When designing models in Promela, what are the design trade-offs for channels when there are a many different types of messages being sent?
Many examples in documentation use a simple case with something like this
mtype { M1, M2, M3 }
chan req = [0] of { mtype, chan, byte};
However in practice some models may have processes that handle a wide variety of different message types, each of which have a unique set of parameters.
So there seems to be a design decision between channels that can represent the parameters of a variety of message types:
mtype { M1, M2, M3 }
chan req = [0] of { mtype, chan, byte, int, byte, etc...};
and channels specific to each message type
chan req1 = [0] of { chan, byte };
chan req2 = [0] of { chan, int };
chan req3 = [0] of { chan, byte, int};
I'm interested in understand any performance benefits to one design over the other, and what is considered best practices.
Related
I am playing around with circom and circomlib.
I am using a simple mimcsponge hashing circuit and seeing if I can create a correct input through javascript frontend.
The circuit I am running
template sponge_test() {
signal input l;
signal input r;
signal input o;
// instantiate - 2 inputs 220 rounds of hashing and 1 output
component hasher = MiMCSponge(2, 220, 1);
// signals in hasher
hasher.ins[0] <== l;
hasher.ins[1] <== r;
// addition constant
hasher.k <== 0;
o === hasher.outs[0];
}
component main = sponge_test();
In my javascript front end I am importing circomlib
import { buildMimcSponge } from 'circomlibjs';
function toHexString(byteArray) {
return Array.from(byteArray, function(byte) {
return ('0' + (byte & 0xFF).toString(16)).slice(-2);
}).join('')
}
export async function getProof(message) {
var hasher = await buildMimcSponge();
var h = hasher.multiHash([BigInt("0x3"), BigInt("0x4")]);
// returns byte array
console.log(h);
// back to hexstring
console.log(toHexString(h));
}
I then create an input.json that looks like this:
{
"l": "0x3",
"r": "0x4",
"o": "0x690f48aba976f2786371b7fa3e941df623e96329e0570dc610f59b7fcfa94723"
}
Which includes the values I used for the input of the hashing and the output I got from printing the hex value, and then run the following script
# Compile the circuit
circom ${CIRCUIT}.circom --r1cs --wasm --sym --c
# Generate the witness.wtns
node ${CIRCUIT}_js/generate_witness.js ${CIRCUIT}_js/${CIRCUIT}.wasm input.json ${CIRCUIT}_js/witness.wtns
And I get the error that the assert (o===hasher.outs[0]) fails.
Now, I know that that mimcsponge circuit uses 220 rounds as well in the javascript implementation of circomlib by looking at the node lib, where else could I be reaching inconsistent results for the hashing?
So I found that reading the has is done using the following. I believe it is because it is specific to the elliptic curves being used.
hasher.F.toString(h, 16);
This produces the expected result which gets accepted by the circuit.
If anyone has further insights, I would be happy to understand it further.
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.
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
Is there any method to specify the size of message?. For example if i want to send message data through channel AB then how can I specify the size of data in PROMELA language?
The syntax for declaring a channel is, for example:
chan ab = [16] of { short }
ab is the identifier bound to the channel. 16 is the number of messages in the channel. short is the data type of each message.
When you specify the message type you have a number of additional options:
char ab = [16] of { byte, short, bit }
which creates a channel with each message being: byte, short, and bit. In such a case it is often better to create a new type with:
typedef message {
byte operator;
short data;
bit what;
};
and then
chan ab = [16] of { message }
I have to generate a big file on the fly. Reading to the database and send it to the client.
I read some documentation and i did this
val streamContent: Enumerator[Array[Byte]] = Enumerator.outputStream {
os =>
// new PrintWriter() read from database and for each record
// do some logic and write
// to outputstream
}
Ok.stream(streamContent.andThen(Enumerator.eof)).withHeaders(
CONTENT_DISPOSITION -> s"attachment; filename=someName.csv"
)
Im rather new to scala in general only a week so don't guide for my reputation.
My questions are :
1) Is this the best way? I found this if i have a big file, this will load in memory, and also don't know what is the chunk size in this case, if it will send for each write() is not to convenient.
2) I found this method Enumerator.fromStream(data : InputStream, chunkedSize : int) a little better cause it has a chunk-size, but i don't have an inputStream cause im creating the file on the fly.
There's a note in the docs for Enumerator.outputStream:
Not [sic!] that calls to write will not block, so if the iteratee that is being fed to is slow to consume the input, the OutputStream will not push back. This means it should not be used with large streams since there is a risk of running out of memory.
If this can happen depends on your situation. If you can and will generate Gigabytes in seconds, you should probably try something different. I'm not exactly sure what, but I'd start at Enumerator.generateM(). For many cases though, your method is perfectly fine. Have a look at this example by Gaëtan Renaudeau for serving a Zip file that's generated on the fly in the same way you're using it:
val enumerator = Enumerator.outputStream { os =>
val zip = new ZipOutputStream(os);
Range(0, 100).map { i =>
zip.putNextEntry(new ZipEntry("test-zip/README-"+i+".txt"))
zip.write("Here are 100000 random numbers:\n".map(_.toByte).toArray)
// Let's do 100 writes of 1'000 numbers
Range(0, 100).map { j =>
zip.write((Range(0, 1000).map(_=>r.nextLong).map(_.toString).mkString("\n")).map(_.toByte).toArray);
}
zip.closeEntry()
}
zip.close()
}
Ok.stream(enumerator >>> Enumerator.eof).withHeaders(
"Content-Type"->"application/zip",
"Content-Disposition"->"attachment; filename=test.zip"
)
Please keep in mind that Ok.stream has been replaced by Ok.chunked in newer versions of Play, in case you want to upgrade.
As for the chunk size, you can always use Enumeratee.grouped to gather a bunch of values and send them as one chunk.
val grouper = Enumeratee.grouped(
Traversable.take[Array[Double]](100) &>> Iteratee.consume()
)
Then you'd do something like
Ok.stream(enumerator &> grouper >>> Enumerator.eof)