cloudstack | Apache CloudStack is an opensource Infrastructure | Continuous Deployment library

I found the Compatibility Matrix.

The answer is yes for xenserver 7.

Use from_json function from Spark-2.4+

Read the parquet data then use from_json by passing the schema that matches with your json column.

Spark will read the matching data and adds non matching fields with null values.

Example:

After editing the required file for example "cloudstack.css" , save it and then update the corresponding zip file by give command. Each file in cloudstack has corresponding zip file that should be updated(overwritten).

Command to use in linux: gzip -k filename(complete path)

Thanks to gyx-hh, this has been resolved:

I used following function (details can be found here):

What you want to do is:

• Output the ELB resource Ref in your load balancer stack
• Change over to using an AWS::AutoScaling::AutoScalingGroup instead of an AWS::EC2::Instance. You can utilize the min/max/desired to mimic a single instance behavior.
• In your parent stack, pass the output of the load balancer stack as a Parameter to the EC2 stack, e.g.: LoadBalancerName: !GetAtt 'ElasticLoadBalancer.Outputs.LoadBalancerOutputName'
• Use a reference to the parameter LoadBalancerName for the LoadBalancerNames property of your AWS::AutoScaling::AutoScalingGroup

I have cloned your repo and try that in my local machine. Here is the steps :

1. Git clone

2. executing Dry run (testing everything before doing a release "for real" :

   $ goreleaser release --skip-publish

3. show there is no error

Since namespaced_function() takes the (old) function get_image() as an argument and returns the (new) get_image() function, it's more apt to say that it's modifying the function, rather than creating it. Of course, it is creating a function and returning it, but one that very closely resembles the input function. namespaced_function() works a bit like a decorator, but decorators usually just wrap the whole input function inside another function that calls the original, rather than actually creating a modified version of the original function. The original get_image() is defined at libcloudfuncs.py.

So the question becomes, "How does namespaced_function() modify the input function?". If you look at what types.FunctionType() is getting as its arguments, you see that most values get copied over directly from the original function. The only argument that isn't directly copied is the function's globals. In other words, namespaced_function() is creating a new function, that is identical in every way to the input function, except that when the function refers to global variables, it looks for them in a different place.

So, they're creating a new version of get_image() that also has access to the current module's global variables. Why are they doing that? Well, either to override some global variables, or to provide ones that weren't there at all in the original module (in which case the original function would have been deliberately broken before the modification). But I can't really answer the "Why?" except by summarily saying that they probably judged that it was easier than the alternatives.

So what are the alternatives? Well, global variables are often frowned upon - when they aren't constant - because, well, you might want to change them. They could have used extra arguments instead of global variables, but probably didn't want to have to keep passing the same arguments around, when most their functions use them. You can inject arguments too, though, like they injected the globals - and it's less hacky, too! So why didn't they do that? Well, again, I kind of have to guess, but they probably have more than one global variable they're changing/providing.

Automatically providing arguments is easy:

I found the resolution to this after trial and error. The cause if that I had not set the "Storage Tags" on the Primary Storage and the Template/ISO.

Bit more info for newbies like me.. So for example if you have 1 Primary storage, and 1 CentOS template, then In Cent OS Template, go to the Storage Tags and type in a tag (say for example HDD). Then go to the Primary Storage and in Tags, type in the same tag - HDD.

This way CloudStack can now match up that the Template can be setup on this Primary storage.

Imagine you want to offer HDD and SSD storage, create 2 templates, one for HDD and one for SSD. Have 1 Primary Storagem one that has tag HDD and other is SSD. Now the SSD template can deploy to the SSD Dissk, and HDD can deploy to HDD.

This is my understanding... IF someone knows this is not correct, please add to the thread.

It sounds to me like the cs_instance module is possibly using the system Python rather than conda virtual environment. I've had this problem before with other Ansible modules running locally.

You can make Ansible use the Python interpreter in your Conda virtual environment by setting the ansible_python_interpreter fact in the inventory of localhost. You can do this as a pre-task, if you're running Ansible in the same Conda environment that you want the local task to use as follows: