apollographql/apollo-kotlin
Apollo Kotlin
Apollo Kotlin is a strongly-typed GraphQL client and code generation library designed for Kotlin and JVM applications. It functions as a comprehensive development tool that transforms GraphQL schema definitions and query documents into type-safe models during the build process, ensuring that data access errors are identified at compile time rather than at runtime.
The project distinguishes itself through its multiplatform runtime abstraction, which allows developers to share data fetching and caching logic across Android, iOS, and desktop environments. It provides a normalized local caching system that maintains a single source of truth for application data, enabling consistent state management and offline functionality. Furthermore, the library supports advanced networking patterns, including incremental response streaming and real-time subscriptions, to facilitate dynamic updates in reactive application architectures.
Beyond core client operations, the library offers a broad suite of capabilities for managing complex data lifecycles. This includes federated API orchestration to unify multiple backend services, declarative request management for handling network states, and extensive tooling for schema-driven development. It also provides built-in support for performance monitoring, security policy enforcement, and the integration of REST services into a unified GraphQL interface.
The project is distributed as a Kotlin multiplatform library, providing a unified execution environment that abstracts platform-specific networking and concurrency primitives.
Features
- GraphQL Clients - Provides a strongly-typed client for defining, executing, and managing GraphQL queries against remote endpoints.
- Multiplatform Libraries - Shares data fetching and caching logic across Android, iOS, and desktop platforms using a unified codebase.
- GraphQL Result Caches - Stores query results in local storage to minimize network requests and ensure data remains accessible offline.
- Operation Execution - Manages the complete network lifecycle including request building, response validation, error handling, and automatic synchronization with local data caches.
- Normalized Data Caching - Centralizes remote information in flat structures to ensure consistent data updates across the application.
- GraphQL Normalized Caches - Maintains a normalized local cache of GraphQL query results to ensure data consistency and enable offline functionality across platforms.
- GraphQL Clients - Provides a strongly-typed GraphQL client for Kotlin and JVM applications with automatic code generation.
- GraphQL Parsers - Converts raw query strings into structured objects using a high-performance parser to optimize memory usage.
- Compile-Time Code Generation - Transforms GraphQL schema definitions and query documents into type-safe models during the build process to ensure type safety.
- GraphQL Data Fetching - Requests specific data fields from a single source to minimize network latency and eliminate the performance overhead of fetching unnecessary information.
- Federation Engines - Composes multiple independent subgraphs into a single unified API for simplified data access.
- Type-Safe Model Mapping - Creates strongly-typed objects from data definitions to ensure that access errors are caught during compilation rather than at runtime.
- GraphQL Code Generation Frameworks - Transforms GraphQL schema definitions and queries into type-safe models during the build process.
- API Orchestrators - Aggregates multiple backend services into a single unified graph by planning and executing efficient data fetching across distributed endpoints.
- REST to GraphQL Translation - Maps external service endpoints to schema fields using declarative instructions to orchestrate HTTP requests and transform response data into compatible formats.
- GraphQL Query Performance Optimizations - Improves GraphQL query performance through document caching and execution optimization.
- HTTP Request Orchestrators - Manages the complete network lifecycle by mapping operations to HTTP requests and handling validation, error states, and response parsing automatically.
- Gateway Deployment - Hosts a dedicated runtime environment that acts as a central access point to aggregate and serve data.
- Payload Optimization - Reduces bandwidth usage by optimizing data payloads and combining multiple queries into single requests.
- Multiplatform Runtimes - Provides a unified execution environment that abstracts platform-specific networking and concurrency primitives to support consistent behavior across different operating systems.
- Database Query Security - Secures database interactions by requiring pre-registered query hashes instead of raw strings.
- Operational Restrictions - Limits execution to pre-approved operations to prevent malicious or resource-intensive queries.
- Declarative Data Fetching - Executes data queries while managing loading, error, and data states automatically to simplify how components request and display information.
- Schema Compatibility Lifecycle Checks - Coordinates the evolution of data structures through automated validation checks and integrated pipelines.
- Single-Source-of-Truth Modeling - Combines multiple backend services into a single secure source of truth to simplify data access and reduce technical debt.
- Data-to-UI Bindings - Connects data sources directly to user interface components to enable efficient state management and keep data requirements close to the view logic.
- Caching HTTP Clients - Provides built-in response caching for HTTP clients to reduce network calls and support offline access.
- External Service Integrations - Extends existing data entities with external service information to create a unified graph that combines multiple service types into one interface.
- Incremental Response Streaming - Processes partial query results as they arrive from the server to update application state dynamically without waiting for full payload delivery.
- Reactive Stream Integrations - Adapts asynchronous network responses and real-time subscription events into observable data streams for seamless integration with reactive application architectures.
- Real-Time Data Streaming - Delivers live updates to clients through aggregated subscriptions that combine data from multiple services.
- Single-Request Data Fetchers - Fetches complex data structures in one query to eliminate request chaining and reduce latency.
- Request Orchestration - Orchestrates data requests across multiple backend services to minimize latency and improve system performance.
- REST-to-Graph Integrators - Integrates existing services into a federated architecture to create a single cohesive data graph.
- Real-Time Data Pushing - Maintains persistent connections to receive live data pushes from the server as specific events occur.
