Overview

Send logs to Datadog from your Android or iOS applications with Datadog’s dd-sdk-kotlin-multiplatform-logs client-side logging library and use the following features:

• Log to Datadog in JSON format natively.
• Add context and extra custom attributes to each log sent.
• Forward Java or Kotlin caught exceptions.
• Forward iOS errors.
• Record real client IP addresses and User-Agents.
• Optimize network usage with automatic bulk posts.

Setup

1. Add the Gradle dependency to the common source set by declaring the library as a dependency in the module-level build.gradle.kts file. Make sure to replace x.x.x in the following example with the latest version of dd-sdk-kotlin-multiplatform-logs.

kotlin {
  sourceSets {
    commonMain.dependencies {
      implementation("com.datadoghq:dd-sdk-kotlin-multiplatform-logs:x.x.x")
    }
  }
}

2. Add native dependencies for iOS.

   Note: Kotlin 2.0.20 or higher is required if crash tracking is enabled on iOS. Otherwise, due to the compatibility with PLCrashReporter, the application may hang if crash tracking is enabled .

   Add the following Datadog iOS SDK dependencies, which are needed for the linking step:

   • DatadogObjc
   • DatadogCrashReporting

   Note: Versions of these dependencies should be aligned with the version used by the Datadog Kotlin Multiplatform SDK itself.

   Datadog Kotlin Multiplatform SDK version	Datadog iOS SDK version
   0.0.1	2.14.1
   0.0.2	2.17.0
   0.0.3	2.17.0

   Adding native iOS dependencies using the CocoaPods plugin

   If you are using Kotlin Multiplatform library as a CocoaPods dependency for your iOS application, you can add dependencies as following:

   cocoapods {
       // ...
   
       framework {
           baseName = "sharedLib"
       }
   
       pod("DatadogObjc") {
           linkOnly = true
           version = 2.17.0
       }
   
       pod("DatadogCrashReporting") {
           linkOnly = true
           version = 2.17.0
       }
   }
   

   Adding native iOS dependencies using Xcode

   If you are integrating Kotlin Multiplatform library as a framework with an embedAndSignAppleFrameworkForXcode Gradle task as a part of your Xcode build, you can add the necessary dependencies directly in Xcode as following:

   1. Click on your project in Xcode and go to the Package Dependencies tab.
   2. Add the iOS SDK package dependency by adding https://github.com/DataDog/dd-sdk-ios.git as a package URL.
   3. Select the version from the table above.
   4. Click on the necessary application target and open the General tab.
   5. Scroll down to the Frameworks, Libraries, and Embedded Content section and add the dependencies mentioned above.

3. Initialize the Datadog SDK with your application context (only for Android; can be null for iOS), tracking consent, and the Datadog client token. For security reasons, you must use a client token; you cannot use Datadog API keys to configure the Datadog SDK, as they would be exposed client-side in the Android application APK byte code.

   For more information about setting up a client token, see the client token documentation.

    // in common source set
    fun initializeDatadog(context: Any? = null) {
        // context should be application context on Android and can be null on iOS
        val appClientToken = <CLIENT_TOKEN>
        val appEnvironment = <ENV_NAME>
        val appVariantName = <APP_VARIANT_NAME>
   
        val configuration = Configuration.Builder(
                clientToken = appClientToken,
                env = appEnvironment,
                variant = appVariantName
        )
            .build()
   
        Datadog.initialize(context, configuration, trackingConsent)
    }
    ```
   

    // in common source set
    fun initializeDatadog(context: Any? = null) {
        // context should be application context on Android and can be null on iOS
        val appClientToken = <CLIENT_TOKEN>
        val appEnvironment = <ENV_NAME>
        val appVariantName = <APP_VARIANT_NAME>
   
        val configuration = Configuration.Builder(
                clientToken = appClientToken,
                env = appEnvironment,
                variant = appVariantName
        )
            .useSite(DatadogSite.EU1)
            .trackCrashes(true)
            .build()
   
        Datadog.initialize(context, configuration, trackingConsent)
    }
   

    // in common source set
    fun initializeDatadog(context: Any? = null) {
        // context should be application context on Android and can be null on iOS
        val appClientToken = <CLIENT_TOKEN>
        val appEnvironment = <ENV_NAME>
        val appVariantName = <APP_VARIANT_NAME>
   
        val configuration = Configuration.Builder(
                clientToken = appClientToken,
                env = appEnvironment,
                variant = appVariantName
        )
            .useSite(DatadogSite.US3)
            .trackCrashes(true)
            .build()
   
        Datadog.initialize(context, configuration, trackingConsent)
    }
   

    // in common source set
    fun initializeDatadog(context: Any? = null) {
        // context should be application context on Android and can be null on iOS
        val appClientToken = <CLIENT_TOKEN>
        val appEnvironment = <ENV_NAME>
        val appVariantName = <APP_VARIANT_NAME>
   
        val configuration = Configuration.Builder(
                clientToken = appClientToken,
                env = appEnvironment,
                variant = appVariantName
        )
            .useSite(DatadogSite.US5)
            .trackCrashes(true)
            .build()
   
        Datadog.initialize(context, configuration, trackingConsent)
    }
   

    // in common source set
    fun initializeDatadog(context: Any? = null) {
        // context should be application context on Android and can be null on iOS
        val appClientToken = <CLIENT_TOKEN>
        val appEnvironment = <ENV_NAME>
        val appVariantName = <APP_VARIANT_NAME>
   
        val configuration = Configuration.Builder(
                clientToken = appClientToken,
                env = appEnvironment,
                variant = appVariantName
        )
            .useSite(DatadogSite.US1_FED)
            .trackCrashes(true)
            .build()
   
        Datadog.initialize(context, configuration, trackingConsent)
    }
   

    // in common source set
    fun initializeDatadog(context: Any? = null) {
        // context should be application context on Android and can be null on iOS
        val appClientToken = <CLIENT_TOKEN>
        val appEnvironment = <ENV_NAME>
        val appVariantName = <APP_VARIANT_NAME>
   
        val configuration = Configuration.Builder(
                clientToken = appClientToken,
                env = appEnvironment,
                variant = appVariantName
        )
            .useSite(DatadogSite.AP1)
            .trackCrashes(true)
            .build()
   
        Datadog.initialize(context, configuration, trackingConsent)
    }
   

   To be compliant with GDPR, the SDK requires the tracking consent value at initialization. Tracking consent can be one of the following values:

   • TrackingConsent.PENDING: (Default) The SDK starts collecting and batching the data but does not send it to the collection endpoint. The SDK waits for the new tracking consent value to decide what to do with the batched data.
   • TrackingConsent.GRANTED: The SDK starts collecting the data and sends it to the data collection endpoint.
   • TrackingConsent.NOT_GRANTED: The SDK does not collect any data. You are not able to manually send any logs, traces, or RUM events.

   To update the tracking consent after the SDK is initialized, call Datadog.setTrackingConsent(<NEW CONSENT>). The SDK changes its behavior according to the new consent. For example, if the current tracking consent is TrackingConsent.PENDING and you update it to:

   • TrackingConsent.GRANTED: The SDK sends all current batched data and future data directly to the data collection endpoint.
   • TrackingConsent.NOT_GRANTED: The SDK wipes all batched data and does not collect any future data.

   Use the utility method isInitialized to check if the SDK is properly initialized:

   if (Datadog.isInitialized()) {
      // your code here
   }
   

   When writing your application, you can enable development logs by calling the setVerbosity method. All internal messages in the library with a priority equal to or higher than the provided level are then logged to Logcat (Android) or Xcode’s debugger console (iOS):

   Datadog.setVerbosity(SdkLogVerbosity.INFO)
   

4. Configure and enable the Logs feature:

   val logsConfig = LogsConfiguration.Builder().build()
   Logs.enable(logsConfig)
   

5. Configure the Logger:

   val logger = Logger.Builder()
       .setNetworkInfoEnabled(true)
       .setPrintLogsToConsole(true)
       .setRemoteSampleRate(100f)
       .setBundleWithRumEnabled(true)
       .setName("<LOGGER_NAME>")
       .build()
   

6. Send a custom log entry directly to Datadog with one of the following functions:

   logger.debug("A debug message.")
   logger.info("Some relevant information?")
   logger.warn("An important warning...")
   logger.error("An error was met!")
   logger.critical("What a Terrible Failure!")
   

