Overview

This page describes how to add Datadog Feature Flags to a Java application. Starting in version 1.65.0, dd-openfeature loads flag configuration directly from the Datadog-managed CDN by default. This agentless source simplifies onboarding for long-running servers and supports serverless runtimes that cannot connect to a Datadog Agent.

The Datadog provider implements the OpenFeature standard. It uses dd-java-agent for configuration delivery. Agentless delivery removes the external Datadog Agent requirement, but dd-java-agent must still load in the JVM.

Compatibility requirements

For the default agentless setup, you need:

• Java 11 or higher
• Datadog Java agent (dd-java-agent, loaded with -javaagent): Version 1.65.0 or later
• Datadog OpenFeature provider (com.datadoghq:dd-openfeature, added as a build dependency): Version 1.65.0 or later
• OpenFeature SDK: Version 1.20.1 or later
• A Datadog API key
• Your Datadog site

Use the same version of dd-java-agent and dd-openfeature. Agentless delivery does not require a separate Datadog Agent service.

For a full list of Datadog’s Java version and framework support, read Compatibility Requirements.

Getting started

Install the OpenFeature dependencies and add the Java agent to the JVM.

Installation

You need the Datadog OpenFeature provider and the OpenFeature SDK dependencies.

dependencies {
    // OpenFeature SDK for flag evaluation
    implementation 'dev.openfeature:sdk:1.20.1'

    // Datadog OpenFeature Provider
    implementation 'com.datadoghq:dd-openfeature:1.65.0'
}

dependencies {
    // OpenFeature SDK for flag evaluation
    implementation("dev.openfeature:sdk:1.20.1")

    // Datadog OpenFeature Provider
    implementation("com.datadoghq:dd-openfeature:1.65.0")
}

<dependencies>
    <!-- OpenFeature SDK for flag evaluation -->
    <dependency>
        <groupId>dev.openfeature</groupId>
        <artifactId>sdk</artifactId>
        <version>1.20.1</version>
    </dependency>

    <!-- Datadog OpenFeature Provider -->
    <dependency>
        <groupId>com.datadoghq</groupId>
        <artifactId>dd-openfeature</artifactId>
        <version>1.65.0</version>
    </dependency>
</dependencies>

The Gradle and Maven installation examples pin specific versions of dd-openfeature and the OpenFeature SDK. See Compatibility requirements for the minimum supported versions.

Flag evaluation metrics use a separate telemetry path from flag configuration. See Set Up Server-Side Flag Evaluation Metrics.

Add the Java agent to the JVM

Load dd-java-agent with the -javaagent JVM option. For installation instructions, see Add the Java SDK to the JVM.

If the runtime controls the Java command, set the option through JAVA_TOOL_OPTIONS. See the Java setup for Cloud Run Functions or Cloud Run containers for examples.

Configuration

Configure agentless delivery

Configure the API key, Datadog site, and environment in the application process:

export DD_API_KEY=<YOUR_API_KEY>
export DD_SITE=<YOUR_DATADOG_SITE>
export DD_ENV=<YOUR_ENVIRONMENT>
export DD_SERVICE=<YOUR_SERVICE_NAME>
export DD_VERSION=<YOUR_APP_VERSION>

java -javaagent:/path/to/dd-java-agent.jar -jar your-application.jar

No Feature Flags enablement or source setting is required. Initialize the Datadog OpenFeature provider to begin polling.

Initialize the OpenFeature provider

Initialize the Datadog OpenFeature provider in your application startup code. The provider starts the selected configuration source.

import dev.openfeature.sdk.OpenFeatureAPI;
import dev.openfeature.sdk.Client;
import datadog.trace.api.openfeature.Provider;
import dev.openfeature.sdk.exceptions.ProviderNotReadyError;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class App {
    private static final Logger logger = LoggerFactory.getLogger(App.class);
    private static Client client;

    public static void main(String[] args) throws Exception {
        // Initialize the Datadog provider
        logger.info("Initializing Datadog OpenFeature Provider...");
        OpenFeatureAPI api = OpenFeatureAPI.getInstance();

        try {
            // Set provider and wait for initial configuration (recommended)
            api.setProviderAndWait(new Provider());
            client = api.getClient("my-app");
            logger.info("OpenFeature provider initialized successfully");
        } catch (ProviderNotReadyError e) {
            // Handle gracefully - app will use default flag values
            logger.warn("Provider not ready (configuration unavailable), continuing with defaults", e);
            client = api.getClient("my-app");
            logger.info("App will use default flag values until provider is ready");
        } catch (Exception e) {
            logger.error("Failed to initialize OpenFeature provider", e);
            throw e;
        }

        // Your application code here
    }
}

Use setProviderAndWait() to block evaluation until the selected source provides the initial flag configuration. This loads flags before the application starts serving traffic. The default initialization timeout is 30 seconds.

ProviderNotReadyError is an OpenFeature SDK exception thrown when the provider times out during initialization. Catching it allows the application to start with default flag values if configuration delivery is unavailable. If not caught, the exception propagates and may prevent application startup. Handle this based on your availability requirements.

Asynchronous initialization

For non-blocking initialization, use setProvider() and listen for provider events:

import dev.openfeature.sdk.ProviderEvent;

OpenFeatureAPI api = OpenFeatureAPI.getInstance();
Client client = api.getClient();

// Listen for provider state changes
client.on(ProviderEvent.PROVIDER_READY, (event) -> {
    logger.info("Feature flags ready!");
});

client.on(ProviderEvent.PROVIDER_ERROR, (event) -> {
    logger.error("Provider error: {}", event.getMessage());
});

client.on(ProviderEvent.PROVIDER_STALE, (event) -> {
    logger.warn("Provider configuration is stale");
});

// Set provider asynchronously
api.setProvider(new Provider());

Set the evaluation context

The evaluation context defines the subject (user, device, session) for flag evaluation. It determines which flag variations are returned based on targeting rules.

import dev.openfeature.sdk.EvaluationContext;
import dev.openfeature.sdk.MutableContext;

// Create an evaluation context with a targeting key and attributes
EvaluationContext context = new MutableContext("user-123")
    .add("email", "user@example.com")
    .add("tier", "premium");

// Use the context for flag evaluations (see next section)

The targetingKey (for example, user-123) is the primary identifier used for consistent flag evaluations and percentage-based rollouts. It’s typically a user ID, session ID, or device ID.

Evaluate flags

Evaluate feature flags using the OpenFeature client. All flag types are supported: Boolean, string, integer, double, and object.

// Simple Boolean evaluation
boolean enabled = client.getBooleanValue("checkout.new", false, context);

if (enabled) {
    // New checkout flow
} else {
    // Old checkout flow
}

// Get detailed evaluation result
import dev.openfeature.sdk.FlagEvaluationDetails;

FlagEvaluationDetails<Boolean> details =
    client.getBooleanDetails("checkout.new", false, context);

logger.info("Value: {}", details.getValue());
logger.info("Variant: {}", details.getVariant());
logger.info("Reason: {}", details.getReason());

// Evaluate string flags (e.g., UI themes, API endpoints)
String theme = client.getStringValue("ui.theme", "light", context);

String apiEndpoint = client.getStringValue(
    "payment.api.endpoint",
    "https://api.example.com/v1",
    context
);

// Integer flags (e.g., limits, quotas)
int maxRetries = client.getIntegerValue("retries.max", 3, context);

// Double flags (e.g., thresholds, rates)
double discountRate = client.getDoubleValue("pricing.discount.rate", 0.0, context);

import dev.openfeature.sdk.Value;

// Evaluate object/JSON flags for complex configuration
Value config = client.getObjectValue("ui.config", new Value(), context);

// Access structured data
if (config.isStructure()) {
    Value timeout = config.asStructure().getValue("timeout");
    Value endpoint = config.asStructure().getValue("endpoint");
}

Error handling

The OpenFeature SDK uses a default value pattern. If evaluation fails for any reason, the default value you provide is returned.

import dev.openfeature.sdk.ErrorCode;

// Check evaluation details for errors
FlagEvaluationDetails<Boolean> details =
    client.getBooleanDetails("checkout.new", false, context);

if (details.getErrorCode() != null) {
    switch (details.getErrorCode()) {
        case FLAG_NOT_FOUND:
            logger.warn("Flag does not exist: {}", "checkout.new");
            break;
        case PROVIDER_NOT_READY:
            logger.warn("Provider not initialized yet");
            break;
        case TARGETING_KEY_MISSING:
            logger.warn("Evaluation context missing targeting key");
            break;
        case TYPE_MISMATCH:
            logger.error("Flag value type doesn't match requested type");
            break;
        default:
            logger.error("Evaluation error for flag: {}", "checkout.new", details.getErrorCode());
    }
}

