I've Spark Streaming job which captures the near real time data from Azure Eventhub and runs 24/7.
More interestingly, my job fails at least 2 times a day with the below error. if I google the error, Microsoft docs gives me 'This exception is thrown if two or more PartitionReceiver instances connect to the same partition with different epoch values'. I'm not worried about data loss because spark Checkpointing will automatically take care of data when i restart the job, but my question is why the spark streaming job fails 2-3 times a day with the same error.
Has anybody faced the same issue, is there any solution/workaround available of this. Any help would be much appreciated.
error:
This exception is thrown if two or more Partitions Receiver instances connect to the same partition with different epoch values.
What is Partition Receiver?
This is a logical representation of receiving from a EventHub partition.
A PartitionReceiver is tied to a ConsumerGroup + Partition combination. If you are creating an epoch based PartitionReceiver (i.e. PartitionReceiver.Epoch != 0) you cannot have more than one active receiver per ConsumerGroup + Partition combo. You can have multiple receivers per ConsumerGroup + Partition combination with non-epoch receivers.
It sounds like you are running two instances of the application, two concurrent classes, or two applications that use the same event hub consumer group. Event hub consumer groups are effectively pointers to a point in time on the event stream. If you try and use one consumer group pointing with two instances of code, then you get a conflict like the one you are seeing.
Either:
Ensure you only have a single instance reading the consumer group at a time.
Use two consumer groups when you need two separate programs or sets of functionality to process the event hub at the same time.
If you are looking to parallelize for performance, look in to event hub Partitioning and how to take advantage of processing each partition independently.
There is also an alternative scenario where an event hub partition is switched over to another host as part of the event hub's internal load balancing. In this case you may see the error you are receiving. In this case, just log it and continue on.
For more details, refer "Features and terminology in Azure Event Hubs" and "Event Hubs Receiver Epoch".
Hope this helps.
Related
When we have multiple consumers of Event Hub (or any messaging service, for that matter), how to make sure that no message is processed twice especially in a situation when consumer auto-scales out to multiple instances?
I know we could keep track of last message processed but then again, between the check if message was processed and actuall, processing it,other instance could process it already (race condition?.
so, how to solve that in a scalable way?
[UPDATE]
i am aware there is a recommendation to have at least as many partitions as there are consumers but what to do in case when a single consumer cannot process messages directed to it but needs to scale out to multiple instances?
Each processor takes a lease on a partition, see the docs
An event processor instance typically owns and processes events from one or more partitions. Ownership of partitions is evenly distributed among all the active event processor instances associated with an event hub and consumer group combination.
So scaling out doesn't result in duplicate message processing because a new processor cannot take a lease on a partition that is already being handled by another processor.
Then, regarding your comment:
i am aware there is a recommendation to have at least as many partitions as there are consumers
It is the other way around: it is recommended to have as many consumers as you have partitions. If you have more consumers than partitions the consumers will compete with each other to obtain a lock on a partition.
Now, regarding duplicate messages, since Event Hub guarantees at-least-once delivery there isn't much you can do to prevent this. There aren't that many scalable services that offer at-most-once deliveries, I know that Azure Service Bus Queues do offer this if you really need it.
The question may arise what can cause duplicate message processing. Well, when processing message the processor does some checkpointing: once in a while it will store its position within a partition event sequence (remember, a partition is bound to a single processor). Now when the processer instance crashes between two checkpoint events a new instance will resume processing messages from the position of the last checkpoint. That may very well lead to older messages being processed again.
If a reader disconnects from a partition, when it reconnects it begins reading at the checkpoint that was previously submitted by the last reader of that partition in that consumer group.
So, that means you need to make sure your processing logic is idempotent. How, that is up to you as I don't know your use case.
One option is to track each individual message to see whether it is already processed or not. If you do not have a unique ID to check on maybe you can generate a hash of the whole message and compare with that.
I'm using:
Azure platform to run some microservice architecture software solution.
microservices are using the Azure-EventHub for communicating in special cases.
Kubernetes with 2 clusters (primary, secondary)
per application namespace, there is 1 event-listener pod running per cluster for consuming from eventhub
The last point is relevant to my current problem:
The load balancers will share traffic between the primary and secondary clusters. This means that 2 event-listener-pods are running per application at the same time. So they are just reacting to events but some times they are consuming the same event from the event hub and this causes some duplicated notification mails.
So finally my question is: How can I avoid reading the same event twice the same time? I thought event hub index is always increasing but starting at the same moment is not "secured".
You will need to use separate consumer groups per pod to avoid EPOCH error.
That said, both pods will read the same events, so you have two options.
Have an active-passive set up. One consumer group, one pod that reads the events and delegates the work out on each event. If that pod fails, then a health/heart beat mechanism brings the second pod online.
Have an active-active set up. Two consumer groups, two active pods. You will need to implement idempotent processing.
Idempotent processing, where processing the same message multiple times produces the same result, is good practice regardless of approach. This would allow you to replay batches of events in which one errored and not have adverse affects on the integrity of your data.
I would opt for the first option, a single event hub reader will process thousands of events per second and pass off the work to your micro services.
If you have lower volumes of messages and need guaranteed message processing, then using Service Bus may be a better choice where messages can be locked, completed and abandoned.
Requirement I want to process event data (same data) from multiple consumers parallel
What I understand from documentation to process same data form event hub we need to create multiple consumer groups.
Consumer groups enable multiple consuming applications to each have a separate view of the event stream, and to read the stream independently at its own pace and with its own offsets.
Message Retention 1,
Partition count 3
Problem: I'm not getting data form log1 consumer group, Instead log1 When I tried $Default and log2 consumer group I was getting data in parallel.
Can anyone help me understand this problem?
Based on the official doc , your understanding is correct. I tried to create my event hub as same as yours to reproduce your issue.
And I start 2 EPH via java code to monitor Log1 and log2 separately.
Then I send 10 messages to event hub to observe the 2 EPH console log.
It seems that I can receiver messages from both of them,however the order is messy. I guess the your issue is delay.
Update Answer:
Per my knowledge, delay in Azure event hub may be affected by many aspects. Maybe network , internal internal mechanisms. However ,Azure event hub ensure that your data is not lost during effective storage time.
If you need to ensure real time data , you could use Kafka which is used for building real-time data pipelines and streaming apps.
I have Azure Service Bus setup with a bunch of queues on it. Some are partitioned. When I tried to read dead letter messages from one of those queues I've deferred messages, then did some massaging and then try to complete those deferred messages. And this is where trouble came in. On the call to the QueueClient.ReceiveBatch() I'm getting InavlidOperationExceptionexception with following message:
ReceiveBatch of sequence numbers from different partitions is not supported
for an entity with partitioning enabled.
Inner exception contains following justification:
BR0012ReceiveBatch of sequence numbers from different partitions is not
supported for an entity with partitioning enabled.
Here is actual line of code, which produces the error:
var deferredMessages = queueClient?.ReceiveBatch(lstSequenceNums);
where lstSequenceNums is type of List<long>and contains sequence numbers of deferred messages; queueClient is of type QueueClient
So I wonder how should this situation be handled? I don't quite understand why that exception has been thrown in the first place? If that expected behavior how I could figure out relation between partition and Service Bus message sequence number?
Any help would be greatly appreciated.
From Azure Service Bus partitioning documentation, for a single message requested
When a client wants to receive a message from a partitioned queue, or from a subscription to a partitioned topic, Service Bus queries all fragments for messages, then returns the first message that is obtained from any of the messaging stores to the receiver.
and
Each partitioned queue or topic consists of multiple fragments. Each fragment is stored in a different messaging store and handled by a different message broker.
I suspect you ask for messages from multiple partitions by requesting a batch using Sequence numbers from different partitions, it would force to query too many brokers and Azure Service Bus service doesn't allow that.
SequenceNumber can help to determine what partition your messages are coming from. Top 16 bits are used to encode the partition ID. You could you that to break your batch into separate calls.
ReceiveBatch(IEnumerable<int64>) does say anything about this. I suggest to raise an issue with the team to clarify the documentation.
I made it work by reading messages one by one(getting all messages in the loop) and abandoning batch reading approach completely, because it's too fragile and issues-pron. It's the only option one have at the moment, imho, if there are partitioned queues on one's Azure Service Bus. Sean Feldman provided some insights(thank you again), but it doesn't provide any acceptable way of making ReceiveBatch viable solution for the task at hand.
