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Introducing the location button: simplified access for one time use

For many common tasks, like finding a nearby shop or tagging a social post, your app doesn't need permanent or background access to a user's precise location.With Android 17, we are introducing the location button, a new UI element designed to provide a well-lit path for responsible one time precise location access. Industry partners have requested this new feature as a way to bring a simpler, and more private location flow to their users.

Users get better privacy protection

Moving the decision making for location sharing to the point where a user takes action, helps the user make a clearer choice about how much information they want to share and for how long. This empowers users to limit data sharing to only what apps need in that session. Once consent is provided, this session based access eliminates repeated prompts for location dependent features. This benefits developers by creating a smoother experience for their users and providing high confidence in user intent, as access is explicitly requested at the moment of action.

Full UI customization to match your app's aesthetic

The location button provides extensive customization options to ensure integration with your app's aesthetic while maintaining system-wide recognizability. You can modify the button's visual style including:

• Background and icon color scheme
• Outline style
• Size and shape

Additionally, you can select the appropriate text label from a predefined list of options. To ensure security and trust, the location icon itself remains mandatory and non-customizable, while the font size is system-managed to respect user accessibility settings

Simplified Integration with Jetpack and automatic backwards compatibility

The location button will be provided as a Jetpack library, ensuring easy integration into your existing app layouts similar to any other Jetpack view implementation, and simplifying how you request permission to access precise location. Additionally, when you implement location button with the Jetpack library it will automatically handle backwards compatibility by defaulting to the existing location prompt when a user taps it on a device running Android 16 or below.

The Android location button is available for testing as of Android 17 Beta 3.

Location access transparency

Users often struggle to understand the tools they can use to monitor and control access to their location data. In Android 17, we are aligning location permission transparency with the high standards already set for the Microphone and Camera.

• Updated Location Indicator: A persistent indicator will now appear to inform a user whenever a non-system app accesses their location
• Attribution & Control: Users can tap the indicator to see exactly which apps have recently accessed their location and manage those permissions immediately through a "Recent app use" dialog.

Strengthening user privacy with density-based Coarse Location

Android 17 is also improving the algorithm for approximate (coarse) locations to be aware of population density. Previously, coarse locations used a static 2 km-wide grid, which in low-population areas may not be sufficiently private since a 2km square could often contain only a handful of users. The new approach replaces this fixed grid with a dynamically-sized area based on local population density. By increasing the grid for areas with lower population density, Android ensures a more consistent privacy guarantee across different environments from dense urban centers to remote regions.

Improved runtime permission dialog

Start building for Android 17

The new location privacy tools are available now in Beta 3. We're looking for your feedback to help refine these features before the general release.

26 Mar 2026 11:00pm GMT

Android 17 has officially reached platform stability today with Beta 3. That means that the API surface is locked; you can perform final compatibility testing and push your Android 17-targeted apps to the Play Store. In addition, Beta 3 brings a host of new capabilities to help you build better, more secure, and highly integrated applications.

Get your apps, libraries, tools, and game engines ready!

If you develop an SDK, library, tool, or game engine, it's even more important to prepare any necessary updates now to prevent your downstream app and game developers from being blocked by compatibility issues and allow them to target the latest SDK features. Please let your downstream developers know if updates are needed to fully support Android 17.

Testing involves installing your production app or a test app making use of your library or engine using Google Play or other means onto a device or emulator running Android 17 Beta 3. Work through all your app's flows and look for functional or UI issues. Review the behavior changes to focus your testing. Each release of Android contains platform changes that improve privacy, security, and overall user experience, and these changes can affect your apps. Here are some changes to focus on:

• Resizability on large screens: Once you target Android 17, you can no longer opt out of maintaining orientation, resizability and aspect ratio constraints on large screens.
• Dynamic code loading: If your app targets Android 17 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load() must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError.
• Enable CT by default: Certificate transparency (CT) is enabled by default. (On Android 16, CT is available but apps had to opt in.)
• Local network protections: Apps targeting Android 17 or higher have local network access blocked by default. Switch to using privacy preserving pickers if possible, and use the new ACCESS_LOCAL_NETWORK for broad, persistent access.

Media and camera enhancements

Photo Picker customization options

Android now allows you to tailor the visual presentation of the photo picker to better complement your app's user interface. By leveraging the new PhotoPickerUiCustomizationParams API, you can modify the grid view aspect ratio from the standard 1:1 square to a 9:16 portrait display. This flexibility extends to both the ACTION_PICK_IMAGES intent and the embedded photo picker, enabling you to maintain a cohesive aesthetic when users interact with media.

This is all part of our effort to help make the privacy-preserving Android photo picker fit seamlessly with your app experience. Learn more about how you can embed the photo picker directly into your app for the most native experience.

val params = PhotoPickerUiCustomizationParams.Builder()
    .setAspectRatio(PhotoPickerUiCustomizationParams.ASPECT_RATIO_PORTRAIT_9_16)
    .build()

val intent = Intent(MediaStore.ACTION_PICK_IMAGES).apply {
    putExtra(MediaStore.EXTRA_PICK_IMAGES_UI_CUSTOMIZATION_PARAMS, params)
}

startActivityForResult(intent, REQUEST_CODE)

Support for the RAW14 image format: Android 17 introduces support for the RAW14 image format - the de-facto industry standard for high-end digital photography - via the new ImageFormat.RAW14 constant. RAW14 is a single-channel, 14-bit per pixel format that uses a densely packed layout where every four consecutive pixels are packed into seven bytes.

Vendor-defined camera extensions: Android 17 adds Vendor-defined extensions to enable hardware partners define and implement custom camera extension modes to provide you access to the best and latest camera features, such as 'Super Resolution' or cutting-edge AI-driven enhancements. You can query for these modes using the isExtensionSupported(int) API.

Camera device type APIs: New Android 17 APIs allow you to query the underlying device type to identify if a camera is built-in hardware, an external USB webcam, or a virtual camera.

Bluetooth LE Audio hearing aid support

Android now includes a specific device category for Bluetooth Low Energy (BLE) Audio hearing aids. With the addition of the AudioDeviceInfo.TYPE_BLE_HEARING_AID constant, your app can now distinguish hearing aids from regular headsets.

val audioManager = getSystemService(Context.AUDIO_SERVICE) as AudioManager
val devices = audioManager.getDevices(AudioManager.GET_DEVICES_OUTPUTS)
val isHearingAidConnected = devices.any { it.type == AudioDeviceInfo.TYPE_BLE_HEARING_AID }

Granular audio routing for hearing aids

Android 17 allows users to independently manage where specific system sounds are played. They can choose to route notifications, ringtones, and alarms to connected hearing aids or the device's built-in speaker.

Extended HE-AAC software encoder

Android 17 introduces a system-provided Extended HE-AAC software encoder. This encoder supports both low and high bitrates using unified speech and audio coding. You can access this encoder via the MediaCodec API using the name c2.android.xheaac.encoder or by querying for the audio/mp4a-latm MIME type.

val encoder = MediaCodec.createByCodecName("c2.android.xheaac.encoder")
val format = MediaFormat.createAudioFormat(MediaFormat.MIMETYPE_AUDIO_AAC, 48000, 1)
format.setInteger(MediaFormat.KEY_BIT_RATE, 24000)
format.setInteger(MediaFormat.KEY_AAC_PROFILE, MediaCodecInfo.CodecProfileLevel.AACObjectXHE)
encoder.configure(format, null, null, MediaCodec.CONFIGURE_FLAG_ENCODE)

Performance and Battery Enhancements

Reduce wakelocks with listener support for allow-while-idle alarms

Android 17 introduces a new variant of AlarmManager.setExactAndAllowWhileIdle that accepts an OnAlarmListener instead of a PendingIntent. This new callback-based mechanism is ideal for apps that currently rely on continuous wakelocks to perform periodic tasks, such as messaging apps maintaining socket connections.

val alarmManager = getSystemService(AlarmManager::class.java)
val listener = AlarmManager.OnAlarmListener {
    // Do work here
}

alarmManager.setExactAndAllowWhileIdle(
    AlarmManager.ELAPSED_REALTIME_WAKEUP,
    SystemClock.elapsedRealtime() + 60000,
    listener,
    null
)

Privacy updates

System-provided Location Button

Android is introducing a system-rendered location button that you will be able to embed directly into your app's layout using an Android Jetpack library. When a user taps this system button, your app is granted precise location access for the current session only. To implement this, you need to declare the USE_LOCATION_BUTTON permission.

Discrete password visibility settings for touch and physical keyboards

This feature splits the existing "Show passwords" system setting into two distinct user preferences: one for touch-based inputs and another for physical (hardware) keyboard inputs. Characters entered via physical keyboards are now hidden immediately by default.

val isPhysical = event.source and InputDevice.SOURCE_KEYBOARD == InputDevice.SOURCE_KEYBOARD
val shouldShow = android.text.ShowSecretsSetting.shouldShowPassword(context, isPhysical)

Security

Enforced read-only dynamic code loading

To improve security against code injection attacks, Android now enforces that dynamically loaded native libraries must be read-only. If your app targets Android 17 or higher, all native files loaded using System.load() must be marked as read-only beforehand.

val libraryFile = File(context.filesDir, "my_native_lib.so")
// Mark the file as read-only before loading to comply with Android 17+ security requirements
libraryFile.setReadOnly()

System.load(libraryFile.absolutePath)

Post-Quantum Cryptography (PQC) Hybrid APK Signing

To prepare for future advancements in quantum computing, Android is introducing support for Post-Quantum Cryptography (PQC) through the new v3.2 APK Signature Scheme. This scheme utilizes a hybrid approach, combining a classical signature with an ML-DSA signature.

User experience and system UI

Better support for widgets on external displays

This feature improves the visual consistency of app widgets when they are shown on connected or external displays with different pixel densities using DP or SP units.

val options = appWidgetManager.getAppWidgetOptions(appWidgetId)
val displayId = options.getInt(AppWidgetManager.OPTION_APPWIDGET_DISPLAY_ID)

val remoteViews = RemoteViews(context.packageName, R.layout.widget_layout)
remoteViews.setViewPadding(
    R.id.container,
    16f, 8f, 16f, 8f,
    TypedValue.COMPLEX_UNIT_DIP
)

Hidden app labels on the home screen

Android now provides a user setting to hide app names (labels) on the home screen workspace. Ensure your app icon is distinct and recognizable.

Desktop Interactive Picture-in-Picture

Unlike traditional Picture-in-Picture, these pinned windows remain interactive while staying always-on-top of other application windows in desktop mode.

val appTask: ActivityManager.AppTask = activity.getSystemService(ActivityManager::class.java).appTasks[0]
appTask.requestWindowingLayer(
    ActivityManager.AppTask.WINDOWING_LAYER_PINNED,
    context.mainExecutor,
    object : OutcomeReceiver<Int, Exception> {
        override fun onResult(result: Int) {
            if (result == ActivityManager.AppTask.WINDOWING_LAYER_REQUEST_GRANTED) {
                // Task successfully moved to pinned layer
            }
        }
        override fun onError(error: Exception) {}
    }
)

Redesigned screen recording toolbar

Core functionality

VPN app exclusion settings

By using the new ACTION_VPN_APP_EXCLUSION_SETTINGS Intent, your app can launch a system-managed Settings screen where users can select applications to bypass the VPN tunnel.

val intent = Intent(Settings.ACTION_VPN_APP_EXCLUSION_SETTINGS)
if (intent.resolveActivity(packageManager) != null) {
    startActivity(intent)
}

OpenJDK 25 and 21 API updates

This update brings extensive features and refinements from OpenJDK 21 and OpenJDK 25, including the latest Unicode support and enhanced SSL support for named groups in TLS.

Get started with Android 17

You can enroll any supported Pixel device or use the 64-bit system images with the Android Emulator.

• Compile against the new SDK and report issues on the feedback page.
• Test your current app for compatibility and learn whether your app is affected by changes in Android 17.

For complete information, visit the Android 17 developer site.

26 Mar 2026 8:00pm GMT

The wait is over! We are incredibly excited to share the Google Play Apps Accelerator class of 2026. We've handpicked a group of high-potential studios from across the globe to embark on a 12-week journey designed to supercharge their success.

Here's what's in store for the program's first ever class:

• Curated learning: virtual masterclasses and workshops led by industry trailblazers.
• Guidance & mentorship: 1-to-1 sessions covering everything from technical scaling to leadership.
• Direct access: exclusive sessions with experts from Google and the world's top studios.

Without further ado, join us in congratulating them!

Google Play Apps Accelerator | Class of 2026

Americas Anytune AstroVeda BetterYou Changed Focus Forge Human Program Know Your Lemons kweliTV Language Innovation Matraquinha MR ROCCO MUU nutrition NKENNE Skarvo Starcrossed Wishfinity

Asia Pacific Human Health Kitakuji Lazy Surfers Mellers Tech Reehee Company

Europe, Middle East & Africa cabuu Class54 Education Digital Garden EverPixel Geolives HelloMind ifal Idea Accelerator Maposcope Ochy Picastro Pixelbite Record Scanner Talkao unorderly Xeropan International

Congratulations again to all the founders selected, we can't wait to see your apps grow on our platform.

The Google Play Apps Accelerator is part of our mission to help businesses of all sizes grow on Google Play and reach their full potential. Discover more about Google Play's programs, resources and tools.

25 Mar 2026 5:00pm GMT

Privacy and user control remain at the heart of the Android experience. Just as the photo picker made media sharing secure and easy to implement, we are now bringing that same level of privacy, simplicity, and great user experience to contact selection.

A New Standard for Contact Privacy

Historically, applications requiring access to a specific user's contacts relied on the broad READ_CONTACTS permission. While functional, this approach often granted apps more data than necessary. The new Android Contact Picker, introduced in Android 17, changes this dynamic by providing a standardized, secure, and searchable interface for contact selection.

This feature allows users to grant apps access only to the specific contacts they choose, aligning with Android's commitment to data transparency and minimized permission footprints.

How It Works

Developers can integrate the Contact Picker using the Intent.ACTION_PICK_CONTACTS intent. This updated API offers several powerful capabilities:

• Granular Data Requests: Apps can specify exactly which fields they need, such as phone numbers or email addresses, rather than receiving the entire contact record.

• Multi-Selection Support: The picker supports both single and multiple contact selections, giving developers more flexibility for features like group invitations.

• Selection Limits: Developers can set custom limits on the number of contacts a user can select at one time.

• Temporary Access: Upon selection, the system returns a Session URI that provides temporary read access to the requested data, ensuring that access does not persist longer than necessary.

• Access to other profiles: When using this new intent, the interface will allow users to select contents from other user profiles such as a work profile, cloned profile or a private space.

// State to hold the list of selected contacts
var contacts by remember { mutableStateOf<List<Contact>>(emptyList()) }

// Launcher for the Contact Picker intent
val pickContact = rememberLauncherForActivityResult(StartActivityForResult()) {
    if (it.resultCode == Activity.RESULT_OK) {
        val resultUri = it.data?.data ?: return@rememberLauncherForActivityResult

        // Process the result URI in a background thread
        coroutine.launch {
            contacts = processContactPickerResultUri(resultUri, context)
        }
    }
}

// Define the specific contact data fields you need
val requestedFields = arrayListOf(
    Email.CONTENT_ITEM_TYPE,
    Phone.CONTENT_ITEM_TYPE,
)

// Set up the intent for the Contact Picker
val pickContactIntent = Intent(ACTION_PICK_CONTACTS).apply {
    putExtra(EXTRA_PICK_CONTACTS_SELECTION_LIMIT, 5)
    putStringArrayListExtra(
        EXTRA_PICK_CONTACTS_REQUESTED_DATA_FIELDS,
        requestedFields
    )
    putExtra(EXTRA_PICK_CONTACTS_MATCH_ALL_DATA_FIELDS, false)
}

// Launch the picker
pickContact.launch(pickContactIntent)

After the user makes a selection, the app processes the result by querying the returned Session URI to extract the requested contact information.

// Data class representing a parsed Contact with selected details
data class Contact(val id: String, val name: String, val email: String?, val phone: String?)

// Helper function to query the content resolver with the URI returned by the Contact Picker.
// Parses the cursor to extract contact details such as name, email, and phone number
private suspend fun processContactPickerResultUri(
    sessionUri: Uri,
    context: Context
): List<Contact> = withContext(Dispatchers.IO) {
    // Define the columns we want to retrieve from the ContactPicker ContentProvider
    val projection = arrayOf(
        ContactsContract.Contacts._ID,
        ContactsContract.Contacts.DISPLAY_NAME_PRIMARY,
        ContactsContract.Data.MIMETYPE, // Type of data (e.g., email or phone)
        ContactsContract.Data.DATA1, // The actual data (Phone number / Email string)
    )

    val results = mutableListOf<Contact>()

    // Note: The Contact Picker Session Uri doesn't support custom selection & selectionArgs.
    context.contentResolver.query(sessionUri, projection, null, null, null)?.use { cursor ->
        // Get the column indices for our requested projection
        val contactIdIdx = cursor.getColumnIndex(ContactsContract.Contacts._ID)
        val mimeTypeIdx = cursor.getColumnIndex(ContactsContract.Data.MIMETYPE)
        val nameIdx = cursor.getColumnIndex(ContactsContract.Contacts.DISPLAY_NAME_PRIMARY)
        val data1Idx = cursor.getColumnIndex(ContactsContract.Data.DATA1)

        while (cursor.moveToNext()) {
            val contactId = cursor.getString(contactIdIdx)
            val mimeType = cursor.getString(mimeTypeIdx)
            val name = cursor.getString(nameIdx) ?: ""
            val data1 = cursor.getString(data1Idx) ?: ""

            // Determine if the current row represents an email or a phone number
            val email = if (mimeType == Email.CONTENT_ITEM_TYPE) data1 else null
            val phone = if (mimeType == Phone.CONTENT_ITEM_TYPE) data1 else null

            // Add the parsed contact to our results list
            results.add(Contact(contactId, name, email, phone))
        }
    }

    return@withContext results
}

24 Mar 2026 8:00pm GMT

At Google we're deeply committed to the automotive industry--not just as a technology provider, but as a partner in the industry's transformation. We believe that car makers and users should have choice and flexibility, and that open platforms are the best enablers. For over a decade, we have provided Android Automotive OS (AAOS) as an open platform for infotainment, enabling rich innovation and differentiation in the in-vehicle digital experience. However, as vehicles modernize, car makers face new hurdles: fragmented software across compute components, poor portability between architectures, and a lack of granular update capabilities. To address these problems, we are expanding AAOS beyond infotainment with Android Automotive OS for Software Defined Vehicles (AAOS SDV)--an open platform featuring a modular structure, a topology-agnostic communication layer, and the support for granular updates.

The transition toward SDVs is an incredible industry transformation, and we are eager to contribute to the broader ecosystem making it happen. Later this year, AAOS SDV will be available in the Android Open Source Project (AOSP) for uses beyond infotainment. By bringing our SDV platform into the open-source domain, we empower the industry to develop or enhance features that lower costs, accelerate time to market, and provide significant advantages across the automotive landscape.

A Foundation for the Software-Defined Vehicle

AAOS SDV is engineered to address the core challenges of modern vehicle development. This new AAOS expansion provides a compact, performant and scalable software foundation based on a headless Android native stack, extending much deeper into the vehicle architecture to power software components throughout the vehicle such as the seat actuator, instrument cluster, climate control, lighting, cameras, mirrors, telemetry, and more.

AAOS SDV's core is a lightweight Android-based operating system incorporating low-level automotive specific frameworks for communications, diagnostics, software updates, and more. This enables AAOS SDV to power many different vehicle controllers, tackling Core Compute, Body Controls, and Cluster domains.

In addition, the AAOS SDV platform includes a new framework, Display Safety, for implementing instrument cluster applications including audible chimes, regulatory camera, and sophisticated graphics that blend seamlessly with AAOS IVI content. Display Safety includes a safety design toolchain and a reference safety monitor, allowing OEMs to meet functional safety requirements leveraging the diverse platform safety mechanisms of Automotive SoCs.

Flexible Deployment for AAOS SDV

Engineered for flexibility, the AAOS SDV framework can utilize hypervisor-backed virtualization with virtio support to separate software domains, or it can be deployed on bare metal for optimal low-latency performance.

Transforming the Developer Experience

AAOS SDV is designed to power modern vehicles, but it was also designed to change how modern vehicle software is developed, tested and delivered with the goals to reduce development time and cost while increasing innovation and agility. With its optimized development workflows, our open-source SDV platform provides a wide range of benefits across the automotive industry:

• Accelerated Time-to-Market: AAOS SDV components can accelerate development with production ready software for various components that can be further modified.

• Standard Signal Catalog: A new standard signal catalog to bring OEMs and automotive suppliers onto the same page eliminates redundant engineering efforts and significantly reduces platform development costs.

• Optimized for virtual cloud development: AAOS SDV was designed ground-up to support virtual cloud development - enabling partners to design, test and validate components in the car well ahead of hardware availability. AAOS SDV already runs on Android Virtual Device (Cuttlefish), and works well with existing Google Cloud integrations such as Google Cloud Horizon, enabling a digital twin solution at scale.

• A Service-Oriented Architecture: Vehicle functions are developed as topology-agnostic services which are reusable across different architectures. The platform treats the vehicle as a dynamic, connected system. This allows for granular, service-level updates with built-in dependency handling, enabling you to deploy new features over-the-air and create continuous improvement loops.

• Future-Ready for new services: The platform is designed to simplify the development of telemetry, AI training feedback loops, accelerating the deployment of advanced features for both enterprise fleets and consumer vehicles.

Production Ready: Partnering with Renault

We are proud to highlight our deep partnership with Renault to underscore the production readiness of the AAOS SDV platform. Renault is currently leveraging the Android Automotive OS SDV platform for its upcoming Renault Trafic e-Tech, "[...] production set to begin in late 2026". The Renault Trafic e-Tech validates the platform's ability to accelerate development and enable a new generation of software-defined commercial vehicles.

Scaling Ready: Partnering with Qualcomm

Qualcomm is scaling the Android Automotive OS SDV platform through our strategic partnership. At CES 2026, Qualcomm introduced Snapdragon vSoC on Google Cloud and announced a scaling collaboration to deliver a turnkey, pre-integrated AAOS SDV stack on Snapdragon Digital Chassis platforms.

Building an Open AAOS Ecosystem

The power of AAOS comes from its vibrant ecosystem. To prepare for the open source release later this year, we are proactively working with leading industry carmakers, suppliers, silicon platforms, and software vendors to ensure that the AAOS SDV platform is well supported and robustly integrated within the automotive ecosystem. We look forward to sharing more updates with our partners in the months ahead.

24 Mar 2026 4:00pm GMT

Android proves you don't have to choose between an open ecosystem and a secure one. Since announcing updated verification requirements, we've worked with the community to ensure these protections are robust yet respectful of platform freedom. We've heard from power users that they want to take educated risks to install software from unverified developers. Today, we're sharing details on a new advanced flow that provides this option.

Advanced flow safeguards against coercion

This flow is a one-time process for power users - but it was designed carefully to prevent those in the midst of a scam attempt from being coerced by high pressure tactics to install malicious software. In these scenarios, scammers exploit fear - using threats of financial ruin, legal trouble, or harm to a loved one - to create a sense of extreme urgency. They stay on the phone with victims, coaching them to bypass security warnings and disable security settings before the victim has a chance to think or seek help. According to a 2025 report from the Global Anti-Scam Alliance (GASA), 57% of surveyed adults experienced a scam in the past year, resulting in a global consumer loss of $442 billion. Because the consequences of these scams that use sophisticated social engineering tactics are so severe, we have carefully engineered the advanced flow to provide the critical time and space needed to break the cycle of coercion.

