RxJS interleaving merged observables (priority queue?) - merge

UPDATE
I think I've figured out the solution. I explain it in this video. Basically, use timeoutWith, and some tricks with zip (within zip).
https://youtu.be/0A7C1oJSJDk
If I have a single observable like this:
A-1-2--B-3-4-5-C--D--6-7-E
I want to put the "numbers" as lower priority; it should wait until the "letters" is filled up (a group of 2 for example) OR a timeout is reached, and then it can emit. Maybe the following illustration (of the desired result) can help:
A------B-1-----C--D-2----E-3-4-5-6-7
I've been experimenting with some ideas... one of them: first step is to split that stream (groupBy), one containing letters, and the other containing numbers..., then "something in the middle" happen..., and finally those two (sub)streams get merged.
It's that "something in the middle" what I'm trying to figure out.
How to achieve it? Is that even possible with RxJS (ver 5.5.6)? If not, what's the closest one? I mean, what I want to avoid is having the "numbers" flooding the stream, and not giving enough chance for the "letters" to be processed in timely manner.
Probably this video I made of my efforts so far can clarify as well:
Original problem statement: https://www.youtube.com/watch?v=mEmU4JK5Tic
So far: https://www.youtube.com/watch?v=HWDI9wpVxJk&feature=youtu.be
The problem with my solution so far (delaying each emission in "numbers" substream using .delay) is suboptimal, because it keeps clocking at slow pace (10 seconds) even after the "characters" (sub)stream has ended (not completed -- no clear boundary here -- just not getting more value for indeterminate amount of time). What I really need is, to have the "numbers" substream raise its pace (to 2 seconds) once that happen.

Unfortunately I don't know RxJs5 that much and use xstream myself (authored by one of the contributor to RxJS5) which is a little bit simpler in terms of the number of operators.
With this I crafted the following example:
(Note: the operators are pretty much the same as in Rx5, the main difference is with flatten wich is more or less like switch but seems to handle synchronous streams differently).
const xs = require("xstream").default;
const input$ = xs.of("A",1,2,"B",3,4,5,"C","D",6,7,"E");
const initialState = { $: xs.never(), count: 0, buffer: [] };
const state$ = input$
.fold((state, value) => {
const t = typeof value;
if (t === "string") {
return {
...state,
$: xs.of(value),
count: state.count + 1
};
}
if (state.count >= 2) {
const l = state.buffer.length;
return {
...state,
$: l > 0 ? xs.of(state.buffer[0]) : xs.of(value) ,
count: 0,
buffer: state.buffer.slice(1).concat(value)
};
}
return {
...state,
$: xs.never(),
buffer: state.buffer.concat(value),
};
}, initialState);
xs
.merge(
state$
.map(s => s.$),
state$
.last()
.map(s => xs.of.apply(xs, s.buffer))
)
.flatten()
.subscribe({
next: console.log
});
Which gives me the result you are looking for.
It works by folding the stream on itself, looking at the type of values and emitting a new stream depending on it. When you need to wait because not enough letters were dispatched I emit an emptystream (emits no value, no errors, no complete) as a "placeholder".
You could instead of emitting this empty stream emit something like
xs.empty().endsWith(xs.periodic(timeout)).last().mapTo(value):
// stream that will emit a value only after a specified timeout.
// Because the streams are **not** flattened concurrently you can
// use this as a "pending" stream that may or may not be eventually
// consumed
where value is the last received number in order to implement timeout related conditions however you would then need to introduce some kind of reflexivity with either a Subject in Rx or xs.imitate with xstream because you would need to notify your state that your "pending" stream has been consumed wich makes the communication bi-directionnal whereas streams / observables are unidirectionnal.

The key here the use of timeoutWith, to switch to the more aggresive "pacer", when the "events" kicks in. In this case the "event" is "idle detected in the higher-priority stream".
The video: https://youtu.be/0A7C1oJSJDk

Related

Is there an Operation to block onComplete?