Common error codes

Advanced configuration

Use Server SDK Configuration Sources as the canonical reference for source selection and operational settings:

• Configure agentless delivery, including polling, request timeout, and endpoint settings
• Use a custom agentless endpoint for advanced testing, local development, or an operator-managed proxy
• Use Agent Remote Configuration to retain Agent-managed delivery
• Migrate an existing Remote Configuration setup and remove the deprecated DD_EXPERIMENTAL_FLAGGING_PROVIDER_ENABLED setting

Agentless mode changes only flag configuration. It does not configure or enable feature_flag.evaluations, exposure logging, or experimentation use cases. These features require a supported Datadog Agent or serverless telemetry path. For more information on available graphing, see Feature Flag Graphs.

Custom initialization timeout

Configure how long the provider waits for initial configuration:

import datadog.trace.api.openfeature.Provider;
import java.util.concurrent.TimeUnit;

Provider.Options options = new Provider.Options()
    .initTimeout(10, TimeUnit.SECONDS);

api.setProviderAndWait(new Provider(options));

Configuration change events

Listen for updates from the selected configuration source:

import dev.openfeature.sdk.ProviderEvent;

client.on(ProviderEvent.PROVIDER_CONFIGURATION_CHANGED, (event) -> {
    logger.info("Flag configuration updated: {}", event.getMessage());
    // Optionally re-evaluate flags or trigger cache refresh
});

PROVIDER_CONFIGURATION_CHANGED is an optional OpenFeature event. Check the Datadog provider documentation to verify this event is supported in your version.

Multiple clients

Use named clients to organize context and flags by domain or team:

// Named clients share the same provider instance but can have different contexts
Client checkoutClient = api.getClient("checkout");
Client analyticsClient = api.getClient("analytics");

// Each client can have its own evaluation context
EvaluationContext checkoutContext = new MutableContext("session-abc");
EvaluationContext analyticsContext = new MutableContext("user-123");

boolean newCheckout = checkoutClient.getBooleanValue(
    "checkout.ui.new", false, checkoutContext
);

boolean enhancedAnalytics = analyticsClient.getBooleanValue(
    "analytics.enhanced", false, analyticsContext
);

The Provider instance is shared globally. Client names are for organizational purposes only and don’t create separate provider instances. All clients use the same underlying Datadog provider and flag configurations.

Best practices

Initialize early

Initialize the OpenFeature provider as early as possible in your application lifecycle (for example, in main() or application startup). This helps ensure flags are ready before business logic executes.

Use meaningful default values

Always provide sensible default values that maintain safe behavior if flag evaluation fails:

// Good: Safe default that maintains current behavior
boolean useNewAlgorithm = client.getBooleanValue("algorithm.new", false, context);

// Good: Conservative default for limits
int rateLimit = client.getIntegerValue("rate.limit", 100, context);

Create context once

Create the evaluation context once per request/user/session and reuse it for all flag evaluations:

// In a web filter or request handler
EvaluationContext userContext = new MutableContext(userId)
    .add("email", user.getEmail())
    .add("tier", user.getTier());

// Reuse context for all flags in this request
boolean featureA = client.getBooleanValue("feature.a", false, userContext);
boolean featureB = client.getBooleanValue("feature.b", false, userContext);

Rebuilding the evaluation context for every flag evaluation adds unnecessary overhead. Create the context once at the start of the request lifecycle, then pass it to all subsequent flag evaluations.

Handle initialization failures (optional)

Consider handling initialization failures if your application can function with default flag values:

try {
    api.setProviderAndWait(new Provider());
} catch (ProviderNotReadyError e) {
    // Log error and continue with defaults
    logger.warn("Feature flags not ready, using defaults", e);
    // Application will use default values for all flags
}

If feature flags are critical for your application to function, let the exception propagate to prevent startup.

Use consistent targeting keys

Use consistent, stable identifiers as targeting keys:

• Good: User IDs, session IDs, device IDs
• Avoid: Timestamps, random values, frequently changing IDs

Monitor flag evaluation

Use the detailed evaluation results for logging and debugging:

FlagEvaluationDetails<Boolean> details =
    client.getBooleanDetails("feature.critical", false, context);

logger.info("Flag: {} | Value: {} | Variant: {} | Reason: {}",
    "feature.critical",
    details.getValue(),
    details.getVariant(),
    details.getReason()
);

Testing

You can test against a dedicated Datadog test environment with the real DatadogProvider, or swap it for OpenFeature’s InMemoryProvider to control flag values directly in test code. This section shows the in-memory approach, which keeps tests hermetic and offline. InMemoryProvider ships in dev.openfeature:sdk (already a test-scope dependency), so no additional library is required. Add dev.openfeature:sdk to your test configuration if it is not already present.

import dev.openfeature.sdk.Client;
import dev.openfeature.sdk.OpenFeatureAPI;
import dev.openfeature.sdk.providers.memory.Flag;
import dev.openfeature.sdk.providers.memory.InMemoryProvider;
import java.util.Map;
import org.junit.jupiter.api.*;
import static org.junit.jupiter.api.Assertions.*;

class CheckoutFlagTest {
    private Client client;

    @BeforeEach
    void setUp() {
        Map<String, Flag<?>> flags = Map.of(
            "new-checkout-flow", Flag.<Boolean>builder()
                .variant("on", true)
                .variant("off", false)
                .defaultVariant("on")
                .build(),
            "ui-theme", Flag.<String>builder()
                .variant("dark", "dark")
                .variant("light", "light")
                .defaultVariant("light")
                .build()
        );

        OpenFeatureAPI api = OpenFeatureAPI.getInstance();
        api.setProviderAndWait(new InMemoryProvider(flags));
        client = api.getClient();
    }

    @AfterEach
    void tearDown() {
        OpenFeatureAPI.getInstance().shutdown();
    }

    @Test
    void newCheckoutEnabledByDefault() {
        assertTrue(client.getBooleanValue("new-checkout-flow", false));
    }

    @Test
    void missingFlagReturnsDefault() {
        assertFalse(client.getBooleanValue("does-not-exist", false));
    }
}

OpenFeatureAPI.getInstance() is a singleton. Always call shutdown() in @AfterEach (or equivalent); otherwise, provider state leaks between test classes and causes flaky suites.

In Spring Boot tests, register the InMemoryProvider through a @TestConfiguration bean or in a @BeforeAll hook on an @SpringBootTest class — the OpenFeature API singleton persists for the lifetime of the Spring context, so initialization only needs to run once.

Troubleshooting

Follow the flag data path from the Flagging Platform through the selected configuration source to the Java SDK. Agentless and Remote Configuration have different requirements. Verify the active source before you troubleshoot connectivity.

1. Flagging platform: Verify flag configuration

Before checking infrastructure, confirm the flag itself is set up correctly:

1. The flag is enabled for the target environment, not disabled. Flags are disabled by default in each environment.
2. The flag targets the correct environment (DD_ENV). Flags do not target specific services—they apply to all services within the enabled environment.
3. Your DD_ENV value appears in Feature Flag Environments. If it is absent, the environment has not received any flag traffic yet.

2. Verify the configuration source

Agentless

1. Confirm that dd-openfeature and dd-java-agent are version 1.65.0 or later. Use the same version for both components.
2. Confirm that the JVM loads dd-java-agent with -javaagent, either in the Java command or through JAVA_TOOL_OPTIONS.
3. Confirm that DD_FEATURE_FLAGS_ENABLED is unset or set to true.
4. Confirm that DD_FEATURE_FLAGS_CONFIGURATION_SOURCE=agentless is set, or that the source and legacy provider settings are not set.
5. Confirm that application code initializes the Datadog OpenFeature provider.
6. Confirm that DD_API_KEY, DD_SITE, and DD_ENV are configured in the application process.
7. Confirm that the application can make outbound HTTPS requests to Datadog.
8. Enable DD_TRACE_DEBUG=true and check for authentication, timeout, or malformed-payload messages from the Feature Flags agentless endpoint.

