Android Datastore 动态创建与源码解析

涉及到的知识点

1、协程原理---->很好的博客介绍，一个小故事讲明白进程、线程、Kotlin 协程到底啥关系？
2、Channel知识点---->Android—kotlin-Channel超详细讲解
3、Coroutines : CompletableDeferred and structured concurrency

封装的DataStoreUtils工具—>gitHub

本篇博客目的

公司使用SharedPreferences容易导致ANR，调研能否使用DataStore替换公司目前的SharedPreferences解决ANR问题，所以需要先研究一下源码

版本引入

implementation "androidx.datastore:datastore-preferences:1.0.0"

迁移SharedPreferences数据到dataStore

既然是迁移数据，那么需要将SharedPreferences已存储的数据迁移到dataStore，所以需要先构建dataStore。
目前网上构建迁移DataStore的案例Demo如下

//迁移使用
private val Context.dataStore: DataStore<Preferences> by preferencesDataStore(name = "userSharePreFile",produceMigrations = { context ->listOf(SharedPreferencesMigration(context,"userSharePreFile"))}
)//或 
//这种构建DataStore写法是alpha版本有的，在1.0.0版本就找不到了
var dataStore: DataStore<Preferences> = context.createDataStore(name = "userSharePreFile")
//或
//直接构建
private val Context.dataStore: DataStore<Preferences> by preferencesDataStore(name = "userSharePreFile"
)

上面3种写法都是对Context进行扩展创建的DataStore，所以上面创建的方式，都有一个缺点，就是需要提前知道name才能创建，如果你之前创建SharedPreferences的方式，是通过外部传递进来name构建的话，上面直接创建DataStore方式就显然不适合你了。

翻阅旧版本(alpha版本)源码，一探究竟如何构建DataStore

//alpha版本构建方式
var dataStore: DataStore<Preferences> = context.createDataStore(name = "userSharePreFile")fun Context.createDataStore(name: String,corruptionHandler: ReplaceFileCorruptionHandler<Preferences>? = null,//①migrations: List<DataMigration<Preferences>> = listOf(),//②scope: CoroutineScope = CoroutineScope(Dispatchers.IO + SupervisorJob())
): DataStore<Preferences> =PreferenceDataStoreFactory.create(//③produceFile = {File(this.filesDir, "datastore/$name.preferences_pb")},corruptionHandler = corruptionHandler,migrations = migrations,scope = scope)

可以明显看到是使用PreferenceDataStoreFactory.create返回DataStore
① 是构建需要迁移SharedPreferences文件名称
② 指明协程是在IO运行
③ 新文件存储的位置
再看看另外一种通过 by preferencesDataStore 创建DataStore方式

private val Context.dataStore: DataStore<Preferences> by preferencesDataStore(name = "userSharePreFile"
)public fun preferencesDataStore(name: String,corruptionHandler: ReplaceFileCorruptionHandler<Preferences>? = null,//①produceMigrations: (Context) -> List<DataMigration<Preferences>> = { listOf() },//②scope: CoroutineScope = CoroutineScope(Dispatchers.IO + SupervisorJob())
): ReadOnlyProperty<Context, DataStore<Preferences>> {return PreferenceDataStoreSingletonDelegate(name, corruptionHandler, produceMigrations, scope)
}internal class PreferenceDataStoreSingletonDelegate internal constructor(private val name: String,private val corruptionHandler: ReplaceFileCorruptionHandler<Preferences>?,private val produceMigrations: (Context) -> List<DataMigration<Preferences>>,private val scope: CoroutineScope
) : ReadOnlyProperty<Context, DataStore<Preferences>> {private val lock = Any()@GuardedBy("lock")@Volatileprivate var INSTANCE: DataStore<Preferences>? = nulloverride fun getValue(thisRef: Context, property: KProperty<*>): DataStore<Preferences> {return INSTANCE ?: synchronized(lock) {if (INSTANCE == null) {val applicationContext = thisRef.applicationContextINSTANCE = PreferenceDataStoreFactory.create(corruptionHandler = corruptionHandler,migrations = produceMigrations(applicationContext),scope = scope) {applicationContext.preferencesDataStoreFile(name)}}INSTANCE!!}}
}//文件存储位置
public fun Context.preferencesDataStoreFile(name: String): File =this.dataStoreFile("$name.preferences_pb")

题外话：这里有利用kotlin委托属性by关键字语法
① 需要迁移的SharedPreferences文件
② 协程运行在IO

可以看出旧版本(alpha) 与 by preferencesDataStore 2种方案，都最终通过PreferenceDataStoreFactory.create，返回DataStore，我们就继续再看看PreferenceDataStoreFactory.kt的具体实现逻辑

//PreferenceDataStoreFactory.ktpublic fun create(corruptionHandler: ReplaceFileCorruptionHandler<Preferences>? = null,//迁移的share文件集合migrations: List<DataMigration<Preferences>> = listOf(),//IOscope: CoroutineScope = CoroutineScope(Dispatchers.IO + SupervisorJob()),//dataStore文件存储的目录位置produceFile: () -> File ): DataStore<Preferences> {val delegate = DataStoreFactory.create(//创建SingleProcessDataStoreserializer = PreferencesSerializer,corruptionHandler = corruptionHandler,migrations = migrations,scope = scope) {//省略代码} //传入SingleProcessDataStorereturn PreferenceDataStore(delegate)}//这里有主动的去调用updateData 方法，如果不去主动调用，就不会触发迁移的逻辑
//下文的扩展函数DataStore<Preferences>.edit会说到这里
internal class PreferenceDataStore(private val delegate: DataStore<Preferences>) :DataStore<Preferences> by delegate {override suspend fun updateData(transform: suspend (t: Preferences) -> Preferences):Preferences {return delegate.updateData {val transformed = transform(it)(transformed as MutablePreferences).freeze()transformed}}
}

继续看DataStoreFactory.create

//DataStoreFactory.kt
fun <T> create(produceFile: () -> File,serializer: Serializer<T>,corruptionHandler: ReplaceFileCorruptionHandler<T>? = null,migrations: List<DataMigration<T>> = listOf(),scope: CoroutineScope = CoroutineScope(Dispatchers.IO + SupervisorJob())): DataStore<T> =//找到最终创建的类SingleProcessDataStore(produceFile = produceFile,serializer = serializer,corruptionHandler = corruptionHandler ?: NoOpCorruptionHandler(),initTasksList = listOf(DataMigrationInitializer.getInitializer(migrations)),scope = scope)

到目前为止已经知道真相了，最终是通过SingleProcessDataStore返回DataStore。

下面我们通过一张图片来小结一下，旧版本alpha版本的创建与新版本 by preferencesDataStore的调用逻辑链

好，已经知道这么多了，那么我们就开始动态构建DataStore

动态创建DataStore

 fun preferencesMigrationDataStore(sharedPreferName: String) {val dataStore = PreferenceDataStoreFactory.create(corruptionHandler =  ReplaceFileCorruptionHandler<Preferences>(produceNewData = { emptyPreferences() }),//需要迁移的sharePrefer文件的名称migrations = listOf(SharedPreferencesMigration(mContext, sharedPreferName)),//IOscope = CoroutineScope(Dispatchers.IO + SupervisorJob())) {//dataStore文件名称mContext.preferencesDataStoreFile(sharedPreferName)}runBlocking {//必须要执行这行代码，否是不会走迁移的逻辑dataStore.updateData {it.toPreferences()}}}

migrations:表示你要迁移的sharedPreference文件
scope :表示写数据是在IO
执行完上述代码后，.xml就会消失，然后会在files目录下多出一个/datastore/xxx.preferences_pb文件
切勿重复对某个SharedPreferences执行文件迁移方案，否则会报错。比如你前一秒在执行迁移，后一秒又继续执行迁移

####存储参数

/*** @key 参数* @value 具体的值*/private fun putInt(key:String, value: Int) {runBlocking {dataStore.edit {//①it[intPreferencesKey(key)] = value}}}
//类似的还有如下，这些都是google提供的参数
intPreferencesKey
doublePreferencesKey
stringPreferencesKey
....

看①详情，点击edit，发现他是一个扩展函数

public suspend fun DataStore<Preferences>.edit(transform: suspend (MutablePreferences) -> Unit
): Preferences {return this.updateData {//调用的是PreferenceDataStore.updateData()//it.toMutablePreferences() 返回类似mapit.toMutablePreferences().apply { transform(this) }}
}

transform 就是调用者{}里面的内容，接下来我们看看 PreferenceDataStore 类的代码

//由前部分的代码，可以得知，delegate = SingleProcessDataStore 
internal class PreferenceDataStore(private val delegate: DataStore<Preferences>) :DataStore<Preferences> by delegate {override suspend fun updateData(transform: suspend (t: Preferences) -> Preferences):Preferences {return delegate.updateData {//调用SingleProcessDataStore.updateData //返回给上一个{}也就是  it.toMutablePreferences().apply { transform(this) }val transformed = transform(it)(transformed as MutablePreferences).freeze()transformed //拿到用户的需要更改的内容数据}}
}

代码里调用了delegate.updateData()，所以继续看SingleProcessDataStore的updateData

SingleProcessDataStore.ktoverride suspend fun updateData(transform: suspend (t: T) -> T): T {val ack = CompletableDeferred<T>()val currentDownStreamFlowState = downstreamFlow.value//协程体封装进Message.Update，coroutineContext 是协程的上下文，就是我们的 runBlocking 启动的线程，我这里是mainval updateMsg = Message.Update(transform, ack, currentDownStreamFlowState, coroutineContext)//对消息进行分发，他的类是 SimpleActoractor.offer(updateMsg)//这里会拿到Preferences，如何拿？后面会有一个update.ack.completeWith方法，会返回回来return ack.await()}

internal class SimpleActor<T>(private val scope: CoroutineScope,//Dispatchers.IO + SupervisorJob()onComplete: (Throwable?) -> Unit,onUndeliveredElement: (T, Throwable?) -> Unit,private val consumeMessage: suspend (T) -> Unit
) {private val messageQueue = Channel<T>(capacity = UNLIMITED)private val remainingMessages = AtomicInteger(0)//......  省去//这里就是将刚刚封装的消息体，添加进这里fun offer(msg: T) {check(//发送封装的消息体messageQueue.trySend(msg).onClosed { throw it ?: ClosedSendChannelException("Channel was closed normally") }.isSuccess)if (remainingMessages.getAndIncrement() == 0) {scope.launch {check(remainingMessages.get() > 0)do {// scope = Dispatchers.IO + SupervisorJob()scope.ensureActive()//取出封装的消息体，然后进行任务处理consumeMessage(messageQueue.receive())} while (remainingMessages.decrementAndGet() != 0)}}}
}

tip:这里有利用Channel进行协程通信，Channel是可以处理并发的情况
到这里，我们可以知道，我们由runBlocking(main主线程) 协程到 Dispatchers.IO的任务分发

private val actor = SimpleActor<Message<T>>(scope = scope,// CoroutineScope(Dispatchers.IO + SupervisorJob())onComplete = {//.....省略},onUndeliveredElement = { msg, ex ->//.....省略) { msg ->//处理分发的任务，msg 为刚刚封装的updateMsg when (msg) { is Message.Read -> {//读取handleRead(msg)}is Message.Update -> {//更新handleUpdate(msg)}}}

 private suspend fun handleUpdate(update: Message.Update<T>) {update.ack.completeWith(runCatching {when (val currentState = downstreamFlow.value) {is Data -> {//写数据到filetransformAndWrite(update.transform, update.callerContext)}is ReadException, is UnInitialized -> {if (currentState === update.lastState) {           //读取file文件      ①          readAndInitOrPropagateAndThrowFailure()//写数据到file       ②transformAndWrite(update.transform, update.callerContext)} else {throw (currentState as ReadException).readException}}is Final -> throw currentState.finalException // won't happen}})}

第一次使用 downstreamFlow.value = UnInitialized 。
这里要注意一下update.ack.completeWith这个函数，他是拿到结果成功返回

这里再次展示出来，是告诉大家，在哪里会等待结果返回override suspend fun updateData(transform: suspend (t: T) -> T): T {val ack = CompletableDeferred<T>()val currentDownStreamFlowState = downstreamFlow.valueval updateMsg =Message.Update(transform, ack, currentDownStreamFlowState, coroutineContext)actor.offer(updateMsg)return ack.await() //这里就是等待 update.ack.completeWith的结果返回，所以如果不加这行，是不会卡主线程的}

所以使用runBlocking是会卡主线程的，如果你还有UI刷新情况，严重的情况会导致ANR问题

不扯之前的了，我们继续继续，看① 的读取

 private suspend fun readAndInitOrPropagateAndThrowFailure() {try {readAndInit()} catch (throwable: Throwable) {downstreamFlow.value = ReadException(throwable)throw throwable}}private suspend fun readAndInit() {check(downstreamFlow.value == UnInitialized || downstreamFlow.value is ReadException)//这个是锁，协程里面专有的，详情可以看 https://www.kotlincn.net/docs/reference/coroutines/shared-mutable-state-and-concurrency.htmlval updateLock = Mutex()//读取dataStore文件var initData = readDataOrHandleCorruption()var initializationComplete: Boolean = false//这里就是shareprefence转dataStoreval api = object : InitializerApi<T> {override suspend fun updateData(transform: suspend (t: T) -> T): T {return updateLock.withLock() {if (initializationComplete) {throw IllegalStateException("InitializerApi.updateData should not be " +"called after initialization is complete.")}//transform里面就是去迁移数据的方法val newData = transform(initData)//这里有做，新 旧值比较，如果不同，就去写入if (newData != initData) {//写文件writeData(newData)initData = newData}initData}}}//initTasks 里面装的就是需要转换的 SharedPreferences集合initTasks?.forEach { it(api) }initTasks = nullupdateLock.withLock {initializationComplete = true}//这里有将迁移完成后的数据，存储在flow.value里面downstreamFlow.value = Data(initData, initData.hashCode())}//读取dataStore文件
private suspend fun readDataOrHandleCorruption(): T {try {return readData()} catch (ex: CorruptionException) {val newData: T = corruptionHandler.handleCorruption(ex)try {writeData(newData)} catch (writeEx: IOException) {ex.addSuppressed(writeEx)throw ex}return newData}}private suspend fun readData(): T {try {FileInputStream(file).use { stream ->return serializer.readFrom(stream)}} catch (ex: FileNotFoundException) {if (file.exists()) {throw ex}return serializer.defaultValue}}

file就是我们存储的dataStore，目录是在 “datastore/$name.preferences_pb”

看完了①，再来看看② 写入数据到file，写数据的方法是 transformAndWrite()

//....
transformAndWrite(update.transform, update.callerContext)
//...private suspend fun transformAndWrite(//来源于 Message.Update.transform封装transform: suspend (t: T) -> T,//来源于 Message.Update.callerContext封装callerContext: CoroutineContext): T {val curDataAndHash = downstreamFlow.value as Data<T>curDataAndHash.checkHashCode()val curData = curDataAndHash.value//这里callerContext  就是我们的 runBlocking，main(主线程)//这里是将旧的值给回调用者，然后从调用者获取到新参数val newData = withContext(callerContext) { transform(curData) }curDataAndHash.checkHashCode()//这里有做数据比较return if (curData == newData) {curData} else {//写入数据writeData(newData)//保存到flow.value里面downstreamFlow.value = Data(newData, newData.hashCode())newData}}private val SCRATCH_SUFFIX = ".tmp"
//写入数据
internal suspend fun writeData(newData: T) {file.createParentDirectories()//这里创建出来的文件是"datastore/$name.preferences_pb.tmp"val scratchFile = File(file.absolutePath + SCRATCH_SUFFIX)try {FileOutputStream(scratchFile).use { stream ->serializer.writeTo(newData, UncloseableOutputStream(stream))stream.fd.sync()}//重新命名回去file，这里的file是我们目标的文件dataStore名称if (!scratchFile.renameTo(file)) {//重新命名失败，抛出异常throw IOException("Unable to rename $scratchFile." +"This likely means that there are multiple instances of DataStore " +"for this file. Ensure that you are only creating a single instance of " +"datastore for this file.")}} catch (ex: IOException) {if (scratchFile.exists()) {scratchFile.delete() }throw ex}}

到此，更新值的操作，我们已经全部走完了流程

总结

1、文件的写入是发生在IO层面
2、使用runBlocking是会卡主线程，如果此时存在需要刷新UI的情况，严重会ANR


/*** @key 参数* @value 具体的值*/private fun putInt(key:String, value: Int) {runBlocking {dataStore.edit {it[intPreferencesKey(key)] = value}}}public suspend fun DataStore<Preferences>.edit(transform: suspend (MutablePreferences) -> Unit
): Preferences {return this.updateData {it.toMutablePreferences().apply { transform(this) }}
}//更新逻辑private suspend fun handleUpdate(update: Message.Update<T>) {update.ack.completeWith(//通知结果回调//.....省去)}//transform 就是上面的{}里面的内容override suspend fun updateData(transform: suspend (t: T) -> T): T {val ack = CompletableDeferred<T>()val currentDownStreamFlowState = downstreamFlow.valueval updateMsg =Message.Update(transform, ack, currentDownStreamFlowState, coroutineContext)actor.offer(updateMsg)return ack.await() //这里就是等待 update.ack.completeWith的结果返回，所以如果不加这行，是不会卡主线程的//题外话不加ack.await() 代码也会执行}

所以，可以考虑使用withContext(IO){读取/更新等待操作}

3、更新参数的时候，是会跟旧的值比较，如果值相同就不写入了，否则就写入到文件里面，并且更新flow.value的值

 return if (curData == newData) {curData} else {writeData(newData)downstreamFlow.value = Data(newData, newData.hashCode())newData}

4、解决并发问题，使用channel解决协程之间沟通与并发，单线程的IO更新文件与并发

5、如果已将SharedPreference迁移到DataStore，你就不要继续使用SharedPreferences了，如果继续使用SharedPreferences，会与DataStore的值不同了