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Which way should I choose between WorkManager and Handler when I run a task periodically?

In the application’s life-cycle, I hope to get the maxAmplitude of MediaRecorder every 100ms just like Code A
I have read the article both this one and that one
I was told that
WorkManager is the primary recommended API for background processing. Scheduled tasks that start at a later time and can run periodically
and
When application is running and we want to schedule or run a task at a specific time then it is recommended to use Handler class in conjunction with Timer and Thread. Instead of using Alarm Manger, Job Scheduler etc. it is easier and much more efficient to use Handler.
1: Which way should I choose between WorkManager and Handler when I run a task periodically ?
2: And more, is Timer().scheduleAtFixedRate(timerTask{ }) good way?
Code A
private fun getAmplitude(): Int {
var result = 0
mRecorder?.let {
result = it.maxAmplitude
}
return result
}

how to implement executors for splash screen in android

I read on post that Coroutine isn't a good practice for using on splash screen and Executors is the best for that because Coroutine takes a time to start more than Executors, but I didn't find an example of implementing that, is the Executors is the normal java Executor class which used to manage thread pool?

First, for splash best performance you must read the link mentioned in comments by William Reed here and here and also this other link here
and when you read the articles focus on these words
The measurements were made on a debuggable app differ surprisingly
from production performance
As pointed out by Jake Wharton, the difference is partly due to
ExecutorService being preloaded by the Zygote, a special part of the
Android framework that shares code between processes. Other
concurrency frameworks which, like coroutines, aren’t preloaded, will
also have a comparatively high initialization cost. That said,
coroutines have a lot of advantages over ExecutorService. They’ve got
scopes, suspending functions, they’re much more lightweight than
threads, etc. The general recommendation to use them in Android
applications is sound, but their impressive feature set has an
initialization cost at the moment. Perhaps the Kotlin and Android
teams will be able to optimize this in the future. Until then, best to
avoid using coroutines in your Application class, or in your main
Activity, if startup time is a primary concern.
Second, for your other part of the question I think that is the Executor you asked about
And here how to use it
1 - To execute code in the main thread
// Create an executor that executes tasks in the main thread.
val mainExecutor = ContextCompat.getMainExecutor(this)
// Execute a task in the main thread
mainExecutor.execute {
// You code logic goes here.
}
2 - To execute code in a background thread
// Create an executor that executes tasks in a background thread.
val backgroundExecutor: ScheduledExecutorService = Executors.newSingleThreadScheduledExecutor()
// Execute a task in the background thread.
backgroundExecutor.execute {
// Your code logic goes here.
}
// Execute a task in the background thread after 3 seconds.
backgroundExecutor.schedule({
// Your code logic goes here
}, 3, TimeUnit.SECONDS)
Remember to shut down the executor after using.
backgroundExecutor.shutdown(); // or backgroundExecutor.shutdownNow();
3 - To execute code in a background thread and update UI on the main thread.
// Create an executor that executes tasks in the main thread.
val mainExecutor: Executor = ContextCompat.getMainExecutor(this)
// Create an executor that executes tasks in a background thread.
val backgroundExecutor = Executors.newSingleThreadScheduledExecutor()
// Execute a task in the background thread.
backgroundExecutor.execute {
// Your code logic goes here.
// Update UI on the main thread
mainExecutor.execute {
// You code logic goes here.
}
}
what is the best for performance on Debug (Production May vary as mentioned before)?- Executor, and Coroutine takes much time based on trying both of them with this function
fun TestPerformance(){
val startTime = System.nanoTime()
// DEBUG in handler 3002952707
Handler(Looper.getMainLooper()).postDelayed({
val endTime = System.nanoTime()
println("DEBUG in handler ${endTime-startTime}")
}, 3000)
// DEBUG in backgroundExecutor 3001161822
val backgroundExecutor: ScheduledExecutorService = Executors.newSingleThreadScheduledExecutor()
backgroundExecutor.schedule({
val endTime = System.nanoTime()
println("DEBUG in backgroundExecutor ${endTime-startTime}")
}, 3, TimeUnit.SECONDS)
// DEBUG in GlobalScope 3046312603
GlobalScope.launch {
delay(3000)
val endTime = System.nanoTime()
println("DEBUG in GlobalScope ${endTime-startTime}")
}
}
As you can see I add another way to the comparison with handler too, and look at the comments in the code, both handler and Executor faster than Coroutine and sometimes handler wins and sometimes Executor wins, both of them gives the same performance.
and if you want more examples for how to use handler check this answer it's a great help and I used it in my answer.

once you have an Executor instance, you can submit multiple tasks to it, and have them executed one after another. You can't do that simply with a raw Thread.
Creates an Executor that uses a single worker thread operating off an unbounded queue. (Note however that if this single thread terminates due to a failure during execution prior to shutdown, a new one will take its place if needed to execute subsequent tasks.) Tasks are guaranteed to execute sequentially, and no more than one task will be active at any given time.
Executors.newSingleThreadExecutor().execute {
// todo.
}

ExecutorService one threaded threadpool is blocking the queue if too many requests are executed

I am using a threadpool with only one thread to execute some network tasks (They need to be executed sequentially)through a viewholder in a RecyclerView in Android. It is working, if I am scrolling slow. But if I am scrolling fast, too many tasks will be pushed to the queue, which I have discovered through debugging, is blocking the queue entirely.
I have simplified the code (much) where I am using the one threaded threadpool:
if (executorService == null) {
executorService = Executors.newFixedThreadPool(1);
}
executorService.execute(new Runnable() {
#Override
public void run() {
RessourceService.getCoverPage(url, callback);
while (waiting) {}
}
});
So what is the alternatives, if I want to execute network tasks sequentially without getting a blocked queue?

Have you tried using newSingleThreadExecutor()
public static ExecutorService newSingleThreadExecutor()
Creates an Executor that uses a single worker thread operating off an
unbounded queue.
(Note however that if this single thread terminates
due to a failure during execution prior to shutdown, a new one will
take its place if needed to execute subsequent tasks.)
Tasks are guaranteed to execute sequentially, and no more than one task will be
active at any given time. Unlike the otherwise equivalent
newFixedThreadPool(1) the returned executor is guaranteed not to be
reconfigurable to use additional threads.

(Posted answer on behalf of the question author).
The problem was somewhere else in my code and not in that posted in the question. As described in my question, I am executing the requests through a viewholder of a recyclerview. When scrolling fast, the images are not loaded. I found out that the problem was occurring when an item was out of sight in the recyclerview. The reason for this, was that the request was locked due to my "waiting" variable being true. I could solve it by making it static and setting it to false right before each request and thereby stopping the request in progress in another viewholder.
Nevertheless, the suggesting by #Jessie Brian Revil on using newSingleThreadExecutor, makes perfectly sense. Not a direct solution to my problem, but a reasonable solution for the code above (therefore accept).

Remove and terminate all tasks from AsyncTask.THREAD_POOL_EXECUTOR

I want to run a number of AsyncTask's on AsyncTask.THREAD_POOL_EXECUTOR.
I am using the following code.
Tasks[i].executeOnExecutor(AsyncTask.THREAD_POOL_EXECUTOR,
taskParams);
After certain number of threads was sent to the executor, it no longer accepts new AsyncTasks. (I do not see any errors, but doInBackGroundMethod is not started for some reason).
I suspect this happens because it's pool is full.
Is there any way to reset the executor?
I want to completely terminate all the tasks so that it will start to accept new tasks again and process them immediately.
I tried to terminate the Tasks using their cancel method, but it didn't help.

Are your tasks finishing? Are you handling the apropriate exceptions? How many tasks are you creating?
Keep in mind that AsyncTask.THREAD_POOL_EXECUTOR (at least in API 17; this doc page has a bunch of useful details), is set up with
public static final Executor THREAD_POOL_EXECUTOR = new ThreadPoolExecutor(CORE_POOL_SIZE, MAXIMUM_POOL_SIZE, KEEP_ALIVE, TimeUnit.SECONDS, sPoolWorkQueue, sThreadFactory);
Where CORE_POOL_SIZE is 5 and MAXIMUM_POOL_SIZE is 128.
The sPoolWorkQueue with
private static final BlockingQueue<Runnable> sPoolWorkQueue = new LinkedBlockingQueue<Runnable>(10);
This means that the queue has a maximum capacity of 10 items.
The THREAD_POOL_EXECUTOR keeps at least 5 threads alive. As tasks are added, they are added to the queue. When the queue fills, then new threads will be spawned, up to 128 (there are other limitations tho).
When this happens (128 threads, the max, are running and the queue is full) then any new tasks will be rejected, and handled with the default handler (since it's not being set explicitly, is AbortPolicy), which throws a RejectedExecutionException.
Make sure that you're not just catching and not handling such exceptions.
One way to deal with this is to create your own ExecutorService (even if it's just your instance of ThreadPoolExecutor with different parameters) and configure the parameters that fit your situation best. Parameters like the queue (you can control the amount of items it can hold, or if it's unbounded, etc) or how to handle rejected tasks.

I can see two reasons to having cancelled AsyncTasks still running:
Either you don't call cancel with mayInterruptIfRunning set to true
Either you do call AsyncTask.cancel(true) but the InterruptedException is miss-handled by the executed code: Make sure your code (and the possible subsequent calls to libraries) don't obfuscate/silence InterruptedExceptions by a try/catch on InterruptedExceptions or even on Exception.

How AsyncTask works in Android

I want to know how AsyncTask works internally.
I know it uses the Java Executor to perform the operations but still some of the questions I am not understanding. Like:
How many AsyncTask can be started at a time in an Android app?
When I start 10 AsyncTask, will all tasks will run simultaneously or one by one?
I have tried with 75000 AsyncTask to test the same. I don't get any problem and seems like all the tasks will be pushed to stack and will run one by one.
Also when I start 100000 AsyncTasks, I start getting OutOfMemoryError.
So is there any limit of no of AsyncTask which can be run at a time?
Note: I have tested these on SDK 4.0

AsyncTask has a rather long story.
When it first appeared in Cupcake (1.5) it handled background operations with a single additional thread (one by one). In Donut (1.6) it was changed, so that a pool of thread had begun to be used. And operations could be processed simultaneously until the pool had been exhausted. In such case operations were enqueued.
Since Honeycomb default behavior is switched back to use of a single worker thread (one by one processing). But the new method (executeOnExecutor) is introduced to give you a possibility to run simultaneous tasks if you wish (there two different standard executors: SERIAL_EXECUTOR and THREAD_POOL_EXECUTOR).
The way how tasks are enqueued also depends on what executor you use. In case of a parallel one you are restricted with a limit of 10 (new LinkedBlockingQueue<Runnable>(10)). In case of a serial one you are not limited (new ArrayDeque<Runnable>()).
So the way your tasks are processed depends on how you run them and what SDK version you run them on.
As for thread limits, we are not guaranteed with any, yet looking at the ICS source code we can say that number of threads in the pool can vary in range 5..128.
When you start 100000 with default execute method serial executor is used.
Since tasks that cannot be processed immediately are enqueued you get OutOfMemoryError (thousands of tasks are added to the array backed queue).
Exact number of tasks you can start at once depends on the memory class of the device you are running on and, again, on executor you use.

let us dive deep into the Android’s Asynctask.java file to understand it from a designer’s perspective and how it has nicely implemented Half Sync-Half Async design pattern in it.
In the beginning of the class few lines of codes are as follows:
private static final ThreadFactory sThreadFactory = new ThreadFactory() {
private final AtomicInteger mCount = new AtomicInteger(1);
public Thread newThread(Runnable r) {
return new Thread(r, "AsyncTask #" + mCount.getAndIncrement());
}
};
private static final BlockingQueue<Runnable> sPoolWorkQueue =
new LinkedBlockingQueue<Runnable>(10);
/**
* An {#link Executor} that can be used to execute tasks in parallel.
*/
public static final Executor THREAD_POOL_EXECUTOR
= new ThreadPoolExecutor(CORE_POOL_SIZE, MAXIMUM_POOL_SIZE, KEEP_ALIVE,
TimeUnit.SECONDS, sPoolWorkQueue, sThreadFactory);
The first is a ThreadFactory which is responsible for creating worker threads. The member variable of this class is the number of threads created so far. The moment it creates a worker thread, this number gets increased by 1.
The next is the BlockingQueue. As you know from the Java blockingqueue documentation, it actually provides a thread safe synchronized queue implementing FIFO logic.
The next is a thread pool executor which is responsible for creating a pool of worker threads which can be taken as and when needed to execute different tasks.
If we look at the first few lines we will know that Android has limited the maximum number of threads to be 128 (as evident from private static final int MAXIMUM_POOL_SIZE = 128).
Now the next important class is SerialExecutor which has been defined as follows:
private static class SerialExecutor implements Executor {
final ArrayDeque<Runnable> mTasks = new ArrayDeque<Runnable>();
Runnable mActive;
public synchronized void execute(final Runnable r) {
mTasks.offer(new Runnable() {
public void run() {
try {
r.run();
} finally {
scheduleNext();
}
}
});
if (mActive == null) {
scheduleNext();
}
}
protected synchronized void scheduleNext() {
if ((mActive = mTasks.poll()) != null) {
THREAD_POOL_EXECUTOR.execute(mActive);
}
}
}
The next important two functions in the Asynctask is
public final AsyncTask<Params, Progress, Result> execute(Params... params) {
return executeOnExecutor(sDefaultExecutor, params);
}
and
public final AsyncTask<Params, Progress, Result> executeOnExecutor(Executor exec,
Params... params) {
if (mStatus != Status.PENDING) {
switch (mStatus) {
case RUNNING:
throw new IllegalStateException("Cannot execute task:"
+ " the task is already running.");
case FINISHED:
throw new IllegalStateException("Cannot execute task:"
+ " the task has already been executed "
+ "(a task can be executed only once)");
}
}
mStatus = Status.RUNNING;
onPreExecute();
mWorker.mParams = params;
exec.execute(mFuture);
return this;
}
AS it becomes clear from the above code we can call the executeOnExecutor from exec function of Asynctask and in that case it takes a default executor. If we dig into the sourcecode of Asynctask, we will find that this default executor is nothing but a serial executor, the code of which has been given above.
Now lets delve into the SerialExecutor class. In this class we have final ArrayDeque<Runnable> mTasks = new ArrayDeque<Runnable>();.
This actually works as a serializer of the different requests at different threads. This is an example of Half Sync Half Async pattern.
Now lets examine how the serial executor does this. Please have a look at the portion of the code of the SerialExecutor which is written as
if (mActive == null) {
scheduleNext();
}
So when the execute is first called on the Asynctask, this code is executed on the main thread (as mActive will be initialized to NULL) and hence it will take us to the scheduleNext() function.
The ScheduleNext() function has been written as follows:
protected synchronized void scheduleNext() {
if ((mActive = mTasks.poll()) != null) {
THREAD_POOL_EXECUTOR.execute(mActive);
}
}
So in the schedulenext() function we initialize the mActive with the Runnable object which we have already inserted at the end of the dequeue. This Runnable object (which is nothing but the mActive) then is executed on a thread taken from the threadpool. In that thread, then "finally "block gets executed.
Now there are two scenarios.
another Asynctask instance has been created and we call the execute method on it when the first task is being executed.
execute method is called for the second time on a same instance of the Asynctask when the first task is getting executed.
Scenario I : if we look at the execute function of the Serial Executor, we will find that we actually create a new runnable thread (Say thread t) for processing the background task.
Look at the following code snippet-
public synchronized void execute(final Runnable r) {
mTasks.offer(new Runnable() {
public void run() {
try {
r.run();
} finally {
scheduleNext();
}
}
});
As it becomes clear from the line mTasks.offer(new Runnable), every call to the execute function creates a new worker thread. Now probably you are able to find out the similarity between the Half Sync - Half Async pattern and the functioning of SerialExecutor. Let me, however, clarify the doubts. Just like the Half Sync - Half Async pattern's Asynchronous layer, the
mTasks.offer(new Runnable() {
....
}
part of the code creates a new thread the moment execute function is called and push it to the queue (the mTasks). It is done absolutely asynchronously, as the moment it inserts the task in the queue, the function returns. And then background thread executes the task in a synchronous manner. So its similar to the Half Sync - Half Async pattern. Right?
Then inside that thread t, we run the run function of the mActive. But as it is in the try block, the finally will be executed only after the background task is finished in that thread. (Remember both try and finally are happening inside the context of t). Inside finally block, when we call the scheduleNext function, the mActive becomes NULL because we have already emptied the queue. However, if another instance of the same Asynctask is created and we call execute on them, the execute function of these Asynctask won’t be executed because of the synchronization keyword before execute and also because the SERIAL_EXECUTOR is a static instance (hence all the objects of the same class will share the same instance… its an example of class level locking) i mean no instance of the same Async class can preempt the background task that is running in thread t. and even if the thread is interrupted by some events, the finally block which again calls the scheduleNext() function will take care of it.what it all means that there will be only one active thread running the task. this thread may not be the same for different tasks, but only one thread at a time will execute the task. hence the later tasks will be executed one after another only when the first task complets. thats why it is called SerialExecutor.
Scenario II: In this case we will get an exception error. To understand why the execute function cannot be called more than once on the same Asynctask object, please have a look at the below code snippet taken from executorOnExecute function of Asynctask.java especially in the below mentioned portion:
if (mStatus != Status.PENDING) {
switch (mStatus) {
case RUNNING:
throw new IllegalStateException("Cannot execute task:"
+ " the task is already running.");
case FINISHED:
throw new IllegalStateException("Cannot execute task:"
+ " the task has already been executed "
+ "(a task can be executed only once)");
}
}
AS from the above code snippet it becomes clear that if we call execute function twice when a task is in the running status it throws an IllegalStateException saying “Cannot execute task: the task is already running.”.
if we want multiple tasks to be executed parallely, we need to call the execOnExecutor passing Asynctask.THREAD_POOL_EXECUTOR (or maybe an user defined THREAD_POOL as the exec parameter.
You can read my discussion on Asynctask internals here.

AsyncTasks has a fixed size queue internally for storing delayed tasks. The queue size by default is 10. For example if you start 15 your tasks in a row, then first 5 will enter their doInBackground(), but the rest will wait in the queue for free worker thread. As one of the first 5 finishes, and thus releases the worker thread, a task from the queue will start execution. In this case at most 5 tasks will run together.
Yes, there is limit of how many tasks can be run run at a time. So AsyncTask uses thread pool executor with limited max number of the worker threads and the delayed tasks queue use fixed size 10. Max number of worker threads is 128. If you try to execute more than 138 custom tasks your application will throw the RejectedExecutionException.

How many AsyncTask can be started at a time in an Android app?
AsyncTask is backed by a LinkedBlockingQueue with a capacity of 10 (in ICS and gingerbread). So it really depends on how many tasks you are trying to start & how long they take to finish - but it's definitely possible to exhaust the queue's capacity.
When I start 10 AsyncTask, will all tasks will run simultaneously or one by one?
Again, this depends on the platform. The maximum pool size is 128 in both gingerbread and ICS - but the *default behavior* changed between 2.3 and 4.0 - from parallel by default to serial. If you want to execute in parallel on ICS you need to call [executeOnExecutor][1] in conjunction with THREAD_POOL_EXECUTOR
Try switching to the parallel executor and spam it with 75 000 tasks - the serial impl. has an internal ArrayDeque that has no upper capacity bound (except OutOfMemoryExceptions ofc).