Agent Remote Configuration

1. Confirm that dd-openfeature and dd-java-agent are version 1.65.0 or later. Use the same version for both components.
2. Confirm that DD_FEATURE_FLAGS_CONFIGURATION_SOURCE=remote_config is set. During the migration window, DD_EXPERIMENTAL_FLAGGING_PROVIDER_ENABLED=true also selects Remote Configuration when no source is set.
3. Confirm that DD_FEATURE_FLAGS_ENABLED is unset or set to true.
4. Confirm that Agent 7.55 or later is running and reachable. See APM Connection Errors.
5. Confirm that Remote Configuration is enabled on the Agent. If it is disabled, set remote_configuration.enabled: true in datadog.yaml or DD_REMOTE_CONFIGURATION_ENABLED=true. See Remote Configuration.
6. Confirm that DD_API_KEY is valid on the Agent and belongs to the target organization.
7. Confirm that DD_SITE is set correctly on the Agent. See Agent Site Issues.
8. Run datadog-agent status and review the Remote Configuration section. See Agent Commands.

3. SDK: Verify Java SDK state

Enable debug logging

Set DD_TRACE_DEBUG=true to enable Feature Flags startup messages. For the default agentless source, confirm that CDN polling starts after provider initialization.

With remote_config, the provider uses the bridge in the Java agent. An older agent produces a provider initialization error that states the required agent version. It does not fall back to CDN delivery.

Monitor provider state changes

Add event listeners early in application startup to observe provider life cycle transitions. Event listeners detect connectivity changes after initialization:

import dev.openfeature.sdk.ProviderEvent;

client.on(ProviderEvent.PROVIDER_READY, (event) -> {
    logger.info("Feature flag provider is ready");
});

client.on(ProviderEvent.PROVIDER_ERROR, (event) -> {
    logger.error("Feature flag provider error: {}", event.getMessage());
});

client.on(ProviderEvent.PROVIDER_STALE, (event) -> {
    logger.warn("Feature flag provider configuration is stale");
});

client.on(ProviderEvent.PROVIDER_CONFIGURATION_CHANGED, (event) -> {
    logger.info("Feature flag configuration updated");
});

A PROVIDER_ERROR or PROVIDER_STALE event after normal operation indicates a disruption in the selected configuration source.

Provider not ready

PROVIDER_NOT_READY is returned when flag evaluation is attempted before the provider receives its first configuration from the selected source.

Common causes:

• Asynchronous initialization: setProvider() was used instead of setProviderAndWait(). Evaluations before the first configuration arrives return PROVIDER_NOT_READY.
• Initialization timeout: setProviderAndWait() timed out (default 30 seconds) and threw ProviderNotReadyError, which was caught. The application continues evaluating flags while waiting for the first configuration.

If PROVIDER_NOT_READY persists beyond the polling and initialization intervals, verify the selected source again.

Debug flag evaluations

If flags return unexpected values, use getBooleanDetails() instead of getBooleanValue(). The Details variant returns a FlagEvaluationDetails object exposing the provider’s internal state:

FlagEvaluationDetails<Boolean> details =
    client.getBooleanDetails("your.flag.key", false, context);

logger.info("Flag evaluation details: value={}, variant={}, reason={}, errorCode={}",
    details.getValue(),
    details.getVariant(),
    details.getReason(),
    details.getErrorCode());

Review reason and errorCode to understand why the provider returned a given result.

Type mismatch errors

TYPE_MISMATCH is returned when the evaluation method does not match the flag’s configured type. Use the correct method for each flag type: getBooleanValue(), getStringValue(), getIntegerValue(), getDoubleValue().

4. Flagging platform: Verify data appears in Datadog

Flag evaluation metrics

Flag evaluation counts appear in Datadog as a feature_flag.evaluations counter metric tagged with the flag key, result variant, and evaluation reason. See Set Up Server-Side Flag Evaluation Metrics for the full setup guide and troubleshooting steps.

Experiment exposures

When the selected configuration path supports exposures, exposures appear only for flags associated with an experiment. Standard feature flags do not generate exposure events. If exposures are missing:

1. Verify the flag is associated with an experiment in the Datadog UI.
2. Verify the Agent API key and connectivity.

Further reading

お役に立つドキュメント、リンクや記事:

Server-Side Feature FlagsDOCUMENTATION Java APM and Distributed TracingDOCUMENTATION Set Up Server-Side Flag Evaluation MetricsGUIDE Feature Flag GraphsCONCEPT Server SDK Configuration SourcesCONCEPT