every request
Stores PostgreSQL schema metadata between introspection runs to avoid expensive system catalog queries
Caches parsed and planned GraphQL operations to skip parsing and planning for repeated queries
Feedback Loops
- Schema rebuilding (cache-invalidation, balancing) — Trigger: Database schema changes detected. Action: Invalidates introspection cache and rebuilds GraphQL schema with new metadata. Exit: New schema successfully generated and cached.
- Plan optimization loop (convergence, balancing) — Trigger: ExecutionPlan creation starts. Action: Iteratively optimizes step batching and dependency ordering until no more improvements possible. Exit: No additional optimizations found in current iteration.
- Plugin hook chain (recursive, reinforcing) — Trigger: Hook execution during schema building. Action: Each plugin can modify the build state and trigger additional hooks, creating cascading schema modifications. Exit: All plugins complete and no new hooks triggered.
Delays
- Initial introspection (warmup, ~seconds) — First request blocked while PostgreSQL schema metadata is extracted and processed
- Schema compilation (compilation, ~milliseconds) — GraphQL schema construction through plugin system adds startup latency but enables caching
- Plan optimization (async-processing, ~microseconds) — Each operation requires planning phase before execution, traded for significant query performance improvement
Control Points
- Schema caching (feature-flag) — Controls: Whether compiled schemas are cached between requests or rebuilt each time. Default: enabled by default
- Introspection depth (threshold) — Controls: How much PostgreSQL metadata to extract (tables only vs full schema including functions, triggers, etc). Default: configurable via options
- Batch size limits (threshold) — Controls: Maximum number of database operations that can be batched together in a single query. Default: runtime configurable
- Debug mode (env-var) — Controls: Enables detailed logging of execution plans, SQL queries, and performance metrics. Default: NODE_ENV=development
Technology Stack
Provides core GraphQL parsing, validation, and schema types that Grafast extends with plan resolvers
Primary database target for introspection and query optimization through dataplan-pg steps
Provides type safety across the entire monorepo with extensive use of generic types for step composition
Powers the Ruru GraphQL IDE interface through ruru-components package
Testing framework used across all packages with custom serializers for GraphQL schemas
Manages the monorepo structure and dependencies between the 31 packages
Coordinates versioning and publishing of multiple related packages with semantic versioning
Key Components
- OperationPlan (orchestrator) — Coordinates the entire GraphQL execution by analyzing operation dependencies, batching steps, and orchestrating data fetching across multiple layers
grafast/grafast/src/engine/OperationPlan.ts - PgSelectStep (processor) — Transforms GraphQL field requests into optimized PostgreSQL SELECT queries with automatic JOIN optimization and WHERE clause batching
grafast/dataplan-pg/src/steps/pgSelect.ts - SchemaBuilder (factory) — Constructs GraphQL schemas through a plugin system, providing hooks for extending types, fields, and resolvers during the building process
graphile-build/graphile-build/src/SchemaBuilder.ts - makeGrafastSchema (factory) — Creates GraphQL schemas optimized for Grafast execution by replacing traditional resolvers with plan resolvers that declare data dependencies
grafast/grafast/src/makeGrafastSchema.ts - grafastSync/grafast (executor) — Replaces GraphQL.js execute() function with optimized execution that batches operations, eliminates N+1 queries, and parallelizes data fetching
grafast/grafast/src/execute.ts - introspect (loader) — Extracts comprehensive PostgreSQL schema metadata by querying system catalogs for tables, columns, constraints, functions, and relationships
utils/pg-introspection/src/introspect.ts - PersistedPlugin (adapter) — Intercepts GraphQL requests to replace operation hashes with stored query documents, enabling persisted operations for security and performance
grafast/grafserv-persisted/src/index.ts - sql (encoder) — Provides tagged template literals for safe SQL construction with automatic parameter binding and SQL injection prevention
utils/pg-sql2/src/index.ts
Package Structure
Benchmarking tool for Grafast GraphQL schemas that measures execution performance across multiple operations and runs.
GraphQL Code Generator plugin that generates TypeScript types for Grafast step classes and plan resolvers.
Grafast data plan library providing steps for JSON parsing and manipulation operations.
Comprehensive PostgreSQL data plan library for Grafast providing steps for database operations, introspection, and query building.
High-performance GraphQL execution engine that replaces GraphQL.js execute() with declarative plan resolvers for optimized query execution.
GraphQL server framework built on Grafast providing HTTP handling, middleware support, and integration with various Node.js servers.
Grafserv plugin that enables persisted GraphQL operations by mapping operation hashes to stored query documents.
GraphQL IDE server that provides a web interface for querying GraphQL APIs with subscription support and CORS proxy capabilities.
React components library for building GraphQL IDE interfaces, providing the UI components used by Ruru.
TypeScript type definitions for Ruru GraphQL IDE interfaces and configuration options.
Automatically generates a high-performance GraphQL API from PostgreSQL database schema with extensive customization capabilities.
Plugin-based GraphQL schema builder that constructs schemas through composable hooks and event-driven architecture.
PostgreSQL-specific plugins for graphile-build that introspect database schemas and generate corresponding GraphQL types.
PostgreSQL database introspection library that extracts comprehensive schema metadata including tables, columns, constraints, and functions.
PostgreSQL SQL query builder with tagged template literals providing type-safe SQL construction and parameter binding.
Configuration management system providing preset merging, plugin resolution, and typed configuration for Graphile tools.
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Frequently Asked Questions
What is crystal used for?
Builds high-performance GraphQL APIs from PostgreSQL databases using declarative execution planning graphile/crystal is a 8-component fullstack written in TypeScript. Data flows through 6 distinct pipeline stages. The codebase contains 913 files.
How is crystal architected?
crystal is organized into 5 architecture layers: Execution Layer, Data Planning Layer, Schema Generation Layer, Application Layer, and 1 more. Data flows through 6 distinct pipeline stages. This layered structure keeps concerns separated and modules independent.
How does data flow through crystal?
Data moves through 6 stages: Database introspection → Schema generation → Operation parsing → Execution planning → Step optimization → .... The system starts with PostgreSQL database introspection to extract schema metadata, uses this to generate GraphQL schemas with plan resolvers, then executes GraphQL operations through declarative planning. When a query arrives, Grafast analyzes the operation to build an execution plan that batches database operations, eliminates redundant queries, and executes steps in optimal order. The plan resolvers declare what data they need rather than how to fetch it, allowing the execution engine to optimize across the entire operation. This pipeline design reflects a complex multi-stage processing system.
What technologies does crystal use?
The core stack includes GraphQL.js (Provides core GraphQL parsing, validation, and schema types that Grafast extends with plan resolvers), PostgreSQL (Primary database target for introspection and query optimization through dataplan-pg steps), TypeScript (Provides type safety across the entire monorepo with extensive use of generic types for step composition), React (Powers the Ruru GraphQL IDE interface through ruru-components package), Jest (Testing framework used across all packages with custom serializers for GraphQL schemas), Yarn Workspaces (Manages the monorepo structure and dependencies between the 31 packages), and 1 more. A focused set of dependencies that keeps the build manageable.
What system dynamics does crystal have?
crystal exhibits 3 data pools (Schema cache, Introspection cache), 3 feedback loops, 4 control points, 3 delays. The feedback loops handle cache-invalidation and convergence. 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 Resolver Pattern, Plugin Hook System, Step Batching, Dependency Graph Execution, Tagged Template SQL.
Analyzed on April 20, 2026 by CodeSea. Written by Karolina Sarna.