GraphQL Mastery
Master GraphQL — schema design, resolvers, queries/mutations/subscriptions, federation, security, and performance. 14 phases from basics to production APIs.
Your Progress
Complete all 14 phases to master this track
Master GraphQL — schema design, resolvers, queries/mutations/subscriptions, federation, security, and performance. 14 phases from basics to production APIs.
Phase 1: GraphQL Foundations and REST Comparison
Compare GraphQL's query-driven API model with REST and execute your first GraphQL operation.
GraphQL and REST API Models Compared
How GraphQL's single-endpoint query model differs from REST's fixed resource endpoints.
GraphQL Specification History
The origins of GraphQL at Facebook and the yearly spec releases that shaped the language.
Anatomy of a GraphQL Operation
Operation types, operation names, and the three top-level parts of a GraphQL document.
GraphQL over HTTP Transport
How GraphQL requests travel over HTTP with POST bodies, status codes, and content types.
Choosing GraphQL for a Project
Decision criteria for adopting GraphQL versus keeping a REST or RPC API.
Phase 2: Schema Foundations: Types and the Query Root
Define object types, scalars, and the Query root so you can publish a valid executable schema.
Object Types and Fields
Defining object types with typed fields that form the shape of your API graph.
The Query Root Type
How the Query type defines every readable entry point clients can call.
Built-in Scalar Types
The built-in scalars Int, Float, String, Boolean, and ID and how they serialize.
Enums, Lists, and Non-Null Modifiers
Enumeration types plus list and non-null wrappers for precise field contracts.
Schema Definition Language Documents
Writing schemas in SDL with type, extend, and schema blocks and multiple root types.
Phase 3: Mutations, Arguments, and Input Types
Build reliable write operations with typed arguments, input objects, and well-shaped payloads.
Mutation Operations and the Mutation Root
Declaring writable entry points on the Mutation type and executing write operations.
Field Arguments and Input Design
Designing consistent argument names, types, and defaults across mutation fields.
Input Object Types
Grouping mutation parameters into reusable input types with clear validation rules.
Mutation Return Payloads
Returning updated entities, client mutation IDs, and field errors from writes.
Serial Execution and Write Ordering
Why mutation root fields run in order and how that affects dependent writes.
Phase 4: Resolvers and Data Source Integration
Wire resolver functions to context, databases, and services while controlling errors and concurrency.
Resolver Functions Fundamentals
How resolvers map each schema field to the function that produces its value.
The Context Object
Passing request-scoped data like auth, data sources, and loaders through context.
Wiring Database and Service Data Sources
Connecting resolvers to databases, ORMs, and downstream HTTP services cleanly.
Asynchronous Resolvers and Concurrency
Awaiting promises correctly and controlling parallel work inside a request.
Error Propagation in Resolvers
How thrown errors become GraphQL errors and how nullability shapes their spread.
Phase 5: Nested Selections and Object Relationships
Model and query related objects at depth while guarding against cycles and runaway cost.
Nested Field Selection
Requesting related objects several levels deep within a single GraphQL query.
Parent Objects and Argument Flow
How parent values and arguments combine to fetch exactly the requested relationship.
Bidirectional Object Relationships
Modeling two-way references like posts to authors and authors back to posts.
Relay-style Connections and Pagination
Edges, nodes, cursors, and page info for paginating nested GraphQL lists.
Circular Queries and Cycle Guards
Preventing runaway recursive queries and resolver cycles in highly connected graphs.
Phase 6: Variables, Aliases, Fragments, and Directives
Write reusable, parameterized operations using variables, aliases, fragments, and directives.
Query Variables
Passing dynamic values into operations with typed variable declarations.
Field Aliases for Renaming
Requesting the same field twice with different arguments using aliases.
Named Fragments for Field Reuse
Extracting shared selection sets into fragments used across many queries.
Inline Fragments on Unions and Interfaces
Selecting type-specific fields with inline fragments during polymorphic queries.
Built-in Include and Skip Directives
Conditionally including fields at query time with @include and @skip.
Phase 7: Subscriptions and Realtime Delivery
Implement server-pushed subscription streams over WebSocket or SSE with resilient delivery.
Subscription Lifecycle and Event Flow
How a client starts a subscription and receives a stream of server-pushed events.
Realtime Feature Patterns
Choosing subscriptions versus polling for chat, dashboards, and live notifications.
GraphQL over WebSocket
The graphql-ws protocol handshake, message types, and connection lifecycle.
GraphQL over Server-Sent Events
Delivering subscriptions through HTTP server-sent events when WebSockets are blocked.
Subscription Resilience and Delivery Guarantees
Handling reconnects, missed events, and backpressure in production subscriptions.
Phase 8: Schema Design Patterns and Type System Depth
Apply naming, deprecation, polymorphism, and domain composition rules to design schemas that scale.
Schema Naming Conventions
Consistent names for types, fields, and arguments across a growing schema.
Deprecating Fields Safely
Using @deprecated annotations and usage data to remove fields without breakage.
Interfaces and Unions for Polymorphism
Sharing fields across related types with interfaces and grouping unrelated types with unions.
Custom Type System Directives
Defining and applying your own directives for auth, validation, and code generation.
Schema Composition and Domain Boundaries
Structuring a schema so teams own clear domains without duplicating shared types.
Phase 9: Solving the N+1 Problem with DataLoader
Diagnose N+1 query storms and fix them with request-scoped batching and caching strategies.
The N+1 Query Problem
Recognizing how nested GraphQL resolvers trigger one database query per parent row.
Configuring DataLoader
Creating loader instances with batch and cache functions that group concurrent requests.
Batching Versus Request Caching
What a loader's memoized cache does and does not fix across a request lifecycle.
Request-scoped Loader Instances
Creating loaders per request so cached data never leaks between concurrent users.
Manual Batch Loading Strategies
Grouping ids yourself with WHERE IN queries when a loader library is not available.
Phase 10: Authentication and Authorization for GraphQL APIs
Verify callers, enforce field and object permissions, and isolate multi-tenant data safely.
Resolving Authentication into Context
Verifying tokens at the transport layer and exposing the caller to every resolver.
Role-based Field Access Control
Allowing or denying schema fields based on caller roles checked in resolvers.
Authorization Directives in the Schema
Attaching auth or role directives to fields so access rules live with the schema.
Multi-tenant Data Scoping
Keeping each tenant's rows isolated when resolvers query a shared database.
Controlling Schema Exposure
Deciding whether to expose introspection and how to restrict it in production.
Phase 11: Error Handling, Nullability, and Schema Evolution
Produce structured errors, control null propagation, and evolve the schema without breaking clients.
The Standard GraphQL Error Shape
Understanding the errors array with message, locations, and path fields.
Structured Error Codes with Extensions
Attaching machine-readable codes and metadata through the extensions object.
Null Propagation and Bubble-up Rules
How non-null violations propagate nulls up the response tree in GraphQL.
Evolving a Schema Without Breakage
Additive change rules that keep existing clients working across releases.
Operating a Versionless GraphQL API
Why GraphQL avoids URL versions and how teams manage change over years.
Phase 12: Developer Tooling: Introspection, GraphiQL, and Codegen
Use introspection, IDEs, linters, and code generators to ship typed GraphQL clients faster.
Exploring APIs with GraphiQL
Using the in-browser IDE to write queries, browse docs, and inspect results.
Introspection Query Mechanics
The __schema and __type queries that power tooling and documentation sites.
Generating TypeScript Types from Schemas
Running GraphQL codegen to emit typed operations and eliminate untyped query strings.
Schema Linting and Style Rules
Enforcing naming, deprecation, and connection conventions with automated linters.
Schema Checks in Continuous Integration
Diffing proposed schema changes against production to catch breaking edits early.
Phase 13: Federation and Federated Architecture
Split a GraphQL API into subgraphs, define entities, and compose them behind a query-planning gateway.
Federation Architecture Overview
Splitting a GraphQL API into subgraphs composed behind a single gateway.
Entities, Keys, and Entity Resolution
Defining shared entities with @key so subgraphs can reference each other's types.
Composing Subgraphs into a Supergraph
Running composition to merge subgraph schemas and resolve ownership conflicts.
Query Planning at the Gateway
How the gateway plans multi-subgraph fetches and merges partial results.
Schema Stitching Versus Federation
Choosing between runtime stitching and build-time federation composition.
Phase 14: Performance, Caching, and Production Hardening
Constrain expensive queries, cache responses, and instrument the API for production operations.
Depth Limiting and Query Validation
Rejecting overly nested queries before they reach resolver execution.
Query Cost Analysis and Budgets
Assigning point costs to fields and rejecting operations that exceed a budget.
Persisted Queries and CDN Caching
Registering query hashes so the server only executes known documents.
Response and Field-level Caching
Applying cache hints, HTTP headers, and entity caches to GraphQL responses.
Observability for Production GraphQL APIs
Tracing, metrics, and field-level logging needed to operate a GraphQL service.