We use Application insights from Azure Functions, currently documenting outage scenarios for different Azure Components. What'll be the impact on Functions if Application Insights service goes down? I hope it doesn't impact Function executions and they continue to operate as normal.
Also when it comes back online, let's say half an hour after the outage, would all the logs done during that time will be lost?
What'll be the impact on Functions if Application Insights service goes down? I hope it doesn't impact Function executions and they continue to operate as normal.
It does not. Functions will continue to run as usual.Telemetry submitting is done in the background anyway.
Also when it comes back online, let's say half an hour after the outage, would all the logs done during that time will be lost?
It depends on what Channel is configured. The Application Insights .NET and .NET Core SDKs ship with two built-in channels:
InMemoryChannel: A lightweight channel that buffers items in memory until they're sent. Items are buffered in memory and flushed once every 30 seconds, or whenever 500 items are buffered. This channel offers minimal reliability guarantees because it doesn't retry sending telemetry after a failure. This channel also doesn't keep items on disk, so any unsent items are lost permanently upon application shutdown (graceful or not). This channel implements a Flush() method that can be used to force-flush any in-memory telemetry items synchronously.
ServerTelemetryChannel: A more advanced channel that has retry policies and the capability to store data on a local disk. This channel retries sending telemetry if transient errors occur. This channel also uses local disk storage to keep items on disk during network outages or high telemetry volumes.
When using the ServerTelemetryChannel you may need to configure the location where telemetry will be stored during the downtime. See also the docs regarding offline storage
Related
We have a doubt about Azure because in some cases we have some dead times when we received requests in one of our AppServices or when a Service Bus triggers, for example, an Azure Functions.
If you see this image, you will see an example:
AppInsight Example Image
We execute a Request and at 5 seconds, but Azure delays more than 30 seconds to start the execution. We made a lot of optimizations in our apps, but we have no visibility about this delay.
Did someone face the same issue and found some solution? We believe it is a performance issue in the Workers, but, this happens also when the Workers are with a low load of memory and CPU. So we don't know how to scale horizontally automatically the resource if it is without load.
This happens also in our AZF, but we believe it's an issue between the Service Bus and the container of the AZF. In these cases we found the AZF has a higher consumption of CPU, but we don't why, because in the local environment we process a lot of messages with multithreading without any problem.
I already faced similar problem few times:
Azure Function with ServiceBusTrigger by some reason (misconfiguration, infrastructure issues, doesn't really matter) fails to connect to ServiceBus (so it happens on trigger level) and it leads to two issues:
It tries to restart all the time, increasing CPU consumption
It generates literally a millions of exceptions in AppInsights, which leads to quota exceedance
Practically every error in configuration means significantly increased bills and requires thorough monitoring after every deployment, which is annoying and error prone solution.
So, my question: If there is a way to set some delay between restart attempts to (for example) one second? And, as addition - is there way to limit amount of restart attempts and then shut down the Function?
Establishing a connection to the broker to fetch messages is Functions responsibility, Scale Controller. That aspect is entirely abstracted from customers and not configurable. I suggest raising an issue with Azure Functions team, likely under the Runtime repo.
I have a Node.js application that receives data via a Websocket connection and pushes each message to an Azure Redis cache. It stores a persistent array of messages in a variable for downstream use, and at regular intervals syncs that array from the cache. Bit convoluted, but at a later point I want to separate out the half of the application that writes to the cache from the half of it that reads from it..
At around 02:00 GMT, based on the Azure portal stats, I appear to have started getting "cache misses" on that sync, which last for a couple of hours before I started getting "cache hits" again sometime around 05:00.
The cache misses correspond to a sudden increase in CPU usage, which peaks at around 05:00. And when I say peaks, I mean it hits 81%, vs a previous max of about 6%.
So sometime around 05:00, the CPU peaks, then drops back to normal, the "cache misses" go away, but looking at the cache memory usage, I drop from about 37.4mb used to about 3.85mb used (which I suspect is the "empty" state), and the list that's being used by this application was emptied.
