The volume of our application is too huge i.e around 500million per day. We are observing some loss of events. Please share if anyone has observed loss of events while consuming using #JmsListener.
There could be many reasons:
some of your messages are producing errors and they are generating rollbacks which you didn't handle carefully
you are not using transactions while you are having connectivity / sync / infrastructure issues
it can be your configuration: not using persistent messaging, caching messages on client side within driver (etc..)
Whatever it is it is NOT issue of #JmsListner annotation, but of code, driver and messaging configuration.
Related
I have .Net core App deployed on azure and enabled application insights.
Sometimes Azure application insights End-to-end transaction details do not display all telemetry.
Here it only logs the error and not request or maybe request logged but both do not display together over here(difficult to find out due to many people use it)
Should be like:
Sometimes request log but with no error log.
What could be the reason for happening this? do I need to look into application insights specific set-up/feature?
Edit:
As suggested by people here, try to disable the Sampling feature but still not works, Here is open question as well.
This usually happens due to sampling. By default, adaptive sampling is enabled in the ApplicationInsights.config which basically means that only a certain percentage of each telemetry item type (Event, Request, Dependency, Exception, etc.) is sent to Application insights. In your example probably one part of the end to end transaction got sent to the server, another part got sampled out. If you want, you can turn off sampling for specific types, or completely remove the
AdaptiveSamplingTelemetryProcessor
from the config which completely disables sampling. Bear in mind that this leads to higher ingestion traffic and higher costs.
You can also configure sampling in the code itself, if you prefer.
Please find here a good overview of how sampling works and can be configured.
This may be related to :
When using SDK 2.x, you have to track all events and send the telemetries to Application insights
When using auto-instrumentation with 3.x agent, in this case the agent collect automatically the traffic, logs ... and you have to pay attention to the sampling file applicationinsights.json where you can filter the events.
If you are using java, below the accepted Logging libraries :
-java.util.logging
-Log4j, which includes MDC properties
-SLF4J/Logback, which includes MDC properties
I developed an Android application where I use Firebase as my main service for storing data, authenticating users, storage, and more.
I recently went deeper into the service and wanted to see the API usage in my Google Cloud Platform.
In order to do so, I navigated to https://console.cloud.google.com/ to see what it has to show inside APIs and Services:
And by checking what might cause it I got:
Can someone please explain what is the meaning of "Latency" and what could be the reason that specifically this service has so much higher Latency value compared to the other API's?
Does this value have any impact on my application such as slowing the response or something else? If yes, are there any guidelines to lower this value?
Thank you
Latency is the "delay" until an operation starts. Cloud Functions, in particular, have to actually load and start a container (if they have paused), or at least load from memory (it depends on how often the function is called).
Can this affect your client? Holy heck, yes. but what you can do about it is a significant study in and of itself. For Cloud Functions, the biggest latency comes from starting the "container" (assuming cold-start, which your low Request count suggests) - it will have to load and initialize modules before calling your code. Same issue applies here as for browser code: tight code, minimal module loads, etc.
Some latency is to be expected from Cloud Functions (I'm pretty sure a couple hundred ms is typical). Design your client UX accordingly. Cloud Functions real power isn't instantaneous response; rather it's the compute power available IN PARALLEL with browser operations, and the ability to spin up multiple instances to respond to multiple browser sessions. Use it accordingly.
Listen and Write are long lived streams. In this case a 8 minute latency should be interpreted as a connection that was open for 8 minutes. Individual queries or write operations on those streams will be faster (milliseconds).
My team and I have been at this for 4 full days now, analyzing every log available to us, Azure Application Insights, you name it, we've analyzed it. And we can not get down to the cause of this issue.
We have a customer who is integrated with our API to make search calls and they are complaining of intermittent but continual 502.3 Bad Gateway errors.
Here is the flow of our architecture:
All resources are in Azure. The endpoint our customers call is a .NET Framework 4.7 Web App Service in Azure that acts as the stateless handler for all the API calls and responses.
This API app sends the calls to an Azure Service Fabric Cluster - that cluster load balances on the way in and distributes the API calls to our Search Service Application. The Search Service Application then generates and ElasticSearch query from the API call, and sends that query to our ElasticSearch cluster.
ElasticSearch then sends the results back to Service Fabric, and the process reverses from there until the results are sent back to the customer from the API endpoint.
What may separate our process from a typical API is that our response payload can be relatively large, based on the search. On average these last several days, the payload of a single response can be anywhere from 6MB to 12MB. Our searches simply return a lot of data from ElasticSearch. In any case, a normal search is typically executed and returned in 15 seconds or less. As of right now, we have already increased our timeout window to 5 minutes just to try to handle what is happening and reduce timeout errors for the fact their searches are taking so long. However, we increased the timeout via the following code in Startup.cs:
services.AddSingleton<HttpClient>(s => {
return new HttpClient() { Timeout = TimeSpan.FromSeconds(300) };
});
I've read in some places that you actually have to do this in the web.config file as opposed to here, or at least in addition to it. Not sure if this is true?
So The customer who is getting the 502.3 errors have significantly increased the volumes they are sending us over the last week, but we believe we are fully scaled to be able to handle it. They are still trying to put the issue on us, but after many days of research, I'm starting to wonder if the problem is actually on their side. Could it be possible that they are not equipped to take the increased payload on their side. Can it be that their integration architecture is not scaled enough to take the return payload from the increased volumes? When we observe our resources usages (CPU/RAM/IO) on all of the above applications, they are all normal - all below 50%. This also makes me wonder if this is on their side.
I know it's a bit of a subjective question, but I'm hoping for some insight from someone who may have experienced this before, but even more importantly, from someone who has experience with a .Net API app in Azure which return large datasets in it's responses.
Any code blocks of our API app, or screenshots from Application Insights are available to post upon request - just not sure what exactly anyone would want to see yet as I type this.
I am building a project from scratch using event-sourcing with Java and Cassandra.
My apps we be based on microservices and in some use cases information will be processed asynchronously. I was wondering what part a Message Queue (such as Rabbit, Active MQ Artemis, Kafka, etc) would play to improve the technology stack in this environment and if I understand the scenarios if I won't use it.
I would start with separating messaging infrastructure like RabbitMQ from event streaming/storing/processing like Kafka. These are two different things made for two (or more) different purposes.
Concerning the event sourcing, you have to have a place where you must store events. This storage must be append-only and support fast reads of unstructured data based on an identity. One example of such persistence is the EventStore.
Event sourcing goes together with CQRS, which means you have to project your changes (event) to another store, which you can query. This is done by projecting events to that store, this is where events get processed to change the domain object state. It is important to understand that using message infrastructure for projections is generally a bad idea. This is due to the nature of messaging and two-phase commit issue.
If you look at how events get persisted, you can see that they get saved to the store as one transaction. If you then need to publish events, this will be another transaction. Since you are dealing with two different pieces of infrastructure, things can get broken.
The messaging issue as such is that messages are usually guaranteed to be delivered "at least once" and the order of messages is usually not guaranteed. Also, when your message consumer fails and NACKs the message, it will be redelivered but usually a bit later, again breaking the sequence.
The ordering and duplication concerns, whoever, do not apply to event streaming servers like Kafka. Also, the EventStore will guarantee once only event delivery in order if you use catch-up subscription.
In my experience, messages are used to send commands and to implement event-driven architecture to connect independent services in a reactive way. Event stores, at the other hand, are used to persist events and only events that get there are then projected to the query store and also get published to the message bus.
Make sure you are clear on the distinction between send(command) and publish(event). Udi Dahan touches on that topic in his essay on busses and brokers.
In most cases where you are event sourcing, you do not want to be reconstructing state from published events. If you need state, then query the technical authority/book of record for the history, and reconstruct the state from the history.
On the other hand, event driven activity off of a message queue should be fine. When a single event (plus the subscriber's state) has everything you need, then running off of the bus is fine.
In some cases, you might do both. For example, if you were updating cached views, you'd subscribe to various BobChanged events to know when your cached data was stale; to rebuild a stale view, you would reload a representation of the history and transform it into an updated view.
In the world of event-sourcing applications, message queues usually allow you to implement publish-subscribe pattern style of communication between producers and consumers. Also, they usually help you with delivery guarantees: which messages were delivered to which subscribers and which ones were not.
But they don't store all messages indefinitely. You need to have an event store to do any kind of event sourcing.
The question is not 'to queue or not to queue', but it is more like:
can this thing store huge volume of events indefinitely?
does it have publish-subscribe capabilities?
does it provide at-least-once delivery guarantees?
So, you should use something like Kafka or EventStore to have all that out-of-the-box. Alternatively, you can combine event store with message queue manually, but this is going to be more involved.
I am interested in creating an application using the the Meteor framework that will be disconnected from the network for long periods of time (multiple hours). I believe meteor stores local data in RAM in a mini-mongodb js structure. If the user closes the browser, or refreshes the page, all local changes are lost. It would be nice if local changes were persisted to disk (localStorage? indexedDB?). Any chance that's coming soon for Meteor?
Related question... how does Meteor deal with document conflicts? In other words, if 2 users edit the same MongoDB JSON doc, how is that conflict resolved? Optimistic locking?
Conflict resolution is "last writer wins".
More specifically, each MongoDB insert/update/remove operation on a client maps to an RPC. RPCs from a given client always play back in order. RPCs from different clients are interleaved on the server without any particular ordering guarantee.
If a client tries to issue RPCs while disconnected, those RPCs queue up until the client reconnects, and then play back to the server in order. When multiple clients are executing offline RPCs, the order they finally run on the server is highly dependent on exactly when each client reconnects.
For some offline mutations like MongoDB's $inc and $addToSet, this model works pretty well as is. But many common modifiers like $set won't behave very well across long disconnects, because the mutation will likely conflict with intervening changes from other clients.
So building "offline" apps is more than persisting the local database. You also need to define RPCs that implement some type of conflict resolution. Eventually we hope to have turnkey packages that implement various resolution schemes.