How the advanced flow works for users

• Enable developer mode in system settings: Activating this is simple. This prevents accidental triggers or "one-tap" bypasses often used in high-pressure scams.

• Confirm you aren't being coached: There is a quick check to make sure that no one is talking you into turning off your security. While power users know how to vet apps, scammers often pressure victims into disabling protections.

• Restart your phone and reauthenticate: This cuts off any remote access or active phone calls a scammer might be using to watch what you're doing.

• Come back after the protective waiting period and verify: There is a one-time, one-day wait and then you can confirm that this is really you who's making this change with our biometric authentication (fingerprint or face unlock) or device PIN. Scammers rely on manufactured urgency, so this breaks their spell and gives you time to think.

• Install apps: Once you confirm you understand the risks, you're all set to install apps from unverified developers, with the option of enabling for 7 days or indefinitely. For safety, you'll still see a warning that the app is from an unverified developer, but you can just tap "Install Anyway."

A secure Android for every developer

We know a "one size fits all" approach doesn't work for our diverse ecosystem. We want to ensure that identity verification isn't a barrier to entry, so we're providing different paths to fit your specific needs.

In addition to the advanced flow we're building free, limited distribution accounts for students and hobbyists. This allows you to share apps with a small group (up to 20 devices) without needing to provide a government-issued ID or pay a registration fee. This ensures Android remains an open platform for learning and experimentation while maintaining robust protections for the broader community.

Limited distribution accounts and advanced flow for users will be available in August before the new developer verification requirements take effect.

Visit our website for more details. We look forward to sharing more in the coming days and weeks.

19 Mar 2026 2:00pm GMT

We're thrilled to announce major updates to our design resources, giving you the comprehensive guidance you need to create polished, adaptive Android apps across all form factors! We now have Desktop Experience guidance and a refreshed Android Design Gallery.

New Desktop Experience Design Guidance

Your users are engaging with Android apps on more diverse devices than ever before-from phones and foldables to laptops and external monitors. A "desktop experience" occurs anytime your app is in a desktop-like mode, typically involving a non-touch input device like a keyboard or mouse, or another display such as a monitor (read more in the connected display announcement). This means designing for larger screens and accommodating additional input states. These new design experiences are meant to maximize productivity for your users with higher information density, multi-tasking capabilities.

Dive into desktop experience guidance to help optimize your app with desktop design principles, input interaction guidance, and system UI considerations.

The new guidance includes foundational guides where you can learn design principles that make desktop experiences unique, such as how multitasking is at the core of desktop experiences.

When your app is in a desktop experience, keep in mind crucial interaction experiences, such as how to best design around unique input interactions, like choosing cursors from system provided cursors.

For specialized actions not covered by system icons, consider creating a custom cursor icon, while ensuring it remains easy for users to find on the page.

A desktop experience brings more multitasking features, like windowing, so expect your app to take on a variety of dimensions with a header bar.

Want to get started quickly? Check out the walkthrough to go from mobile to desktop and design along with the updated Adaptive Design lab.

16 Mar 2026 5:00pm GMT

The first alpha of Room 3.0 has been released! Room 3.0 is a major breaking version of the library that focuses on Kotlin Multiplatform (KMP) and adds support for JavaScript and WebAssembly (WASM) on top of the existing Android, iOS and JVM desktop support.

In this blog we outline the breaking changes, the reasoning behind Room 3.0, and the various things you can do to migrate from Room 2.0.

13 Mar 2026 5:00pm GMT

Posted by Ajesh R Pai, Developer Relations Engineer & Ben Trengrove, Developer Relations Engineer

TikTok is a global short-video platform known for its massive user base and innovative features. The team is constantly releasing updates, experiments, and new features for their users. Faced with the challenge of maintaining velocity while managing technical debt, the TikTok Android team turned to Jetpack Compose.

The team wanted to enable faster, higher-quality iteration of product requirements. By leveraging Compose, the team sought to improve engineering efficiency by writing less code and reducing cognitive load, while also achieving better performance and stability.

Furthermore, Compose's measurement strategy helps reduce double taxation, making measure performance easier to optimize.

To improve developer productivity, TikTok's central Design System team provides a component library for teams working on different app features. The team observed that Development in Compose is simple; leveraging small composables is highly effective, while incorporating large UI blocks with conditional logic is both straightforward and has minimal overhead.

Building a path forward through strategic migration

By strategically adopting Jetpack Compose, TikTok was able to stay on top of technical debt, while also continuing to focus on creating great experiences for their users. The ability of Compose to handle conditional logic cleanly and streamline composition allowed the team to achieve up to a 78% reduction in page loading time on new or fully rewritten pages. This improvement was 20-30% in smaller cases, and 70-80% for full rewrites and new features. They also were able to reduce their code size by 58%, when compared to the same feature built in Views. The team has further shared a couple of learnings:

TikTok team's overall strategy was to incrementally migrate specific user journeys. This gave them an opportunity to migrate, confirm measurable benefits, then scale to more screens. They started with using Compose to simplify the overall structure in the QR code feature and saw the improvements. The team later expanded the migration to the Login and Sign-up experiences.

The team shared some additional learnings:

While checking performance during migration, the TikTok team found that using many small ComposeViews to replace elements inside a single ViewHolder, caused composition overhead. They achieved better results by expanding the migration to use one single ComposeView for the entire ViewHolder.

When migrating a Fragment inside ViewPager, which has custom height logic and conditional logic to hide and show ui based on experiments, the performance wasn't impacted. In this case, migrating the ViewPager to Composable performed better than migrating the Fragment.

Jun Shen really likes that Compose "reduces the amount of code required for feature development, improves testability, and accelerates delivery". The team plans to steadily increase Compose adoption, making it their preferred framework in the long term. Jetpack Compose proved to be a powerful solution for improving both their developer experience and production metrics at scale.

Get Started with Jetpack Compose

Learn more about how Jetpack Compose can help your team.

13 Mar 2026 1:00pm GMT

Last September, we shared our vision for the future of Google Play Games grounded in a core belief: the best way to drive your game's success is to deliver a world-class player experience. We launched the Google Play Games Level Up program to recognize and reward great gaming experiences, while providing you with a powerful toolkit and new promotional opportunities to grow your games.

The momentum since our announcement has been incredibly positive, with more than 600 million gamers now using Play Games Services every month. Developers are also finding success, with one-third of all game installs on the Play Store now coming from editorially-driven organic discovery. In fact, in 2025, Level Up features have driven over 2.5 billion incremental acquisitions for featured games, in addition to an average uplift of 25% in installs during the featuring windows.

Today, we're inviting you to start testing Play Games Sidekick to keep your players in the action, sharing new Play Console updates to optimize your reach, and helping you prepare for our upcoming program milestones.

Deep Rock Galactic: Survivor keeps players in the action with Play Games Sidekick

Today, Sidekick officially debuts in over 90 games, with the experience expanding to all Level Up titles later this year. But you don't need to wait for the broader rollout to get your game ready. You can now enable Sidekick through Play Console to preview and test how your players will interact with features like Achievements, Streaks, Play Points Coupons, and Game Tips. Upon completing your testing, be sure to push Sidekick for production to ensure your game meets the Level Up user experience guidelines.

Enable Play Games Sidekick in Play Console to begin testing

Identify launch-day risks and optimize performance for your players

with new pre-registration device breakdowns

• Real-time feedback: With Level Up+, our tier for high-performing games, qualifying titles can unlock promotional content featuring and tools like deep-links and audience targeting. While submissions must meet Play's quality guidelines, you no longer have to wait 24 hours to learn about issues. You can now get immediate feedback on quality whenever possible.

Your 2026 checklist: Securing your Level Up benefits

Today, all games on Google Play qualify for Google Play Games Level Up. However, in order to maintain access to Level Up benefits like Play Points offers, expanded APK size limits, Play Store collections and campaigns consideration, or access to high-visibility surfaces like You tab and Sidekick, you'll need to ensure your game meets user experience guidelines by their upcoming milestones:

By July 2026:

• Integrate Play Games Sidekick to offer a quick and easy entry point to access rewards, offers, and achievements through an in-game overlay.
• Implement achievements with Play Games Services, to support authentication with the modern Gamer Profile, and to keep players engaged across the lifespan of your game.