The only functions that the application is running against the cache are LPUSH and LRANGE, there's nothing that has any capability to remove data, and in case anybody was wondering, when the CPU ramped up the memory usage did not so there's nothing to suggest that rogue additions of data cropped up.
It's only on the Basic plan, so I'm not expecting it to be invulnerable or anything, but even without the replication features of the Standard plan I had expected that it wouldn't be in a position to completely wipe itself - I was under the impression that Redis periodically writes itself to disk and restores from that when it recovers from an error.
All of which is my way of asking:
Does anybody have any idea what might have happened here?
If this is something that others have been able to accidentally trigger themselves, are there any gotchas I should be looking out for that I might have in other applications using the same cache that could have caused it to fail so catastrophically?
I would welcome a chorus of people telling me that the Standard plan won't suffer from this sort of issue, because I've already forked out for it and it would be nice to feel like that was the right call.
Many thanks in advance..
Here my thoughts:
Azure Redis Cache stores information in memory. By default, it won't save a "backup" on disk, so, you had information in memory, for some reason the server got restarted and you lost your data.
PS: See this feedback, there is no option to persist information on disk using azure-redis cache yet http://feedback.azure.com/forums/169382-cache/suggestions/6022838-redis-cache-should-also-support-persistence
Make sure you don't use Basic plan. Basic plan doesn't suppose SLA and from my expirience it lost data quite often
Standard plan provides SLA and utilize 2 instances of Redis Cache. It's quite stable and it didn't lose our data, although such case still possible.
Now, if you're going to use Azure Redis as database, but not as a cache you need to utilize data persistance feature, which is already available in Azure Redis Cache Premium Tier: https://azure.microsoft.com/en-us/documentation/articles/cache-premium-tier-intro (see Redis data persistence)
James, using the Standards instance should give you much improved availability.
With the Basic tier any Azure Fabric update to the Master Node (or hardware failure), will cause you to loose all data.
Azure Redis Cache does not support persistence (writing to disk/blob) yet, even in Standard Tier. But the Standard tier does give you a replicated slave node, that can take over if you Master goes down.
Short version: What reasons could there be for a sudden, dramatic, and seemingly permanent increase in the rate of timing-out Azure queue requests?
It's going to be difficult to provide all of the details that could possibly be relevant here, but here's a start:
This is an Azure application (SDK v2.0) with a WCF service placing work requests on a queue (roughly 100k calls a day) and a couple of worker roles which process the queue. We've got New Relic monitoring with the latest .NET agent (3.3.38).
We've run into an issue in our latest release, deployed a few days ago -- after it ran normally for about 24 hours, all of a sudden we started seeing a greatly increased rate of timeouts when our worker roles fetch messages from the queue, along with a catastrophic drop in throughput (our application can now barely keep up with its own queue using 40 workers, whereas it usually gets by with just 2!) Ever since the timeouts started, they show no signs of letting up, keeping up at the same rate since it started happening.
A couple images from New Relic to illustrate:
While this isn't nearly enough information to provide a good answer, I'm just trying to figure out where I might start looking. I've got support tickets open with New Relic and Microsoft, but we're trying to investigate on our own as well. Could this be throttling? Some kind of resource exhaustion in my queue processor worker role? We don't see increased load on the WCF service, and we haven't changed Azure client libraries or changed much of anything in the code that processes the queue.
I suggest you enable analytics on your storage account to determine if the bottleneck is server side or client side/network related. Specifically, you can look at Storage Analytics Metrics table - AverageE2ELatency and AverageServerLatency properties to check if the issue is server side or client side.
You can learn more about Azure storage analytics from links below
Overview:
http://msdn.microsoft.com/en-us/library/hh343270.aspx
How to enable in portal:
http://azure.microsoft.com/en-us/documentation/articles/storage-monitor-storage-account/
Metrics table Schema:
http://msdn.microsoft.com/en-us/library/hh343264.aspx
Blog post:
http://blogs.msdn.com/b/windowsazurestorage/archive/2011/08/03/windows-azure-storage-analytics.aspx
My application relies heavily on a queue in in Windows Azure Storage (not Service Bus). Until two days ago, it worked like a charm, but all of a sudden my worker role is no longer able to process all the items in the queue. I've added several counters and from that data deduced that deleting items from the queue is the bottleneck. For example, deleting a single item from the queue can take up to 1 second!
