Google is committed to advancing racial equity for Black communities. See how.

DataStore   Part of Android Jetpack.

Jetpack DataStore is a data storage solution that allows you to store key-value pairs or typed objects with protocol buffers. DataStore uses Kotlin coroutines and Flow to store data asynchronously, consistently, and transactionally.

If you're currently using SharedPreferences to store data, consider migrating to DataStore instead.

Preferences DataStore and Proto DataStore

DataStore provides two different implementations: Preferences DataStore and Proto DataStore.

  • Preferences DataStore stores and accesses data using keys. This implementation does not require a predefined schema, and it does not provide type safety.
  • Proto DataStore stores data as instances of a custom data type. This implementation requires you to define a schema using protocol buffers, but it provides type safety.


To use Jetpack DataStore in your app, add the following to your Gradle file depending on which implementation you want to use:

dependencies {
  // Preferences DataStore
  implementation "androidx.datastore:datastore-preferences:1.0.0-alpha01"

  // Proto DataStore
  implementation "androidx.datastore:datastore-core:1.0.0-alpha01"

Store key-value pairs with Preferences DataStore

The Preferences DataStore implementation uses the DataStore and Preferences classes to persist simple key-value pairs to disk.

Create a Preferences DataStore

Use the Context.createDataStore() extension function to create an instance of DataStore<Preferences>. The mandatory name parameter is the name of the Preferences DataStore.

val dataStore: DataStore<Preferences> = context.createDataStore(
  name = "settings"

Read from a Preferences DataStore

Because Preferences DataStore does not use a predefined schema, you must use preferencesKey() to define a key for each value that you need to store in the DataStore<Preferences> instance. Then, use the property to expose the appropriate stored value using a Flow.

val EXAMPLE_COUNTER = preferencesKey<Int>("example_counter")
val exampleCounterFlow: Flow<Int> =
  .map { preferences ->
    // No type safety.
    preferences[EXAMPLE_COUNTER] ?: 0

Write to a Preferences DataStore

Preferences DataStore provides an edit() function that transactionally updates the data in a DataStore. The function's transform parameter accepts a block of code where you can update the values as needed. All of the code in the transform block is treated as a single transaction.

suspend fun incrementCounter() {
  dataStore.edit { settings ->
    val currentCounterValue = settings[EXAMPLE_COUNTER] ?: 0
    settings[EXAMPLE_COUNTER] = currentCounterValue + 1

Store typed objects with Proto DataStore

The Proto DataStore implementation uses DataStore and protocol buffers to persist typed objects to disk.

Define a schema

Proto DataStore requires a predefined schema in a proto file in the app/src/main/proto/ directory. This schema defines the type for the objects that you persist in your Proto DataStore. To learn more about defining a proto schema, see the protobuf language guide.

syntax = "proto3";

option java_package = "com.example.application";
option java_multiple_files = true;

message Settings {
  int example_counter = 1;

Create a Proto DataStore

There are two steps involved in creating a Proto DataStore to store your typed objects:

  1. Define a class that implements Serializer<T>, where T is the type defined in the proto file. This serializer class tells DataStore how to read and write your data type.
  2. Use the Context.createDataStore() extension function to create an instance of DataStore<T>, where T is the type defined in the proto file. The filename parameter tells DataStore which file to use to store the data, and the serializer parameter tells DataStore the name of the serializer class defined in step 1.
object SettingsSerializer : Serializer<Settings> {
  override fun readFrom(input: InputStream): Settings {
    try {
      return Settings.parseFrom(input)
    } catch (exception: InvalidProtocolBufferException) {
      throw CorruptionException("Cannot read proto.", exception)

  override fun writeTo(
    t: Settings,
    output: OutputStream) = t.writeTo(output)

val settingsDataStore: DataStore<Settings> = context.createDataStore(
  fileName = "settings.pb",
  serializer = SettingsSerializer

Read from a Proto DataStore

Use to expose a Flow of the appropriate property from your stored object.

val exampleCounterFlow: Flow<Int> =
  .map { settings ->
    // The exampleCounter property is generated from the proto schema.

Write to a Proto DataStore

Proto DataStore provides an updateData() function that transactionally updates a stored object. updateData() gives you the current state of the data as an instance of your data type and updates the data transactionally in an atomic read-write-modify operation.

suspend fun incrementCounter() {
  settingsDataStore.updateData { currentSettings ->
      .setExampleCounter(currentSettings.exampleCounter + 1)

Use DataStore in synchronous code

One of the primary benefits of DataStore is the asynchronous API, but it may not always be feasible to change your surrounding code to be asynchronous. This might be the case if you're working with an existing codebase that uses synchronous disk I/O or if you have a dependency that doesn't provide an asynchronous API.

Kotlin coroutines provide the runBlocking() coroutine builder to help bridge the gap between synchronous and asynchronous code. You can use runBlocking() to read data from DataStore synchronously. The following code blocks the calling thread until DataStore returns data:

val exampleData = runBlocking { }

Performing synchronous I/O operations on the UI thread can cause ANRs or UI jank. You can mitigate these issues by asynchronously preloading the data from DataStore:

override fun onCreate(savedInstanceState: Bundle?) {
    lifecycleScope.launch {
        // You should also handle IOExceptions here.

This way, DataStore asynchronously reads the data and caches it in memory. Later synchronous reads using runBlocking() may be faster or may avoid a disk I/O operation altogether if the initial read has completed.

Provide feedback

Share your feedback and ideas with us through these resources:

Issue tracker
Report issues so we can fix bugs.

Additional resources

To learn more about Jetpack DataStore, see the following additional resources: