I've following InboundChannelAdapter with Poller to process files every 30 seconds. The files are not large but I realize the memory consumptions keeps going up even when there's no files coming.
#Bean
#InboundChannelAdapter(value = "flowFileInChannel" ,poller = #Poller(fixedDelay ="30000", maxMessagesPerPoll = "1"))
public MessageSource<File> flowInboundFileAdapter(#Value("${integration.path}") File directory) {
FileReadingMessageSource source = new FileReadingMessageSource();
source.setDirectory(directory);
source.setFilter(flowPathFileFilter);
source.setUseWatchService(true);
source.setScanEachPoll(true);
source.setAutoCreateDirectory(false);
return source;
}
Is there an internal queue that is not cleared after each poll? How do I configure to avoid eating up memory.
After digging deeper, it looks like the below Spring IntegrationFlows which processes the data from the InboundChannelDapter is holding up the memory after each file polling. After I commenting out the middle part, the memory consumption seems stable (instead of increasing consumption). Now I'm wondering how do we force Spring IntegrationFlows to clear those Messages and Headers after they're passed through different channels (i.e. after the last channel below)
public IntegrationFlow incomingLocateFlow(){
return IntegrationFlows.from(locateIncomingChannel())
// .split("locateItemSplitter","split")
// .transform(locateItemEnrichmentTransformer)
// .transform(locateRequestTransformer)
// .aggregate(new Consumer<AggregatorSpec>() { // 32
//
// #Override
// public void accept(AggregatorSpec aggregatorSpec) {
// aggregatorSpec.processor(locateRequestProcessor, null); // 33
// }
//
// }, null)
// .transform(locateIncomingResultTransformer)
// .transform(locateExceptionReportWritingHandler)
.channel(locateIncomingCompleteChannel())
.get();
}
Indeed there is an AcceptOnceFileListFilter with the code like:
private final Queue<F> seen;
private final Set<F> seenSet = new HashSet<F>();
On each poll those internal collections are replenished with new files.
For this purpose you can consider to use FileSystemPersistentAcceptOnceFileListFilter with the persistent MetadataStore implementation to avoid memory consumption.
Also consider to use some tool to analyze the memory content. You might have something else downstream on the flowFileInChannel.
UPDATE
Since you use .aggregate() it is definitely the point where memory is consumed by default. That's because there is SimpleMessageStore to keep messages for grouping. Plus there is an option expireGroupsUponCompletion(boolean) which is false by default. Therefore even after successful releasing some info is still in the MessageStore. That's how your memory is consumed a bit from time to time.
That option is false by default to let to have logic when we discard late message for completed group. When it is true, you are able to form fresh group for the same correlationKey.
See more info about Aggregator in the Reference Manual.
Related
I have a Integration flow configured using Java DSL which pulls file from Ftp server using Ftp.inboundChannelAdapter then transforms it to JobRequest, then I have a .handle() method which triggers my batch job, everything is working as per required but the process in running sequentially for each file inside the FTP folder
I added currentThreadName in my Transformer Endpoint it was printing same thread name for each file
Here is what I have tried till now
1.task executor bean
#Bean
public TaskExecutor taskExecutor(){
return new SimpleAsyncTaskExecutor("Integration");
}
2.Integration flow
#Bean
public IntegrationFlow integrationFlow(JobLaunchingGateway jobLaunchingGateway) throws IOException {
return IntegrationFlows.from(Ftp.inboundAdapter(myFtpSessionFactory)
.remoteDirectory("/bar")
.localDirectory(localDir.getFile())
,c -> c.poller(Pollers.fixedRate(1000).taskExecutor(taskExecutor()).maxMessagesPerPoll(20)))
.transform(fileMessageToJobRequest(importUserJob(step1())))
.handle(jobLaunchingGateway)
.log(LoggingHandler.Level.WARN, "headers.id + ': ' + payload")
.route(JobExecution.class,j->j.getStatus().isUnsuccessful()?"jobFailedChannel":"jobSuccessfulChannel")
.get();
}
3.I also read in another SO thread that I need ExecutorChannel so I configured one but I don't know how to inject this channel into my Ftp.inboundAdapter, from logs is see that the channel is always integrationFlow.channel#0 which I guess is a DirectChannel
#Bean
public MessageChannel inputChannel() {
return new ExecutorChannel(taskExecutor());
}
I dont know what I'm missing here, or I might have not properly understood Spring Messaging System as I'm very much new to Spring and Spring-Integration
Any help is appreciated
Thanks
The ExecutorChannel you can simply inject into the flow and it is going to be applied to the SourcePollingChannelAdapter by the framework. So, having that inputChannel defined as a bean you just do this:
.channel(inputChannel())
before your .transform(fileMessageToJobRequest(importUserJob(step1()))).
See more in docs: https://docs.spring.io/spring-integration/docs/current/reference/html/dsl.html#java-dsl-channels
On the other hand to process your files in parallel according your .taskExecutor(taskExecutor()) configuration, you just need to have a .maxMessagesPerPoll(20) as 1. The logic in the AbstractPollingEndpoint is like this:
this.taskExecutor.execute(() -> {
int count = 0;
while (this.initialized && (this.maxMessagesPerPoll <= 0 || count < this.maxMessagesPerPoll)) {
if (pollForMessage() == null) {
break;
}
count++;
}
So, we do have tasks in parallel, but only when they reach that maxMessagesPerPoll where it is 20 in your current case. There is also some explanation in the docs: https://docs.spring.io/spring-integration/docs/current/reference/html/messaging-endpoints.html#endpoint-pollingconsumer
The maxMessagesPerPoll property specifies the maximum number of messages to receive within a given poll operation. This means that the poller continues calling receive() without waiting, until either null is returned or the maximum value is reached. For example, if a poller has a ten-second interval trigger and a maxMessagesPerPoll setting of 25, and it is polling a channel that has 100 messages in its queue, all 100 messages can be retrieved within 40 seconds. It grabs 25, waits ten seconds, grabs the next 25, and so on.
I have an EventHub configured in Azure, also a consumer group for reading the data. It was working fine for some days. Suddenly, I see there is a delay in incoming data(around 3 days). I use Windows Service to consume data in my server. I have around 500 incoming messages per minute. Can anyone help me out to figure this out ?
It might be that you are processing them items too slow. Therefore the work to be done grows and you will lag behind.
To get some insight in where you are in the event stream you can use code like this:
private void LogProgressRecord(PartitionContext context)
{
if (namespaceManager == null)
return;
var currentSeqNo = context.Lease.SequenceNumber;
var lastSeqNo = namespaceManager.GetEventHubPartition(context.EventHubPath, context.ConsumerGroupName, context.Lease.PartitionId).EndSequenceNumber;
var delta = lastSeqNo - currentSeqNo;
logWriter.Write(
$"Last processed seqnr for partition {context.Lease.PartitionId}: {currentSeqNo} of {lastSeqNo} in consumergroup '{context.ConsumerGroupName}' (lag: {delta})",
EventLevel.Informational);
}
the namespaceManager is build like this:
namespaceManager = NamespaceManager.CreateFromConnectionString("Endpoint=sb://xxx.servicebus.windows.net/;SharedAccessKeyName=yyy;SharedAccessKey=zzz");
I call this logging method in the CloseAsync method:
public Task CloseAsync(PartitionContext context, CloseReason reason)
{
LogProgressRecord(context);
return Task.CompletedTask;
}
logWriter is just some logging class I have used to write info to blob storage.
It now outputs messages like
Last processed seqnr for partition 3: 32780931 of 32823804 in consumergroup 'telemetry' (lag: 42873)
so when the lag is very high you could be processing events that have occurred a long time ago. In that case you need to scale up/out your processor.
If you notice a lag you should measure how long it takes to process a given number of item. You can then try to optimize performance and see whether it improves. We did it like:
public async Task ProcessEventsAsync(PartitionContext context, IEnumerable<EventData> events)
{
try
{
stopwatch.Restart();
// process items here
stopwatch.Stop();
await CheckPointAsync(context);
logWriter.Write(
$"Processed {events.Count()} events in {stopwatch.ElapsedMilliseconds}ms using partition {context.Lease.PartitionId} in consumergroup {context.ConsumerGroupName}.",
EventLevel.Informational);
}
}
I have got a Worker Role running in azure.
This worker processes a queue in which there are a large number of integers. For each integer I have to do processings quite long (from 1 second to 10 minutes according to the integer).
As this is quite time consuming, I would like to do these processings in parallel. Unfortunately, my parallelization seems to not be efficient when I test with a queue of 400 integers.
Here is my implementation :
public class WorkerRole : RoleEntryPoint {
private readonly CancellationTokenSource cancellationTokenSource = new CancellationTokenSource();
private readonly ManualResetEvent runCompleteEvent = new ManualResetEvent(false);
private readonly Manager _manager = Manager.Instance;
private static readonly LogManager logger = LogManager.Instance;
public override void Run() {
logger.Info("Worker is running");
try {
this.RunAsync(this.cancellationTokenSource.Token).Wait();
}
catch (Exception e) {
logger.Error(e, 0, "Error Run Worker: " + e);
}
finally {
this.runCompleteEvent.Set();
}
}
public override bool OnStart() {
bool result = base.OnStart();
logger.Info("Worker has been started");
return result;
}
public override void OnStop() {
logger.Info("Worker is stopping");
this.cancellationTokenSource.Cancel();
this.runCompleteEvent.WaitOne();
base.OnStop();
logger.Info("Worker has stopped");
}
private async Task RunAsync(CancellationToken cancellationToken) {
while (!cancellationToken.IsCancellationRequested) {
try {
_manager.ProcessQueue();
}
catch (Exception e) {
logger.Error(e, 0, "Error RunAsync Worker: " + e);
}
}
await Task.Delay(1000, cancellationToken);
}
}
}
And the implementation of the ProcessQueue:
public void ProcessQueue() {
try {
_queue.FetchAttributes();
int? cachedMessageCount = _queue.ApproximateMessageCount;
if (cachedMessageCount != null && cachedMessageCount > 0) {
var listEntries = new List<CloudQueueMessage>();
listEntries.AddRange(_queue.GetMessages(MAX_ENTRIES));
Parallel.ForEach(listEntries, ProcessEntry);
}
}
catch (Exception e) {
logger.Error(e, 0, "Error ProcessQueue: " + e);
}
}
And ProcessEntry
private void ProcessEntry(CloudQueueMessage entry) {
try {
int id = Convert.ToInt32(entry.AsString);
Service.GetData(id);
_queue.DeleteMessage(entry);
}
catch (Exception e) {
_queueError.AddMessage(entry);
_queue.DeleteMessage(entry);
logger.Error(e, 0, "Error ProcessEntry: " + e);
}
}
In the ProcessQueue function, I try with different values of MAX_ENTRIES: first =20 and then =2.
It seems to be slower with MAX_ENTRIES=20, but whatever the value of MAX_ENTRIES is, it seems quite slow.
My VM is a A2 medium.
I really don't know if I do the parallelization correctly ; maybe the problem comes from the worker itself (which may be it is hard to have this in parallel).
You haven't mentioned which Azure Messaging Queuing technology you are using, however for tasks where I want to process multiple messages in parallel I tend to use the Message Pump Pattern on Service Bus Queues and Subscriptions, leveraging the OnMessage() method available on both Service Bus Queue and Subscription Clients:
QueueClient OnMessage() - https://msdn.microsoft.com/en-us/library/microsoft.servicebus.messaging.queueclient.onmessage.aspx
SubscriptionClient OnMessage() - https://msdn.microsoft.com/en-us/library/microsoft.servicebus.messaging.subscriptionclient.onmessage.aspx
An overview of how this stuff works :-) - http://fabriccontroller.net/blog/posts/introducing-the-event-driven-message-programming-model-for-the-windows-azure-service-bus/
From MSDN:
When calling OnMessage(), the client starts an internal message pump
that constantly polls the queue or subscription. This message pump
consists of an infinite loop that issues a Receive() call. If the call
times out, it issues the next Receive() call.
This pattern allows you to use a delegate (or anonymous function in my preferred case) that handles the receipt of the Brokered Message instance on a separate thread on the WaWorkerHost process. In fact, to increase the level of throughput, you can specify the number of threads that the Message Pump should provide, thereby allowing you to receive and process 2, 4, 8 messages from the queue in parallel. You can additionally tell the Message Pump to automagically mark the message as complete when the delegate has successfully finished processing the message. Both the thread count and AutoComplete instructions are passed in the OnMessageOptions parameter on the overloaded method.
public override void Run()
{
var onMessageOptions = new OnMessageOptions()
{
AutoComplete = true, // Message-Pump will call Complete on messages after the callback has completed processing.
MaxConcurrentCalls = 2 // Max number of threads the Message-Pump can spawn to process messages.
};
sbQueueClient.OnMessage((brokeredMessage) =>
{
// Process the Brokered Message Instance here
}, onMessageOptions);
RunAsync(_cancellationTokenSource.Token).Wait();
}
You can still leverage the RunAsync() method to perform additional tasks on the main Worker Role thread if required.
Finally, I would also recommend that you look at scaling your Worker Role instances out to a minimum of 2 (for fault tolerance and redundancy) to increase your overall throughput. From what I have seen with multiple production deployments of this pattern, OnMessage() performs perfectly when multiple Worker Role Instances are running.
A few things to consider here:
Are your individual tasks CPU intensive? If so, parallelism may not help. However, if they are mostly waiting on data processing tasks to be processed by other resources, parallelizing is a good idea.
If parallelizing is a good idea, consider not using Parallel.ForEach for queue processing. Parallel.Foreach has two issues that prevent you from being very optimal:
The code will wait until all kicked off threads finish processing before moving on. So, if you have 5 threads that need 10 seconds each and 1 thread that needs 10 minutes, the overall processing time for Parallel.Foreach will be 10 minutes.
Even though you are assuming that all of the threads will start processing at the same time, Parallel.Foreach does not work this way. It looks at number of cores on your server and other parameters and generally only kicks off number of threads it thinks it can handle, without knowing too much about what's in those threads. So, if you have a lot of non-CPU bound threads that /can/ be kicked off at the same time without causing CPU over-utilization, default behaviour will not likely run them optimally.
How to do this optimally:
I am sure there are a ton of solutions out there, but for reference, the way we've architected it in CloudMonix (that must kick off hundreds of independent threads and complete them as fast as possible) is by using ThreadPool.QueueUserWorkItem and manually keeping track number of threads that are running.
Basically, we use a Thread-safe collection to keep track of running threads that are started by ThreadPool.QueueUserWorkItem. Once threads complete, remove them from that collection. The queue-monitoring loop is indendent of executing logic in that collection. Queue-monitoring logic gets messages from the queue if the processing collection is not full up to the limit that you find most optimal. If there is space in the collection, it tries to pickup more messages from the queue, adds them to the collection and kick-start them via ThreadPool.QueueUserWorkItem. When processing completes, it kicks off a delegate that cleans up thread from the collection.
Hope this helps and makes sense
I have the following scenario
I am writing a server that process files (jobs)
a file has a "prefix" and a time
the files should be processed according to time (older file first) but also take into account the prefix (files with same prefix can't be processed concurrently)
I have a thread (Task with Timer) that watches over a directory and adds files to a "queue" (producer)
I have several consumers that take the file from "queue" (consumer) - they should conform to the above rules.
the job of each task is kept in some list (this indicates the constraints)
There are several consumers, the number of consumers is determined at startup.
One of the requirement is to be able to gracefully stop the consumers (either immediately or let ongoing processes to finish).
I did something along this line:
while (processing)
{
//limits number of concurrent tasks
_processingSemaphore.Wait(queueCancellationToken);
//Take next job when available or wait for cancel signal
currentwork = workQueue.Take(taskCancellationToken);
//check that it can actually process this work
if (CanProcess(currnetWork)
{
var task = CreateTask(currentwork)
task.ContinueWith((t) => { //release processing slot });
}
else
//release slot, return job? something else?
}
The cancellation tokens sources are in the caller code and can be cancelled. There are two in order to be able to stop queuing while not cancelling running tasks.
I tired to implement the "queue" as BlockingCollection wrapping a "safe" SortedSet. The general idea work (ordering by time) except the case in which I need to find a new job that matches the constraint. If I return the job to the queue and try to take again I will get the same one.
It is possible to take jobs from the queue until I find a proper one and then returning the "illegal" jobs back but this may cause issues with other consumers processing out of order jobs
Another alternative is to pass a simple collection and a way to lock it and just lock and do a simple search according to current constraints. Again, this means writing code that will possibly not be thread-safe.
Any other suggestion / pointers / data structures that can help?
I think Hans is right: if you already have a thread-safe SortedSet (that implements IProducerConsumerCollection, so it can be used in BlockingCollection), then all you need is to put only files that can be processed right now into the collection. If you finish a file which makes another file available for processing, add the other file to the collection at this point, not earlier.
I would have implemented your requirement(s) with TPL Dataflow. Look at the way you could implement the Producer-Consumer pattern with it. I believe this will meet all the requirements you have (including cancellation on the consumers).
EDIT (for those that do not like to read documentation, but who does...)
Here is an example of how you could implement the requirements with TPL Dataflow. The beauty of this implementation is that consumers are not bound to a single thread and only uses a pool thread when it needs to process data.
static void Main(string[] args)
{
BufferBlock<string> source = new BufferBlock<string>();
var cancellation = new CancellationTokenSource();
LinkConsumer(source, "A", cancellation.Token);
LinkConsumer(source, "B", cancellation.Token);
LinkConsumer(source, "C", cancellation.Token);
// Link an action that will process source values that are not processed by other
source.LinkTo(new ActionBlock<string>((s) => Console.WriteLine("Default action")));
while (cancellation.IsCancellationRequested == false)
{
ConsoleKey key = Console.ReadKey(true).Key;
switch (key)
{
case ConsoleKey.Escape:
cancellation.Cancel();
break;
default:
Console.WriteLine("Posted value {0} on thread {1}.", key, Thread.CurrentThread.ManagedThreadId);
source.Post(key.ToString());
break;
}
}
source.Complete();
Console.WriteLine("Done.");
Console.ReadLine();
}
private static void LinkConsumer(ISourceBlock<string> source, string prefix, CancellationToken token)
{
// Link a consumer that will buffer and process all input of the specified prefix
var consumer = new ActionBlock<string>(new Action<string>(Process), new ExecutionDataflowBlockOptions() { MaxDegreeOfParallelism = 1, SingleProducerConstrained = true, CancellationToken = token, TaskScheduler = TaskScheduler.Default });
var linkDisposable = source.LinkTo(consumer, (p) => p == prefix);
// Dispose the link (remove the link) when cancellation is requested.
token.Register(linkDisposable.Dispose);
}
private static void Process(string arg)
{
Console.WriteLine("Processed value {0} in thread {1}", arg, Thread.CurrentThread.ManagedThreadId);
// Simulate work
Thread.Sleep(500);
}
I'm implementing my own logging framework. Following is my BaseLogger which receives the log entries and push it to the actual Logger which implements the abstract Log method.
I use the C# TPL for logging in an Async manner. I use Threads instead of TPL. (TPL task doesn't hold a real thread. So if all threads of the application end, tasks will stop as well, which will cause all 'waiting' log entries to be lost.)
public abstract class BaseLogger
{
// ... Omitted properties constructor .etc. ... //
public virtual void AddLogEntry(LogEntry entry)
{
if (!AsyncSupported)
{
// the underlying logger doesn't support Async.
// Simply call the log method and return.
Log(entry);
return;
}
// Logger supports Async.
LogAsync(entry);
}
private void LogAsync(LogEntry entry)
{
lock (LogQueueSyncRoot) // Make sure we ave a lock before accessing the queue.
{
LogQueue.Enqueue(entry);
}
if (LogThread == null || LogThread.ThreadState == ThreadState.Stopped)
{ // either the thread is completed, or this is the first time we're logging to this logger.
LogTask = new new Thread(new ThreadStart(() =>
{
while (true)
{
LogEntry logEntry;
lock (LogQueueSyncRoot)
{
if (LogQueue.Count > 0)
{
logEntry = LogQueue.Dequeue();
}
else
{
break;
// is it possible for a message to be added,
// right after the break and I leanve the lock {} but
// before I exit the loop and task gets 'completed' ??
}
}
Log(logEntry);
}
}));
LogThread.Start();
}
}
// Actual logger implimentations will impliment this method.
protected abstract void Log(LogEntry entry);
}
Note that AddLogEntry can be called from multiple threads at the same time.
My question is, is it possible for this implementation to lose log entries ?
I'm worried that, is it possible to add a log entry to the queue, right after my thread exists the loop with the break statement and exits the lock block, and which is in the else clause, and the thread is still in the 'Running' state.
I do realize that, because I'm using a queue, even if I miss an entry, the next request to log, will push the missed entry as well. But this is not acceptable, specially if this happens for the last log entry of the application.
Also, please let me know whether and how I can implement the same, but using the new C# 5.0 async and await keywords with a cleaner code. I don't mind requiring .NET 4.5.
Thanks in Advance.
While you could likely get this to work, in my experience, I'd recommend, if possible, use an existing logging framework :) For instance, there are various options for async logging/appenders with log4net, such as this async appender wrapper thingy.
Otherwise, IMHO since you're going to be blocking a threadpool thread during your logging operation anyway, I would instead just start a dedicated thread for your logging. You seem to be kind-of going for that approach already, just via Task so that you'd not hold a threadpool thread when nothing is logging. However, the simplification in implementation I think benefits just having the dedicated thread.
Once you have a dedicated logging thread, you then only need have an intermediate ConcurrentQueue. At that point, your log method just adds to the queue and your dedicated logging thread just does that while loop you already have. You can wrap with BlockingCollection if you need blocking/bounded behavior.
By having the dedicated thread as the only thing that writes, it eliminates any possibility of having multiple threads/tasks pulling off queue entries and trying to write log entries at the same time (painful race condition). Since the log method is now just adding to a collection, it doesn't need to be async and you don't need to deal with the TPL at all, making it simpler and easier to reason about (and hopefully in the category of 'obviously correct' or thereabouts :)
This 'dedicated logging thread' approach is what I believe the log4net appender I linked to does as well, FWIW, in case that helps serve as an example.
I see two race conditions off the top of my head:
You can spin up more than one Thread if multiple threads call AddLogEntry. This won't cause lost events but is inefficient.
Yes, an event can be queued while the Thread is exiting, and in that case it would be "lost".
Also, there's a serious performance issue here: unless you're logging constantly (thousands of times a second), you're going to be spinning up a new Thread for each log entry. That will get expensive quickly.
Like James, I agree that you should use an established logging library. Logging is not as trivial as it seems, and there are already many solutions.
That said, if you want a nice .NET 4.5-based approach, it's pretty easy:
public abstract class BaseLogger
{
private readonly ActionBlock<LogEntry> block;
protected BaseLogger(int maxDegreeOfParallelism = 1)
{
block = new ActionBlock<LogEntry>(
entry =>
{
Log(entry);
},
new ExecutionDataflowBlockOptions
{
MaxDegreeOfParallelism = maxDegreeOfParallelism,
});
}
public virtual void AddLogEntry(LogEntry entry)
{
block.Post(entry);
}
protected abstract void Log(LogEntry entry);
}
Regarding the loosing waiting messages on app crush because of unhandled exception, I've bound a handler to the event AppDomain.CurrentDomain.DomainUnload. Goes like this:
protected ManualResetEvent flushing = new ManualResetEvent(true);
protected AsyncLogger() // ctor of logger
{
AppDomain.CurrentDomain.DomainUnload += CurrentDomain_DomainUnload;
}
protected void CurrentDomain_DomainUnload(object sender, EventArgs e)
{
if (!IsEmpty)
{
flushing.WaitOne();
}
}
Maybe not too clean, but works.