ssds | Skinning Decomposition with Similarity Transformations

I am trying to create multi instance in GCP with cloud function, use golang programing.

I refer tutorial in https://medium.com/google-cloud/using-cloud-scheduler-and-cloud-functions-to-deploy-a-periodic-compute-engine-vm-worker-2b897ef68dc5 then write some customize in my context. Here is my code

I'm using SQL Server running on an Azure VM with 8 SSDs. The SSDs are grouped together in Storage Spaces as 1 disk - in order to increase the capacity and also to combine the IOPS/Throughput. But the "combine the IOPS" part just doesn't seem to be working as far as I can tell by all of my tests/benchmarks (the "combine the throughput" part is working though). In fact, it looks like the SSD performance (IOPS) are better on 1 single disk than the whole 8-physical-disk virtual disk. So, I'm thinking about just forgetting about Storage Spaces and splitting up my data across 8 disks.

But what would be the best way to do that? (I don't have much experience with mulitple files, or filegroups, or partitioning tables, and that sort of thing.)

Just make 8 mdf files (1 on each disk) and let SQL Server redistribute the data across all of these files? If so, I would like to know how SQL Server knows which disk a given record is on. Would doing this speed things up?

And maybe split up the ldf files too?

What about multiple filegroups? I really don't know what the practical difference is between multiple files and filegroups.

What about splitting up the big tables somehow by using a partitioning function? Would that help, since now, maybe, SQL Server would "have a better idea" of where (in which file) a given record would be - since that is defined by a partition function?

Please don't try to close this question because it seems very general or open-ended. Life is tough enough as it is. This is a very good question. And I'm sure there are a lot of people out there who could give very helpful, experienced answers to this which would help a lot of people. Just because there might not be one exact answer to this question, doesn't mean it's a bad question. And anyway, if you think about it, there IS one best answer to this question - there is a best way to do things in this - very common - situation.

I have a job running on Flink 1.14.3 (Java 11) that uses rocksdb as the state backend. The problem is that the job requires an amount of memory pretty similar to the overall state size.

Indeed, for making it stable (and capable of taking snapshots) this is what I'm using:

• 4 TMs with 30 GB of RAM and 7 CPUs
• Everything is run on top of Kubernetes on AWS using nodes with 32 GB of RAM and locally attached SSD disks (M5ad instances for what it's worth)

I have these settings in place:

If one looks at the pricing tiers for disks in Azure storage (Premium SSDs one below as of today for the East US region), the higher the IOPS offered the higher the disk size.

One can choose a higher performance tier but use a lower disk size (although it won't make much sense probably, as you'd still be paying for the respective tier and corresponding size). But why isn't there an option to get a small disk (e.g. 32 GB) with a high number of IOPS?

In another words, why is there this forced correlation between disk size and IOPS?

I want to generate a large amount (10 TB) of seemingly random, but predictable numbers. The generation speed should exceed that of fast SSDs, so I want 3000 MB/s to 4000 MB/s.

After the file has been written, the numbers will be read again and generated again, so that they can be compared. The total program is supposed to check disks.

At the moment I'm thinking of hashes. The data to be hashed is just a 8 byte number (ulong) for the predictability. So in the binary file it looks like this

Im a currently working on mutliple Unity projects that share quite a lot of prefabs, scripts etc. More or less those are multiple "minigames" that all have the same foundation. I am currently showing those Games via WebGL on a Website that requests the data files (wasm, data etc.) from an Azure Blob Storage where I upload the files to. This upload happens through a separate Page I set up for the various Developers we have in the Team. The Problem is the following:

• The developers all build the Games on their dev Machines which "locks" the PC from any other use / further development
• They have the "work" of uploading the game via the second webapp

Because of these concerns I wanted to automate this process. My Ideas so far where using Unity Cloud Build (as we already have Unity plus and Teams advanced) or using Github Actions and source controlling it via a Git repository.

My Problems with these 2 options are:

• Cloud build / Collaborate apparently does not support branching (always builds on commit but sometimes those are used for "transferring" progress to another machine), Cloud build is quite difficult to customize to our needs (would have to somehow upload the files after build to the blob storage (supposedly via FTP or something similar)
• I have no experience with source controlling Unity files and Projects with Github (I have knowledge of Github itself and how to use it))

The final Problem I have is that, as said in the beginning, all "Games" are in the same Unity Project in order to make it easier and faster to develop the next games (weekly) and using a source control I do not want to build all games everytime but rather select which one I want to build (cloud build / github action).

So my questions in particular are:

• Is Cloud Build / Github Action feasible for my needs
• How would I control which games to build and deploy and which not (I thought of a triggered build on master/main branch commit and then checking which files have changed and getting the according "game" for those files)
• How much faster / slower would a cloud build solution be compared to a local building option (The devs have quite the machines (i7-10xxx, i9-11xxx + rtx 30xx and m.2 ssds)

Maybe someone with more experience can help me out!

Cheers

So, since ages I have been told that hard disks (I am specifically interested in HDDs and not SSDs) write data in units of 512 bytes, sectors. But when I interrogate the folder with stat . then I get told that blocks are 4096 bytes. How do I find out the sector size for the HDD and not the filesystem?

In my current system, I have 3 SSDs, sda, sdb and sdc. The OS is installed in sdc. I am trying to extract the SSDs without the OS installed in it. So, this command

I'm trying to write a custom renderer to change Text color in my JTable based on the values inside.

This is the code I have so far. I tried to simplify it as much as possible without actually changing the code so much: