Environment: I am running Apache Ignite v2.13.0 for the cache and the cache store is persisting to a Mongo DB v3.6.0. I am also utilizing Spring Boot (Java).
Question: When I have an expiration policy set, how do I remove the corresponding data from my persistent database?
What I have attempted: I have attempted to utilize CacheEntryExpiredListener but my print statement is not getting triggered. Is this the proper way to solve the problem?
Here is a sample bit of code:
#Service
public class CacheRemovalListener implements CacheEntryExpiredListener<Long, Metrics> {
#Override
public void onExpired(Iterable<CacheEntryEvent<? extends Long, ? extends Metrics>> events) throws CacheEntryListenerException {
for (CacheEntryEvent<? extends Long, ? extends Metrics> event : events) {
System.out.println("Received a " + event);
}
}
}
Use Continuous Query to get notifications about Ignite data changes.
ExecutorService mongoUpdateExecutor = Executors.newSingleThreadExecutor();
CacheEntryUpdatedListener<Integer, Integer> lsnr = new CacheEntryUpdatedListener<Integer, Integer>() {
#Override
public void onUpdated(Iterable<CacheEntryEvent<? extends Integer, ? extends Integer>> evts) {
for (CacheEntryEvent<?, ?> e : evts) {
if (e.getEventType() == EventType.EXPIRED) {
// Use separate executor to avoid blocking Ignite threads
mongoUpdateExecutor.submit(() -> removeFromMongo(e.getKey()));
}
}
}
};
var qry = new ContinuousQuery<Integer, Integer>()
.setLocalListener(lsnr)
.setIncludeExpired(true);
// Start receiving updates.
var cursor = cache.query(qry);
// Stop receiving updates.
cursor.close();
Note 1: EXPIRED events should be enabled explicitly with ContinuousQuery#setIncludeExpired.
Note 2: Query listeners should not perform any heavy/blocking operations. Offload that work to a separate thread/executor.
Related
I have a springboot with springdata mongodb application where I am connecting to mongo change stream to save the changes to a audit collection. My application is running multiple instances (2 instances) and will be scaled up to n number instances when the load increased. When records are created in the original collection (“my collection”), the listeners will be triggered in all running instances and creates duplicate records. Following is my setup
build.gradle
…
// spring data mingodb version 3.1.5
implementation 'org.springframework.boot:spring-boot-starter-data-mongodb'
…
Listener config
#Configuration
#Slf4j
public class MongoChangeStreamListenerConfig {
#Bean
MessageListenerContainer changeStreamListenerContainer(
MongoTemplate template,
PartyConsentAuditListener consentAuditListener,
ErrorHandler errorHandler) {
MessageListenerContainer messageListenerContainer =
new MongoStreamListenerContainer(template, errorHandler);
ChangeStreamRequest<PartyConsentEntity> request =
ChangeStreamRequest.builder(consentAuditListener)
.collection("my-collection")
.filter(newAggregation(match(where("operationType").in("insert", "update", "replace"))))
.fullDocumentLookup(FullDocument.UPDATE_LOOKUP)
.build();
messageListenerContainer.register(request, MyEntity.class, errorHandler);
log.info("mongo stream listener is registered");
return messageListenerContainer;
}
#Bean
ErrorHandler getLoggingErrorHandler() {
return new ErrorHandler() {
#Override
public void handleError(Throwable throwable) {
log.error("error in creating audit records {}", throwable);
}
};
}
}
Listener container
public class MongoStreamListenerContainer extends DefaultMessageListenerContainer {
public MongoStreamListenerContainer(MongoTemplate template, ErrorHandler errorHandler) {
super(template, Executors.newFixedThreadPool(15), errorHandler);
}
#Override
public boolean isAutoStartup() {
return true;
}
}
ChangeListener
#Component
#Slf4j
#RequiredArgsConstructor
/**
* This class will listen to mongodb change stream and process changes. The onMessage will triggered
* when a record added, updated or replaced in the mongo db.
*/
public class MyEntityAuditListener
implements MessageListener<ChangeStreamDocument<Document>, MyEntity> {
#Override
public void onMessage(Message<ChangeStreamDocument<Document>, MyEntity > message) {
var update = message.getBody();
log.info("db change event received");
if (update != null) {
log.info("creating audit entries for id {}", update.getId());
// This will execute in all the instances and creating duplicating records
}
}
}
Is there a way to control the execution on one instance at a given time and share the load between nodes?. It would be really nice to know if there is a config from spring data mongodb to control the flow.
Also, I have checked the following post in stack overflow and I am not sure how to use this with spring data.
Mongo Change Streams running multiple times (kind of): Node app running multiple instances
Any help or tip to resolve this issue is highly appreciated. Thank you very much in advance.
Whenever I use addListenerForSingleValueEvent with setPersistenceEnabled(true), I only manage to get a local offline copy of DataSnapshot and NOT the updated DataSnapshot from the server.
However, if I use addValueEventListener with setPersistenceEnabled(true), I can get the latest copy of DataSnapshot from the server.
Is this normal for addListenerForSingleValueEvent as it only searches DataSnapshot locally (offline) and removes its listener after successfully retrieving DataSnapshot ONCE (either offline or online)?
Update (2021): There is a new method call (get on Android and getData on iOS) that implement the behavior you'll like want: it first tries to get the latest value from the server, and only falls back to the cache when it can't reach the server. The recommendation to use persistent listeners still applies, but at least there's a cleaner option for getting data once even when you have local caching enabled.
How persistence works
The Firebase client keeps a copy of all data you're actively listening to in memory. Once the last listener disconnects, the data is flushed from memory.
If you enable disk persistence in a Firebase Android application with:
Firebase.getDefaultConfig().setPersistenceEnabled(true);
The Firebase client will keep a local copy (on disk) of all data that the app has recently listened to.
What happens when you attach a listener
Say you have the following ValueEventListener:
ValueEventListener listener = new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot snapshot) {
System.out.println(snapshot.getValue());
}
#Override
public void onCancelled(FirebaseError firebaseError) {
// No-op
}
};
When you add a ValueEventListener to a location:
ref.addValueEventListener(listener);
// OR
ref.addListenerForSingleValueEvent(listener);
If the value of the location is in the local disk cache, the Firebase client will invoke onDataChange() immediately for that value from the local cache. If will then also initiate a check with the server, to ask for any updates to the value. It may subsequently invoke onDataChange() again if there has been a change of the data on the server since it was last added to the cache.
What happens when you use addListenerForSingleValueEvent
When you add a single value event listener to the same location:
ref.addListenerForSingleValueEvent(listener);
The Firebase client will (like in the previous situation) immediately invoke onDataChange() for the value from the local disk cache. It will not invoke the onDataChange() any more times, even if the value on the server turns out to be different. Do note that updated data still will be requested and returned on subsequent requests.
This was covered previously in How does Firebase sync work, with shared data?
Solution and workaround
The best solution is to use addValueEventListener(), instead of a single-value event listener. A regular value listener will get both the immediate local event and the potential update from the server.
A second solution is to use the new get method (introduced in early 2021), which doesn't have this problematic behavior. Note that this method always tries to first fetch the value from the server, so it will take longer to completely. If your value never changes, it might still be better to use addListenerForSingleValueEvent (but you probably wouldn't have ended up on this page in that case).
As a workaround you can also call keepSynced(true) on the locations where you use a single-value event listener. This ensures that the data is updated whenever it changes, which drastically improves the chance that your single-value event listener will see the current value.
