I'm new with Azure Service Fabric. I have created the smallest possible (3xA0) cluster for testing my stateless application. Ideally I wanted to use F1 instances but they were not available for some reason in Cluster Creation dialog wizard.
Now I'm trying to understand how can I manage instance count and size for my existing cluster but I can't see any menu options in Resource Manager related to this.
Please advise.
I've decided to convert my comment to an answer. So there are a lot of help documents covering this.
https://learn.microsoft.com/en-us/azure/service-fabric/service-fabric-cluster-resource-manager-introduction
https://learn.microsoft.com/en-us/azure/service-fabric/service-fabric-cluster-scale-up-down
https://learn.microsoft.com/en-us/azure/service-fabric/service-fabric-cluster-fabric-settings
Related
I have managed to get the C# and db setup using ListMappings. However, when I try to deploy the split/merge tool to Azure cloud classic the service it states 'The requested VM tier is currently not available in East US for this subscription. Please try another tier or deploy to a different location.' We tried a few other regions with the same result. Do you know if there is a workaround or updated version? Is the split / merge service even still relevant? Has anyone got this service to run on Azure lately?
https://learn.microsoft.com/en-us/azure/azure-sql/database/elastic-scale-overview-split-and-merge
The answer to the question on whether it is still relevant, in my opinion is ...no. Split\merge is no longer relevant with the maturation of elastic pools. Elastic pools with one data base per tenant seem the sustainable way to implement multi tenancy with legacy code. The initial plan was to add keys to each of our tables to have multiple tenants per database. Elastic pools give us the same flexibility without having to make breaking changes our existing code.
Late post here, but we are implementing ElasticScale for a client to split ~50 clients into a database-per-tenant model. I don't think the SplitMerge tool will be used over the long term, just for the initial data migration from one db to many shards, but it has been handy for that purpose. We are using the ElasticScale SDK to allow a single API to route queries to the appropriate shard(s) based on sharding key. Happy to compare notes with you if you are still working on this.
Is there any easy way to find out which applications are using a particular application insights from azure portal?
I have checked the various options in the portals but don't find any easy to understand interface where I can find the list of applications which are sending data to that particular application insights.
The application map should provide you with a good view of various resources using the app insights resource
The application map is good. You can also go to Performance, then choose Roles. Roles is in the same tab group as Operations and Dependencies. This will give you a listing of all services that use that Application Insights instance. This has the added benefit of allowing you to expand a particular node and see the actual instances.
This same approach also works for the Failures tab. You can see the number of calls and failures rolled up per service, and also see the breakout metrics per instance.
We have Azure environment with 3 different subscription and around 5 project resources are deployed in this environment.
Each project team has rights to create resources under specific Resource Group (RG) within Azure.
Now from Azure Admin perspective, i would like to know Who, When
This is basic requirements for any organization to track their cost, resource information. When i looked in Azure, this information is not available directly at resource level.
Few posts are mentioning to use Tagging for this or use logs (2 years back, really?)? Is it? I am surprised.
Can i use Application Insight for this? or only available for App Service kind of services?
Please help me to get this information in efficient way
Your only option is to implement some sort of logging (like poll Azure Subscription events) and save it somewhere. You can use Azure Monitor to achieve that rather easily. But by itself Azure doesnt offer anything like that out of the box.
you can use tagging, but with obvious challenges. logs only go 3 months back.
I have multiple services running on Service Fabric. I would like to add Application Insight for logging. I'm just wondering whether I have to add an Application Insight resource for each microservice or only one is common for all. What is the best practice?
There is no such thing a the best practice for this. It really depends. Some considerations:
Pricing: depending on the level (basic or enterprise) you will get an amount of data for free / included in the base price. See the docs. So in some cases, depending on the amount of traffic you can reduce costs by having a dedicated AI resource per service. AI resources for services that send data below the threshold of the AI pricing plan are then (almost) free.
Querying: if you split up services per AI resource getting an overview of the whole system is difficult since at the moment you cannot create queries spanning multiple AI resources.
Responsibility: If you have multiple teams working on multiple services it might be an option to have an AI resource per team so they have a good insight in only the parts they are responsible for.
If you do decide to use a shared AI resource there are options like custom telemetry initializers to include custom data that further identify which ASF application or service is sending the data if it is not included by default.
See also Add Application Insight to a existing Azure Service Fabric cluster for more info about how to integrate AI.
Now, when it comes to bring data together you do have some additional options that may or may not need additional services or configuration. For example:
PowerBi: You can visualize data of AI resources using dashboards, see https://learn.microsoft.com/en-us/azure/application-insights/app-insights-export-power-bi
OMS: Operation Management Suite, See https://blogs.technet.microsoft.com/msoms/2016/09/26/application-insights-connector-in-oms/. As Jesse mentions you can link multiple AI Resources
Custom dashboards: Using the rest api you can create your own solution that displays data for one or more AI resources.
I want to host an Orleans project on Azure, but don't want to use the (classic) Cloud Services model (I want an ARM template project). The web app sample uses the old web / worker model - what is best option? There is a Service Fabric sample - is that the best route? The nearest equivalent to the web/worker model is VM Scale Sets - is that a well tested option?
IMO, app service is closet to web role.
Worker role however, depending on the point of view
From system architecture point of view, I think Scale Set is the closet. You get an identical set of VMs running your application. However you lost all management features. How your cluster handle application configurations, work loads on each node, service interruptions from server failure or deployments are pretty much DIY. Also you need to provision the VM with dependencies for your application.
From operations point of view, I think Service Fabric is the closest. It handles problems above but then you are dealing with design/implementation changes and learning curve from the added fabric layer in the architecture. Could be small, could be big depending on the complexity of your project. Besides, service fabric is still relatively new and nothing is for sure. Best case you follow the sample change a few lines of code and it works like a charm. Worst case you may want to complete refactor orleans solution into service fabric solution.
App service would be the easiest among the three. If it doesn't meet your requirement, I personally would try Service Fabric. Same reason why people are moving to cloud and you would opt for ARM solution.