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How vertx handlers work?

For the last week I read documentation about vertx. What i don't get it's how vertx handlers are work? For example
public class Client extends AbstractVerticle{
#Override
public void start() throws Exception {
final HttpClient httpClient = this.vertx.createHttpClient();
this.vertx.setPeriodic(1000, handler->{
httpClient.getNow(8080, "localhost", "/", responseHandler -> {
System.out.println("response");
});
});
}
}
And server is:
public class JdbcVertx extends AbstractVerticle{
#Override
public void start() throws Exception {
JDBCClient client = JDBCClient.createNonShared(this.vertx, new JsonObject()
.put("url", "jdbc:postgresql://localhost:5432/test")
.put("user", "user")
.put("password", "password")
.put("driver_class", "org.postgresql.Driver")
.put("max_pool_size", 30));
this.vertx.createHttpServer()
.requestHandler(r -> {
client.getConnection(handler -> {
final SQLConnection connection = handler.result();
connection.execute(execute(), hndlr -> {
connection.close(closehndlr -> {
r.response().putHeader("content-type", "text/html").end("Response");
});
});
});
}).listen(8080);
}
private String execute(){
return "insert into rubish (name) values ('test')";
}
}
(P.S i know that i firstly should to check if handler is succeded and then make some action but i remove this checking to simplify code and also from official docs if there is no any response during 30 sec there will be an exception in handler)
From the code above, client send request each second and doesn't wait for response , but it has a handler that will be executed when response was comming.
'JdbcVertx' listen on port 8080 , get request, make insertion to db with sleep for example 3 s (i put 1_000_000 rows to db and create index to slow down insertion time) and then send response, therefore each request is non blocking.
As i know , vertx has only one thread named EventLoop event loop from jdbcVertx get reqests but doesn't return response immediately , instead it put a handler that will be executed when db insertion was succeed. How event loop know when IO action is done. I think that it use somthing like this
if(Thread.currentThread().getState != 'blocked' && sec != 30){
this.object.getHandler().execute();
} else if(sec == 30){
Thread.currentThread.inerrupt();
} else{
sec++;
}
But we have only one thread, and when we have blocking call it doesn't has a thread, only handler.
The problem is , how event loop know when blocking operation is ended and it's time to execute handler

But we have only one thread, and when we have blocking call it doesn't has a thread, only handler. How it work , and why do we need to use Worker Verticle if we can use handlers instead?
The handlers are just actions triggered upon receipt of an eventbus message or an http call. They are not designed to handle scalability for you. If you only use handlers and if your actions starts to take a long time or if you have any increase in the number of requests, you will block the eventloop of your verticle and will have a lot of Thread xxxx has been blocked warns.
To answer on how handler works and why the event loop doesn't wait the end of a handler to start another, according to this : https://vertx.io/docs/vertx-core/java/#_reactor_and_multi_reactor
Instead of a single event loop, each Vertx instance maintains several event loops. By default we choose the number based on the number of available cores on the machine, but this can be overridden.
This means a single Vertx process can scale across your server, unlike Node.js.
We call this pattern the Multi-Reactor Pattern to distinguish it from the single threaded reactor pattern.
But that's not enough to handle all scalability and thread blocking problematics for you in my opinion, you schould read this too : https://vertx.io/docs/vertx-core/java/#golden_rule
There are many ways to design verticles but you have to stay as non-blocking as possible. In my opinion, using vert.x with a traditional blocking approach (like blocking restfull endpoints for example) is not relevant.
Personally I'd design my verticles as follows :
verticle A : which expose a restfull endpoint and take a callback url (whatever the action GET/POST/PUT/PATCH/DELETE). The verticle always respond a 202 Accepted immediatly without result and send a message in the eventbus to a verticle B.
verticle B : get the message, do the action (eventually invoke other verticles asynchronously with the eventbus and waiting the replies) and reply invoking the callback url.
I'd avoid to use worker verticle or the executeBlocking method or even creating a pool of thread. I'd privilege multiplying the instances of my verticles B (in seperate pids) that listen to the same eventbus cluster (and eventually verticle A with a http reverse proxy). We can even imagine having a variable number of verticle B instances (in seperate pids) depending on the number of requests in real time.
P.S : sometimes I use a more powerfull message broker tool like Apache Kafka instead of the native eventbus (when I need to respect a sort of message, or when I need to replay some messages).

Answering the question:
how event loop know when blocking operation is ended and it's time to execute handler?
According to the non-blocking model, the event-loop upon the call
connection.execute( execute(), hndlr )
spawns a new thread, executes your blocking piece of code and upon it's completion (something like Thread.join()) invokes the hndlr callback in the event-loop thread. Thus the main loop doesn't get blocked although the blocking code can be executed.

java - multiple http requests at same time With multi threading in java

I'm calling multiple request(10) at same time HTTP GET method.Here calling method will create different threads (Like Thread 1,Thread 2 ....)
Caller Method:
enter code here: for(int i=0;i<10;i++){Thread.currentThread().getId();HttpClient httpClient = new HtpClient(url);res = httpClient.get(5000);}
Then Request will hit application entry point.The entry point will create new threads for each and every request (Like Thread 11,Thread 12 ....).
enter code here//public void DoProcess(){Thread.currentThread().getId();// New threads for each request.else........}
But i want to know which caller thread request created application thread.
Like Thread 1 belongs Thread 11
Thread 2 belongs Thread 12
Please let me know,how to achieved this.

Client connects through TCP, so there is a socket client ip and port involved.
I don't know HTTPClient api by heart but if there is a getClientPort() then you should be able to printout the time, thread name and client ip+port. On the server, whatever accept the socket will have the client ip and port too. If this is a servlet container, the servlet request has getRemoteAddress() and getRemotePort(). There too you can print out the time, ip+port and thread name. If you pile those events in 2 tables, you should be able to join by ip+port with a tolerance on the client time vs server time (try less than 2 seconds apart, assuming C and S are on same time with NTP).
The other trivial way (but it changes the http payload) is to inject a HTTP header from the client into the http request, stating the current thread name/tid. Ex: "my_custom_remote_thread_id: Thread-11". This way on the server you can pull the request header to figure the client thread name/tid.

Vert.x Event loop - How is this asynchronous?

I'm playing around with Vert.x and quite new to the servers based on event loop as opposed to the thread/connection model.
public void start(Future<Void> fut) {
vertx
.createHttpServer()
.requestHandler(r -> {
LocalDateTime start = LocalDateTime.now();
System.out.println("Request received - "+start.format(DateTimeFormatter.ISO_DATE_TIME));
final MyModel model = new MyModel();
try {
for(int i=0;i<10000000;i++){
//some simple operation
}
model.data = start.format(DateTimeFormatter.ISO_DATE_TIME) +" - "+LocalDateTime.now().format(DateTimeFormatter.ISO_DATE_TIME);
} catch (Exception e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
r.response().end(
new Gson().toJson(model)
);
})
.listen(4568, result -> {
if (result.succeeded()) {
fut.complete();
} else {
fut.fail(result.cause());
}
});
System.out.println("Server started ..");
}
I'm just trying to simulate a long running request handler to understand how this model works.
What I've observed is the so called event loop is blocked until my first request completes. Whatever little time it takes, subsequent request is not acted upon until the previous one completes.
Obviously I'm missing a piece here and that's the question that I have here.
Edited based on the answers so far:
Isn't accepting all requests considered to be asynchronous? If a new
connection can only be accepted when the previous one is cleared
off, how is it async?
Assume a typical request takes anywhere between 100 ms to 1 sec (based on the kind and nature of the request). So it means, the
event loop can't accept a new connection until the previous request
finishes(even if its winds up in a second). And If I as a programmer
have to think through all these and push such request handlers to a
worker thread , then how does it differ from a thread/connection
model?
I'm just trying to understand how is this model better from a traditional thread/conn server models? Assume there is no I/O op or
all the I/O op are handled asynchronously? How does it even solve
c10k problem, when it can't start all concurrent requests parallely and have to wait till the previous one terminates?
Even if I decide to push all these operations to a worker thread(pooled), then I'm back to the same problem isn't it? Context switching between threads?
Edits and topping this question for a bounty
Do not completely understand how this model is claimed to asynchronous.
Vert.x has an async JDBC client (Asyncronous is the keyword) which I tried to adapt with RXJava.
Here is a code sample (Relevant portions)
server.requestStream().toObservable().subscribe(req -> {
LocalDateTime start = LocalDateTime.now();
System.out.println("Request for " + req.absoluteURI() +" received - " +start.format(DateTimeFormatter.ISO_DATE_TIME));
jdbc.getConnectionObservable().subscribe(
conn -> {
// Now chain some statements using flatmap composition
Observable<ResultSet> resa = conn.queryObservable("SELECT * FROM CALL_OPTION WHERE UNDERLYING='NIFTY'");
// Subscribe to the final result
resa.subscribe(resultSet -> {
req.response().end(resultSet.getRows().toString());
System.out.println("Request for " + req.absoluteURI() +" Ended - " +LocalDateTime.now().format(DateTimeFormatter.ISO_DATE_TIME));
}, err -> {
System.out.println("Database problem");
err.printStackTrace();
});
},
// Could not connect
err -> {
err.printStackTrace();
}
);
});
server.listen(4568);
The select query there takes 3 seconds approx to return the complete table dump.
When I fire concurrent requests(tried with just 2), I see that the second request completely waits for the first one to complete.
If the JDBC select is asynchronous, Isn't it a fair expectation to have the framework handle the second connection while it waits for the select query to return anything.?

Vert.x event loop is, in fact, a classical event loop existing on many platforms. And of course, most explanations and docs could be found for Node.js, as it's the most popular framework based on this architecture pattern. Take a look at one more or less good explanation of mechanics under Node.js event loop. Vert.x tutorial has fine explanation between "Don’t call us, we’ll call you" and "Verticles" too.
Edit for your updates:
First of all, when you are working with an event loop, the main thread should work very quickly for all requests. You shouldn't do any long job in this loop. And of course, you shouldn't wait for a response to your call to the database.
- Schedule a call asynchronously
- Assign a callback (handler) to result
- Callback will be executed in the worker thread, not event loop thread. This callback, for example, will return a response to the socket.
So, your operations in the event loop should just schedule all asynchronous operations with callbacks and go to the next request without awaiting any results.
Assume a typical request takes anywhere between 100 ms to 1 sec (based on the kind and nature of the request).
In that case, your request has some computation expensive parts or access to IO - your code in the event loop shouldn't wait for the result of these operations.
I'm just trying to understand how is this model better from a traditional thread/conn server models? Assume there is no I/O op or all the I/O op are handled asynchronously?
When you have too many concurrent requests and a traditional programming model, you will make thread per each request. What this thread will do? They will be mostly waiting for IO operations (for example, result from database). It's a waste of resources. In our event loop model, you have one main thread that schedule operations and preallocated amount of worker threads for long tasks. + None of these workers actually wait for the response, they just can execute another code while waiting for IO result (it can be implemented as callbacks or periodical checking status of IO jobs currently in progress). I would recommend you go through Java NIO and Java NIO 2 to understand how this async IO can be actually implemented inside the framework. Green threads is a very related concept too, that would be good to understand. Green threads and coroutines are a type of shadowed event loop, that trying to achieve the same thing - fewer threads because we can reuse system thread while green thread waiting for something.
How does it even solve c10k problem, when it can't start all concurrent requests parallel and have to wait till the previous one terminates?
For sure we don't wait in the main thread for sending the response for the previous request. Get request, schedule long/IO tasks execution, next request.
Even if I decide to push all these operations to a worker thread(pooled), then I'm back to the same problem isn't it? Context switching between threads?
If you make everything right - no. Even more, you will get good data locality and execution flow prediction. One CPU core will execute your short event loop and schedule async work without context switching and nothing more. Other cores make a call to the database and return response and only this. Switching between callbacks or checking different channels for IO status doesn't actually require any system thread's context switching - it's actually working in one worker thread. So, we have one worker thread per core and this one system thread await/checks results availability from multiple connections to database for example. Revisit Java NIO concept to understand how it can work this way. (Classical example for NIO - proxy-server that can accept many parallel connections (thousands), proxy requests to some other remote servers, listen to responses and send responses back to clients and all of this using one or two threads)
About your code, I made a sample project for you to demonstrate that everything works as expected:
public class MyFirstVerticle extends AbstractVerticle {
#Override
public void start(Future<Void> fut) {
JDBCClient client = JDBCClient.createShared(vertx, new JsonObject()
.put("url", "jdbc:hsqldb:mem:test?shutdown=true")
.put("driver_class", "org.hsqldb.jdbcDriver")
.put("max_pool_size", 30));
client.getConnection(conn -> {
if (conn.failed()) {throw new RuntimeException(conn.cause());}
final SQLConnection connection = conn.result();
// create a table
connection.execute("create table test(id int primary key, name varchar(255))", create -> {
if (create.failed()) {throw new RuntimeException(create.cause());}
});
});
vertx
.createHttpServer()
.requestHandler(r -> {
int requestId = new Random().nextInt();
System.out.println("Request " + requestId + " received");
client.getConnection(conn -> {
if (conn.failed()) {throw new RuntimeException(conn.cause());}
final SQLConnection connection = conn.result();
connection.execute("insert into test values ('" + requestId + "', 'World')", insert -> {
// query some data with arguments
connection
.queryWithParams("select * from test where id = ?", new JsonArray().add(requestId), rs -> {
connection.close(done -> {if (done.failed()) {throw new RuntimeException(done.cause());}});
System.out.println("Result " + requestId + " returned");
r.response().end("Hello");
});
});
});
})
.listen(8080, result -> {
if (result.succeeded()) {
fut.complete();
} else {
fut.fail(result.cause());
}
});
}
}
#RunWith(VertxUnitRunner.class)
public class MyFirstVerticleTest {
private Vertx vertx;
#Before
public void setUp(TestContext context) {
vertx = Vertx.vertx();
vertx.deployVerticle(MyFirstVerticle.class.getName(),
context.asyncAssertSuccess());
}
#After
public void tearDown(TestContext context) {
vertx.close(context.asyncAssertSuccess());
}
#Test
public void testMyApplication(TestContext context) {
for (int i = 0; i < 10; i++) {
final Async async = context.async();
vertx.createHttpClient().getNow(8080, "localhost", "/",
response -> response.handler(body -> {
context.assertTrue(body.toString().contains("Hello"));
async.complete();
})
);
}
}
}
Output:
Request 1412761034 received
Request -1781489277 received
Request 1008255692 received
Request -853002509 received
Request -919489429 received
Request 1902219940 received
Request -2141153291 received
Request 1144684415 received
Request -1409053630 received
Request -546435082 received
Result 1412761034 returned
Result -1781489277 returned
Result 1008255692 returned
Result -853002509 returned
Result -919489429 returned
Result 1902219940 returned
Result -2141153291 returned
Result 1144684415 returned
Result -1409053630 returned
Result -546435082 returned
So, we accept a request - schedule a request to the database, go to the next request, we consume all of them and send a response for each request only when everything is done with the database.
About your code sample I see two possible issues - first, it looks like you don't close() connection, which is important to return it to pool. Second, how your pool is configured? If there is only one free connection - these requests will serialize waiting for this connection.
I recommend you to add some printing of a timestamp for both requests to find a place where you serialize. You have something that makes the calls in the event loop to be blocking. Or... check that you send requests in parallel in your test. Not next after getting a response after previous.

How is this asynchronous? The answer is in your question itself
What I've observed is the so called event loop is blocked until my
first request completes. Whatever little time it takes, subsequent
request is not acted upon until the previous one completes
The idea is instead of having a new for serving each HTTP request, same thread is used which you have blocked by your long running task.
The goal of event loop is to save the time involved in context switching from one thread to another thread and utilize the ideal CPU time when a task is using IO/Network activities. If while handling your request it had to other IO/Network operation eg: fetching data from a remote MongoDB instance during that time your thread will not be blocked and instead an another request would be served by the same thread which is the ideal use case of event loop model (Considering that you have concurrent requests coming to your server).
If you have long running tasks which does not involve Network/IO operation, you should consider using thread pool instead, if you block your main event loop thread itself other requests would be delayed. i.e. for long running tasks you are okay to pay the price of context switching for for server to be responsive.
EDIT:
The way a server can handle requests can vary:
1) Spawn a new thread for each incoming request (In this model the context switching would be high and there is additional cost of spawning a new thread every time)
2) Use a thread pool to server the request (Same set of thread would be used to serve requests and extra requests gets queued up)
3) Use a event loop (single thread for all the requests. Negligible context switching. Because there would be some threads running e.g: to queue up the incoming requests)
First of all context switching is not bad, it is required to keep application server responsive, but, too much context switching can be a problem if the number of concurrent requests goes too high (roughly more than 10k). If you want to understand in more detail I recommend you to read C10K article
Assume a typical request takes anywhere between 100 ms to 1 sec (based
on the kind and nature of the request). So it means, the event loop
can't accept a new connection until the previous request finishes(even
if its winds up in a second).
If you need to respond to large number of concurrent requests (more than 10k) I would consider more than 500ms as a longer running operation. Secondly, Like I said there are some threads/context switching involved e.g.: to queue up incoming requests, but, the context switching amongst threads would be greatly reduced as there would be too few threads at a time. Thirdly, if there is a network/IO operation involved in resolving first request second request would get a chance to be resolved before first is resolved, this is where this model plays well.
And If I as a programmer have to think
through all these and push such request handlers to a worker thread ,
then how does it differ from a thread/connection model?
Vertx is trying to give you best of threads and event loop, so, as programmer you can make a call on how to make your application efficient under both the scenario i.e. long running operation with and without network/IO operation.
I'm just trying to understand how is this model better from a
traditional thread/conn server models? Assume there is no I/O op or
all the I/O op are handled asynchronously? How does it even solve c10k
problem, when it can't start all concurrent requests parallely and
have to wait till the previous one terminates?
The above explanation should answer this.
Even if I decide to push all these operations to a worker
thread(pooled), then I'm back to the same problem isn't it? Context
switching between threads?
Like I said, both have pros and cons and vertx gives you both the model and depending on your use case you got to choose what is ideal for your scenario.

In these sort of processing engines, you are supposed to turn long running tasks in to asynchronously executed operations and these is a methodology for doing this, so that the critical thread can complete as quickly as possible and return to perform another task. i.e. any IO operations are passed to the framework to call you back when the IO is done.
The framework is asynchronous in the sense that it supports you producing and running these asynchronous tasks, but it doesn't change your code from being synchronous to asynchronous.

How to Cancel a REST api request?

I use REST API based system, in which there are some requests that take long time to complete. I want to give user an option to cancel the request.

First, support
POST /requests
which will return a reference to the status of the request
{
"id": 1234,
"self"": "/requests/1234"
"status": "Running"
}
Then add support for
PUT /requests/1234
{
"status": "Canceled:"
}
That will let clients cancel a request if it hasn't finished yet. If the request is to create some other kind of resource, then instead of POST /requests, do POST /myResource, but still return the status object with the pointer to /requests in the response.
Clients can then poll /requests to see when the request is complete.

Firstly you need to use multiple threads because your program will be on hold while it is sending the request so you cannot click on something until it is back from hold.
Create a thread which calls the rest API in background without hanging the overall application and terminate that thread on click of a button.
note for terminating the thread you need to use stop function which is depreciated now because you cannot interrupt the thread or check a Boolean during the process.
#Deprecated
public final void stop()
Alternatively, you can use Maximum Time for a HTTP Request call by
HttpConnectionParams.setConnectionTimeout(httpParams, 30000);
_For all scenario
Make thread pool method
executorService = Executors.newFixedThreadPool(1);
Put your logical method in callable store in future object
Future<Boolean> futureObjects = executorService.submit(newCallable<Boolean>() { ....call your logical method which you going run in multiple thread....});
3.Gets your results future object
return (futureObjects != null)
? futureObjects.get(timeout, TimeUnit.SECONDS)
: null;
The default waits until getting separate calls response.
4.IN between calling multiple threads requesting user want to stop or break their multiple thread calls then simply check executor is not terminated then terminate immediately
if (executorService != null && !executorService.isTerminated(){
executorService.shutdownNow();
}

Sending and receiving an acknowledgement using Datagram UDP

I'm new to socket programming and programming a Java UDP simple client-server application. I'm writing a time/date server client. The client can ask the server for the time and date and it waits for a response. Also, every minute, the server updates all clients with the current time. The client needs to
be able to initiate contact with the server and wait for a message back
listen for periodic updates from the server
How can I do this using a single DatagramSocket?
I was thinking of creating two threads: one that listens and one that writes. The problem is that in the case that the client initiates contact with the server, it needs to wait to receive an acknowledgement from the server. So, the writing thread also needs to listen for packets from the server sometimes. But in this case, I have two threads listening and the wrong thread will get the acknowledgement.
Is there a way to specify which thread gets the input? Or is there some other way to solve this problem?
I've been searching for this answer but unable to find it. The closest I've found is Java sockets: can you send from one thread and receive on another?

If there is just one writer thread then it could send the request and go into a wait loop. The listener thread would then get the response, add it to a shared variable (maybe an AtomicReference), and then notify the writer that response has been received.
// both write and listener threads will need to share this
private final AtomicReference<Response> responseRef =
new AtomicReference<Response>();
...
// writer-thread
writeRequest(request);
synchronize (responseRef) {
while (responseRef.get() == null) {
// maybe put a timeout here
responseRef.wait();
}
}
processResponse(response);
...
// listener-thread
Response response = readResponse();
synchronize (responseRef) {
responseRef.set(response);
responseRef.notify();
}
If you have multiple writers or multiple requests being sent at the same time then it gets more complicated. You'll need to send some sort of unique id with each request and return it in the response. Then the response thread can match up the request with the response. You'd need a ConcurrentHashMap or other shared collection so that the responder can match up the particular request, add the response, and notify the appropriate waiting thread.