I have a Node.js timerTrigger Azure function that processes a collection and queues the processing results for further processing (by a Node.js queueTrigger function).
The code is something like the following:
module.exports = function (context, myTimer) {
collection.forEach(function (item) {
var items = [];
// do some work and fill 'items'
var toBeQueued = { items: items };
context.bindings.myQueue = toBeQueued;
});
context.done();
};
This code will only queue the last toBeQueued and not each one I'm trying to queue.
Is there any way to queue more than one item?
Update
To be clear, I'm talking about queueing a toBeQueued in each iteration of forEach, not just queueing an array. Yes, there is an issue with Azure Functions because of which I cannot queue an array, but I have a workaround for it; i.e., { items: items }.
Not yet, but we'll address that within the week, stay tuned :) You'll be able to pass an array to the binding as you're trying to do above.
We have an issue tracking this in our public repo here. Thanks for reporting.
Mathewc's answer is the correct one wrt Node.
For C# you can today by specifying ICollector<T> as the type of your output queue parameter.
Below is an example I have of two output queues, one of which I add via a for loop.
public static void Run(Item inbound, DateTimeOffset InsertionTime, ICollector<Item> outbound, ICollector<LogItem> telemetry, TraceWriter log)
{
log.Verbose($"C# Queue trigger function processed: {inbound}");
telemetry.Add(new LogItem(inbound, InsertionTime));
if(inbound.current_generation < inbound.max_generation)
{
for(int i = 0; i < inbound.multiplier; i++) {
outbound.Add(Item.nextGen(inbound));
}
}
}
Related
I am creating an architecture to process our orders from an ecommerce website who gets 10,000 orders or more every hour. We are using an external third party order fulfillment service and they have about 5 Steps/APIs that we have to run which are dependent upon each other.
I was thinking of using Fan in/Fan Out approach where we can use durable functions.
My plan
Once the order is created on our end, we store in a table with a flag of Order completed.
Run a time trigger azure function that runs the durable function orchestrator which calls the activity functions for each step
Now if it fails, timer will pick up the order again until it is completed. But my question is should we put this order in service bus and pick it up from there instead of time trigger.
Because there can be more than 10,000 records each hour so we have to run a query in the time trigger function and find orders that are not completed and run the durable orchestrator 10,000 times in a loop. My first question - Can I run the durable function parallelly for 10,000 records?
If I use service bus trigger to trigger durable orchestrator, it will automatically run azure function and durable 10,000 times parallelly right? But in this instance, I will have to build a dead letter queue function/process so if it fails, we are able to move it to active topic
Questions:
Is durable function correct approach or is there a better and easier approach?
If yes, Is time trigger better or Service bus trigger to start the orchestrator function?
Can I run the durable function orchestrator parallelly through time trigger azure function. I am not talking about calling activity functions because those cannot be run parallelly because we need output of one to be input of the next
This usecase fits function chaining. This can be done by
Have the ordering system put a message on a queue (storage or servicebus)
Create an azure function with storage queue trigger or service bus trigger. This would also be the client function that triggers the orchestration function
Create an orchestration function that invokes the 5 step APIs, one activity function for each (similar to as given in function chaining example.
Create five activity function, one f for each API
Ordering system
var clientOptions = new ServiceBusClientOptions
{
TransportType = ServiceBusTransportType.AmqpWebSockets
};
//TODO: Replace the "<NAMESPACE-NAME>" and "<QUEUE-NAME>" placeholders.
client = new ServiceBusClient(
"<NAMESPACE-NAME>.servicebus.windows.net",
new DefaultAzureCredential(),
clientOptions);
sender = client.CreateSender("<QUEUE-NAME>");
var message = new ServiceBusMessage($"{orderId}");
await sender.SendMessageAsync(message);
Client function
public static class OrderFulfilment
{
[Function("OrderFulfilment")]
public static string Run([ServiceBusTrigger("<QUEUE-NAME>", Connection = "ServiceBusConnection")] string orderId,
[DurableClient] IDurableOrchestrationClient starter)
{
var logger = context.GetLogger("OrderFulfilment");
logger.LogInformation(orderId);
return starter.StartNewAsync("ChainedApiCalls", orderId);
}
}
Orchestration function
[FunctionName("ChainedApiCalls")]
public static async Task<object> Run([OrchestrationTrigger] IDurableOrchestrationContext fulfillmentContext)
{
try
{
// .... get order with orderId
var a = await context.CallActivityAsync<object>("ApiCaller1", null);
var b = await context.CallActivityAsync<object>("ApiCaller2", a);
var c = await context.CallActivityAsync<object>("ApiCaller3", b);
var d = await context.CallActivityAsync<object>("ApiCaller4", c);
return await context.CallActivityAsync<object>("ApiCaller5", d);
}
catch (Exception)
{
// Error handling or compensation goes here.
}
}
Activity functions
[FunctionName("ApiCaller1")]
public static string ApiCaller1([ActivityTrigger] IDurableActivityContext fulfillmentApiContext)
{
string input = fulfillmentApiContext.GetInput<string>();
return $"API1 result";
}
[FunctionName("ApiCaller2")]
public static string ApiCaller2([ActivityTrigger] IDurableActivityContext fulfillmentApiContext)
{
string input = fulfillmentApiContext.GetInput<string>();
return $"API2 result";
}
// Repeat 3 more times...
Can I use an output binding argument in a foreach, multiple times?
[FunctionName("OnClientConnectedDisconnected")]
public async Task Run(
[EventGridTrigger] EventGridEvent eventGridEvent,
[SignalR(HubName = "Lobby")] IAsyncCollector<SignalRMessage> signalRMessage,
[SignalR(HubName = "Lobby")] IAsyncCollector<SignalRGroupAction> signalRGroupMessage,
ILogger log)
{
...
...
foreach (var player in onlineFriends)
{
await signalRGroupMessage.AddAsync(
new SignalRGroupAction
{
GroupName = $"Group_{player}",
Action = GroupAction.Add,
UserId = eventGridData.UserId
}
);
}
}
Whether you can call an output binding multiple times in a function depends on the binding type and how it is written. For example, the table storage output binding will add multiple entities to a table (once for each call), but the queue binding will only post one message. I'm not seeing that the SignalR output binding allows multiple calls.
Remember that binding are optional--an alternate strategy would be to write your own code to create your group and exit the function after the loop.
Yes, you can.
In C#, you can use SignalR output binding multiple times and the invocation just happens at where you call await signalRGroupMessage.AddAsync()
I'm trying to get a service fabric to consistently pull messages from an azure event hub. I seem to have everything wired up but notice that my consumer just stops pulling events.
I have a hub with a couple thousand events I've pushed to it. Configured the hub with 1 partition and have my service fabric service with also only 1 partition to ease debugging.
Service starts, creates the EventHubClient, from there uses it to create a PartitionReceiver. The receiver is passed to an "EventLoop" that enters an "infinite" while that calls receiver.ReceiveAsync. The code for the EventLoop is below.
What I am observing is the first time through the loop I almost always get 1 message. Second time through I get somewhere between 103 and 200ish messages. After that, I get no messages. Also seems like if I restart the service, I get the same messages again - but that's because when I restart the service I'm having it start back at the beginning of the stream.
Would expect this to keep running until my 2000 messages were consumed and then it would wait for me (polling ocassionally).
Is there something specific I need to do with the Azure.Messaging.EventHubs 5.3.0 package to make it keep pulling events?
//Here is how I am creating the EventHubClient:
var connectionString = "something secret";
var connectionStringBuilder = new EventHubsConnectionStringBuilder(connectionString)
{
EntityPath = "NameOfMyEventHub"
};
try
{
m_eventHubClient = EventHubClient.Create(connectionStringBuilder);
}
//Here is how I am getting the partition receiver
var receiver = m_eventHubClient.CreateReceiver("$Default", m_partitionId, EventPosition.FromStart());
//The event loop which the receiver is passed to
private async Task EventLoop(PartitionReceiver receiver)
{
m_started = true;
while (m_keepRunning)
{
var events = await receiver.ReceiveAsync(m_options.BatchSize, TimeSpan.FromSeconds(5));
if (events != null) //First 2/3 times events aren't null. After that, always null and I know there are more in the partition/
{
var eventsArray = events as EventData[] ?? events.ToArray();
m_state.NumProcessedSinceLastSave += eventsArray.Count();
foreach (var evt in eventsArray)
{
//Process the event
await m_options.Processor.ProcessMessageAsync(evt, null);
string lastOffset = evt.SystemProperties.Offset;
if (m_state.NumProcessedSinceLastSave >= m_options.BatchSize)
{
m_state.Offset = lastOffset;
m_state.NumProcessedSinceLastSave = 0;
await m_state.SaveAsync();
}
}
}
}
m_started = false;
}
**EDIT, a question was asked on the number of partitions. The event hub has a single partition and the SF service also has a single one.
Intending to use service fabric state to keep track of my offset into the hub, but that's not the concern for now.
Partition listeners are created for each partition. I get the partitions like this:
public async Task StartAsync()
{
// slice the pie according to distribution
// this partition can get one or more assigned Event Hub Partition ids
string[] eventHubPartitionIds = (await m_eventHubClient.GetRuntimeInformationAsync()).PartitionIds;
string[] resolvedEventHubPartitionIds = m_options.ResolveAssignedEventHubPartitions(eventHubPartitionIds);
foreach (var resolvedPartition in resolvedEventHubPartitionIds)
{
var partitionReceiver = new EventHubListenerPartitionReceiver(m_eventHubClient, resolvedPartition, m_options);
await partitionReceiver.StartAsync();
m_partitionReceivers.Add(partitionReceiver);
}
}
When the partitionListener.StartAsync is called, it actually creates the PartitionListener, like this (it's actually a bit more than this, but the branch taken is this one:
m_eventHubClient.CreateReceiver(m_options.EventHubConsumerGroupName, m_partitionId, EventPosition.FromStart());
Thanks for any tips.
Will
How many partition do you have? I can't see in your code how you make sure you read all partitions in the default consumer group.
Any specific reason why you are using PartitionReceiver instead of using an EventProcessorHost?
To me, SF seems like a perfect fit for using the event processor host. I see there is already a SF integrated solution that uses stateful services for checkpointing.
I have a use case which fits well with the durable functions sequence example: push a json payload through three functions, each of which modifies the json graph and forwards it to the next function.
In the sequence example the result of the sequence is retrieved by issuing a query to the orchestrator.
In my use case I want to directly return the result of the three functions, essentially as the response of the third function.
Is there a way to do this? Is it even wise?
This is certainly doable. You can start with an HTTP trigger to start the orchestration and use the GetStatusAsync API inside your function to poll and wait for it to complete. Once completed, you can return the result from your HTTP trigger.
Something like this, perhaps:
public static async Task<JObject> Run(JObject input, DurableOrchestrationClient client)
{
string instanceId = await client.StartAsync("MyOrchestration", input);
for (int i = 0; i < 60; i++)
{
var status = await client.GetStatusAsync(instanceId);
if (status?.RuntimeStatus == "Completed")
{
return (JObject)status.Output;
}
// handle other status conditions, like failure
await Task.Delay(TimeSpan.FromSeconds(1));
}
// handle timeouts
}
As you can see from the code, the issue you'll have is dealing with error conditions. For example, what does your function do if the orchestration fails? Also, what if it takes a long time to finish? Those are things you can certainly figure out, but you'll want to code defensively to handle these cases.
I have an async method that gets api data from a server. When I run this code on my local machine, in a console app, it performs at high speed, pushing through a few hundred http calls in the async function per minute. When I put the same code to be triggered from an Azure WebJob queue message however, it seems to operate synchronously and my numbers crawl - I'm sure I am missing something simple in my approach - any assistance appreciated.
(1) .. WebJob function that listens for a message on queue and kicks off the api get process on message received:
public class Functions
{
// This function will get triggered/executed when a new message is written
// on an Azure Queue called queue.
public static async Task ProcessQueueMessage ([QueueTrigger("myqueue")] string message, TextWriter log)
{
var getAPIData = new GetData();
getAPIData.DoIt(message).Wait();
log.WriteLine("*** done: " + message);
}
}
(2) the class that outside azure works in async mode at speed...
class GetData
{
// wrapper that is called by the message function trigger
public async Task DoIt(string MessageFile)
{
await CallAPI(MessageFile);
}
public async Task<string> CallAPI(string MessageFile)
{
/// create a list of sample APIs to call...
var apiCallList = new List<string>();
apiCallList.Add("localhost/?q=1");
apiCallList.Add("localhost/?q=2");
apiCallList.Add("localhost/?q=3");
apiCallList.Add("localhost/?q=4");
apiCallList.Add("localhost/?q=5");
// setup httpclient
HttpClient client =
new HttpClient() { MaxResponseContentBufferSize = 10000000 };
var timeout = new TimeSpan(0, 5, 0); // 5 min timeout
client.Timeout = timeout;
// create a list of http api get Task...
IEnumerable<Task<string>> allResults = apiCallList.Select(str => ProcessURLPageAsync(str, client));
// wait for them all to complete, then move on...
await Task.WhenAll(allResults);
return allResults.ToString();
}
async Task<string> ProcessURLPageAsync(string APIAddressString, HttpClient client)
{
string page = "";
HttpResponseMessage resX;
try
{
// set the address to call
Uri URL = new Uri(APIAddressString);
// execute the call
resX = await client.GetAsync(URL);
page = await resX.Content.ReadAsStringAsync();
string rslt = page;
// do something with the api response data
}
catch (Exception ex)
{
// log error
}
return page;
}
}
First because your triggered function is async, you should use await rather than .Wait(). Wait will block the current thread.
public static async Task ProcessQueueMessage([QueueTrigger("myqueue")] string message, TextWriter log)
{
var getAPIData = new GetData();
await getAPIData.DoIt(message);
log.WriteLine("*** done: " + message);
}
Anyway you'll be able to find usefull information from the documentation
Parallel execution
If you have multiple functions listening on different queues, the SDK will call them in parallel when messages are received simultaneously.
The same is true when multiple messages are received for a single queue. By default, the SDK gets a batch of 16 queue messages at a time and executes the function that processes them in parallel. The batch size is configurable. When the number being processed gets down to half of the batch size, the SDK gets another batch and starts processing those messages. Therefore the maximum number of concurrent messages being processed per function is one and a half times the batch size. This limit applies separately to each function that has a QueueTrigger attribute.
Here is a sample code to configure the batch size:
var config = new JobHostConfiguration();
config.Queues.BatchSize = 50;
var host = new JobHost(config);
host.RunAndBlock();
However, it is not always a good option to have too many threads running at the same time and could lead to bad performance.
Another option is to scale out your webjob:
Multiple instances
if your web app runs on multiple instances, a continuous WebJob runs on each machine, and each machine will wait for triggers and attempt to run functions. The WebJobs SDK queue trigger automatically prevents a function from processing a queue message multiple times; functions do not have to be written to be idempotent. However, if you want to ensure that only one instance of a function runs even when there are multiple instances of the host web app, you can use the Singleton attribute.
Have a read of this Webjobs SDK documentation - the behaviour you should expect is that your process will run and process one message at a time, but will scale up if more instances are created (of your app service). If you had multiple queues, they will trigger in parallel.
In order to improve the performance, see the configurations settings section in the link I sent you, which refers to the number of messages that can be triggered in a batch.
If you want to process multiple messages in parallel though, and don't want to rely on instance scaling, then you need to use threading instead (async isn't about multi-threaded parallelism, but making more efficient use of the thread you're using). So your queue trigger function should read the message from the queue, the create a thread and "fire and forget" that thread, and then return from the trigger function. This will mark the message as processed, and allow the next message on the queue to be processed, even though in theory you're still processing the earlier one. Note you will need to include your own logic for error handling and ensuring that the data wont get lost if your thread throws an exception or can't process the message (eg. put it on a poison queue).
The other option is to not use the [queuetrigger] attribute, and use the Azure storage queues sdk API functions directly to connect and process the messages per your requirements.