On a SO post How to achive more 10 inserts per second with azure storage tables and on the MSDN blog
http://blogs.msdn.com/b/jnak/archive/2010/01/22/windows-azure-instances-storage-limits.aspx I found some info on how to speed up the communication with the queue, but those posts only look at insertion of new items. So far, I haven't been able to find anything on why deletion of queue items should be slow. So the questions are:
(1) Does anyone have a general idea why deletion suddenly may be slow?
(2) On Azure's status pages (https://azure.microsoft.com/en-us/status/#history) there is no mentioning of any service disruption in West Europe (which is where my stuff is situated); can I rely on the service pages?
(3) In the same storage, I have a lot of data in blobs and tables. Could that amount of data interfere with the ability to delete items from the queue? Also, does anyone know what happens if you're pushing the data limit of 2TB?
1) Sorry, no. Not a general one.
2) Can you rely on the service pages? They certainly will give you information, but there is always a lag from the time an issue occurs and when it shows up on the status board. They are getting better at automating the updates and in the management portal you are starting to see where they will notify you if your particular deployments might be affected. With that said, it is not unheard of that small issues crop up from time to time that may never be shown on the board as they don't break SLA or are resolved extremely quickly. It's good you checked this though, it's usually a good first step.
3) In general, no the amount of data you have within a storage account should NOT affect your throughput; however, there is a limit to the amount of throughput you'll get on a storage account (regardless of the data amount stored). You can read about the Storage Scalability and Performance targets, but the throughput target is up to 20,000 entities or messages a second for all access of a storage account. If you have a LOT of applications or systems attempting to access data out of this same storage account you might see some throttling or failures if you are approaching that limit. Note that as you saw with the posts on improving throughput for inserts these are the performance targets and how your code is written and configurations you use have a drastic affect on this. The data limit for a storage account (everything in it) is 500 TB, not 2TB. I believe once you hit the actual storage limit all writes will simply fail until more space is available (I've never even got close to it, so I'm not 100% sure on that).
Throughput is also limited at the partition level, and for a queue that's a target of Up to 2000 messages per second, which you clearly aren't getting at all. Since you have only a single worker role I'll take a guess that you don't have that many producers of the messages either, at least not enough to get near the 2,000 msgs per second.
I'd turn on storage analytics to see if you are getting throttled as well as check out the AverageE2ELatency and AverageServerLatency values (as Thomas also suggested in his answer) being recorded in the $MetricsMinutePrimaryTransactionQueue table that the analytics turns on. This will help give you an idea of trends over time as well as possibly help determine if it is a latency issue between the worker roles and the storage system.
The reason I asked about the size of the VM for the worker role is that there is a (unpublished) amount of throughput per VM based on it's size. An XS VM gets much less of the total throughput on the NIC than larger sizes. You can sometimes get more than you expect across the NIC, but only if the other deployments on the physical machine aren't using their portion of that bandwidth at the time. This can often lead to varying performance issues for network bound work when testing. I'd still expect much better throughput than what you are seeing though.
There is a network in between you and the Azure storage, which might degrade the latency.
Sudden peaks (e.g. from 20ms to 2s) can happen often, so you need to deal with this in your code.
To pinpoint this problem further down the road (e.g. client issues, network errors etc.) You can turn on storage analytics to see where the problem exists. There you can also see if the end2end latency is too big or just the server latency is the limiting factor. The former usually tells about network issues, the latter about something beeing wrong on the Queue itself.
Usually those latency issues a transient (just temporary) and there is no need to announce that as a service disruption, because it isn't one. If it has constantly bad performance, you should open a support ticket.