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Azure Data API Builder: Turn Databases into REST and GraphQL APIs

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Azure’s Data API Builder (DAB) is the kind of developer tool that quietly changes the rules of engagement: it turns an existing database into a production-ready HTTP API — both REST and GraphQL — with no custom backend code required, letting you “bring your data to your code” without building or maintaining a service layer.

A glowing holographic data cube hovers above a neon platform labeled REST and GraphQL.Background​

Over the past decade Microsoft has shifted much of its internal operational tooling outward, converting infrastructure patterns used to run Microsoft services into consumable products. Data API Builder follows that model: it packages a lightweight, container-friendly runtime and a declarative configuration model so teams can expose database entities as HTTP endpoints without writing boilerplate CRUD APIs. The project is open source (MIT) and available on GitHub, but it’s also backed by official Microsoft documentation and a growing set of enterprise features.
This article summarizes the current technical state of DAB, verifies key capabilities against authoritative documentation, and evaluates strengths, limitations, and operational trade-offs for Windows and Azure-centric development teams. Wherever possible, claims and specifications are cross-checked against Microsoft’s official docs and the project repository to ensure accuracy; unverifiable or ambiguous statements are clearly flagged.

What Data API Builder is — a concise technical summary​

Data API Builder (DAB) is a stateless, configuration-driven service that:
  • Exposes REST endpoints (CRUD semantics with OData-like query operators) and GraphQL endpoints from existing database objects (tables, views, stored procedures).
  • Supports multiple backend data sources across relational and NoSQL systems, including SQL Server, Azure SQL Database, Azure Cosmos DB for NoSQL, PostgreSQL, MySQL, and Azure SQL Data Warehouse.
  • Ships as a .NET tool and a container image, and is designed to run in development locally, on Azure (Static Web Apps, Container Apps, AKS), or on-premises.
  • Is open source under the MIT license and maintained in the azure/data-api-builder GitHub repository.
DAB is intentionally narrow in scope: it removes the repetitive and error-prone work of writing generic data APIs and focuses on secure, observable, and performant exposure of data models to client code. That single-purpose focus is what makes it attractive for teams who want fast prototyping as well as production-grade endpoints without a heavy API stack.

Key features and verified capabilities​

REST and GraphQL parity​

DAB exposes both REST and GraphQL interfaces for configured entities. REST endpoints support the common verbs (GET, POST, PUT, PATCH, DELETE) and OData-like query keywords such as $select, $filter, and $orderBy. GraphQL endpoints support queries, mutations, relationship navigation, and — in more recent releases — multiple mutations within a single transaction. The project documentation and release notes explicitly call out GraphQL multiple-mutation support as a significant capability.

Supported data sources and per-source differences​

While DAB advertises support for a broad set of backends, the feature availability table shows real differences depending on the data source. For example:
  • DAB does not generate REST endpoints for Azure Cosmos DB for NoSQL because that database already provides a native REST API; instead, DAB can provide GraphQL for Cosmos DB.
  • Some GraphQL relationship and mutation features are restricted by backend: many-to-many and some mutation types are not supported on certain platforms. The feature table should be consulted when mapping complex relationships.
These per-backend distinctions are critical in planning: DAB’s capabilities vary by engine and you must confirm support for your intended operations before relying on parity across data stores.

Security and authentication​

DAB is designed to integrate with modern identity patterns:
  • Supports JWT token validation and OAuth2 flows, and can integrate with Azure Static Web Apps’ EasyAuth or Microsoft Entra (Azure AD) identity providers.
  • Claims from validated tokens can be automatically passed into SQL session context, enabling role-based authorization and fine-grained, item-level policies.
The combination of token validation, claims-to-database-context plumbing, and a policy engine makes DAB practical for multi-tenant or RBAC-driven APIs — provided teams design their SQL and policy rules carefully.

Observability and developer tooling​

DAB includes built-in telemetry hooks and developer tooling:
  • Native support for OpenTelemetry-style telemetry and Application Insights integration.
  • Built-in OpenAPI (Swagger) for REST endpoints and interactive GraphQL tooling (Nitro; formerly Banana Cake Pop) to test queries.
  • A CLI with dab init, dab add, and dab start flows to initialize configs, scaffold entities, and run the runtime locally for development.
This integration lowers the barrier to production readiness: endpoints can be inspected with Swagger and GraphQL playgrounds in dev, and telemetry can flow into existing observability pipelines in production.

Performance and caching​

DAB supports an in-memory Level 1 cache for REST endpoints and exposes cache-control headers for HTTP clients. Redis-backed Level 2 caching is mentioned in some contexts but feature availability shows limited support across engines (verify per-backend). For high-throughput scenarios, containerized deployments on AKS or Container Apps and careful connection pooling are necessary.

Deployment models and operational patterns​

DAB is flexible in where and how it runs:
  • Local development via CLI is fast and provides hot-reload for configuration changes.
  • In production, the typical pattern is to run DAB as a container (Azure Container Registry image) behind the application’s standard ingress, or as a sidecar/proxy in containerized environments like Azure Container Apps or AKS. It also integrates with Azure Static Web Apps for Jamstack scenarios where a static front end needs direct access to a database-driven API.
Operational best practices include:
  • Running DAB statelessly in containers behind an API gateway or managed ingress.
  • Securing secrets and connection strings using environment variables, Key Vault, or managed identity flows.
  • Enabling application and telemetry collection (OpenTelemetry / Application Insights) for end-to-end observability.
  • Using SQL session claims and DAB policy expressions to enforce row-level security where needed.

Developer experience: configuration-first, not code-first​

DAB’s central model is configuration: a JSON file describes data sources, entities, relationships, permissions, and the endpoint shapes. The CLI scaffolds this config and the runtime enforces it.
  • Pros: Faster time-to-market, fewer unit tests for boilerplate code, consistent endpoint behavior, built-in auth.
  • Cons: Complex transformations or business logic still require a bespoke service (stored procedures, triggers, or a small middleware layer). When you need complex validation, workflow orchestration, or heavy server-side business logic, DAB should be used alongside serverless functions or a microservice layer.
This configuration-first approach suits applications where the database is the canonical model and most operations are CRUD or relationship navigation. For applications with heavy domain logic, DAB can still be the data-access façade while the complex logic sits in stored procedures or separate services.

Where DAB shines — practical use cases​

  • Rapid prototyping and MVPs where developers want a fully functional API with minimal infrastructure.
  • Jamstack front ends (Static Web Apps) that need direct, secure access to backend data without a custom API layer.
  • Internal admin UIs and dashboards where the endpoint security and OpenAPI/GraphQL surfaces accelerate front-end development.
  • Data-driven automation or low-code integration scenarios where a standardized API over data is more valuable than bespoke endpoints.
These are scenarios where speed and consistency matter more than bespoke server logic. DAB reduces dev and test surface area and enforces consistent API shapes across teams.

Limitations and operational risks — verified caveats​

No tool is a silver bullet. The following constraints are drawn from product documentation and the GitHub project:
  • Not feature-equal across all backends. Some GraphQL mutations, relationship types, and REST features vary by the underlying database — consult the feature-availability table before designing cross-engine APIs.
  • Azure Cosmos DB for NoSQL does not get a generated REST API (because Cosmos already has one). If your app expects a homogeneous REST surface across stores, you’ll need architecture adjustments.
  • DAB is intentionally limited to data-access patterns. Complex business logic, file transformations, long-running workflows, or advanced data validation are still the domain of a dedicated service or serverless functions. Overloading DAB with non-data responsibilities can lead to brittle systems.
  • Operationally, ensure connection pooling and scaling behavior at the database level are sufficient. Because DAB converts client HTTP traffic into database activity, poorly-designed client queries or excessive nested GraphQL requests can produce heavy database loads. Mitigation requires rate limiting, pagination, parameterized queries, and possibly a caching tier.
Any claim about performance at scale is highly context-dependent. Benchmarks and scaling behavior should be validated against your workload; the documentation provides guidance but not substitute for environment-specific testing.

Security checklist: what to verify before production​

  • Use a managed identity or a secrets vault for connection strings; do not embed credentials in configs.
  • Configure JWT/OAuth provider and validate audiences and issuers in DAB.
  • Map token claims to SQL session context carefully, and test every policy expression for SQL injection and logic bypasses.
  • Enable telemetry and auditing: log requests, failed authorizations, and unusual query patterns.
  • Perform load testing with realistic GraphQL queries and REST $filter expressions to see how database indexes react.
This checklist reflects a conservative, defense-in-depth approach: DAB makes it easy to expose data, so governance and observability must be equally easy to apply.