So I have a working solution for this. All you have to do is use ValueEventListener and remove the listener after 0.5 seconds to make sure you've grabbed the updated data by then if needed. Realtime database has very good latency so this is safe. See safe code example below;
public class FirebaseController {
private DatabaseReference mRootRef;
private Handler mHandler = new Handler();
private FirebaseController() {
FirebaseDatabase.getInstance().setPersistenceEnabled(true);
mRootRef = FirebaseDatabase.getInstance().getReference();
}
public static FirebaseController getInstance() {
if (sInstance == null) {
sInstance = new FirebaseController();
}
return sInstance;
}
Then some method you'd have liked to use "addListenerForSingleEvent";
public void getTime(final OnTimeRetrievedListener listener) {
DatabaseReference ref = mRootRef.child("serverTime");
ref.addValueEventListener(new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot dataSnapshot) {
if (listener != null) {
// This can be called twice if data changed on server - SO DEAL WITH IT!
listener.onTimeRetrieved(dataSnapshot.getValue(Long.class));
}
// This can be called twice if data changed on server - SO DEAL WITH IT!
removeListenerAfter2(ref, this);
}
#Override
public void onCancelled(DatabaseError databaseError) {
removeListenerAfter2(ref, this);
}
});
}
// ValueEventListener version workaround for addListenerForSingleEvent not working.
private void removeListenerAfter2(DatabaseReference ref, ValueEventListener listener) {
mHandler.postDelayed(new Runnable() {
#Override
public void run() {
HelperUtil.logE("removing listener", FirebaseController.class);
ref.removeEventListener(listener);
}
}, 500);
}
// ChildEventListener version workaround for addListenerForSingleEvent not working.
private void removeListenerAfter2(DatabaseReference ref, ChildEventListener listener) {
mHandler.postDelayed(new Runnable() {
#Override
public void run() {
HelperUtil.logE("removing listener", FirebaseController.class);
ref.removeEventListener(listener);
}
}, 500);
}
Even if they close the app before the handler is executed, it will be removed anyways.
Edit: this can be abstracted to keep track of added and removed listeners in a HashMap using reference path as key and datasnapshot as value. You can even wrap a fetchData method that has a boolean flag for "once" if this is true it would do this workaround to get data once, else it would continue as normal.
You're Welcome!
You can create transaction and abort it, then onComplete will be called when online (nline data) or offline (cached data)
I previously created function which worked only if database got connection lomng enough to do synch. I fixed issue by adding timeout. I will work on this and test if this works. Maybe in the future, when I get free time, I will create android lib and publish it, but by then it is the code in kotlin:
/**
* #param databaseReference reference to parent database node
* #param callback callback with mutable list which returns list of objects and boolean if data is from cache
* #param timeOutInMillis if not set it will wait all the time to get data online. If set - when timeout occurs it will send data from cache if exists
*/
fun readChildrenOnlineElseLocal(databaseReference: DatabaseReference, callback: ((mutableList: MutableList<#kotlin.UnsafeVariance T>, isDataFromCache: Boolean) -> Unit), timeOutInMillis: Long? = null) {
var countDownTimer: CountDownTimer? = null
val transactionHandlerAbort = object : Transaction.Handler { //for cache load
override fun onComplete(p0: DatabaseError?, p1: Boolean, data: DataSnapshot?) {
val listOfObjects = ArrayList<T>()
data?.let {
data.children.forEach {
val child = it.getValue(aClass)
child?.let {
listOfObjects.add(child)
}
}
}
callback.invoke(listOfObjects, true)
}
override fun doTransaction(p0: MutableData?): Transaction.Result {
return Transaction.abort()
}
}
val transactionHandlerSuccess = object : Transaction.Handler { //for online load
override fun onComplete(p0: DatabaseError?, p1: Boolean, data: DataSnapshot?) {
countDownTimer?.cancel()
val listOfObjects = ArrayList<T>()
data?.let {
data.children.forEach {
val child = it.getValue(aClass)
child?.let {
listOfObjects.add(child)
}
}
}
callback.invoke(listOfObjects, false)
}
override fun doTransaction(p0: MutableData?): Transaction.Result {
return Transaction.success(p0)
}
}
In the code if time out is set then I set up timer which will call transaction with abort. This transaction will be called even when offline and will provide online or cached data (in this function there is really high chance that this data is cached one).
Then I call transaction with success. OnComplete will be called ONLY if we got response from firebase database. We can now cancel timer (if not null) and send data to callback.
This implementation makes dev 99% sure that data is from cache or is online one.
If you want to make it faster for offline (to don't wait stupidly with timeout when obviously database is not connected) then check if database is connected before using function above:
DatabaseReference connectedRef = FirebaseDatabase.getInstance().getReference(".info/connected");
connectedRef.addValueEventListener(new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot snapshot) {
boolean connected = snapshot.getValue(Boolean.class);
if (connected) {
System.out.println("connected");
} else {
System.out.println("not connected");
}
}
#Override
public void onCancelled(DatabaseError error) {
System.err.println("Listener was cancelled");
}
});
When workinkg with persistence enabled, I counted the times the listener received a call to onDataChange() and stoped to listen at 2 times. Worked for me, maybe helps:
private int timesRead;
private ValueEventListener listener;
private DatabaseReference ref;
private void readFB() {
timesRead = 0;
if (ref == null) {
ref = mFBDatabase.child("URL");
}
if (listener == null) {
listener = new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot dataSnapshot) {
//process dataSnapshot
timesRead++;
if (timesRead == 2) {
ref.removeEventListener(listener);
}
}
#Override
public void onCancelled(DatabaseError databaseError) {
}
};
}
ref.removeEventListener(listener);
ref.addValueEventListener(listener);
}
Whenever I use addListenerForSingleValueEvent with setPersistenceEnabled(true), I only manage to get a local offline copy of DataSnapshot and NOT the updated DataSnapshot from the server.
However, if I use addValueEventListener with setPersistenceEnabled(true), I can get the latest copy of DataSnapshot from the server.
Is this normal for addListenerForSingleValueEvent as it only searches DataSnapshot locally (offline) and removes its listener after successfully retrieving DataSnapshot ONCE (either offline or online)?
Update (2021): There is a new method call (get on Android and getData on iOS) that implement the behavior you'll like want: it first tries to get the latest value from the server, and only falls back to the cache when it can't reach the server. The recommendation to use persistent listeners still applies, but at least there's a cleaner option for getting data once even when you have local caching enabled.
How persistence works
The Firebase client keeps a copy of all data you're actively listening to in memory. Once the last listener disconnects, the data is flushed from memory.
If you enable disk persistence in a Firebase Android application with:
Firebase.getDefaultConfig().setPersistenceEnabled(true);
The Firebase client will keep a local copy (on disk) of all data that the app has recently listened to.
What happens when you attach a listener
Say you have the following ValueEventListener:
ValueEventListener listener = new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot snapshot) {
System.out.println(snapshot.getValue());
}
#Override
public void onCancelled(FirebaseError firebaseError) {
// No-op
}
};
When you add a ValueEventListener to a location:
ref.addValueEventListener(listener);
// OR
ref.addListenerForSingleValueEvent(listener);
If the value of the location is in the local disk cache, the Firebase client will invoke onDataChange() immediately for that value from the local cache. If will then also initiate a check with the server, to ask for any updates to the value. It may subsequently invoke onDataChange() again if there has been a change of the data on the server since it was last added to the cache.
What happens when you use addListenerForSingleValueEvent
When you add a single value event listener to the same location:
ref.addListenerForSingleValueEvent(listener);
The Firebase client will (like in the previous situation) immediately invoke onDataChange() for the value from the local disk cache. It will not invoke the onDataChange() any more times, even if the value on the server turns out to be different. Do note that updated data still will be requested and returned on subsequent requests.
This was covered previously in How does Firebase sync work, with shared data?
Solution and workaround
The best solution is to use addValueEventListener(), instead of a single-value event listener. A regular value listener will get both the immediate local event and the potential update from the server.
A second solution is to use the new get method (introduced in early 2021), which doesn't have this problematic behavior. Note that this method always tries to first fetch the value from the server, so it will take longer to completely. If your value never changes, it might still be better to use addListenerForSingleValueEvent (but you probably wouldn't have ended up on this page in that case).
As a workaround you can also call keepSynced(true) on the locations where you use a single-value event listener. This ensures that the data is updated whenever it changes, which drastically improves the chance that your single-value event listener will see the current value.
