In MT4, there exists a stage/state: when we switch from AccountA to AccountB, when Connection is established and init() and start() are triggered by MT4; but before the "blinnnggg" (sound) when all the historical/outstanding trades are loaded from Server.
Switch Account>Establish Connection>Trigger Init()/Start() events>Start Downloading of Outstanding/Historical trades>Completed Downloading (issue "bliinng" sound).
I need to know (in MQL4) that all the trades are completed downloaded from the tradeServer --to know that the account is truly empty -vs- still downloading history from tradeServer.
Any pointer will be appreciated. I've explored IsTradeAllowed() IsContextBusy() and IsConnected(). All these are in "normal" state and the init() and start() events are all fired ok. But I cannot figure out if the history/outstanding trade lists has completed downloading.
UPDATE: The final workaround I finally implemented was to use the OrdersHistoryTotal(). Apparently this number will be ZERO (0) during downloading of order history. And it will NEVER be zero (due to initial deposit). So, I ended-up using this as a "flag".
Observation
As the problem was posted, there seems no such "integrated" method for MT4-Terminal.
IsTradeAllowed() reflects an administrative state of the account/access to the execution of the Trading Services { IsTradeAllowed | !IsTradeAllowed }
IsConnected() reflects a technical state of the visibility / login credentials / connection used upon an attempt to setup/maintain an online connection between a localhost <-> Server { IsConnected() | !IsConnected() }
init() {...} is a one-stop setup facility, that is/was being called once an MT4-programme { ExpertAdvisor | Script | TechnicalIndicator } was launched on a localhost machine. This facility is strongly advised to be non-blocking and non-re-entrant. A change from the user account_A to another user account_B is typically ( via an MT4-configuration options ) a reason to stop an execution of a previously loaded MQL4-code ( be it an EA / a Script / a Technical Indicator ) )
start() {...} is an event-handler facility, that endlessly waits, for a next occurrence of an FX-Market Event appearance ( being propagated down the line by the Broker MT4-Server automation ) that is being announced via an established connection downwards, to the MT4-Terminal process, being run on a localhost machine.
A Workaround Solution
As understood, the problem may be detected and handled indirectly.
While the MT4 platform seems to have no direct method to distinguish between the complete / in-complete refresh of the list of { current | historical } trades, let me propose a method of an indirect detection thereof.
Try to launch a "signal"-trade ( a pending order, placed geometrically well far away, in the PriceDOMAIN, from the current Ask/Bid-levels ).
Once this trade would be end-to-end registered ( Server-side acknowledged ), the local-side would have confirmed the valid state of the db.POOL
Making this a request/response pattern between localhost/MT4-Server processes, the localhost int init(){...} / int start(){...} functionality may thus reflect a moment, when the both sides have synchronised state of the records in db.POOL
Related
For a mental health app project, I need to intercept the startup of specific apps (like Instagram) and check if they used instagram the n-th time, possibly opening a questionair etc.
Searching for a solutions online, I came across the "android.app.usage" API. I could not get my around how to use this.
Do I need a for every running background service which does active polling with the usage api?
Or is their a way to say "run this code or start this app/service when appXY launches"?
Looking forward to any kind of input :)
Greetings Pascal
You can't listen out for an "app is being opened" intent unfortunately, Android doesn't support it. Your approach is likely the best workaround, to state it explicitly:
Have a foreground service running (so it is less likely to be killed by the OS) at all times.
Check regularly the currently running app, and see if it's the one you're trying to look for.
If this is different to last time you checked, do whatever you need to. Perhaps this will include keeping track of last time the app was opened, how many times it's been opened etc.
As a warning however, the OS won't really like this, and there's likely to be an impact on battery life. Whatever you do, make sure this check isn't happening when the screen is off, happening as infrequently as possible, and doesn't include any unnecessary computation, otherwise the user will quickly notice the negative impact.
Here's an extract from an article that looks like it'll be able to fetch the latest app even on later versions:
var foregroundAppPackageName : String? = null
val currentTime = System.currentTimeMillis()
// The `queryEvents` method takes in the `beginTime` and `endTime` to retrieve the usage events.
// In our case, beginTime = currentTime - 10 minutes ( 1000 * 60 * 10 milliseconds )
// and endTime = currentTime
val usageEvents = usageStatsManager.queryEvents( currentTime - (1000*60*10) , currentTime )
val usageEvent = UsageEvents.Event()
while ( usageEvents.hasNextEvent() ) {
usageEvents.getNextEvent( usageEvent )
Log.e( "APP" , "${usageEvent.packageName} ${usageEvent.timeStamp}" )
}
I implemented two different solution to discover service on my BLE device. One use a handler then return what .discoverService have found, the other one is really similar but give the size of the service discovered list that is always 0. I tried it with my realme buds 2 as test and some other device publically visible. The result is always 0. What can the problem be?