Just have a couple of questions regarding the usage of Azure Functions with an EventHub in an IoT scenario.
EventHub has partitions. Typically messages from a specific device go to the same partition. How are the instances of an Azure Function distributed across EventHub partitions? Is it based on the performance? In case one instance of an Azure Function manages to process events from all partitions then it is enough otherwise one might end up with one instance of an Azure Function per EventHub partition?
What about the read-offset? Does this binding somehow records where it stopped reading the event stream? I thought the functions are meant to be stateless and here we have some state.
Thanks
Each instance of an Event Hub-Triggered Function is backed by only 1 EventProcessorHost(EPH) instance. Event Hub ensures that only 1 EPH can get a lease on a given partition.
Answer to Question 1:
Let's elaborate on this with a contrived example. Suppose we begin with the following setup and assumptions for an EventHub:
10 partitions.
1000 events distributed evenly across all partitions => 100 messages in each partition.
When your Function is first enabled, there is only 1 instance of the Function. Let's call this Function instance Function_0. Function_0 will have 1 EPH that manages to get a lease on all 10 partitions. Let this EPH be called EPH_0, and it will start reading events from partitions 0-9. From this point forward, one of the following will happen:
Only 1 Function instance is needed - Function_0 is able to process all 1000 before the Azure Functions' scaling logic kicks in.
Hence, all 1000 messages are processed by Function_0.
Add 1 more Function instance - Azure Functions' scaling logic determines that Function_0 seems sluggish, so a new instance
Function_1 is created, resulting in EPH_1. Event Hub detects that a new EPH instance is trying read messages. Event Hub will start load
balancing the partitions across the EPH instances, e.g., partitions
0-4 are assigned to EPH_0 and partitions 5-9 are assigned to EPH_1.
If all Function execution succeed without errors, both EPH_0 and
EPH_1 checkpoints successfully and all 1000 messages are processed. When check-pointing succeeds, all 1000 messages should never be retrieved again.
Add N more function instances - Azure Functions' scaling logic determines that both Function_0 and Function_1 are still sluggish and
will repeat workflow 2 again for Function_2...N, where N>9. Event Hub will load balance the partitions across Function_0...9 instances.
Unique to Azure Functions' current scaling logic is the fact that N is >(number of partitions). This is done to ensure
that there are always instances of EPH readily available to quickly
get a lock on the partition(s). As a customer, you are only charged for the resources used when your Function instance executes, but you are not charged for this over-provisioning.
Answer to Question 2:
EPH uses a check-pointing mechanism to mark the last known successfully read message. An EventHub-Triggered Function can be setup to process 1 message or a batch of messages at a time. The option you choose needs to consider the following:
1. Speed of message processing - Processing messages in batches instead of a single message at a time is one of the factors that will speed up the ability of your Azure Function workflow to keep up with the incoming messages in your Event Hub.
2. Tolerance for duplicates - If check-pointing fails due to errors in your Function code/(Updated Aug 24th, 2017) timeout/partition least lost, then the next EPH that gets a lease on that partition will start retrieving messages from the last known checkpoint. Event Hub guarantees at-least-once delivery but not at-most-once delivery. Azure Functions will not attempt to change that behavior. If not having duplicate messages is a priority, then you will need to mitigate it in your workflow. As such, when check-pointing fails, there are more duplicate messages to manage if your Function is processing messages at batch level.
Function Apps are based on WebJobs SDK, which use EventHostProcessor to consume events from Event Hubs. So you can lookup information about EventHostProcessor and it will be applicable to your Function App.
Particularly, you can find the implementation of IEventProcessor
here.
To your questions:
Not sure what you mean by "one instance". One listener will be created per partition, but they can be both hosted inside a single App Plan Instance if the load is low. On the high level, you should not care much: in Consumption Plan you pay per execution time, no matter how many servers/processes/threads are running. Of course, you should care whether the auto-scaling works good enough for high load, but that needs to be tested anyway.
Functions are stateless in a sense that you can't save anything in-memory between two function executions. You are totally fine to save state in external storage. Function App will use PartitionContext.CheckpointAsync() for checkpointing of the current offset. Azure Storage is used internally; again you can read more about how it works in Event Hubs and EventHostProcessor docs, e.g. here.