So I have a working solution for this. All you have to do is use ValueEventListener and remove the listener after 0.5 seconds to make sure you've grabbed the updated data by then if needed. Realtime database has very good latency so this is safe. See safe code example below;
public class FirebaseController {
private DatabaseReference mRootRef;
private Handler mHandler = new Handler();
private FirebaseController() {
FirebaseDatabase.getInstance().setPersistenceEnabled(true);
mRootRef = FirebaseDatabase.getInstance().getReference();
}
public static FirebaseController getInstance() {
if (sInstance == null) {
sInstance = new FirebaseController();
}
return sInstance;
}
Then some method you'd have liked to use "addListenerForSingleEvent";
public void getTime(final OnTimeRetrievedListener listener) {
DatabaseReference ref = mRootRef.child("serverTime");
ref.addValueEventListener(new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot dataSnapshot) {
if (listener != null) {
// This can be called twice if data changed on server - SO DEAL WITH IT!
listener.onTimeRetrieved(dataSnapshot.getValue(Long.class));
}
// This can be called twice if data changed on server - SO DEAL WITH IT!
removeListenerAfter2(ref, this);
}
#Override
public void onCancelled(DatabaseError databaseError) {
removeListenerAfter2(ref, this);
}
});
}
// ValueEventListener version workaround for addListenerForSingleEvent not working.
private void removeListenerAfter2(DatabaseReference ref, ValueEventListener listener) {
mHandler.postDelayed(new Runnable() {
#Override
public void run() {
HelperUtil.logE("removing listener", FirebaseController.class);
ref.removeEventListener(listener);
}
}, 500);
}
// ChildEventListener version workaround for addListenerForSingleEvent not working.
private void removeListenerAfter2(DatabaseReference ref, ChildEventListener listener) {
mHandler.postDelayed(new Runnable() {
#Override
public void run() {
HelperUtil.logE("removing listener", FirebaseController.class);
ref.removeEventListener(listener);
}
}, 500);
}
Even if they close the app before the handler is executed, it will be removed anyways.
Edit: this can be abstracted to keep track of added and removed listeners in a HashMap using reference path as key and datasnapshot as value. You can even wrap a fetchData method that has a boolean flag for "once" if this is true it would do this workaround to get data once, else it would continue as normal.
You're Welcome!
You can create transaction and abort it, then onComplete will be called when online (nline data) or offline (cached data)
I previously created function which worked only if database got connection lomng enough to do synch. I fixed issue by adding timeout. I will work on this and test if this works. Maybe in the future, when I get free time, I will create android lib and publish it, but by then it is the code in kotlin:
/**
* #param databaseReference reference to parent database node
* #param callback callback with mutable list which returns list of objects and boolean if data is from cache
* #param timeOutInMillis if not set it will wait all the time to get data online. If set - when timeout occurs it will send data from cache if exists
*/
fun readChildrenOnlineElseLocal(databaseReference: DatabaseReference, callback: ((mutableList: MutableList<#kotlin.UnsafeVariance T>, isDataFromCache: Boolean) -> Unit), timeOutInMillis: Long? = null) {
var countDownTimer: CountDownTimer? = null
val transactionHandlerAbort = object : Transaction.Handler { //for cache load
override fun onComplete(p0: DatabaseError?, p1: Boolean, data: DataSnapshot?) {
val listOfObjects = ArrayList<T>()
data?.let {
data.children.forEach {
val child = it.getValue(aClass)
child?.let {
listOfObjects.add(child)
}
}
}
callback.invoke(listOfObjects, true)
}
override fun doTransaction(p0: MutableData?): Transaction.Result {
return Transaction.abort()
}
}
val transactionHandlerSuccess = object : Transaction.Handler { //for online load
override fun onComplete(p0: DatabaseError?, p1: Boolean, data: DataSnapshot?) {
countDownTimer?.cancel()
val listOfObjects = ArrayList<T>()
data?.let {
data.children.forEach {
val child = it.getValue(aClass)
child?.let {
listOfObjects.add(child)
}
}
}
callback.invoke(listOfObjects, false)
}
override fun doTransaction(p0: MutableData?): Transaction.Result {
return Transaction.success(p0)
}
}
In the code if time out is set then I set up timer which will call transaction with abort. This transaction will be called even when offline and will provide online or cached data (in this function there is really high chance that this data is cached one).
Then I call transaction with success. OnComplete will be called ONLY if we got response from firebase database. We can now cancel timer (if not null) and send data to callback.
This implementation makes dev 99% sure that data is from cache or is online one.
If you want to make it faster for offline (to don't wait stupidly with timeout when obviously database is not connected) then check if database is connected before using function above:
DatabaseReference connectedRef = FirebaseDatabase.getInstance().getReference(".info/connected");
connectedRef.addValueEventListener(new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot snapshot) {
boolean connected = snapshot.getValue(Boolean.class);
if (connected) {
System.out.println("connected");
} else {
System.out.println("not connected");
}
}
#Override
public void onCancelled(DatabaseError error) {
System.err.println("Listener was cancelled");
}
});
When workinkg with persistence enabled, I counted the times the listener received a call to onDataChange() and stoped to listen at 2 times. Worked for me, maybe helps:
private int timesRead;
private ValueEventListener listener;
private DatabaseReference ref;
private void readFB() {
timesRead = 0;
if (ref == null) {
ref = mFBDatabase.child("URL");
}
if (listener == null) {
listener = new ValueEventListener() {
#Override
public void onDataChange(DataSnapshot dataSnapshot) {
//process dataSnapshot
timesRead++;
if (timesRead == 2) {
ref.removeEventListener(listener);
}
}
#Override
public void onCancelled(DatabaseError databaseError) {
}
};
}
ref.removeEventListener(listener);
ref.addValueEventListener(listener);
}
I have a stream processing application built with spring cloud streams & kafka streams,
this system takes logs from an application and compares them to observations made by another stream processor
and produces a score, the log stream is then split by the score (above & below some threshold).
The topology:
The issue:
So my problem is how to properly implement the "Log best observation selector processor",
There are a finite amount of observations at the moment the log is processed but there may be a lot of them.
So I came up with 2 solutions...
Group & Window log-scored-observations topic by log id and then reduce to get the highest score. (Problem: scoring all observations may take longer then the window)
Emit a scoring completed message after every scoring, join with log-relevant-observations, use log-scored-observations global table & interactive query to check that every observation id is in the global table store, when all ids are in the store map to the observation with the highest score. (Problem: global table does not appear to work when only used for interactive query)
What would be the best way to achieve what I'm trying?
I'm hoping not to create any partition, disk or memory bottleneck.
Everything has unique ids and tuples of relevant ids when the value is joined from log & observation.
(Edit: Switched text description of topology with a diagram & change title)
Solution #2 seems to work, but it emitted warnings because interactive queries takes some time to be ready - so I implemented the same solution with a Transformer:
#Slf4j
#Configuration
#RequiredArgsConstructor
#SuppressWarnings("unchecked")
public class LogBestObservationsSelectorProcessorConfig {
private String logScoredObservationsStore = "log-scored-observations-store";
private final Serde<LogEntryRelevantObservationIdTuple> logEntryRelevantObservationIdTupleSerde;
private final Serde<LogRelevantObservationIdsTuple> logRelevantObservationIdsTupleSerde;
private final Serde<LogEntryObservationMatchTuple> logEntryObservationMatchTupleSerde;
private final Serde<LogEntryObservationMatchIdsRelevantObservationsTuple> logEntryObservationMatchIdsRelevantObservationsTupleSerde;
#Bean
public Function<
GlobalKTable<LogEntryRelevantObservationIdTuple, LogEntryObservationMatchTuple>,
Function<
KStream<LogEntryRelevantObservationIdTuple, LogEntryRelevantObservationIdTuple>,
Function<
KTable<String, LogRelevantObservationIds>,
