For Azure event hub 1 though put unit equals 1MB/sec ingress. So it can take 1000 messages of 1 KB. If I select 5 or more throughput units would I be able to ingest 5000 messages/ second of 1KB size with 4 partitions? What would be egress in that case? I am not sure about limitation on Event Hub partition, i read that it is also 1MB/sec. But then does that mean to use event hub effectively i need to have same number of partitions?
Great question.
1 Throughput Unit (TU) means an ingress limit of 1 MB/sec or 1000 msgs/sec - whichever happens first. You pay for TUs and you can change TUs as per your load requirements. This is your knob to control the bill. And TUs are set on a given Event Hubs Namespace!
When you buy 1 TU for an EventHubs Namespace and create a number of EventHubs in it, the the limit of 1 MB/sec or 1000 msgs/sec applies cumulatively across them. The limit also applies to each partition individually. Although, sometimes you might get lucky in some regions where load is low.
Consider these principles while deciding on no. of partitions in eventhub for your service:
The intent of Partitions is to offer high-availability. If you are sending to Eventhubs and you want the sends to succeed NO MATTER WHAT you should create multiple partitions and send using EventHubClient.Send (which doesn't confine the send to a particular partition).
The no. of partitions will determine how fat the event pipe is & how fast/parallelly you can receive & process the events. If you have 10 partitions on your EventHub - it's capacity is effectively capped at 10 TUs. You can create 10 epoch receivers in parallel & consume & process events. If you envision that the EventHub that you are currently creating now can quickly grow 10-fold create as many partitions and keep the TU's matching the current load. Analogy here is having multiple lanes on a freeway!
Another thing to note is, a TU is configured at namespace level. And, one Event Hubs namespace can have multiple EventHubs in it and each EventHub can have a different no. of partitions.
Answers:
If you select 5 or more TUs on the Namespace and have only 1 EventHub with 4 partitions you will get a max. of 4 MB/sec or 4K msgs/sec.
Egress max will be 2X of ingress (8 MBPS or 8K msgs/sec). In other words, you could create 2 patterns of receives (e.g. slow and fast) by creating 2 consumer groups. If you need more than 2X parallel receives then you will need to by more TUs.
Yes, ideally you will need more partitions than TUs. First model your partition count as mentioned above. Start with 1 TU while you are developing your solution. Once done, when you are doing load testing or going live, increase TUs in tune with your load. Remember, you could have multiple EventHubs in a Namespace. So, having 20 TUs at Namespace level and 10 EventHubs with 4 partitions each can deliver 20 MB/sec across the Namespace.
More on EventHubs
One partition goes to one TPU. Think of TPUs as a processing engine. You can't take advantage of more TPUs than you have partitions. If you have 4 partitions, you can't use more than 4 TPUs.
It's typical to have more partitions than TPUs, for the following reasons
You can scale the number of TPUs up if you have a lot of traffic, but you can't change the number of partitions
You can't have more concurrent readers than you have partitions. If you want to have 5 concurrent readers, you need 5 partitions.
As for throughput, the limits are 1 MB ingerss/2 MB egress per TPU. This covers the typical scenario where each event is sent both to cold storage (eg a database) and Stream analytics or an event processor for analysis, monitoring etc.
Related
I've red the documentation and searched the internet for a simple explanation on Azure application gateway auto-scaling and the above quoted line but failed.
It would be really helpful if you can explain/provide a explanation link related to the same for better understanding.
Thank you!
When you enable auto scaling you need to set a minimum and maximum instance count. How do you know how many instances you need to handle the minimum amount of traffic you want to be able to handle? That is where Capacity Units play a role:
Capacity Unit is the measure of capacity utilization for an Application Gateway across multiple parameters.
A single Capacity Unit consists of the following parameters:
2500 Persistent connections
2.22-Mbps throughput
1 Compute Unit
If any one of these parameters are exceeded, then another n capacity unit(s) are necessary, even if the other two parameters don’t exceed this single capacity unit’s limits. The parameter with the highest utilization among the three above will be internally used for calculating capacity units, which is in turn billed.
When configuring the minimum and maximum number of instances you can now calculate how many instances you need because a single instance can handle up to 10 Capacity Units, so for example a maximum number of 10 * 2500 = 25.000 persistent connections.
For example: if you expect to have to deal with 6000 persistent connections you will need at least 3 instances (3 * 2500 = up to 7500 persistent connections)
I have an Event Hub and Azure Function connected to it. With small amounts of data all works well, but when I tested it with 10 000 events, I got very peculiar results.
For test purposes I send into Event hub numbers from 0 to 9999 and log data in application insights and in service bus. For the first test I see in Azure that hub got exactly 10 000 events, but service bus and AI got all messages between 0 and 4500, and every second message after 4500 (so it lost about 30%). In second test, I got all messages from 0 to 9999, but every second message between 3500 and 3200 was duplicated. I would like to get all messages once, what did I do wrong?
public async Task Run([EventHubTrigger("%EventHubName%", Connection = "AzureEventHubConnectionString")] EventData[] events, ILogger log)
{
int id = _random.Next(1, 100000);
_context.Log.TraceInfo("Started. Count: " + events.Length + ". " + id); //AI log
foreach (var message in events)
{
//log with ASB
var mess = new Message();
mess.Body = message.EventBody.ToArray();
await queueClient.SendAsync(mess);
}
_context.Log.TraceInfo("Completed. " + id); //AI log
}
By using EventData[] events, you are reading events from hub in batch mode, thats why you see X events processing at a time then next seconds you process next batch.
Instead of EventData[] use simply EventData.
When you send events to hub check that all events are sent with the same partition key if you want try batch processing otherwise they can be splitted in several partitions depending on TU (throughput units), PU (Processing Units) and CU (Capacity Units).
Egress: Up to 2 MB per second or 4096 events per second.
Refer to this document.
Throughput limits for Basic, Standard, Premium..:
There are a couple of things likely happening, though I can only speculate with the limited context that we have. Knowing more about the testing methodology, tier of your Event Hubs namespace, and the number of partitions in your Event Hub would help.
The first thing to be aware of is that the timing between when an event is published and when it is available in a partition to be read is non-deterministic. When a publish operation completes, the Event Hubs broker has acknowledged receipt of the events and taken responsibility for ensuring they are persisted to multiple replicas and made available in a specific partition. However, it is not a guarantee that the event can immediately be read.
Depending on how you sent the events, the broker may also need to route events from a gateway by performing a round-robin or applying a hash algorithm. If you're looking to optimize the time from publish to availability, taking ownership of partition distribution and publishing directly to a partition can help, as can ensuring that you're publishing with the right degree of concurrency for your host environment and scenario.
With respect to duplication, it's important to be aware that Event Hubs offers an "at least once" guarantee; your consuming application should expect some duplicates and needs to be able to handle them in the way that is appropriate for your application scenario.
Azure Functions uses a set of event processors in its infrastructure to read events. The processors collaborate with one another to share work and distribute the responsibility for partitions between them. Because collaboration takes place using storage as an intermediary to synchronize, there is an overlap of partition ownership when instances are scaled up or scaled down, during which time the potential for duplication is increased.
Functions makes the decision to scale based on the number of events that it sees waiting in partitions to be read. In the case of your test, if your publication pattern increases rapidly and Functions sees "the event backlog" grow to the point that it feels the need to scale by multiple instances, you'll see more duplication than you otherwise would for a period of 10-30 seconds until partition ownership normalizes. To mitigate this, using an approach of gradually increasing speed of publishing over a 1-2 minute period can help to smooth out the scaling and reduce (but not eliminate) duplication.
https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization#calculate-the-maximum-streaming-units-of-a-job
As per the documentation,
Eg.
Query
*The input data stream is partitioned by 16.
*The query contains one step.
*The step is partitioned.
Max SUs for the job
96 (6 * 16 partitions)
What is that means?
Welcome to Stackoverflow!
To understand the example in the document, kindly understand "How many streaming Units are required for a specific job".
If a step don't contain "PARTITION BY", then maximum of 6 SUs for the
job.
If a step contain "PARTITION BY [N]", then maximum of 6 * [N]
SUs for the job.
How many Streaming Units are required for a job?
Choosing the number of required SUs for a particular job depends on the partition configuration for the inputs and the query that's defined within the job. The Scale page allows you to set the right number of SUs. It is a best practice to allocate more SUs than needed. The Stream Analytics processing engine optimizes for latency and throughput at the cost of allocating additional memory.
In general, the best practice is to start with 6 SUs for queries that don't use PARTITION BY. Then determine the sweet spot by using a trial and error method in which you modify the number of SUs after you pass representative amounts of data and examine the SU% Utilization metric. The maximum number of streaming units that can be used by a Stream Analytics job depends on the number of steps in the query defined for the job and the number of partitions in each step.
Hope this helps.
I'm running a Kafka Streams application with three sub-topologies. The stages of activity are roughly as follows:
stream Topic A
selectKey and repartition Topic A to Topic B
stream Topic B
foreach Topic B to Topic C Producer
stream Topic C
Topic C to Topic D
Topics A, B, and C are each materialized, which means that if each topic has 40 partitions, my maximum parallelism is 120.
At first I was running 5 streams applications with 8 threads a piece. With this set up I was experiencing inconsistent performance. It seems like some sub-topologies sharing the same thread were hungrier for CPU than others and after a while, I'd get this error: Member [client_id] in group [consumer_group] has failed, removing it from the group (kafka.coordinator.group.GroupCoordinator). Everything would get rebalanced, which could lead to decreased performance until the next failure and rebalance.
My questions are as follows:
How is it that multiple sub-topologies are able to be run on a single thread? A poll queue?
How does each thread decide how to allocate compute resources to each of its sub-topologies?
How do you optimize your thread to topic-partition ratio in such cases to avoid periodic consumer failures? e.g., will a 1:1 ratio ensure more consistent performance?
If you use a 1:1 ratio, how do you ensure that every thread gets assigned its own topic-partition and some threads aren't left idle?
The thread will poll() for all topics of different sub-topologies and check the records topic metadata to feed it into the correct task.
Each sub-topology is treated the same, ie, available resources are evenly distributed if you wish.
A 1:1 ratio is only useful if you have enough cores. I would recommend to monitor your CPU utilization. If it's too high (larger >80%) you should add more cores/threads.
Kafka Streams handles this for you automatically.
Couple of general comments:
you might consider to increase max.poll.interval.ms config to avoid that a consumer drops out of the group
you might consider to decrease max.poll.records to get less records per poll() call, and thus decrease the time between two consecutive calls to poll().
note, that max.poll.records does not imply increases network/broker communication -- if a single fetch request return more records than max.poll.records config, the data is just buffered within the consumer and the next poll() will be served from the buffered data avoiding a broker round trip
I have a simple requirement, 1 million devices need to send a simple heartbeat to the event hub every 5 seconds, that works out to 200000 events per second. Since 1 throughput unit supports only 1000 events\sec, do I really need to purchase 200 throughput units to implement a simple heartbeat mechanism?
I'm really wondering about the claim of event hub supporting millions of events per second, how is that possible if a throughput unit only supports 1000. I need a HUGE number of throughput units and that's going to burn every last dollar. Unless I'm really missing something.