I have a standard SQS and I need to read data from it using multiple consumer threads.
To achieve the same, I have written the following method ( in Java):
public void consume() {
ExecutorService exService = Executors.newFixedThreadPool(3);
while(true) {
exService.execute(()->{
try {
/* the following code submits a null data to the blocking queue when the queue(sqs) is empty*/
OrderQ order = queueMessagingTemplate.receiveAndConvert(StringConstants.QUEUE_NAME
, OrderQ.class);
if(order!=null)
repstiory.saveOrder(order);
log.debug("Received from queue:"+ order);
}catch(Exception exc) {
log.error("couldn't read msg from queue");
throw new CouldNotReadMessageFromSQSException("couldn't read msg from queue");
}
});
}
}
and, as of now, I have two approaches to call the above method and start consuming the messages from the queue stated as follows along with the issues associated with them:
Approach-1 :
create a rest api and call this method. This approach won't work in production and also when the queue is empty, null will keep getting populated in the blocking queue of the thread pool. So, this is clearly not a good approach. Question-How can I ensure that only 'not null' data is submitted to the blocking queue?
Approach-2:
calling the consume method from CommandLineRunner but the issue is, the queue, most probably, won't have data as soon as the application starts and will run into the same problem as described in the 1st approach.
Problem-1:What can be a better solution to handle the null data problem?
problem-2:what can be better way to call the consume method, considering the production environment?
Kindly suggest solutions and best practices to achieve the same.
I am using DefaultEventExecutorGroup to execute business handler methods. My understanding is, IO event loop thread will enqueue events to DefaultEventExecutorGroup for execution. And any thread from DefaultEventExecutorGroup will poll those events, and executes handler methods when such event arises. If, so, then different threads from DefaultEventExecutorGroup can executing same channel handler methods. So, I need to synchronize channelRead() write() methods. Is it true? Or it is that, always only one of thread from DefaultEventExecutorGroup will always be executing handler methods, same like one of IO event loop thread always handles channel operations, that is, channel handler is always bound to single same thread only, even when there are multiple event executor groups in pipeline?
Going through the Netty 4 release guide, I see some information around the threading model that has been introduced since 4.0 release. Based on my understanding of it, below is my view:
different threads from DefaultEventExecutorGroup can executing same
channel handler methods.
Once a thread is assigned to a handler, this thread-handler link will continue until de-registration. The handler methods will always be invoked by the same thread.
channel handler is always bound to single same thread only, even when
there are multiple event executor groups in pipeline?
If two handlers in the same pipeline are assigned with different EventExecutors, they are invoked simultaneously. A user has to pay attention to thread safety if more than one handler access shared data even if the shared data is accessed only by the handlers in the same pipeline.
To test this scenario, I tried with NettyServer that has two handlers each with its own DefaultEventExecutorGroup. These handlers write and flush to the underlying channel character by character with a delay of 100 ms. First handler writes "Hello" and second one does a " WORLD !!!".
Server Code:
EventLoopGroup group = new NioEventLoopGroup();
try {
ServerBootstrap b = new ServerBootstrap();
b.group(group).channel(NioServerSocketChannel.class).localAddress(new InetSocketAddress(port))
.childHandler(new ChannelInitializer<SocketChannel>() {
#Override
public void initChannel(SocketChannel ch) throws Exception {
ch.pipeline().addLast(new DefaultEventExecutorGroup(10), new EchoServerHandler("Hello"));
ch.pipeline().addLast(new DefaultEventExecutorGroup(10), new EchoServerHandler(" WORLD !!!"));
}
});
Server Handler code:
for(int i = 0; i < message.length(); i++) {
ctx.write(Unpooled.copiedBuffer(new byte[] {message.getBytes()[i]}));
ctx.flush();
try {
Thread.sleep(100);
} catch (InterruptedException e) {}
On the client side once the connection is established and I see the output is jumbled like HeWlOlRoLD !!!, H eWlORlLoD !!! etc. I believe this is the scenario you are asking.
Please refer to the Well-defined thread model and Write Ordering - Mix EventLoop thread and other threads sections of the following link regarding the Netty 4.0 threading model for more information.
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.
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();
}
I'm trying to write a non-blocking proxy with netty 4.1. I have a "FrontHandler" which handles incoming connections, and then a "BackHandler" which handles outgoing ones. I'm following the HexDumpProxyHandler (https://github.com/netty/netty/blob/ed4a89082bb29b9e7d869c5d25d6b9ea8fc9d25b/example/src/main/java/io/netty/example/proxy/HexDumpProxyFrontendHandler.java#L67)
In this code I have found:
#Override
public void channelRead(final ChannelHandlerContext ctx, Object msg) {
if (outboundChannel.isActive()) {
outboundChannel.writeAndFlush(msg).addListener(new ChannelFutureListener() {, I've seen:
Meaning that the incoming message is only written if the outbound client connection is already ready. This is obviously not ideal in a HTTP proxy case, so I am thinking what would be the best way to handle it.
I am wondering if disabling auto-read on the front-end connection (and only trigger reads manually once the outgoing client connection is ready) is a good option. I could then enable autoRead over the child socket again, in the "channelActive" event of the backend handler. However, I am not sure about how many messages would I get in the handler for each "read()" invocation (using HttpDecoder, I assume I would get the initial HttpRequest, but I'd really like to avoid getting the subsequent HttpContent / LastHttpContent messages until I manually trigger the read() again and enable autoRead over the channel).
Another option would be to use a Promise to get the Channel from the client ChannelPool:
private void setCurrentBackend(HttpRequest request) {
pool.acquire(request, backendPromise);
backendPromise.addListener((FutureListener<Channel>) future -> {
Channel c = future.get();
if (!currentBackend.compareAndSet(null, c)) {
pool.release(c);
throw new IllegalStateException();
}
});
}
and then do the copying from input to output thru that promise. Eg:
private void handleLastContent(ChannelHandlerContext frontCtx, LastHttpContent lastContent) {
doInBackend(c -> {
c.writeAndFlush(lastContent).addListener((ChannelFutureListener) future -> {
if (future.isSuccess()) {
future.channel().read();
} else {
pool.release(c);
frontCtx.close();
}
});
});
}
private void doInBackend(Consumer<Channel> action) {
Channel c = currentBackend.get();
if (c == null) {
backendPromise.addListener((FutureListener<Channel>) future -> action.accept(future.get()));
} else {
action.accept(c);
}
}
but I'm not sure about how good it is to keep the promise there forever and do all the writes from "front" to "back" by adding listeners to it. I'm also not sure about how to instance the promise so that the operations are performed in the right thread... right now I'm using:
backendPromise = group.next().<Channel> newPromise(); // bad
// or
backendPromise = frontCtx.channel().eventLoop().newPromise(); // OK?
(where group is the same eventLoopGroup as used in the ServerBootstrap of the frontend).
If they're not handled thru the right thread, I assume it could be problematic to have the "else { }" optimization in the "doInBackend" method to avoid using the Promise and write to the channel directly.
The no-autoread approach doesn't work by itself, because the HttpRequestDecoder creates several messages even if only one read() was performed.
I have solved it by using chained CompletableFutures.
I have worked on a similar proxy application based on the MQTT protocol. So it was basically used to create a real-time chat application. The application that I had to design however was asynchronous in nature so I naturally did not face any such problem. Because in case the
outboundChannel.isActive() == false
then I can simply keep the messages in a queue or a persistent DB and then process them once the outboundChannel is up. However, since you are talking about an HTTP application, so this means that the application is synchronous in nature meaning that the client cannot keep on sending packets until the outboundChannel is up and running. So the option you suggest is that the packet will only be read once the channel is active and you can manually handle the message reads by disabling the auto read in ChannelConfig.
However, what I would like to suggest is that you should check if the outboundChannel is active or not. In case the channel is active, send he packet forward for processing. In case the channel is not active, you should reject the packet by sending back a response similar to Error404
Along with this you should configure your client to keep on retrying sending the packets after certain intervals and accordingly handle what needs to be done in case the channel takes too long a time to become active and become readable. Manually handling channelRead is generally not preferred and is an anti pattern. You should let Netty handle that for you in the most efficient way.