I am trying to learn reactive programming, so forgive me if I ask a silly question. I'm also open to advice on changing my design.
I am working in scala-swing to display the results of a simulator. With one setting, a chart is displayed as a histogram; with the other setting the chart is displayed as the cumulative sum. (I'm probably using the wrong word; in the first setting you might have bin1=2, bin2=5, bin3=3; in the second setting the first height is 2, the second is 2 + 5, the third is 2 + 5 + 3, etc.). The simulator can be slow, so I originally used a Future to compute it, and the set the data into the chart. I decided to try a reactive approach, so my requirements are: 1. I don't want to recreate the data when I change the display mode, and 2. I want to set the Observable once for the chart and have the chart listen to the same Observable permanently.
I got this to work when I started the chain with a PublishSubject and the Future set the data into the start of the chain. When the display mode changed, I created a new PublishSubject().map(newRenderingLogic).subscribe(theChartsObservable). I am now trying to do what looks like the "right way," but it's not working correctly. I've tried to simplify what I have done:
val textObservable: Subject[String] = PublishSubject()
textObservable.subscribe(text => {
println(s"Text: ${text}")
})
var textSubscription: Option[Subscription] = None
val start = Observable.from(Future {
"Base text"
}).cache
var i = 0
val button = new Button() {
text = "Click"
reactions += {
case event => {
i += 1
if (textSubscription.isDefined) {
textSubscription.get.unsubscribe()
}
textSubscription = Some(start.map(((j: Int) => { (base: String) => s"${base} ${j}" })(i)).subscribe(textObservable))
}
}
}
On start, an Observable is created and logic to print some text is added to it. Then, an Observable with the generated data is created and a cache is added so that the result is replayed if the next subscription comes in after its results are generated. Then, a button is created. Then on button clicks a middle observable is chained with unique logic (it's a function that creates a function to append the value of i into the string, run with the current value of i; I tried to make something that couldn't just be reused) that is supposed to change with each click. Then the first Observable is subscribed to it so that the results of the whole chain end up being printed.
In theory, the cache operation takes care of not regenerating the data, and this works once, but onComplete is called on textObservable and then it can't be used again. It works if I subscribe it like this:
textSubscription = Some(start.map(((j: Int) => { (base: String) => s"${base} ${j}" })(i)).subscribe(text => textObservable.onNext(text)))
because the call to onComplete is intercepted, but this looks wrong and I wanted to know if there was a more typical way to do this, or architect it. It makes me think that I don't understand how this is supposed to be done if there isn't an out-of-the-box operation to do this.
Thank you.
I'm not 100% sure if I got the essence of your question right, but: if you have an Observable that may complete and you want to turn it into an Observable that never completes, you can just concatenate it with Observable.never.
For example:
// will complete after emitting those three elements:
val completes = Observable.from(List(1, 2, 3))
// will emit those three elements, but will never complete:
val wontComplete = completes ++ Observable.never

Creating Seq after waiting for all results from map/foreach in Scala

I am trying to loop over inputs and process them to produce scores.
Just for the first input, I want to do some processing that takes a while.
The function ends up returning just the values from the 'else' part. The 'if' part is done executing after the function returns the value.
I am new to Scala and understand the behavior but not sure how to fix it.
I've tried inputs.zipWithIndex.map instead of foreach but the result is the same.
def getscores(
inputs: inputs
): Future[Seq[scoreInfo]] = {
var scores: Seq[scoreInfo] = Seq()
inputs.zipWithIndex.foreach {
case (f, i) => {
if (i == 0) {
// long operation that returns Future[Option[scoreInfo]]
getgeoscore(f).foreach(gso => {
gso.foreach(score => {
scores = scores.:+(score)
})
})
} else {
scores = scores.:+(
scoreInfo(
id = "",
score = 5
)
)
}
}
}
Future {
scores
}
}
For what you need, I would drop the mutable variable and replace foreach with map to obtain an immutable list of Futures and recover to handle exceptions, followed by a sequence like below:
def getScores(inputs: Inputs): Future[List[ScoreInfo]] = Future.sequence(
inputs.zipWithIndex.map{ case (input, idx) =>
if (idx == 0)
getGeoScore(input).map(_.getOrElse(defaultScore)).recover{ case e => errorHandling(e) }
else
Future.successful(ScoreInfo("", 5))
})
To capture/print the result, one way is to use onComplete:
getScores(inputs).onComplete(println)
The part your missing is understanding a tricky element of concurrency, and that is that the order of execution when using multiple futures is not guaranteed.
If your block here is long running, it will take a while before appending the score to scores
// long operation that returns Future[Option[scoreInfo]]
getgeoscore(f).foreach(gso => {
gso.foreach(score => {
// stick a println("here") in here to see what happens, for demonstration purposes only
scores = scores.:+(score)
})
})
Since that executes concurrently, your getscores function will also simultaneously continue its work iterating over the rest of inputs in your zipWithindex. This iteration, especially since it's trivial work, likely finishes well before the long-running getgeoscore(f) completes the execution of the Future it scheduled, and the code will exit the function, moving on to whatever code is next after you called getscores
val futureScores: Future[Seq[scoreInfo]] = getScores(inputs)
futureScores.onComplete{
case Success(scoreInfoSeq) => println(s"Here's the scores: ${scoreInfoSeq.mkString(",")}"
}
//a this point the call to getgeoscore(f) could still be running and finish later, but you will never know
doSomeOtherWork()
Now to clean this up, since you can run a zipWithIndex on your inputs parameter, I assume you mean it's something like a inputs:Seq[Input]. If all you want to do is operate on the first input, then use the head function to only retrieve the first option, so getgeoscores(inputs.head) , you don't need the rest of the code you have there.
Also, as a note, if using Scala, get out of the habit of using mutable vars, especially if you're working with concurrency. Scala is built around supporting immutability, so if you find yourself wanting to use a var , try using a val and look up how to work with the Scala's collection library to make it work.
In general, that is when you have several concurrent futures, I would say Leo's answer describes the right way to do it. However, you want only the first element transformed by a long running operation. So you can use the future return by the respective function and append the other elements when the long running call returns by mapping the future result:
def getscores(inputs: Inputs): Future[Seq[ScoreInfo]] =
getgeoscore(inputs.head)
.map { optInfo =>
optInfo ++ inputs.tail.map(_ => scoreInfo(id = "", score = 5))
}
So you neither need zipWithIndex nor do you need an additional future or join the results of several futures with sequence. Mapping the future just gives you a new future with the result transformed by the function passed to .map().

Where does a variable in a match arm in a loop come from?

I am trying to implement an HTTP client in Rust using this as a starting point. I was sent to this link by the rust-lang.org site via one of their rust-by-example suggestions in their TcpStream page. I'm figuring out how to read from a TcpStream. I'm trying to follow this code:
fn handle_client(mut stream: TcpStream) {
// read 20 bytes at a time from stream echoing back to stream
loop {
let mut read = [0; 1028];
match stream.read(&mut read) {
Ok(n) => {
if n == 0 {
// connection was closed
break;
}
stream.write(&read[0..n]).unwrap();
}
Err(err) => {
panic!(err);
}
}
}
}
Where does the n variable come from? What exactly is it? The author says it reads 20 bytes at a time; where is this coming from?
I haven't really tried anything yet because I want to understand before I do.
I strongly encourage you to read the documentation for the tools you use. In this case, The match Control Flow Operator from The Rust Programming Language explains what you need to know.
From the Patterns that Bind to Values section:
In the match expression for this code, we add a variable called state to the pattern that matches values of the variant Coin::Quarter. When a Coin::Quarter matches, the state variable will bind to the value of that quarter’s state. Then we can use state in the code for that arm, like so:
fn value_in_cents(coin: Coin) -> u8 {
match coin {
Coin::Penny => 1,
Coin::Nickel => 5,
Coin::Dime => 10,
Coin::Quarter(state) => {
println!("State quarter from {:?}!", state);
25
},
}
}
If we were to call value_in_cents(Coin::Quarter(UsState::Alaska)), coin would be Coin::Quarter(UsState::Alaska). When we compare that value with each of the match arms, none of them match until we reach Coin::Quarter(state). At that point, the binding for state will be the value UsState::Alaska. We can then use that binding in the println! expression, thus getting the inner state value out of the Coin enum variant for Quarter.
There is an entire chapter about the pattern matching syntax available and where it can be used.
Figured it out, this is what's happening:
match stream.read(&mut read) {
This line is telling the software to pass stream.read(&mut read) to Ok(n) because stream.read returns the number of bytes read. I'm still not sure why they specify 20 bytes at a time as being read.

Batching large result sets using Rx

I've got an interesting question for Rx experts. I've a relational table keeping information about events. An event consists of id, type and time it happened. In my code, I need to fetch all the events within a certain, potentially wide, time range.
SELECT * FROM events WHERE event.time > :before AND event.time < :after ORDER BY time LIMIT :batch_size
To improve reliability and deal with large result sets, I query the records in batches of size :batch_size. Now, I want to write a function that, given :before and :after, will return an Observable representing the result set.
Observable<Event> getEvents(long before, long after);
Internally, the function should query the database in batches. The distribution of events along the time scale is unknown. So the natural way to address batching is this:
fetch first N records
if the result is not empty, use the last record's time as a new 'before' parameter, and fetch the next N records; otherwise terminate
if the result is not empty, use the last record's time as a new 'before' parameter, and fetch the next N records; otherwise terminate
... and so on (the idea should be clear)
My question is:
Is there a way to express this function in terms of higher-level Observable primitives (filter/map/flatMap/scan/range etc), without using the subscribers explicitly?
So far, I've failed to do this, and come up with the following straightforward code instead:
private void observeGetRecords(long before, long after, Subscriber<? super Event> subscriber) {
long start = before;
while (start < after) {
final List<Event> records;
try {
records = getRecordsByRange(start, after);
} catch (Exception e) {
subscriber.onError(e);
return;
}
if (records.isEmpty()) break;
records.forEach(subscriber::onNext);
start = Iterables.getLast(records).getTime();
}
subscriber.onCompleted();
}
public Observable<Event> getRecords(final long before, final long after) {
return Observable.create(subscriber -> observeGetRecords(before, after, subscriber));
}
Here, getRecordsByRange implements the SELECT query using DBI and returns a List. This code works fine, but lacks elegance of high-level Rx constructs.
NB: I know that I can return Iterator as a result of SELECT query in DBI. However, I don't want to do that, and prefer to run multiple queries instead. This computation does not have to be atomic, so the issues of transaction isolation are not relevant.
Although I don't fully understand why you want such time-reuse, here is how I'd do it:
BehaviorSubject<Long> start = BehaviorSubject.create(0L);
start
.subscribeOn(Schedulers.trampoline())
.flatMap(tstart ->
getEvents(tstart, tstart + twindow)
.publish(o ->
o.takeLast(1)
.doOnNext(r -> start.onNext(r.time))
.ignoreElements()
.mergeWith(o)
)
)
.subscribe(...)

Combining parts of Stream

I've got an observable watching a log that is continuously being written too. Each line is a new onNext call. Sometimes the log outputs a single log item over multiple lines. Detecting this is easy, I just can't find the right RX call.
I'd like to find a way to collect the single log items into a List of lines, and onNext the list when the single log item is complete.
Buffer doesn't seem right as this isn't time based, it's algorithm based.
GroupBy might be what I want, but the documentation is confusing for it. It also seems that the observables it creates probably won't have onComplete called until the completion of the source observable.
This solution can't delay the log much (preferably not at all). I need to be reading the log as close to real time as possible, and order matters.
Any push in the right direction would be great.
This is a typical reactive parsing problem. You could use Rxx Parsers, or for a native solution you can build your own state machine with either Scan or by defining an async iterator. Scan is preferable for simple parsers and often uses a Scan-Where-Select pattern.
Async iterator state machine example: Turnstile
Scan parser example (untested):
IObservable<string> lines = ReadLines();
IObservable<IReadOnlyList<string>> parsed = lines.Scan(
new
{
ParsingItem = (IEnumerable<string>)null,
Item = (IEnumerable<string>)null
},
(state, line) =>
// I'm assuming here that items never span lines partially.
IsItem(line)
? IsItemLastLine(line)
? new
{
ParsingItem = (IEnumerable<string>)null,
Item = (state.ParsingItem ?? Enumerable.Empty<string>()).Concat(line)
}
: new
{
ParsingItem = (state.ParsingItem ?? Enumerable.Empty<string>()).Concat(line),
Item = (List<string>)null
}
: new
{
ParsingItem = (IEnumerable<string>)null,
Item = new[] { line }
})
.Where(result => result.Item != null)
.Select(result => result.Item.ToList().AsReadOnly());