Handler(Looper.getMainLooper()).post {
var temp = bluetoothGatt?.discoverServices()
addGlog("discordservice() returned ${temp.toString()}")
}
addGlog("handler discover service reached an end")
val gattServices: List<BluetoothGattService> = gatt.getServices()
addGlog("Services count: " + gattServices.size)
for (gattService in gattServices) {
val serviceUUID = gattService.uuid.toString()
addGlog("Service uuid $serviceUUID")
}
edit: AddGlog is a simple log function to print results
answer: The code is not wrong but it take some time to discover those services so i put this code in a button. In this way there is 3-4 second of time between connecting with the device and make a discoveryservice operation. So a button make the conneting operations and another one the service discovery operations. I am sorry if my answer is pretty lame but I am still a noob on this topic
I have a piece of code that is essentially executing the following with Infinispan in embedded mode, using version 13.0.0 of the -core and -clustered-lock modules:
#Inject
lateinit var lockManager: ClusteredLockManager
private fun getLock(lockName: String): ClusteredLock {
lockManager.defineLock(lockName)
return lockManager.get(lockName)
}
fun createSession(sessionId: String) {
tryLockCounter.increment()
logger.debugf("Trying to start session %s. trying to acquire lock", sessionId)
Future.fromCompletionStage(getLock(sessionId).lock()).map {
acquiredLockCounter.increment()
logger.debugf("Starting session %s. Got lock", sessionId)
}.onFailure {
logger.errorf(it, "Failed to start session %s", sessionId)
}
}
I take this piece of code and deploy it to kubernetes. I then run it in six pods distributed over six nodes in the same region. The code exposes createSession with random Guids through an API. This API is called and creates sessions in chunks of 500, using a k8s service in front of the pods which means the load gets balanced over the pods. I notice that the execution time to acquire a lock grows linearly with the amount of sessions. In the beginning it's around 10ms, when there's about 20_000 sessions it takes about 100ms and the trend continues in a stable fashion.
I then take the same code and run it, but this time with twelve pods on twelve nodes. To my surprise I see that the performance characteristics are almost identical to when I had six pods. I've been digging in to the code but still haven't figured out why this is, I'm wondering if there's a good reason why infinispan here doesn't seem to perform better with more nodes?
For completeness the configuration of the locks are as follows:
val global = GlobalConfigurationBuilder.defaultClusteredBuilder()
global.addModule(ClusteredLockManagerConfigurationBuilder::class.java)
.reliability(Reliability.AVAILABLE)
.numOwner(1)
and looking at the code the clustered locks is using DIST_SYNC which should spread out the load of the cache onto the different nodes.
UPDATE:
The two counters in the code above are simply micrometer counters. It is through them and prometheus that I can see how the lock creation starts to slow down.
It's correctly observed that there's one lock created per session id, this is per design what we'd like. Our use case is that we want to ensure that a session is running in at least one place. Without going to deep into detail this can be achieved by ensuring that we at least have two pods that are trying to acquire the same lock. The Infinispan library is great in that it tells us directly when the lock holder dies without any additional extra chattiness between pods, which means that we have a "cheap" way of ensuring that execution of the session continues when one pod is removed.
After digging deeper into the code I found the following in CacheNotifierImpl in the core library:
private CompletionStage<Void> doNotifyModified(K key, V value, Metadata metadata, V previousValue,
Metadata previousMetadata, boolean pre, InvocationContext ctx, FlagAffectedCommand command) {
if (clusteringDependentLogic.running().commitType(command, ctx, extractSegment(command, key), false).isLocal()
&& (command == null || !command.hasAnyFlag(FlagBitSets.PUT_FOR_STATE_TRANSFER))) {
EventImpl<K, V> e = EventImpl.createEvent(cache.wired(), CACHE_ENTRY_MODIFIED);
boolean isLocalNodePrimaryOwner = isLocalNodePrimaryOwner(key);
Object batchIdentifier = ctx.isInTxScope() ? null : Thread.currentThread();
try {
AggregateCompletionStage<Void> aggregateCompletionStage = null;
for (CacheEntryListenerInvocation<K, V> listener : cacheEntryModifiedListeners) {
// Need a wrapper per invocation since converter could modify the entry in it
configureEvent(listener, e, key, value, metadata, pre, ctx, command, previousValue, previousMetadata);
aggregateCompletionStage = composeStageIfNeeded(aggregateCompletionStage,
listener.invoke(new EventWrapper<>(key, e), isLocalNodePrimaryOwner));
}
The lock library uses a clustered Listener on the entry modified event, and this one uses a filter to only notify when the key for the lock is modified. It seems to me the core library still has to check this condition on every registered listener, which of course becomes a very big list as the number of sessions grow. I suspect this to be the reason and if it is it would be really really awesome if the core library supported a kind of key filter so that it could use a hashmap for these listeners instead of going through a whole list with all listeners.
