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Build the SDK around a small, language-neutral evaluation API and a written behavioral contract—not around matching method names in separate implementations. Keep active flag data local so evaluations can run in process, and update it asynchronously through streaming or polling. Then run the same conformance fixtures against every language binding to catch differences in defaults, types, context, errors, and results.
What belongs in the SDK, and what belongs in the provider?
Separate the API application developers call from the provider-specific machinery that retrieves and evaluates flag data. The public API should offer typed evaluation methods and stable evaluation details. Providers should handle backend protocols, vendor payloads, configuration synchronization, and any provider-specific evaluation engine.
OpenFeature describes its SDK as a vendor-neutral way to interface with an external evaluation engine; the SDK itself does not supply the flag evaluation logic. That separation gives application code a portable surface while allowing providers to connect to different backends.
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Which behaviors must the contract define?
Write down the behavior application developers can rely on before implementing the bindings. Treat the contract as normative: where a language SDK’s conventions conflict with it, specify which behavior takes precedence and why.
- Values and defaults: Define supported flag types, what happens on a type mismatch, how the caller’s default is used, and which evaluation details are returned.
- Context: Specify how global, call-specific, and implicitly propagated values combine, which targeting key is used, and whether later values override earlier ones.
- Errors: Define error categories and say whether each failure returns the caller’s default, evaluation details, or both. Keep this behavior stable across languages.
- Provider lifecycle: Document readiness, initialization, shutdown, and the behavior when no provider is configured. OpenFeature specifies a no-op provider that returns the supplied default in that case.
- Data compatibility: Specify the wire format and its version-compatibility rules, including how unsupported or malformed data is handled.
- Cache identity: Include the flag key and every evaluation-relevant context input in a result or intermediate-data cache key.
- Extensions and observability: Define hook ordering, events, and telemetry behavior where applicable so extensions do not vary silently by language.
How do you keep language bindings in parity?
Use one shared set of fixtures as executable examples of the contract. Each fixture should state the input context, flag data, expected value, default behavior, error category, and evaluation details. Run the same cases in every supported language, ideally in continuous integration, and treat a language-specific exception as an explicit contract decision rather than an accidental difference.
Include boundary cases, not just happy paths
Test missing keys, absent providers, type mismatches, malformed or unsupported data, empty and partial contexts, and failures during initialization or evaluation. Include values that are easy to interpret differently across runtimes. The GO Feature Flag provider specification specifically notes Python’s relationship between bool and int, large Java integers, and .NET numeric conversion behavior as parity risks.
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Keep fixtures independent of the test language where practical, and compare structured outputs rather than only the returned flag value. Otherwise, bindings can agree on the visible value while disagreeing on errors or evaluation details that applications also consume.
How can evaluation stay fast without a network request per call?
For server-side evaluation, keep the active configuration in a local snapshot or in-memory store and evaluate against it in process. In the documented LaunchDarkly architecture, the SDK obtains flag data during initialization, evaluates from an in-memory cache, and receives updates asynchronously. This avoids a remote round trip for each evaluation in that architecture; it is not a measured performance guarantee for every SDK or runtime.
Keep the update path separate from the evaluation path. A flag check should not wait for control-plane traffic to finish. Measure each language implementation under its intended workload, including the cost of context handling, hooks, cache access, and conversion to host-language types. The available documentation does not establish a universal latency or throughput figure, so use benchmarks from the actual runtime and deployment conditions rather than treating an architectural description as a benchmark.
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Should updates arrive by streaming or polling?
Streaming pushes changes over a persistent connection; polling asks for updates on a schedule. These modes have different runtime and operational trade-offs, but neither guarantees a particular update delay under all conditions.
| Consideration | Streaming | Polling |
|---|---|---|
| How updates arrive | The server pushes updates over a persistent connection. | The SDK requests updates on a schedule. |
| Change propagation | Can be prompt while connected; disconnect behavior depends on reconnection and cache policy. | Bounded by the polling interval, subject to request and service delays. |
| Runtime fit | Requires persistent connections and compatible networking. | Can suit environments where persistent connections are unsupported or undesirable. |
| Operational concerns | Reconnection, backoff, update ordering, and stream recovery. | Refresh interval, request load, jitter, and possible staleness. |
LaunchDarkly documents streaming as its default and polling as an option for cases where persistent connections do not fit. That is vendor-specific guidance, not a universal rule. Choose according to runtime constraints, how quickly changes need to propagate, connection costs, and the operational burden each mode creates.
What should happen when configuration cannot be refreshed?
Define failure behavior for startup, network partitions, expired credentials, malformed updates, reconnect storms, and process restarts. For each case, decide whether the SDK keeps using its last known configuration or falls back to caller defaults, and specify how applications can observe provider health.
State the freshness policy in terms the operator can act on: what the SDK knows about the age of its configuration, what happens when updates stop arriving, and how recovery works. The cited specifications and vendor documentation establish local caching and fallback concepts, but not a universal stale-data interval or reconnection algorithm. Set and test values appropriate to the service rather than implying one interval fits all environments.
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OpenFeature describes evaluation context as arbitrary data used to make dynamic evaluations. It can combine global static values, values supplied for an individual call, and runtime-specific implicit context, such as thread-local or asynchronous context. Specify a deterministic merge order across languages, including override behavior, and avoid depending on implicit propagation when it would make the same call behave differently across runtimes.
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When caching evaluation results or intermediate data, key the cache on the flag key and all context inputs that can affect the result. Omitting an input can return a value computed for a different evaluation context. Treat context as potentially sensitive: minimize what is sent to a remote provider and avoid logging attributes by default.
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How should provider state and hooks behave?
Define a predictable owner for API-wide state such as the active provider, global evaluation context, and hooks. OpenFeature recommends a global singleton for this API state and defines no-provider behavior through its no-op provider. If a language binding uses a different lifecycle model, document how applications get equivalent, consistent behavior.
Hooks can support validation, context modification, logging, telemetry, and tracking. Specify their order and failure behavior, and include their overhead in performance measurements. An extension point that runs in a different order—or adds unmeasured work—in one binding can undermine both parity and latency expectations.
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