Editing an instance, we can see two inputs as below snapshot about region and about location.
How would we distinguish the two?
Within a region, there are multiple physical locations your instance can be. By setting your Preferred Location, your instance will come up in the same location as an App Engine or Google Compute Engine instance. This will minimize latency between your Cloud SQL instance and the othe AE/GCE instance.
Additionally, your Cloud SQL instance will follow the instance you've tied it to, so they will both locate next to each other, even if your AE/GCE instance moves.
If you don't care, you can leave Preferred Location to None.
Related
Im using Geode/Gemfire Peer-to-Peer topology with a replicated region. I want this region to be refreshed every X hrs. I think this will require some locking on the region to ensure members dont refresh within X hrs.
Few observations/questions:
Locking is available only on Scope.GLOBAL regions but my region is Scope.DISTRIBUTED_ACK. I can change my region to be Scope.GLOBAL but what is the difference between the two?
Another way to achieve what I want: Create another region (with only one entry with key lastRefreshAt and value an Instant) that I can get a lock on (this region will be Scope.GLOBAL). Member after getting the lock will check if lastRefreshAt < X hrs, if true, refresh and release lock. This will allow me keep my original region as Scope.DISTRIBUTED_ACK.
Last but not least, how to obtain the lock on a region. Im using SDG (Spring Data Geode).
Thank you and Im open for other suggestions.
I have a project with 4 VPC networks. I created a GCDF instance, I had expected that the "default" network would be picked but I see that another one was picked, (the first one alphabetically). Is this the algorithm, the alphabetic order of names?
Is there a way to specify the network to be used, that would be very useful since I would like to isolate the network where those VMs run.
Your observation is correct. Current implementation selects network alphabetically. To use specific network, there are multiple options:
Create a dataproc compute profile that uses default or any other VPC network you have already created.
Use system.profile.properties.network=default as system preference.
I have a service fabric .net application that needs more temporary storage than is available on the instance. The current instance allows for up to 4 data disks. I see how to allocate these from the scale set, but not how to specify mounts, permissions, or even how to properly access them from the file API. I was surprised to find very little in the way of documentation on this so any help is appreciated.
Once you get into the instance of the scale then you add new disk to the VM using instruction here
https://learn.microsoft.com/en-us/azure/virtual-machines/windows/attach-disk-ps
After that you can access them using standard IO i.e System.IO.File.ReadAllText
However it is not recommend to change node type for primary node and for non primary node type it is better to create a new vm scale set. Graduate move services from old scale set to the new one by update placement properties
I'm trying to use Google Cloud Storage's lifecycle management features on a bucket, but I want to circumvent it for certain files (basically auto delete all files after 1 day, except for specific files that I want to keep). If I call the set metadata API endpoint will that update the age of the object and prevent the delete from occurring?
Set metadata changes the last updated time, not the creation time. TTL is keyed off of creation time, so that will not prevent TTL cleanup.
However, you could do a copy operation, and just set the destination to be the same as the source. That would update the creation time, and would be a fast operation as it can copy in the cloud.
That being said, it would probably be safer to just use a different bucket for these files. If your job to keep touching the files goes down they may get deleted.
Google Compute Engine allows for a daily export of a project's itemized bill to a storage bucket (.csv or .json). In the daily file I can see X-number of seconds of N1-Highmem-8 VM usage. Is there a mechanism for further identifying costs, such as per tag or instance group, when a project has many of the same resource type deployed for different functional operations?
As an example, Qty:10 N1-Highmem-8 VM's are deployed to a region in a project. In the daily bill they just display as X-seconds of N1-Highmem-8.
Functionally:
2 VM's might run a database 24x7
3 VM's might run batch analytics operation averaging 2-5 hrs each night
5 VM's might perform a batch operation which runs in sporadic 10 minute intervals through the day
final operation writes data to a specific GS Buckets, other operations read/write to different buckets.
How might costs be broken out across these four operations each day?
The Usage Logs do not provide 'per-tag' granularity at this time and it can be a little tricky to work with the usage logs but here is what I recommend.
To further break down the usage logs and get better information out of em, I'd recommend trying to work like this:
Your usage logs provide the following fields:
Report Date
MeasurementId
Quantity
Unit
Resource URI
ResourceId
Location
If you look at the MeasurementID, you can choose to filter by the type of image you want to verify. For example VmimageN1Standard_1 is used to represent an n1-standard-1 machine type.
You can then use the MeasurementID in combination with the Resource URI to find out what your usage is on a more granular (per instance) scale. For example, the Resource URI for my test machine would be:
https://www.googleapis.com/compute/v1/projects/MY_PROJECT/zones/ZONE/instances/boyan-test-instance
*Note: I've replaced the "MY_PROJECT" and "ZONE" here, so that's that would be specific to your output along with the name of the instance.
If you look at the end of the URI, you can clearly see which instance that is for. You could then use this to look for a specific instance you're checking.
If you are better skilled with Excel or other spreadsheet/analysis software, you may be able to do even better as this is just an idea on how you could use the logs. At that point it becomes somewhat a question of creativity. I am sure you could find good ways to work with the data you gain from an export.
9/2017 update.
It is now possible to add user defined labels, then track usage and billing by these labels for Compute and GCS.
Additionally, by enabling the billing export to Big Query, it is then possible to create custom views or hit Big Query in a tool more friendly to finance people such as Google Docs, Data Studio, or anything which can connect to Big Query. Here is a great example of labels across multiple projects to split costs into something friendlier to organizations, in this case a Data Studio report.