I believe you are creating a clustered lock per session id. Is this what you need ? what is the acquiredLockCounter? We are about to deprecate the "lock" method in favour of "tryLock" with timeout since the lock method will block forever if the clustered lock is never acquired. Do you ever unlock the clustered lock in another piece of code? If you shared a complete reproducer of the code will be very helpful for us. Thanks!
I have a simple Kafka streams scenario where I am doing a groupyByKey then reduce and then an action. There could be duplicate events in the source topic hence the groupyByKey and reduce
The action could error and in that case, I need the streams app to reprocess that event. In the example below I'm always throwing an error to demonstrate the point.
It is very important that the action only ever happens once and at least once.
The problem I'm finding is that when the streams app reprocesses the event, the reduce function is being called and as it returns null the action doesn't get recalled.
As only one event is produced to the source topic TOPIC_NAME I would expect the reduce to not have any values and skip down to the mapValues.
val topologyBuilder = StreamsBuilder()
topologyBuilder.stream(
TOPIC_NAME,
Consumed.with(Serdes.String(), EventSerde())
)
.groupByKey(Grouped.with(Serdes.String(), EventSerde()))
.reduce { current, _ ->
println("reduce hit")
null
}
.mapValues { _, v ->
println(Id: "${v.correlationId}")
throw Exception("simulate error")
}
To cause the issue I run the streams app twice. This is the output:
First run
Id: 90e6aefb-8763-4861-8d82-1304a6b5654e
11:10:52.320 [test-app-dcea4eb1-a58f-4a30-905f-46dad446b31e-StreamThread-1] ERROR org.apache.kafka.streams.KafkaStreams - stream-client [test-app-dcea4eb1-a58f-4a30-905f-46dad446b31e] All stream threads have died. The instance will be in error state and should be closed.
Second run
reduce hit
As you can see the .mapValues doesn't get called on the second run even though it errored on the first run causing the streams app to reprocess the same event again.
Is it possible to be able to have a streams app re-process an event with a reduced step where it's treating the event like it's never seen before? - Or is there a better approach to how I'm doing this?
I was missing a property setting for the streams app.
props["processing.guarantee"]= "exactly_once"
By setting this, it will guarantee that any state created from the point of picking up the event will rollback in case of a exception being thrown and the streams app crashing.
The problem was that the streams app would pick up the event again to re-process but the reducer step had state which has persisted. By enabling the exactly_once setting it ensures that the reducer state is also rolled back.
It now successfully re-processes the event as if it had never seen it before
Situation is the following.
We have setup SSL + ACLs in Kafka Broker.
We are setting up stream, which reads messages from two topics:
KStream<String, String> stringInput
= kBuilder.stream( STRING_SERDE, STRING_SERDE, inTopicName );
stringInput
.filter( streamFilter::passOrFilterMessages )
.map( processor )
.to( outTopicName );
It is done like two times (in the loop).
Then we are setting general error handler:
streams.setUncaughtExceptionHandler( ( Thread t, Throwable e ) -> {
synchronized ( this ) {
LOG.fatal( ... );
this.stop();
}
}
);
Problem is the following. If for example in one topic certificate is no more valid. The stream is throwing exception Not authorized to access topics ...
So far so good.
But the exception is handled by general error handler, so the complete application stops even if the second topic has no problems.
The question is, how to handle this exception per topic?
How to avoid situation that at some moment complete application stops due to the problem that one single topic has problems with authorization?
I understand that if Broker is not available, then complete app may stop. But if only one topic is not available, then single stream shall stop, and not complete application, or?
By design, Kafka Streams treats the topology a one and cannot distinguish between both parts. For your specific case, as you loop and build to independent pipelines, you could run two KafkaStreams instances in parallel (within the same application/JVM) to isolate both from each other. Thus, if one fails, the other one is not affected. You would need to use two different application.id for both instances.