I am writing a singleton class (Object in scala) which uses apache httpclient(4.5.2) to post some file content and return status to caller.
object HttpUtils{
protected val retryHandler = new HttpRequestRetryHandler() {
def retryRequest(exception: IOException, executionCount: Int, context: HttpContext): Boolean = {
//retry logic
true
}
}
private val connectionManager = new PoolingHttpClientConnectionManager()
// Reusing same client for each request that might be coming from different threads .
// Is it correct ????
val httpClient = HttpClients.custom()
.setConnectionManager(connectionManager)
.setRetryHandler(retryHandler)
.build()
def restApiCall (url : String, rDD: RDD[SomeMessage]) : Boolean = {
// Creating new context for each request
val httpContext: HttpClientContext = HttpClientContext.create
val post = new HttpPost(url)
// convert RDD to text file using rDD.collect
// add this file as MultipartEntity to post
var response = None: Option[CloseableHttpResponse] // Is it correct way of using it ?
try {
response = Some(httpClient.execute(post, httpContext))
val responseCode = response.get.getStatusLine.getStatusCode
EntityUtils.consume(response.get.getEntity) // Is it require ???
if (responseCode == 200) true
else false
}
finally {
if (response.isDefined) response.get.close
post.releaseConnection() // Is it require ???
}
}
def onShutDown = {
connectionManager.close()
httpClient.close()
}
}
Multiple threads (More specifically from spark streaming context) are calling restApiCall method. I am relatively new to scala and apache httpClient. I have to make frequent connections to only few fixed server (i.e. 5-6 fixed URL's with different request parameters).
I went through multiple online resource but still not confident about it.
Is it the best way to use http client in multi-threaded environment?
Is it possible to keep live connections and use it for various requests ? Will it be beneficial in this case ?
Am i using/releasing all resources efficiently ? If not please suggest.
Is it good to use it in Scala or there exist some better library ?
Thanks in advance.
It seems the official docs have answers to all your questions:
2.3.3. Pooling connection manager
PoolingHttpClientConnectionManager is a more complex implementation
that manages a pool of client connections and is able to service
connection requests from multiple execution threads. Connections are
pooled on a per route basis. A request for a route for which the
manager already has a persistent connection available in the pool will
be serviced by leasing a connection from the pool rather than creating
a brand new connection.
PoolingHttpClientConnectionManager maintains a maximum limit of
connections on a per route basis and in total. Per default this
implementation will create no more than 2 concurrent connections per
given route and no more 20 connections in total. For many real-world
applications these limits may prove too constraining, especially if
they use HTTP as a transport protocol for their services.
2.4. Multithreaded request execution
When equipped with a pooling connection manager such as
PoolingClientConnectionManager, HttpClient can be used to execute
multiple requests simultaneously using multiple threads of execution.
The PoolingClientConnectionManager will allocate connections based on
its configuration. If all connections for a given route have already
been leased, a request for a connection will block until a connection
is released back to the pool. One can ensure the connection manager
does not block indefinitely in the connection request operation by
setting 'http.conn-manager.timeout' to a positive value. If the
connection request cannot be serviced within the given time period
ConnectionPoolTimeoutException will be thrown.
Related
What is the best configuration I can use to set up the OkHttp3 client correctly in a multi threaded environment? Had 2 main questions:
Connection pool - How do we define the number of available connections in the pool? Can it be scaled at runtime? The number of concurrent users will be very high and need to make sure users aren't waiting a long time for the connection to be available from the pool.
I read the OkHttp might end up doing multiple retries in case of failures or timeouts. Is it possible to only enable this for only the "Gets" and not "Post" while using just 1 OkHttp client?
Also Anything else I should be considering?
Here is my starting code for the client.
private static final int timeout = 15000;
private static final OkHttpClient okClient = new OkHttpClient()
.newBuilder()
.connectTimeout(timeout, TimeUnit.MILLISECONDS)
.readTimeout(timeout, TimeUnit.MILLISECONDS)
.writeTimeout(timeout, TimeUnit.MILLISECONDS)
.retryOnConnectionFailure(false)
.addInterceptor(new HttpLoggingInterceptor().setLevel(HttpLoggingInterceptor.Level.BASIC))
.build();
You can configure the connection pool then pass into the client builder.
https://square.github.io/okhttp/3.x/okhttp/okhttp3/ConnectionPool.html
See Connection Pool - OkHttp for an example.
For the second question, you can disable automatic retries and do this in your application code instead. Use retryOnConnectionFailure(false) as you show above.
To have this applied differently for get and posts you should use customise one client like the following
val postClient = client.newBuilder().retryOnConnectionFailure(false).build()
I have a use case where we are streaming events and for each event I have to do some lookups. The Lookups are in Redis and I am wondering what is the best way to create the connections. The spark streaming would run 40 executors and I have 5 such Streaming jobs all connecting to same Redis Cluster. So I am confused what approach should I be taking to create the Redis connection
Create a connection object on the driver and broadcast it to the executors ( Not sure if it really works as I have to make this object Serializable). Can I do this with broadcast variables?
Create a Redis connection for each partition, however I have the code written this way
val update = xyz.transform(rdd => {
// on driver
if (xyz.isNewDay) {
.....
}
rdd
})
update.foreachRDD(rdd => {
rdd.foreachPartition(partition => {
partition.foreach(Key_trans => {
// perform some lookups logic here
}
}
})
So now if i create a connection inside each partition it would mean that for every RDD and for each partition in that RDD I would be creating a new connection.
Is there a way i can maintain one connection for each partition and cache that object so that I would not have to create connections again and again?
I can add more context/info if required.
1. Create a connection object on the driver and broadcast it to the executors ( Not sure if it really works as I have to make this object Serializable). Can I do this with broadcast variables?
Answer - No. Most of the connection objects are not serializable due to machine dependent data associated with connection.
2. Is there a way i can maintain one connection for each partition and cache that object so that I would not have to create connections again and again?
Ans- Yes, create a connection pool and use it in partition. here is the style. You can create a connection pool like this https://github.com/RedisLabs/spark-redis/blob/master/src/main/scala/com/redislabs/provider/redis/ConnectionPool.scala
and then use it
dstream.foreachRDD { rdd =>
rdd.foreachPartition { partitionOfRecords =>
// ConnectionPool is a static, lazily initialized pool of connections
val connection = ConnectionPool.getConnection()
partitionOfRecords.foreach(record => connection.send(record))
ConnectionPool.returnConnection(connection) // return to the pool for future reuse
}
}
Please check this:
design pattern for using foreachRDD
I know that you utilize a port to address a process and that you have to use sockets for handling multiple requests on web server, but how does it work? Is the process creating multiple socket threads for each connection? Is threading the answer?
Overview
This is a great question, and one that will take a bit to explain fully. I will step through different parts of this topic below. I personally learned multi-threading in Java, which has quite an extensive concurrency library. Although my examples will be in Java, the concepts will stand between languages.
Is threading valid?
In short, yes this is a perfect use case for multi-threading, although single-threaded is fine for simple scenarios as well. However, there does exist better designs that may yield better performance and safer code. The great thing is there are loads of examples on how to do this on the internet!
Multi-Threading
Lets investigate sample code from this article, seen below.
public class Server
{
public static void main(String[] args) throws IOException
{
// server is listening on port 5056
ServerSocket ss = new ServerSocket(5056);
// running infinite loop for getting
// client request
while (true)
{
Socket s = null;
try
{
// socket object to receive incoming client requests
s = ss.accept();
System.out.println("A new client is connected : " + s);
// obtaining input and out streams
DataInputStream dis = new DataInputStream(s.getInputStream());
DataOutputStream dos = new DataOutputStream(s.getOutputStream());
System.out.println("Assigning new thread for this client");
// create a new thread object
Thread t = new ClientHandler(s, dis, dos);
// Invoking the start() method
t.start();
}
catch (Exception e){
s.close();
e.printStackTrace();
}
}
}
}
The Server code is actually quite basic but still does the job well. Lets step through all the logic seen here:
The Server sets up on Socket 5056
The Server begins its infinite loop
The client blocks on ss.accept() until a client request is received on part 5056
The Server does relatively minimal operations (i.e. System.out logging, set up IO streams)
A Thread is created and assigned to this request
The Thread is started
The loop repeats
The mentality here is that the server acts as a dispatcher. Requests enter the server, and the server allocates workers (Threads) to complete the operations in parallel so that the server can wait for and assist the next, incoming request.
Pros
Simple, readable code
Operations in parallel allows for increased performance with proper synchronization
Cons
The dangers of multi-threading
The creation of threads is quite cumbersome and resource intensive, thus should not be a frequent operation
No re-use of threads
Must manually limit threads
Thread Pool
Lets investigate sample code from this article, seen below.
while(! isStopped()){
Socket clientSocket = null;
try {
clientSocket = this.serverSocket.accept();
} catch (IOException e) {
if(isStopped()) {
System.out.println("Server Stopped.") ;
break;
}
throw new RuntimeException("Error accepting client connection", e);
}
this.threadPool.execute(new WorkerRunnable(clientSocket,"Thread Pooled Server"));
}
Note, I excluded the setup because it is rather similar to the Multi-Threaded example. Lets step through the logic in this example.
The server waits for a request to arrive on its alloted port
The server sends the request to a handler that is given to the ThreadPool to run
The ThreadPool receives Runnable code, allocated a worker, and begin code execution in parallel
The loop repeats
The server again acts as a dispatcher; it listens for the request, receives one, and ships it to a ThreadPool. The ThreadPool abstracts the complex resource management from the developer and executes the code optimized fully. This is very similar to the multi-thread example, but all resource management is packaged into the ThreadPool. The code is reduced further from the above example, and it is much safer for non-multi-threading professionals. Note, the WorkerRunnable is only a Runnable, not a raw Thread, whilst the ClientHandler in the Multi-Thread example was a raw Thread.
Pros
Threads are managed and re-used by the pool
Further simplify code base
Inherits all benefits from the Multi-Threaded example
Cons
There is a learning curve to fully understanding pooling and different configurations of them
Notes
In Java, there exists another implementation called RMI, that attempts to abstract away the network, thus allowing the communication of Client-Server to happen as though it is on one JVM, even if it is on many. Although this as well can use multi-threading, it is another approach to the issue instead of sockets.
I have a Function app in Azure that is triggered when an item is put on a queue. It looks something like this (greatly simplified):
public static async Task Run(string myQueueItem, TraceWriter log)
{
using (var client = new HttpClient())
{
client.BaseAddress = new Uri(Config.APIUri);
client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
StringContent httpContent = new StringContent(myQueueItem, Encoding.UTF8, "application/json");
HttpResponseMessage response = await client.PostAsync("/api/devices/data", httpContent);
response.EnsureSuccessStatusCode();
string json = await response.Content.ReadAsStringAsync();
ApiResponse apiResponse = JsonConvert.DeserializeObject<ApiResponse>(json);
log.Info($"Activity data successfully sent to platform in {apiResponse.elapsed}ms. Tracking number: {apiResponse.tracking}");
}
}
This all works great and runs pretty well. Every time an item is put on the queue, we send the data to some API on our side and log the response. Cool.
The problem happens when there's a big spike in "the thing that generates queue messages" and a lot of items are put on the queue at once. This tends to happen around 1,000 - 1,500 items in a minute. The error log will have something like this:
2017-02-14T01:45:31.692 mscorlib: Exception while executing function:
Functions.SendToLimeade. f-SendToLimeade__-1078179529: An error
occurred while sending the request. System: Unable to connect to the
remote server. System: Only one usage of each socket address
(protocol/network address/port) is normally permitted
123.123.123.123:443.
At first, I thought this was an issue with the Azure Function app running out of local sockets, as illustrated here. However, then I noticed the IP address. The IP address 123.123.123.123 (of course changed for this example) is our IP address, the one that the HttpClient is posting to. So, now I'm wondering if it is our servers running out of sockets to handle these requests.
Either way, we have a scaling issue going on here. I'm trying to figure out the best way to solve it.
Some ideas:
If it's a local socket limitation, the article above has an example of increasing the local port range using Req.ServicePoint.BindIPEndPointDelegate. This seems promising, but what do you do when you truly need to scale? I don't want this problem coming back in 2 years.
If it's a remote limitation, it looks like I can control how many messages the Functions runtime will process at once. There's an interesting article here that says you can set serviceBus.maxConcurrentCalls to 1 and only a single message will be processed at once. Maybe I could set this to a relatively low number. Now, at some point our queue will be filling up faster than we can process them, but at that point the answer is adding more servers on our end.
Multiple Azure Functions apps? What happens if I have more than one Azure Functions app and they all trigger on the same queue? Is Azure smart enough to divvy up the work among the Function apps and I could have an army of machines processing my queue, which could be scaled up or down as needed?
I've also come across keep-alives. It seems to me if I could somehow keep my socket open as queue messages were flooding in, it could perhaps help greatly. Is this possible, and any tips on how I'd go about doing this?
Any insight on a recommended (scalable!) design for this sort of system would be greatly appreciated!
I think the code error is because of: using (var client = new HttpClient())
Quoted from Improper instantiation antipattern:
this technique is not scalable. A new HttpClient object is created for
each user request. Under heavy load, the web server may exhaust the
number of available sockets.
I think I've figured out a solution for this. I've been running these changes for the past 3 hours 6 hours, and I've had zero socket errors. Before I would get these errors in large batches every 30 minutes or so.
First, I added a new class to manage the HttpClient.
public static class Connection
{
public static HttpClient Client { get; private set; }
static Connection()
{
Client = new HttpClient();
Client.BaseAddress = new Uri(Config.APIUri);
Client.DefaultRequestHeaders.Add("Connection", "Keep-Alive");
Client.DefaultRequestHeaders.Add("Keep-Alive", "timeout=600");
Client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
}
}
Now, we have a static instance of HttpClient that we use for every call to the function. From my research, keeping HttpClient instances around for as long as possible is highly recommended, everything is thread safe, and HttpClient will queue up requests and optimize requests to the same host. Notice I also set the Keep-Alive headers (I think this is the default, but I figured I'll be implicit).
In my function, I just grab the static HttpClient instance like:
var client = Connection.Client;
StringContent httpContent = new StringContent(myQueueItem, Encoding.UTF8, "application/json");
HttpResponseMessage response = await client.PostAsync("/api/devices/data", httpContent);
response.EnsureSuccessStatusCode();
I haven't really done any in-depth analysis of what's happening at the socket level (I'll have to ask our IT guys if they're able to see this traffic on the load balancer), but I'm hoping it just keeps a single socket open to our server and makes a bunch of HTTP calls as the queue items are processed. Anyway, whatever it's doing seems to be working. Maybe someone has some thoughts on how to improve.
If you use consumption plan instead of Functions on a dedicated web app, #3 more or less occurs out of the box. Functions will detect that you have a large queue of messages and will add instances until queue length stabilizes.
maxConcurrentCalls only applies per instance, allowing you to limit per-instance concurrency. Basically, your processing rate is maxConcurrentCalls * instanceCount.
The only way to control global throughput would be to use Functions on dedicated web apps of the size you choose. Each app will poll the queue and grab work as necessary.
The best scaling solution would improve the load balancing on 123.123.123.123 so that it can handle any number of requests from Functions scaling up/down to meet queue pressure.
Keep alive afaik is useful for persistent connections, but function executions aren't viewed as a persistent connection. In the future we are trying to add 'bring your own binding' to Functions, which would allow you to implement connection pooling if you liked.
I know the question was answered long ago, but in the mean time Microsoft have documented the anti-pattern that you were using.
Improper Instantiation antipattern
I'm new to Spring Integration. The situation is that I've to connect to Tcp server dynamically(i.e. the DNS will be dynamically generated at runtime based on some params). Because of this I'm using Service Activator to manually create Tcp Connections and send messages. I've overridden CachingClientConnectionFactory to make use of shared connections concept(with single-use='false'). I was listening to messages using TcpReceivingChannelAdaptor by overriding "onMessage" method. The problem is that the server either responds with a Success or failure(with Generic messages) with no CorrelationID. Is there any way to correlate the request with the response ?
I tried using TcpOutboundGateway, but with this approach also I get the same problem. I used TcpConnectionSupport to send messages :
//Sample Code.
final String correlationId = "" // Dynamic unique number
TcpOutboundGateway outboundGateway = new TcpOutboundGateway(){
public synchronized boolean onMessage(Message<?> message) {
ByteArrayToStringConverter converter = new ByteArrayToStringConverter();
String response = converter.convert((byte[]) message
.getPayload());
logger.info(correlationId);
return false;
}
};
DefaultCachingClientConnectionFactory connFactory = new DefaultCachingClientConnectionFactory();
TcpConnectionSupport con = connFactory.obtainConnection();
GenericMessage<String> msg = new GenericMessage<String>("Sample Message" + correlationId);
con.registerListener(outboundGateway);
con.send(msg);
// DefaultCachingClientConnectionFactory is the subclass of CachingClientConnectionFactory.
When I send multiple messages, every time I get the same correlation printed in the "onMessage" method.
I read here that Outbound Gateway will correlate messages. Please help me. Maybe I'm doing something wrong.
Thanks
Unless you include correlation data in the message you can't correlate a response to a request.
The gateway achieves this by only allowing one outstanding request on a socket at a time; hence the reply has to be for the request. This is not very useful at high volume with a shared connection; hence the caching client cf was introduced. The gateway keeps a map of outstanding requests based on the connection id.
The gateway, in conjunction with the caching client connection factory should do what you need. However, overriding onMessage is not a good idea, because that's where the reply correlation is done.