Integration with Azure Fabric and the product roadmap​

DAB is positioned as a component of Azure’s broader data ecosystem and is referenced as a foundational element for certain Fabric scenarios. Its open-source evolution and the Microsoft Learn pages indicate active development, with GA (version 1.1) milestones announced and incremental feature releases documented in the “what’s new” pages. Teams should plan to track GitHub releases and Microsoft Learn changelogs for breaking changes or new capabilities.

Alternatives and comparative context​

DAB sits in a space alongside other patterns:
  • Hand-rolled APIs or frameworks (custom .NET/Node services) — best for heavy business logic but costlier to maintain.
  • Serverless functions (Azure Functions) — flexible and event-driven; better for small, specific endpoints but can become fragmented for many CRUD endpoints.
  • Other zero-code GraphQL/REST generators (commercial or open-source products) — some competitors provide richer data-trigger or schema transformation functionality.
The right choice depends on whether the team prioritizes developer velocity and consistency (DAB) or complex domain logic and bespoke orchestration (custom services). For many Azure-native teams, DAB provides a compelling middle ground: fast, secure endpoints coupled with Azure operational tools.

Practical starter checklist (1–2 day pilot plan)​

  • Pick a single, well-indexed data table or view that represents a simple CRUD workload.
  • Scaffold a DAB config with the CLI, expose both REST and GraphQL, and run locally.
  • Hook up Application Insights / OpenTelemetry for traces and metrics.
  • Add JWT-based auth via Azure Static Web Apps or a test JWT issuer and test claims pass-through.
  • Load-test representative client queries and examine DB metrics (CPU, blocking, connections).
  • If results look good, containerize and deploy behind your standard ingress with an API gateway for rate limiting.
This short pilot validates both the developer experience and operational assumptions before a broader rollout.

Example configuration and runtime notes​

A typical DAB configuration is a JSON file that includes:
  • Data source definitions (type, connection string)
  • Entities (name, source table/view, permissions)
  • Relationships (for GraphQL navigation)
  • Authentication settings and policy expressions
The runtime supports hot-reload for config changes in development and a production mode optimized for containerized runs. The GitHub README and Microsoft Learn guide include CLI commands (dab init, dab add, dab start) and best-practice snippets for environment variables and container deployment.

Final analysis: who should adopt Data API Builder, and when​

Data API Builder is highly recommendable for teams that:
  • Manage Azure-hosted or on-prem relational databases and want a fast path to expose data to front ends or integrations.
  • Need both REST and GraphQL surfaces without the maintenance burden of a custom API layer.
  • Prefer configuration-driven tooling and the operational guarantees of containerized deployment.
However, teams should avoid using DAB as a catch-all for business logic, long-running workflows, or unbounded client queries. It is a tool built to simplify data access — not to replace a service-oriented architecture when that architecture is necessary.
Where security, observability, and governance are non-negotiable, DAB already provides meaningful primitives (OpenTelemetry, JWT/OAuth integration, claims-to-session context), but each production deployment must be accompanied by the operational controls described in this article.

Conclusion​

Azure’s Data API Builder delivers a pragmatic, production-capable bridge between databases and modern application code. It removes a significant amount of repetitive plumbing for CRUD-driven applications, surfaces both REST and GraphQL for rapid front-end and integration work, and ships with sensible defaults for telemetry and security. For Azure-first teams building data-driven UIs, prototypes, or even many production workloads, DAB is worth piloting immediately.
At the same time, do not treat DAB as a replacement for careful API design. Verify backend-specific feature availability, validate performance at scale, and keep policy/audit controls central to any rollout. When used deliberately — as a fast, secure data façade rather than a dumping ground for business logic — Data API Builder can dramatically accelerate developer velocity while keeping operational complexity manageable.
Source: InfoWorld Bring your data to your code with Azure’s Data API Builder
 

Click to expand...
Azure’s Data API Builder promises to turn databases into production-ready HTTP APIs — both REST and GraphQL — with minimal code and near-immediate developer velocity, but the tool’s real value depends on matching expectations to backend capabilities, security controls, and operational discipline.

Azure data API visualization showing REST/JSON and GraphiQL API data flows.Background​

Azure has spent years productizing internal developer patterns into outward-facing services, and Data API Builder (DAB) is one of the clearest examples: a configuration-first runtime that creates REST and GraphQL endpoints directly from database objects. Early coverage framed DAB as a pragmatic bridge between data and modern client code — ideal for rapid prototypes, admin UIs, Jamstack front ends, and many production CRUD scenarios. This framing was captured in a recent industry analysis that summarized DAB’s goals and practical trade-offs.
Microsoft’s official documentation likewise defines DAB as a stateless, open-source runtime that exposes REST and GraphQL surfaces for a variety of Azure and on-prem data sources; Microsoft lists supported backends, security options, and developer tooling in a consolidated Learn article updated mid‑2025.

Overview: What Data API Builder is — and what it isn’t​

Data API Builder is intentionally narrow and pragmatic:
  • It’s a configuration-driven runtime that maps database tables, views, and stored procedures to HTTP endpoints.
  • It exposes REST endpoints (with OData-like query parameters such as $select, $filter, $orderBy) and GraphQL endpoints that support relationship traversal and mutations.
  • It’s open source (MIT) and distributed as both a .NET CLI tool and a container image, designed to run locally, in containers, or on Azure services such as Static Web Apps, Container Apps, or AKS.
Crucially, DAB is not a substitute for a full application server when substantial server-side business logic, long-running workflows, or complex orchestration is required. Treat it as a data façade: fast to stand up, but bounded in scope. This distinction is central to safe and effective adoption.

Key features at a glance​

  • REST and GraphQL surfaces out of the box (CRUD, filtering, pagination, relationships).
  • Multi‑engine support: SQL Server / Azure SQL, Azure Cosmos DB for NoSQL, PostgreSQL, MySQL, Azure SQL Data Warehouse.
  • Authentication and authorization hooks: JWT/OAuth2, Microsoft Entra (Azure AD) / Static Web Apps integration, claims-to-session mapping.
  • Telemetry and developer conveniences: OpenTelemetry / Application Insights, OpenAPI (Swagger) for REST, Nitro (formerly Banana Cake Pop) for GraphQL, local hot-reload during development.
  • Caching layers: in-memory Level 1 cache; Level 2 Redis-backed caching appears in recent releases as an opt-in capability.
  • Configuration-first CLI that scaffolds JSON configuration and runs a local dev server with interactive tooling.
These features combine to deliver rapid developer productivity, particularly for teams where the database is the canonical representation of the domain model.

How Data API Builder works (technical view)​

At runtime, DAB reads a JSON configuration file that describes:
  • Data sources and connection strings.
  • Entities (tables, views, stored procedures), and how they map to REST/GraphQL endpoints.
  • Relationships for GraphQL navigation.
  • Authorization policies and claim mappings.
The runtime then translates HTTP requests into parameterized SQL or NoSQL queries, applies policy checks, and returns JSON responses. It supports hot-reload in development, integrated Swagger/Nitro tooling, and health/telemetry endpoints suited to containerized deployments. This architectural simplicity (config → query translation → JSON) is what makes DAB both fast to adopt and predictable to operate.

Security, authentication, and authorization​

Security is a focal point in DAB’s design — but the responsibility is shared between the runtime and the operator:
  • Authentication: DAB validates tokens issued by configured identity providers. In Azure scenarios, it integrates with Azure Static Web Apps EasyAuth and Microsoft Entra (Azure AD) flows; any compliant JWT issuer can be used for non-Azure deployments.
  • Claims mapping: Claims from validated tokens can be injected into the SQL session context, enabling row-level filtering or role-bound database access via session variables.
  • Policy engine: DAB supports entity- and item-level policy expressions that are evaluated before queries run. These are powerful but require careful testing; malformed or overly-broad policy expressions can lead to logic bypasses or accidental data exposure.
  • Secrets: Best practice is to avoid embedding secrets in config files. Use managed identities, environment variables, Key Vault, or other secret stores and ensure connection strings are rotated and audited.
Takeaway: DAB provides the primitives for robust access control, but production security requires deliberate design, claim testing, and well-configured token lifecycles.

Observability, developer tooling, and the developer experience​

DAB ships with useful developer ergonomics designed to shorten the feedback loop:
  • OpenTelemetry and Application Insights integration lets teams feed traces and metrics into existing observability pipelines.
  • Swagger / OpenAPI is generated for REST endpoints, and Nitro provides a GraphQL playground for exploration and testing.
  • The CLI offers quick scaffolding (dab init, dab add, dab start) and hot-reload in dev mode so front-end teams can iterate quickly.
  • Health endpoints and structured logs are available for readiness/liveness checks and diagnostics in container orchestration platforms.
These features reduce friction between front-end and back-end teams and make DAB a good fit for rapid prototyping and iterative product development.

Performance, caching, and scaling considerations​

DAB translates client requests directly into DB activity. That design is efficient for many scenarios but imposes real constraints:
  • GraphQL and REST filters that cause large scans or deep nested relationship traversals can push heavy workloads onto the database.
  • DAB includes an in-memory Level 1 cache and, in recent versions, Level 2 Redis caching support. Use these caches for read-heavy surfaces, but test for cache consistency and invalidation patterns.
  • Connection pooling, proper indexing, and database scaling (compute & I/O) remain the primary levers for throughput. For high-throughput workloads, deploy DAB as containers behind a gateway or API management layer and scale the database appropriately.
  • Any claim about "performance at scale" depends on the workload: DAB’s documentation recommends load-testing representative queries and measuring DB metrics before committing to a production rollout.
Operationally, treat DAB as a stateless layer and rely on proven patterns (API gateway for rate limiting, sidecars for secrets, and robust telemetry) to prevent runaway queries or noisy neighbors.

Verified facts and release cadence​

To avoid relying on hearsay, key product claims were cross‑checked with Microsoft and the project repository:
  • Microsoft Learn’s DAB overview (updated June 23, 2025) describes supported engines, the runtime’s stateless nature, auth options, and bundled developer tools. This official documentation is the canonical anchor for product behavior.
  • The GitHub repository and release notes show active development and frequent releases. For example, the project’s releases page documented v1.6.67 (published in early September 2025), which added features such as Level 2 caching, Azure Key Vault CLI configuration, and Azure Log Analytics support. These release notes confirm that caching and operational telemetry improvements are shipping in stable builds.
When adopting DAB, track the GitHub releases and Microsoft Learn changelogs for breaking changes, feature availability by backend, and security advisories.

Limitations, backend differences, and gotchas​

Not all data engines are feature-equal. Important caveats:
  • Azure Cosmos DB for NoSQL: DAB does not generate a REST API for Cosmos DB the same way it does for relational stores — Cosmos already provides native REST interfaces; DAB focuses on GraphQL surfaces for some Cosmos scenarios. If you expect homogeneous REST parity across engines, plan for special-case architecture.
  • Feature variance by engine: Many-to-many relationships, certain GraphQL mutation types, and advanced REST features may be restricted depending on backend capabilities. Always consult the DAB feature matrix before designing cross-engine APIs.
  • Business logic scope: DAB is not designed for heavy server-side processing or long-running workflows. Complex validations, orchestrations, or transformations should remain in a dedicated service layer (serverless functions, microservices) or be implemented as stored procedures with controlled exposure.
  • Query safety: Because DAB maps HTTP parameters to DB queries, unbounded client queries can generate heavy load. Enforce pagination, rate limits, and limit nesting depth for GraphQL queries, or consider an API gateway with query throttling.
These are not theoretical limitations — they are operational realities that surfaced repeatedly in documentation and community threads. Treat them as design constraints during architecture planning.

Deployment patterns and a 1–2 day pilot checklist​

A recommended pilot validates both developer ergonomics and operational assumptions before wider rollout:
  • Pick a single, well-indexed table or view that represents a simple CRUD workload.
  • Scaffold a DAB config with the CLI, expose both REST and GraphQL, and run locally.
  • Hook up Application Insights/OpenTelemetry and confirm telemetry flows.
  • Enable JWT-based auth (Static Web Apps or a test JWT issuer) and validate claims-to-session mapping.
  • Load-test representative client queries (including GraphQL nesting) and observe DB metrics (CPU, blocking, connections).
  • If results are acceptable, containerize the DAB runtime, deploy behind an ingress or API gateway, and add rate limiting and caching as necessary.
This pilot approach is intentionally short: it surfaces the most common operational shocks — unindexed filters, connection saturation, or misapplied policy expressions — without investing in a full production deployment.

Alternatives and when to choose something else​

DAB sits among a set of architectural choices; use it when the benefits outweigh the trade-offs:
  • Choose DAB when you want rapid API surfaces for CRUD-driven apps, Jamstack UIs, internal admin tools, or low-code integrations.
  • Choose a hand-rolled API (custom .NET/Node service) when you need heavy domain logic, complex validation, or advanced orchestration.
  • Choose serverless functions (Azure Functions) when you need event-driven behavior, durable functions, or fine-grained compute that won’t fit a pure data façade.
For many Azure-native teams, DAB provides a practical middle ground — but teams must avoid treating it as a one-size-fits-all replacement for a well-architected service tier.

Practical examples (quick, real-world use cases)​

  • Rapid MVP backend for a customer dashboard: expose a subset of tables as REST/GraphQL and iterate on the front end while the data model stabilizes.
  • Jamstack site with Azure Static Web Apps: use DAB as the secure data connector between static UI and Azure SQL.
  • Internal tooling and admin panels: speed development for tooling that requires consistent CRUD endpoints and tight telemetry.
  • Low-code integrations: provide standardized APIs for dashboards, RPA, and ETL jobs without writing boilerplate endpoints.
Each case should be accompanied by explicit policy tests, load tests, and secrets management verification to ensure the convenience doesn’t become a vector for data leakage or outages.

Operational checklist before production​

  • Use managed identities or Key Vault for connection strings; never embed credentials in config files.
  • Validate and unit-test every policy expression and claim-to-session mapping.
  • Configure telemetry and alerting for high latencies, authorization failures, and unusual query patterns.
  • Enforce pagination and depth-limits on GraphQL queries; use rate limiting at the edge.
  • Run load tests using representative queries to confirm database scaling behavior and connection pooling.
  • Track the project’s release notes and GitHub issues for security and bug fixes; recent releases show active improvements in cache and observability.

What to watch for in the roadmap and community signals​

The DAB project is actively developed and maintained in the Azure GitHub org. Release notes reveal ongoing investments in caching, telemetry, and enterprise operational features — a positive sign for teams planning production use. Keep an eye on:
  • Feature parity across backends (watch the feature matrix).
  • Official guidance on Level 2 caching and eventual certified patterns for high-scale deployments.
  • Any security advisories or breaking changes documented in release notes.
Following the project on GitHub and subscribing to Microsoft Learn updates is the simplest way to avoid unpleasant surprises during upgrades.

Final assessment: who should adopt Data API Builder — and when​

Data API Builder is a well-considered, pragmatic tool for teams that:
  • Want to cut the boilerplate of CRUD API development and accelerate front-end integration.
  • Prefer configuration-first tooling that produces consistent REST and GraphQL surfaces.
  • Can constrain their domain logic to the database (or a small set of stored procedures) or keep complex logic in separate services.
Risks remain for teams that expect DAB to replace a full application layer: unbounded client queries, backend feature gaps, and policy misconfiguration are common operational pitfalls. For production adoption, pair DAB with an API gateway, rigorous telemetry, secrets management, and a short pilot that validates performance and security under realistic load. The combination of Microsoft’s documentation and active GitHub releases indicates a healthy project trajectory, but teams must still exercise typical engineering diligence before entrusting critical workloads to a convenience tool.

Conclusion​

Azure Data API Builder delivers a meaningful productivity multiplier for Azure-first teams: it creates REST and GraphQL endpoints from database constructs with minimal code, strong developer tooling, and sensible operational primitives. When used as a data façade and not a catch-all service layer, it can dramatically shorten the time from idea to usable API while integrating neatly with Azure’s identity and observability ecosystems.
Adopt with care: verify backend-specific features, validate performance under realistic loads, and bake in defense-in-depth security and governance from day one. For teams that balance convenience with operational rigor, DAB is worth piloting immediately; for teams needing heavy domain logic or complex orchestration, DAB can be part of the solution — but not the entire architecture.
Source: InfoWorld Using Microsoft’s Data API builder for Azure databases
 

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