KStream<String, LogEntryObservationMatchTuple>
>
>
>
logBestObservationSelectorProcessor() {
return (GlobalKTable<LogEntryRelevantObservationIdTuple, LogEntryObservationMatchTuple> logScoredObservationsTable) ->
(KStream<LogEntryRelevantObservationIdTuple, LogEntryRelevantObservationIdTuple> logScoredObservationProcessedStream) ->
(KTable<String, LogRelevantObservationIdsTuple> logRelevantObservationIdsTable) -> {
return logScoredObservationProcessedStream
.selectKey((k, v) -> k.getLogId())
.leftJoin(
logRelevantObservationIdsTable,
LogEntryObservationMatchIdsRelevantObservationsTuple::new,
Joined.with(
Serdes.String(),
logEntryRelevantObservationIdTupleSerde,
logRelevantObservationIdsTupleSerde
)
)
.transform(() -> new LogEntryObservationMatchTransformer(logScoredObservationsStore))
.groupByKey(
Grouped.with(
Serdes.String(),
logEntryObservationMatchTupleSerde
)
)
.reduce(
(match1, match2) -> Double.compare(match1.getScore(), match2.getScore()) != -1 ? match1 : match2,
Materialized.with(
Serdes.String(),
logEntryObservationMatchTupleSerde
)
)
.toStream()
;
};
}
#RequiredArgsConstructor
private static class LogEntryObservationMatchTransformer implements Transformer<String, LogEntryObservationMatchIdsRelevantObservationsTuple, KeyValue<String, LogEntryObservationMatchTuple>> {
private final String stateStoreName;
private ProcessorContext context;
private TimestampedKeyValueStore<LogEntryRelevantObservationIdTuple, LogEntryObservationMatchTuple> kvStore;
#Override
public void init(ProcessorContext context) {
this.context = context;
this.kvStore = (TimestampedKeyValueStore<LogEntryRelevantObservationIdTuple, LogEntryObservationMatchTuple>) context.getStateStore(stateStoreName);
}
#Override
public KeyValue<String, LogEntryObservationMatchTuple> transform(String logId, LogEntryObservationMatchIdsRelevantObservationsTuple value) {
val observationIds = value.getLogEntryRelevantObservationsTuple().getRelevantObservations().getObservationIds();
val allObservationsProcessed = observationIds.stream()
.allMatch((observationId) -> {
val key = LogEntryRelevantObservationIdTuple.newBuilder()
.setLogId(logId)
.setRelevantObservationId(observationId)
.build();
return kvStore.get(key) != null;
});
if (!allObservationsProcessed) {
return null;
}
val observationId = value.getLogEntryRelevantObservationIdTuple().getObservationId();
val key = LogEntryRelevantObservationIdTuple.newBuilder()
.setLogId(logId)
.setRelevantObservationId(observationId)
.build();
ValueAndTimestamp<LogEntryObservationMatchTuple> observationMatchValueAndTimestamp = kvStore.get(key);
return new KeyValue<>(logId, observationMatchValueAndTimestamp.value());
}
#Override
public void close() {
}
}
}
I am new to RxJava and finding it very useful for network and database processing within my Android applications.
I have two use cases that I cannot implement completely in RxJava
Use Case 1
Clear down my target database table Table A
Fetch a list of database records from Table B that contain a key field
For each row retrieved from Table B, call a Remote API and persist all the returned data into Table A
The closest I have managed is this code
final AtomicInteger id = new AtomicInteger(0);
DatabaseController.deleteAll(TableA_DO.class);
DatabaseController.fetchTable_Bs()
.subscribeOn(Schedulers.io())
.toObservable()
.flatMapIterable(b -> b)
.flatMap(b_record -> NetworkController.getTable_A_data(b_record.getKey()))
.flatMap(network -> transformNetwork(id, network, NETWORK_B_MAPPER))
.doOnNext(DatabaseController::persistRealmObjects)
.doOnComplete(onComplete)
.doOnError(onError)
.doAfterTerminate(doAfterTerminate())
.doOnSubscribe(compositeDisposable::add)
.subscribe();
Use Case 2
Clear down my target database table Table X
Clear down my target database table Table Y
Fetch a list of database records from Table Z that contain a key field
For each row retrieved from Table B, call a Remote API and persist some of the returned data into Table X the remainder of the data should be persisted into table Y
I have not managed to create any code for use case 2.
