I'm looking for a script to check the size of a particular LVM volume on CentOS 6.5 and when it reaches a certain threshold, have it automatically extend the partition and online re-size the file system.
I have this particular machine monitored, and could do it manually, but I saw a script once to do just this.
I have plenty of disk space on the physical volumes but, since it's easier to expand when needed than reduce later, I'd rather expand my logical partitions only when they start to fill up. There are several logical volumes on this machine, but only one that regularly grows.
Any tips are appreciated; and, if the overall best thing to do is just expand the volume manually when the time comes that advice is welcome as well!
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I have a datacenter A which has 100GB of the file changing every millisecond. I need to copy and place the file in Datacenter B. In case of failure on Datacenter A, I need to utilize the file in B. As the file is changing every millisecond does r-sync can handle it at 250 miles far datacenter? Is there any possibility of getting the corropted file? As it is continuously updating when we call this as a finished file in datacenter B ?
rsync is a relatively straightforward file copying tool with some very advanced features. This would work great for files and directory structures where change is less frequent.
If a single file with 100GB of data is changing every millisecond, that would be a potential data change rate of 100TB per second. In reality I would expect the change rate to be much smaller.
Although it is possible to resume data transfer and potentially partially reuse existing data, rsync is not made for continuous replication at that interval. rsync works on a file level and is not as commonly used as a block-level replication tool. However there is an --inplace option. This may be able to provide you the kind of file synchronization you are looking for. https://superuser.com/questions/576035/does-rsync-inplace-write-to-the-entire-file-or-just-to-the-parts-that-need-to
When it comes to distance, the 250 miles may result in at least 2ms of additional latency, if accounting for the speed of light, which is not all that much. In reality this would be more due to cabling, routers and switches.
rsync by itself is probably not the right solution. This question seems to be more about physics, link speed and business requirements than anything else. It would be good to know the exact change rate, and to know if you're allowed to have gaps in your restore points. This level of reliability may require a more sophisticated solution like log shipping, storage snapshots, storage replication or some form of distributed storage on the back end.
No, rsync is probably not the right way to keep the data in sync based on your description.
100Gb of data is of no use to anybody without without the means to maintain it and extract information. That implies structured elements such as records and indexes. Rsync knows nothing about this structure therefore cannot ensure that writes to the file will transition from one valid state to another. It certainly cannot guarantee any sort of consistency if the file will be concurrently updated at either end and copied via rsync
Rsync might be the right solution, but it is impossible to tell from what you have said here.
If you are talking about provisioning real time replication of a database for failover purposes, then the best method is to use transaction replication at the DBMS tier. Failing that, consider something like drbd for block replication but bear in mind you will have to apply database crash recovery on the replicated copy before it will be usable at the remote end.
I read in a stackoverflow post that (link here)
By using predictable (e.g. sequential) IDs for documents, you increase the chance you'll hit hotspots in the backend infrastructure. This decreases the scalability of the write operations.
I would like if anyone could explain better on the limitations that can occur when using sequential or user provided id.
Cloud Firestore scales horizontally by allocated key ranges to machines. As load increases beyond a certain threshold on a single machine, it will split the range being served by it and assign it to 2 machines.
Let's say you just starting writing to Cloud Firestore, which means a single server is currently handling the entire range.
When you are writing new documents with random Ids, when we split the range into 2, each machine will end up with roughly the same load. As load increases, we continue to split into more machines, with each one getting roughly the same load. This scales well.
When you are writing new documents with sequential Ids, if you exceed the write rate a single machine can handle, the system will try to split the range into 2. Unfortunately, one half will get no load, and the other half the full load! This doesn't scale well as you can never get more than a single machine to handle your write load.
In the case where a single machine is running more load than it can optimally handle, we call this "hot spotting". Sequential Ids mean we cannot scale to handle more load. Incidentally, this same concept applies to index entries too, which is why we warn sequential index values such as timestamps of now as well.
So, how much is too much load? We generally say 500 writes/second is what a single machine will handle, although this will naturally vary depending on a lot of factors, such as how big a document you are writing, number of transactions, etc.
With this in mind, you can see that smaller more consistent workloads aren't a problem, but if you want something that scales based on traffic, sequential document ids or index values will naturally limit you to what a single machine in the database can keep up with.
My Codename One application downloads around 16000 records of data (approx 10 fields in each record).
On my Android phone (OS6.0, RAM 2GB) it's able to load 8000 to 9000 records but then shows out of memory error.
From the trace, it looks like it run out of heap memory allocated to the app.
Any suggestion what would be the ideal way to handle that large amount of data, please?
Here is the log file
The amount of RAM on the phone doesn't mean much. The OS takes about half and then divides the rest to the various apps running in parallel. You would typically have much less see What is the maximum amount of RAM an app can use?
You need to review your code and check what is eating up memory. 16k records of 1kb each would be 16Mb which probably shouldn't crash an app so the question is where is memory taken, I would suggest reading the performance section of the developer guide to figure out memory usage.
This might not apply to your situation, but would it be possible to only download x number of records at a time? Then, when the user takes some action (scrolls, hits next page, etc) it loads the next batch? Codename one has a great endless scroller implementation. See here for an example - https://www.codenameone.com/blog/property-cross-revisited.html
I'm working on optimizing a separable image downscaler. My next step is reduction of multiple samplings (nearest) of the same texel by reading all necessary texels into local memory. Here begins the fun...
The downscaler is versatile, so it can downscale anything larger into anything smaller and even take sections of an image and downscale it into a destination image. Thus the final resolution divider never is a whole number. Most of the time it will be something around 3.97 or such. This means: I do not know the required size for that local array at compile time.
To me that means: before enqueuing a task, I'll have to create a local mem object of the required size.
How do I know what workgroup sizes OpenCL will select?
If there is no way, is there a "best practice" to overcome this problem?
P.S.: I'm writing for OpenCL 1.1 compatibility.
Since you are using images, the texture cache can be relied upon instead of using shared local memory.
I have a Standard website in Azure with a small instance, (1 core and 1.75 GB memory). It seems to be coping fine and handling the requests smoothly, although I am expecting a lot more within the week.
It is unclear though under what circumstances I should be looking to scale the instance size to the next level ie to Medium. (Besides MemoryWorkingSet of course, rather obvious :))
ie. Will moving up to a Medium instance resolve high CPU time ?
What other telltales should I be watching for ?
I am NOT comfortable scaling the number of instances to more than one at the moment until I resolve some cache issues.
I think the key point I am trying to understand is the link between the metrics provided and the means of scaling available regardless of it being scaled horizontally or vertically.
I am trying to keep the average response time as low as possible as the number of users that interact with the website increase.
Which of the other metrics will alert me when the load on the server is getting to its limits & I will need to scale Vertically ?
The idea behind scaling in Azure is to scale horizontally, i.e. add more instances. Azure can do this for you automatically. If you can't add more instances, Azure can't do the scaling for you automatically.
You can move to Medium instance, overall capacity will increase, but it is impossible to say what your application will require under heavy load. I suggest you run profiler and load test to find out the weak parts of your app and improve these before you have an actual increase in useage.