7. Exceptions caught can be sent with a message:

   try { 
       doSomething() 
   } catch (e: IOException) {
        logger.error("Error while doing something", e) 
   }
   

   Note: All logging methods can have a Throwable (or NSError is called from iOS source set) attached to them.

8. (Optional) Provide a map alongside your log message to add attributes to the emitted log. Each entry of the map is added as an attribute.

   logger.info("onPageStarted", attributes = mapOf("http.url" to url))
   

9. If you need to modify some attributes in your Log events before batching, you can do so by providing an implementation of EventMapper<LogEvent> when initializing the Logs feature:

   val logsConfig = LogsConfiguration.Builder()
               // ...
               .setEventMapper(logEventMapper)
               .build()
   

   Note: If you return null or a different instance from the EventMapper<LogEvent> implementation, the event will be dropped.

Advanced logging

Logger initialization

You can use the following methods in Logger.Builder when initializing the logger to send logs to Datadog:

Global configuration

Use the following functions to add or remove tags and attributes to all logs sent by a given logger.

Global tags

Add tags

Use the addTag("<TAG_KEY>", "<TAG_VALUE>") function to add tags to all logs sent by a specific logger:

// This adds a tag "build_type:debug" or "build_type:release" accordingly
logger.addTag("build_type", BuildConfig.BUILD_TYPE)

// This adds a tag "device:android"
logger.addTag("device", "android")

The <TAG_VALUE> must be a String.

Remove tags

Use the removeTagsWithKey("<TAG_KEY>") function to remove tags from all logs sent by a specific logger:

// This removes any tag starting with "build_type"
logger.removeTagsWithKey("build_type")

For more information, see Getting Started with Tags.

Global attributes

Add attributes

By default, the following attributes are added to all logs sent by a logger:

• http.useragent and its extracted device and OS properties
• network.client.ip and its extracted geographical properties (country, city)

Use the addAttribute("<ATTRIBUTE_KEY>", "<ATTRIBUTE_VALUE>") function to add a custom attribute to all logs sent by a specific logger:

// This adds an attribute "version_code" with an integer value
logger.addAttribute("version_code", BuildConfig.VERSION_CODE)

// This adds an attribute "version_name" with a String value
logger.addAttribute("version_name", BuildConfig.VERSION_NAME)

The <ATTRIBUTE_VALUE> can be any primitive, String, or Date.

Remove attributes

Use the removeAttribute("<ATTRIBUTE_KEY>", "<ATTRIBUTE_VALUE>") function to remove a custom attribute from all logs sent by a specific logger:

// This removes the attribute "version_code" from all further log send.
logger.removeAttribute("version_code")

// This removes the attribute "version_name" from all further log send.
logger.removeAttribute("version_name")

Batch collection

All the logs are first stored on the local device in batches. Each batch follows the intake specification. They are sent as soon as the network is available, and the battery is high enough to ensure the Datadog SDK does not impact the end user’s experience. If the network is not available while your application is in the foreground, or if an upload of data fails, the batch is kept until it can be sent successfully.

This means that even if users open your application while offline, no data is lost.

The data on disk is automatically discarded if it gets too old. This ensures that the SDK does not use too much disk space.

Before data is uploaded to Datadog, it is stored in cleartext in your application’s cache directory.

Further Reading

Más enlaces, artículos y documentación útiles:

dd-sdk-kotlin-multiplatform source codeSOURCE CODE Learn how to explore your logsDOCUMENTATION