I have a number of questions regarding the use of RxJava for these use cases.
Is it possible to achieve both my use cases in RxJava?
Is it "Best Practice" to combine all these steps into an Rx "Stream"
UPDATE
I ended up with this POC test code which seems to work...
I am not sure if its the optimum solution however My API calls return Single and my database operations return Completable so I feel like this is the best solution for me.
public class UseCaseOneA {
public static void main(final String[] args) {
login()
.andThen(UseCaseOneA.deleteDatabaseTableA())
.andThen(UseCaseOneA.deleteDatabaseTableB())
.andThen(manufactureRecords())
.flatMapIterable(x -> x)
.flatMapSingle(record -> NetworkController.callApi(record.getPrimaryKey()))
.flatMapSingle(z -> transform(z))
.flatMapCompletable(p -> UseCaseOneA.insertDatabaseTableA(p))
.doOnComplete(() -> System.out.println("ON COMPLETE"))
.doFinally(() -> System.out.println("ON FINALLY"))
.subscribe();
}
private static Single<List<PayloadDO>> transform(final List<RemotePayload> payloads) {
return Single.create(new SingleOnSubscribe<List<PayloadDO>>() {
#Override
public void subscribe(final SingleEmitter<List<PayloadDO>> emitter) throws Exception {
System.out.println("transform - " + payloads.size());
final List<PayloadDO> payloadDOs = new ArrayList<>();
for (final RemotePayload remotePayload : payloads) {
payloadDOs.add(new PayloadDO(remotePayload.getPayload()));
}
emitter.onSuccess(payloadDOs);
}
});
}
private static Observable<List<Record>> manufactureRecords() {
final List<Record> records = new ArrayList<>();
records.add(new Record("111-111-111"));
records.add(new Record("222-222-222"));
records.add(new Record("3333-3333-3333"));
records.add(new Record("44-444-44444-44-4"));
records.add(new Record("5555-55-55-5-55-5555-5555"));
return Observable.just(records);
}
private static Completable deleteDatabaseTableA() {
return Completable.create(new CompletableOnSubscribe() {
#Override
public void subscribe(final CompletableEmitter emitter) throws Exception {
System.out.println("deleteDatabaseTableA");
emitter.onComplete();
}
});
}
private static Completable deleteDatabaseTableB() {
return Completable.create(new CompletableOnSubscribe() {
#Override
public void subscribe(final CompletableEmitter emitter) throws Exception {
System.out.println("deleteDatabaseTableB");
emitter.onComplete();
}
});
}
private static Completable insertDatabaseTableA(final List<PayloadDO> payloadDOs) {
return Completable.create(new CompletableOnSubscribe() {
#Override
public void subscribe(final CompletableEmitter emitter) throws Exception {
System.out.println("insertDatabaseTableA - " + payloadDOs);
emitter.onComplete();
}
});
}
private static Completable login() {
return Completable.complete();
}
}
This code doesn't address all my use case requirements. Namely being able to transform the remote payload records into multiple Database record types and insert each type into its own specific target databased table.
I could just call the Remote API twice to get the same remote data items and transform first into one database type then secondly into the second database type, however that seems wasteful.
Is there an operand in RxJava where I can reuse the output from my API calls and transform them into another database type?
You have to index the items yourself in some manner, for example, via external counting:
Observable.defer(() -> {
AtomicInteger counter = new AtomicInteger();
return DatabaseController.fetchTable_Bs()
.subscribeOn(Schedulers.io())
.toObservable()
.flatMapIterable(b -> b)
.doOnNext(item -> {
if (counter.getAndIncrement() == 0) {
// this is the very first item
} else {
// these are the subsequent items
}
});
});
The defer is necessary to isolate the counter to the inner sequence so that repetition still works if necessary.