By November 2026:

11 Mar 2026 8:02pm GMT

Posted by Aurash Mahbod, VP and GM, Games on Google Play

Google Play is proud to be the home of over 200,000 games-many of which defined the mobile-first era. But as cross-platform becomes the standard for players, we are evolving our ecosystem to match the scale of your ambitions. In recent years, we focused on elevating Android gaming quality while significantly deepening our support for native PC titles.

We know that maximizing your game's reach across different platforms is complex. The Level Up program serves as your strategic roadmap, helping you prioritize optimizations that drive great experiences on Android. Building on this foundation, we're doubling down on our investment to make Play the most accessible home for every category of play. We're adding new tools for paid games and making the PC game discovery to purchase seamless. Keep reading to learn more about how we're creating a bigger stage for your games.

Scale your discovery across mobile and PC platforms

Building a bigger stage starts with making your games easier to find-and easier to buy-no matter which device your players prefer. We're expanding your reach by bringing cross-platform discovery directly to the mobile storefront.

• With the new PC section in the Games tab, your PC titles gain high visibility placement among our most active mobile players.

• The PC badge ensures your cross-platform investment is recognized. This creates more opportunities to acquire players on mobile and transition them seamlessly to your high-fidelity PC experience.

• With 'buy once play anywhere' pricing, we're making it easier to sell your games across different devices. If you choose to opt-in your mobile game for Google Play Games on PC, you can now offer a single price that covers both mobile and PC versions. We're rolling out this feature in EAP with select games including Brotato: Premium.

• For PC-only games, players can now complete the full purchase journey on Google Play Games on PC with the same trusted security and privacy standards they expect from Google Play.

'Buy once play anywhere' pricing to sell your games across devices

Lower the purchase barrier with Game Trials

To help you convert high-intent buyers with less friction, we're introducing Game Trials, a feature that enables players to experience your game for a limited time before making a purchase on mobile. Accessible directly from your game's store listing, Game Trials provides a fast-track for players to start exploring your world with a single tap. Game trials are now in testing with select titles and we'll roll it out to more titles soon.

• To ensure this is low maintenance for you, Game Trials is added directly into your Android App Bundle. This enables you to offer a high quality trial without the burden of a separate codebase or a demo version of your app.

• Play ensures trials are secure and seamless. Game Trials are once per user and protects your game while the trial is active. When it ends, players can purchase your game and keep their progress.

• We're also working on tools that will give you more control-such as specifying a custom time limit or an in-game event to conclude the trial.

Game Trial for DREDGE to help convert high-intent buyers

Diversify your revenue with a dedicated player community on Play Pass

Play Pass is another way to diversify revenue and grow your player audience. It has been a strong launchpad for indie hits such as Isle of Arrows, Slay the Spire, and Dead Cells. With Play Pass, you can reach highly dedicated players seeking a more curated gaming experience, free of ads and in-app purchases. To help you deepen engagement, paid titles on Play Pass can now opt in to Google Play Games on PC - making it easy for players to find and play your games on a larger screen. Later this year, you can nominate your game through a streamlined opt-in process directly in Play Console.

Drive long term sales with Wishlists and Discounts

Wishlists and Discounts are one of the most effective ways to capture player intent and drive long term sales. To support players at every stage of their purchase journey, we're integrating them directly into Play. Players can save titles to their wishlist and manage them from library settings. To keep your game top-of-mind, players will receive automated notifications for your latest discounts - starting with mobile and expanding soon to PC games.

How leading studios are finding a new path to success on Play

We're thrilled to welcome Sledding Game, 9 Kings, Potion Craft, Moonlight Peaks, and Low Budget Repairs to Play [1]. It marks an exciting expansion of our catalog and a step forward in our mission to build a bigger gaming ecosystem for all developers. This growth is fueled by our developer community, whose feedback continues to shape our roadmap and help us better support your success.

That mission brings us to GDC and the Independent Games Festival (IGF) Awards [2], where the next generation of games awaits! This year, we're inviting you to come along for the ride as we go backstage to chat with the finalists and winners, sharing the moments of triumph and the creative stories behind their development. Not joining us at GDC? You can take the next step in your journey to launch your game on Google Play today.

1. Sledding Game, 9 Kings, Potion Craft, and Moonlight Peaks are coming to Google Play in 2026. Low Budget Repairs is scheduled for release in 2027. [Back]

2. Independent Games Festival (IGF) Awards is hosted by Game Developers Conference (GDC) and requires a valid GDC pass for entry. [Back]

11 Mar 2026 8:02pm GMT

We are the Android LLVM toolchain team. One of our top priorities is to improve Android performance through optimization techniques in the LLVM ecosystem. We are constantly searching for ways to make Android faster, smoother, and more efficient. While much of our optimization work happens in userspace, the kernel remains the heart of the system. Today, we're excited to share how we are bringing Automatic Feedback-Directed Optimization (AutoFDO) to the Android kernel to deliver significant performance wins for users.

What is AutoFDO?

During a standard software build, the compiler makes thousands of small decisions, such as whether to inline a function and which branch of a conditional is likely to be taken, based on static code hints.While these heuristics are useful, they don't always accurately predict code execution during real-world phone usage.

AutoFDO changes this by using real-world execution patterns to guide the compiler. These patterns represent the most common instruction execution paths the code takes during actual use, captured by recording the CPU's branching history. While this data can be collected from fleet devices, for the kernel we synthesize it in a lab environment using representative workloads, such as running the top 100 most popular apps. We use a sampling profiler to capture this data, identifying which parts of the code are 'hot' (frequently used) and which are 'cold'. When we rebuild the kernel with these profiles, the compiler can make much smarter optimization decisions tailored to actual Android workloads.

To understand the impact of this optimization, consider these key facts:

• On Android, the kernel accounts for about 40% of CPU time.
• We are already using AutoFDO to optimize native executables and libraries in the userspace, achieving about 4% cold app launch improvement and a 1% boot time reduction.

Real-World Performance Wins

We have seen impressive improvements across key Android metrics by leveraging profiles from controlled lab environments. These profiles were collected using app crawling and launching, and measured on Pixel devices across the 6.1, 6.6, and 6.12 kernels.

The most noticeable improvements are listed below. Details on the AutoFDO profiles for these kernel versions can be found in the respective Android kernel repositories for android16-6.12 and android15-6.6 kernels.

These aren't just theoretical numbers. They translate to a snappier interface, faster app switching, extended battery life, and an overall more responsive device for the end user.

How It Works: The Pipeline

Our deployment strategy involves a sophisticated pipeline to ensure profiles stay relevant and performance remains stable.

Step 1: Profile Collection

While we rely on our internal test fleet to profile userspace binaries, we shifted to a controlled lab environment for the Generic Kernel Image (GKI). Decoupling profiling from the device release cycle allows for flexible, immediate updates independent of deployed kernel versions. Crucially, tests confirm that this lab-based data delivers performance gains comparable to those from real-world fleets.

• Tools & Environment: We flash test devices with the latest kernel image and use simpleperf to capture instruction execution streams. This process relies on hardware capabilities to record branching history, specifically utilizing ARM Embedded Trace Extension (ETE) and ARM Trace Buffer Extension (TRBE) on Pixel devices.
• Workloads: We construct a representative workload using the top 100 most popular apps from the Android App Compatibility Test Suite (C-Suite). To capture the most accurate data, we focus on:
• • App Launching: Optimizing for the most visible user delays
  • AI-Driven App Crawling: Simulating contiguous, evolving user interactions
  • System-Wide Monitoring: Capturing not only foreground app activities, but also critical background workloads and inter-process communications
• Validation: This synthesized workload shows an 85% similarity to execution patterns collected from our internal fleet.
• Targeted Data: By repeating these tests sufficiently, we capture high-fidelity execution patterns that accurately represent real-world user interaction with the most popular applications. Furthermore, this extensible framework allows us to seamlessly integrate additional workloads and benchmarks to broaden our coverage.

Step 2: Profile Processing

We post-process the raw trace data to ensure it is clean, effective, and ready for the compiler.

• Aggregation: We consolidate data from multiple test runs and devices into a single system view.
• Conversion: We convert raw traces into the AutoFDO profile format, filtering out unwanted symbols as needed.
• Profile Trimming: We trim profiles to remove data for "cold" functions, allowing them to use standard optimization. This prevents regressions in rarely used code and avoids unnecessary increases in binary size.

Step 3: Profile Testing

Before deployment, profiles undergo rigorous verification to ensure they deliver consistent performance wins without stability risks.

• Profile & Binary Analysis: We strictly compare the new profile's content (including hot functions, sample counts, and profile size) against previous versions. We also use the profile to build a new kernel image, analyzing binaries to ensure that changes to the text section are consistent with expectations.
• Performance Verification: We run targeted benchmarks on the new kernel image. This confirms that it maintains the performance improvements established by previous baselines.

Continuous Updates

Code naturally "drifts" over time, so a static profile would eventually lose its effectiveness. To maintain peak performance, we run the pipeline continuously to drive regular updates:

• Regular Refresh: We refresh profiles in Android kernel LTS branches ahead of each GKI release, ensuring every build includes the latest profile data.
• Future Expansion: We are currently delivering these updates to the android16-6.12 and android15-6.6 branches and will expand support to newer GKI versions, such as the upcoming android17-6.18.

Ensuring Stability

A common question with profile-guided optimization is whether it introduces stability risks. Because AutoFDO primarily influences compiler heuristics, such as function inlining and code layout, rather than altering the source code's logic, it preserves the functional integrity of the kernel. This technology has already been proven at scale, serving as a standard optimization for Android platform libraries, ChromeOS, and Google's own server infrastructure for years.

To further guarantee consistent behavior, we apply a "conservative by default" strategy. Functions not captured in our high-fidelity profiles are optimized using standard compiler methods. This ensures that the "cold" or rarely executed parts of the kernel behave exactly as they would in a standard build, preventing performance regressions or unexpected behaviors in corner cases.

Looking Ahead

We are currently deploying AutoFDO across the android16-6.12 and android15-6.6 branches. Beyond this initial rollout, we see several promising avenues to further enhance the technology:

• Expanded Reach: We look forward to deploying AutoFDO profiles to newer GKI kernel versions and additional build targets beyond the current aarch64 support.

• GKI Module Optimization: Currently, our optimization is focused on the main kernel binary (vmlinux). Expanding AutoFDO to GKI modules could bring performance benefits to a larger portion of the kernel subsystem.

• Vendor Module Support: We are also interested in supporting AutoFDO for vendor modules built using the Driver Development Kit (DDK). With support already available in our build system (Kleaf) and profiling tools (simpleperf), this allows vendors to apply these same optimization techniques to their specific hardware drivers.

• Broader Profile Coverage: There is potential to collect profiles from a wider range of Critical User Journeys (CUJs) to optimize them.

By bringing AutoFDO to the Android kernel, we're ensuring that the very foundation of the OS is optimized for the way you use your device every day.

10 Mar 2026 11:00pm GMT

Existing Complexities

Previously, Meta used a combination of warmup (to get players ready) and prefetch (to cache content on disk) for video delivery. While these methods helped improve network efficiency, they introduced significant challenges. Warmup required instantiating multiple player instances sequentially, which consumed significant memory and limited preloading to only a few videos. This high resource demand meant that a more scalable robust solution could be applied to deliver the instant playback expected on modern, fast-scrolling social feeds.

Integrating Media3 PreloadManager

To achieve truly instant playback, Meta's Media Foundation Client team integrated the Jetpack Media3 PreloadManager into Facebook and Instagram. They chose the DefaultPreloadManager to unify their preloading and playback systems. This integration required refactoring Meta's existing architecture to enable efficient resource sharing between the PreloadManager and ExoPlayer instances. This strategic shift provided a key architectural advantage: the ability to parallelize preloading tasks and manage many videos using a single player instance. This dramatically increased preloading capacity while eliminating the high memory complexities of their previous approach.

Optimization and Performance Tuning

The team then performed extensive testing and iterations to optimize performance across Meta's diverse global device ecosystem. Initial aggressive preloading sometimes caused issues, including increased memory usage and scroll performance slowdowns. To solve this, they fine-tuned the implementation by using careful memory measurements, considering device fragmentation, and tailoring the system to specific UI patterns.

Fine tuning implementation to specific UI patterns

Meta applied different preloading strategies and tailored the behavior to match the specific UI patterns of each app:

• Facebook Newsfeed: The UI prioritizes the video currently coming into view. The manager preloads only the current video to ensure it starts the moment the user pauses their scroll. This "current-only" focus minimizes data and memory footprints in an environment where users may see many static posts between videos. While the system is presently designed to preload just the video in view, it can be adjusted to also preload upcoming (future) videos.

• Instagram Reels: This is a pure video environment where users swipe vertically. For this UI, the team implemented an "adjacent preload" strategy. The PreloadManager keeps the videos immediately after the current Reel ready in memory. This bi-directional approach ensures that whether a user swipes up or down, the transition remains instant and smooth. The result was a dramatic improvement in the Quality of Experience (QoE) including improvements in Playback Start and Time to First Frame for the user.

Scaling for a diverse global device ecosystem

Scaling a high-performance video stack across billions of devices requires more than just aggressive preloading; it requires intelligence. Meta faced initial challenges with memory pressure and scroll lag, particularly on mid-to-low-end hardware. To solve this, they built a Device Stress Detection system around the Media3 implementation. The apps now monitor I/O and CPU signals in real-time. If a device is under heavy load, preloading is paused to prioritize UI responsiveness.

This device-aware optimization ensures that the benefit of instant playback doesn't come at the cost of system stability, allowing even users on older hardware to experience a smoother, uninterrupted feed.

Architectural wins and code health

Beyond the user-facing metrics, the migration to Media3 PreloadManageroffered long-term architectural benefits. While the integration and tuning process needed multiple iterations to balance performance, the resulting codebase is more maintainable. The team found that the PreloadManager API integrated cleanly with the existing Media3 ecosystem, allowing for better resource sharing. For Meta, the adoption of Media3 PreloadManager was a strategic investment in the future of video consumption.

By adopting preloading and adding device-intelligent gates, they successfully increased total watch time on their apps and improved the overall engagement of their global community.

Resulting impact on Instagram and Facebook

The proactive architecture delivered immediate and measurable improvements across both platforms.

• Facebook experienced faster playback starts, decreased playback stall rates and a reduction in bad sessions (like rebuffering, delayed start time, lower quality,etc) which overall resulted in higher watch time.

• Instagram saw faster playback starts and an increase in total watch time. Eliminating join latency (the interval from the user's action to the first frame display) directly increased engagement metrics. The fewer interruptions due to reduced buffering meant users watched more content, which showed through engagement metrics.

Key engineering learnings at scale

As media consumption habits evolve, the demand for instant experiences will continue to grow. Implementing proactive memory management and optimizing for scale and device diversity ensures your application can meet these expectations efficiently.

• Prioritize intelligent preloading

Focus on delivering a reliable experience by minimizing stutters and loading times through preloading. Rather than simple disk caching, leveraging memory-level preloading ensures that content is ready the moment a user interacts with it.

• Align your implementation with UI patterns

Customize preloading behavior as per your apps's UI. For example, use a "current-only" focus for mixed feeds like Facebook to save memory, and an "adjacent preload" strategy for vertical environments like Instagram Reels.

• Leverage Media3 for long-term code health

Integrating with Media3 APIs rather than a custom caching solution allows for better resource sharing between the player and the PreloadManager, enabling you to manage multiple videos with a single player instance. This results in a future-proof codebase that is easier for engineering teams to not only maintain and optimize over time but also benefit from the latest feature updates.

• Implement device aware optimizations

Broaden your market reach by testing on various devices, including mid-to-low-end models. Use real-time signals like CPU, memory, and I/O to adapt features and resource usage dynamically.

05 Mar 2026 6:03pm GMT

Posted by Matthew McCullough, VP of Product Management, Android Developer

"Measuring AI's impact on Android is a massive challenge, so it's great to see a framework that's this sound and realistic. While we're active in benchmarking ourselves, Android Bench is a unique and welcome addition. This methodology is exactly the kind of rigorous evaluation Android developers need right now."

- Kirill Smelov, Head of AI Integrations at JetBrains.

The first Android Bench results

For this initial release, we wanted to purely measure model performance and not focus on agentic or tool use. The models were able to successfully complete 16-72% of the tasks. This is a wide range that demonstrates some LLMs already have a strong baseline for Android knowledge, while others have more room for improvement. Regardless of where the models are at now, we're anticipating continued improvement as we encourage LLM makers to enhance their models for Android development.

The LLM with the highest average score for this first release is Gemini 3.1 Pro, followed closely by Claude Opus 4.6. You can try all of the models we evaluated for AI assistance for your Android projects by using API keys in the latest stable version of Android Studio.

Providing developers and LLM makers with transparency

We value an open and transparent approach, so we made our methodology, dataset, and test harness publicly available on GitHub.

One challenge for any public benchmark is the risk of data contamination, where models may have seen evaluation tasks during their training process. We have taken measures to ensure our results reflect genuine reasoning rather than memorization or guessing, including a thorough manual review of agent trajectories, or the integration of a canary string to discourage training.

Looking ahead, we will continue to evolve our methodology to preserve the integrity of the dataset, while also making improvements for future releases of the benchmark-for example, growing the quantity and complexity of tasks.

We're looking forward to how Android Bench can improve AI assistance long-term. Our vision is to close the gap between concept and quality code. We're building the foundation for a future where no matter what you imagine, you can build it on Android.

05 Mar 2026 2:03pm GMT

In recognition that excessive battery drain is top of mind for Android users, Google has been taking significant steps to help developers build more power-efficient apps. On March 1st, 2026, Google Play Store began rolling out the wake lock technical quality treatments to improve battery drain. This treatment will roll out gradually to impacted apps over the following weeks. Apps that consistently exceed the "Excessive Partial Wake Lock" threshold in Android vitals may see tangible impacts on their store presence, including warnings on their store listing and exclusion from discovery surfaces such as recommendations.

Users may see a warning on your store listing if your app exceeds the bad behavior threshold.

This initiative elevated battery efficiency to a core vital metric alongside stability metrics like crashes and ANRs. The "bad behavior threshold" is defined as holding a non-exempted partial wake lock for at least two hours on average while the screen is off in more than 5% of user sessions in the past 28 days. A wake lock is exempted if it is a system held wake lock that offers clear user benefits that cannot be further optimized, such as audio playback, location access, or user-initiated data transfer. You can view the full definition of excessive wake locks in our Android vitals documentation.

As part of our ongoing initiative to improve battery life across the Android ecosystem, we have analyzed thousands of apps and how they use partial wake locks. While wake locks are sometimes necessary, we often see apps holding them inefficiently or unnecessarily, when more efficient solutions exist. This blog will go over the most common scenarios where excessive wake locks occur and our recommendations for optimizing wake locks. We have already seen measurable success from partners like WHOOP, who leveraged these recommendations to optimize their background behavior.

Using a foreground service vs partial wake locks

We've often seen developers struggle to understand the difference between two concepts when doing background execution: foreground service and partial wake locks.

A foreground service is a lifecycle API that signals to the system that an app is performing user-perceptible work and should not be killed to reclaim memory, but it does not automatically prevent the CPU from sleeping when the screen turns off. In contrast, a partial wake lock is a mechanism specifically designed to keep the CPU running even while the screen is off.

While a foreground service is often necessary to continue a user action, a manual acquisition of a partial wake lock is only necessary in conjunction with a foreground service for the duration of the CPU activity. In addition, you don't need to use a wake lock if you're already utilizing an API that keeps the device awake.

Refer to the flow chart in Choose the right API to keep the device awake to ensure you have a strong understanding of what tool to use to avoid acquiring a wake lock in scenarios where it's not necessary.

Third party libraries acquiring wake locks

It is common for an app to discover that it is flagged for excessive wake locks held by a third-party SDK or system API acting on its behalf. To identify and resolve these wake locks, we recommend the following steps:

• Check Android vitals: Find the exact name of the offending wake lock in the excessive partial wake locks dashboard. Cross-reference this name with the Identify wake locks created by other APIs guidance to see if it was created by a known system API or Jetpack library. If it is, you may need to optimize your usage of the API and can refer to the recommended guidance.

• Capture a System Trace: If the wake lock cannot be easily identified, reproduce the wake lock issue locally using a system trace and inspect it with the Perfetto UI. You can learn more about how to do this in the Debugging other types of excessive wake locks section of this blog post.

• Evaluate Alternatives: If an inefficient third-party library is responsible and cannot be configured to respect battery life, consider communicating the issue with the SDK's owners, finding an alternative SDK or building the functionality in-house.

One-Time or Periodic Background Syncs

Example use cases:

• An app performs periodic background syncs to fetch data for offline access.

• Pedometer apps that fetch step count periodically.

How to reduce wake locks:

• Do not acquire a manual wake lock. Use WorkManager configured for one-time or periodic work. WorkManager respects system health by batching tasks and has a minimum periodic interval (15 minutes), which is generally sufficient for background updates.

• If you identify wake locks created by WorkManager or JobScheduler with high wake lock usage, it may be because you've misconfigured your worker to not complete in certain scenarios. Consider analyzing the worker stop reasons, particularly if you're seeing high occurrences of STOP_REASON_TIMEOUT.

workManager.getWorkInfoByIdFlow(syncWorker.id)
  .collect { workInfo ->
      if (workInfo != null) {
        val stopReason = workInfo.stopReason
        logStopReason(syncWorker.id, stopReason)
      }
  }

• In addition to logging worker stop reasons, refer to our documentation on debugging your workers. Also, consider collecting and analyzing system traces to understand when wake locks are acquired and released.

• Finally, check out our case study with WHOOP, where they were able to discover an issue with configuration of their workers and reduce their wake lock impact significantly.

Bluetooth Communication

Example use cases:

• Companion device app prompts the user to pair their Bluetooth external device.

• Companion device app listens for hardware events on an external device and user visible change in notification.

• Companion device app's user initiates a file transfer between the mobile and bluetooth device.

• Companion device app performs occasional firmware updates to an external device via Bluetooth.

How to reduce wake locks:

• Use companion device pairing to pair Bluetooth devices to avoid acquiring a manual wake lock during Bluetooth pairing.

• Consult the Communicate in the background guidance to understand how to do background Bluetooth communication.

• Using WorkManager is often sufficient if there is no user impact to a delayed communication. If a manual wake lock is deemed necessary, only hold the wake lock for the duration of Bluetooth activity or processing of the activity data.

Location Tracking

Example use cases:

• Fitness apps that cache location data for later upload such as plotting running routes

• Food delivery apps that pull location data at a high frequency to update progress of delivery in a notification or widget UI.

How to reduce wake locks:

• Consult our guidance to Optimize location usage. Consider implementing timeouts, leveraging location request batching, or utilizing passive location updates to ensure battery efficiency.

• When requesting location updates using the FusedLocationProvider or LocationManager APIs, the system automatically triggers a device wake-up during the location event callback. This brief, system-managed wake lock is exempted from excessive partial wake lock calculations.

• Avoid acquiring a separate, continuous wake lock for caching location data, as this is redundant. Instead, persist location events in memory or local storage and leverage WorkManager to process them at periodic intervals.

override fun onCreate(savedInstanceState: Bundle?) {
    locationCallback = object : LocationCallback() {
        override fun onLocationResult(locationResult: LocationResult?) {
            locationResult ?: return
            // System wakes up CPU for short duration
            for (location in locationResult.locations){
                // Store data in memory to process at another time
            }
        }
    }
}

High Frequency Sensor Monitoring

Example use cases:

• Pedometer apps that passively collect steps, or distance traveled.

• Safety apps that monitor the device sensors for rapid changes in real time, to provide features such as crash detection or fall detection.

How to reduce wake locks:

• If using SensorManager, reduce usage to periodic intervals and only when the user has explicitly granted access through a UI interaction. High frequency sensor monitoring can drain the battery heavily due to the number of CPU wake-ups and processing that occurs.

• If you're tracking step counts or distance traveled, rather than using SensorManager, leverage Recording API or consider utilizing Health Connect to access historical and aggregated device step counts to capture data in a battery-efficient manner.

• If you're registering a sensor with SensorManager, specify a maxReportLatencyUs of 30 seconds or more to leverage sensor batching to minimize the frequency of CPU interrupts. When the device is subsequently woken by another trigger such as a user interaction, location retrieval, or a scheduled job, the system will immediately dispatch the cached sensor data.

val accelerometer = sensorManager.getDefaultSensor(Sensor.TYPE_ACCELEROMETER)

sensorManager.registerListener(this,
                 accelerometer,
                 samplingPeriodUs, // How often to sample data
                 maxReportLatencyUs // Key for sensor batching 
              )

• If your app requires both location and sensor data, synchronize their event retrieval and processing. By piggybacking sensor readings onto the brief wake lock the system holds for location updates, you avoid needing a wake lock to keep the CPU awake. Use a worker or a short-duration wake lock to handle the upload and processing of this combined data.

val readChannel = socket.openReadChannel()
while (!readChannel.isClosedForRead) {
    // CPU can safely sleep here while waiting for the next packet
    val packet = readChannel.readRemaining(1024) 
    if (!packet.isEmpty) {
         // Data Arrived: The system woke the CPU and we should keep it awake via manual wake lock (urgent) or scheduling a worker (non-urgent)
         performWorkWithWakeLock { 
              val data = packet.readBytes()
              // Additional logic to process data packets
         }
    }
}

05 Mar 2026 12:00am GMT

Posted by Breana Tate, Developer Relations Engineer, Mayank Saini, Senior Android Engineer, Sarthak Jagetia, Senior Android Engineer and Manmeet Tuteja, Android Engineer II

Building an Android app for a wearable means the real work starts when the screen turns off. WHOOP helps members understand how their body responds to training, recovery, sleep, and stress, and for the many WHOOP members on Android, reliable background syncing and connectivity are what make those insights possible.

Earlier this year, Google Play released a new metric in Android vitals: Excessive partial wake locks. This metric measures the percentage of user sessions where cumulative, non-exempt wake lock usage exceeds 2 hours in a 24-hour period. The aim of this metric is to help you identify and address possible sources of battery drain, which is crucial for delivering a great user experience.

Beginning March 1, 2026, apps that continue to not meet the quality threshold may be excluded from Google Play discovery surfaces. A warning may also be placed on the Google Play Store listing, indicating the app might use more battery than expected.

According to Mayank Saini, Senior Android Engineer at WHOOP, this "presented the team with an opportunity to raise the bar on Android efficiency," after Android vitals flagged the app's excessive partial wake lock % as 15%-which exceeded the recommended 5% threshold.

The team viewed the Android vitals metric as a clear signal that their background work was holding the CPU awake longer than necessary. Resolving this would allow them to continue to deliver a great user experience while simultaneously decreasing wasted background time and maintaining reliable and timely Bluetooth connectivity and syncing.

Identifying the issue

To figure out where to get started, the team first turned to Android vitals for more insight into which wake locks were affecting the metric. By consulting the Android vitals excessive partial wake locks dashboard, they were able to identify the biggest contributor to excessive partial wake locks as one of their WorkManager workers (identified in the dashboard as androidx.work.impl.background.systemjob.SystemJobService). To support the WHOOP "always-on experience", the app uses WorkManager for background tasks like periodic syncing and delivering recurring updates to the wearable.

While the team was aware that WorkManager acquires a wake lock while executing tasks in the background, they previously did not have visibility into how all of their background work (beyond just WorkManager) was distributed until the introduction of the excessive partial wake locks metric in Android vitals.

With the dashboard identifying WorkManager as the main contributor, the team was then able to focus their efforts on identifying which of their workers was contributing the most and work towards resolving the issue.

Making use of internal metrics and data to better narrow down the cause

WHOOP already had internal infrastructure set up to monitor WorkManager metrics. They periodically monitor:

1. Average Runtime: For how long does the worker run?

2. Timeouts: How often is the worker timing out instead of completing?

3. Retries: How often does the worker retry if the work timed out or failed?

4. Cancellations: How often was the work cancelled?

Tracking more than just worker successes and failures gives the team visibility into their work's efficiency.

The internal metrics flagged high average runtime for a select few workers, enabling them to narrow the investigation down even further.

In addition to their internal metrics, the team also used Android Studio's Background Task Inspector to inspect and debug the workers of interest, with a specific focus on associated wake locks, to align with the metric flagged in Android vitals.

Investigation: Distinguishing between worker variants

WHOOP uses both one-time and periodic scheduling for some workers. This allows the app to reuse the same Worker logic for identical tasks with the same success criteria, differing only in timing.

Using their internal metrics made it possible to narrow their search to a specific worker, but they couldn't tell if the bug occurred when the worker was one-time, periodic, or both. So, they rolled out an update to use WorkManager's setTraceTag method to distinguish between the one-time and periodic variants of the same Worker.

This extra detail would allow them to definitively identify which Worker variant (periodic or one-time) was contributing the most to sessions with excessive partial wake locks. However, the team was surprised when the data revealed that neither variant appeared to be contributing more than the other.

Manmeet Tuteja, Android Engineer II at WHOOP said "that split also helped us confirm the issue was happening in both variants, which pointed away from scheduling configuration and toward a shared business logic problem inside the worker implementation."

Diving deeper on worker behavior and fixing the root cause

With the knowledge that they needed to take a look at logic within the worker, the team re-examined worker behavior for the workers that had been flagged during their investigation. Specifically, they were looking for instances in which work may have been getting stuck and not completing.

All of this culminated in finding the root cause of the excessive wake locks:

A CoroutineWorker that was designed to wait for a connection to the WHOOP sensor before proceeding.

If the work started with no sensor connected, whoopSensorFlow-which indicates if the sensor is connected- was null. The SensorWorker didn't treat this as an early-exit condition and kept running, effectively waiting indefinitely for a connection. As a result, WorkManager held a partial wake lock until the work timed out, leading to high background wake lock usage and frequent, unwanted rescheduling of the SensorWorker.

To address this, the WHOOP team updated the worker logic to check the connection status before attempting to execute the core business logic.

If the sensor isn't available, the worker exits, avoiding a timeout scenario and releasing the wake lock. The following code snippet shows the solution:

class SensorWorker(appContext: Context, params: WorkerParameters): CoroutineWorker(appContext, params) {
   override suspend fun doWork(): Result {
      ...
      // Check the sensor state and perform work or return failure
       return whoopSensorFlow.replayCache
            .firstOrNull()
            ?.let { cachedData ->
                processSensorData(cachedData)
                Result.success()
            } ?: run {
                Result.failure()
            }
}

Achieving a 90% decrease in sessions with excessive partial wake locks

After rolling out the fix, the team continued to monitor the Android vitals dashboard to confirm the impact of the changes.

Ultimately, WHOOP saw their excessive partial wake lock percentage drop from 15% to less than 1% just 30 days after implementing the changes to their Worker.

As a result of the changes, the team has seen fewer instances of work timing out without completing, resulting in lower average runtimes.

The WHOOP team's advice to other developers that want to improve their background work's efficiency:

Want to dive deeper into the details and hear more insights from the developers? Check out the WHOOP team's blog.

Get Started

If you're interested in trying to reduce your app's excessive partial wake locks or trying to improve worker efficiency, view your app's excessive partial wake locks metric in Android vitals, and review the wake locks documentation for more best practices and debugging strategies.

04 Mar 2026 6:00pm GMT