You can not post a blank message. Please type your message and try again. I used to run ESXi 3.
In many applications it's nice to know that kernel buffers are flushed to disk even if this alone does not necessarily guarantees data is actually written to the disk, as the disk itself can have caching layersbut unfortunately fsync tends to be monkey assess slow.
As I like numbers, slow is, for instance, 55 milliseconds against a small file with not so much writes, while the disk is idle. Slow means a few seconds when the disk is busy and there is fsync-ing the write ahead log in sync thread chart serious amount of data to flush.
With some application this is not a problem. For instance when you save your edited file in vim the worst that can happen is some delay before the editor will quit. But there are applications where both speed and persistence guarantees are required, especially when we talk about databases.
Like in my specific case: Redis supports a persistence mode called Append Only File, where every change to the dataset is written on disk before reporting a success status code to the client performing the operation. In this kind of application it is desirable to fsync in order to make sure the data is actually written on disk, in the event of a system crash or alike.
Since fsyncing is slow, Redis allows the user to select among three different fsync policies: In Linux this usually means that data will be flushed on disk at max in 30 seconds.
But you can change the kernel settings to change this defaults if needed. The first option is the faster, the second is almost as fast as the first but much safer, the third is so slow to be basically impossible to use, at the point I'm thinking about dropping it.
The "fsync everysec" policy is a very good compromise and works well in practice if the disk is not too much busy serving other processes, but since in this mode we just need to sync every second without our sync being blocking from the point of view of reporting the successful status code to the client, an obvious thing to do is moving the fsync call into another thread.
Doing things in this way, in theory, when from time to time an fsync will take too much as the disk is busy, no one will notice and the latency from the point of view of the client talking with the Redis server will be good as usually.
But I started to have the feeling that this would be totally useless, as the write 2 call would block anyway if there was a slow fsync going on against the same file, so I wrote the following test program: The program is pretty simple. It starts one thread doing an fsync call every second, while the other main thread does a write 10 times per second.
Both syscalls are benchmarked in order to check if when a slow fsync is in progress the write will also block for the same time. The output speaks for itself: Write in 11 microseconds Write in 12 microseconds Write in 12 microseconds Write in 12 microseconds Sync in microseconds 0 Write in microseconds Write in 11 microseconds Write in 11 microseconds Write in 11 microseconds Write in 11 microseconds Unfortunately my suspicious is confirmed.
This is really counter intuitive since after all we are talking about flushing buffers on disk. When this operation is started the kernel could allocate new buffers that will be used by new write 2 calls, so my guess is, this is a Linux limitation, not something that must be this way.
Since this behavior seemed so strange I started wondering if fsync actually blocks all the other writes until the buffers are not flushed on disk because it is required to also flush metadata.
So I tried the same thing with fdatasyncthat is much faster, unfortunately it just takes some more time to see the same behavior because fdatasync calls are usually much faster, but from time to time I was able to see this happening again: Write in 13 microseconds Write in 13 microseconds Write in 14 microseconds Write in 14 microseconds Write in 12 microseconds Write in 13 microseconds Write in 12 microseconds Sync in microseconds 0 Write in microseconds Write in 13 microseconds Write in 10 microseconds Write in 13 microseconds Conclusions If you have a Linux write intensive application and are thinking about calling fsync in another thread in order to avoid blocking, don't do it, it's completely useless with the current kernel implementation.
If you are a kernel hacker and know why Linux is behaving in an apparently lame way about this, please make me know.
Write in microseconds Write in microseconds Write in microseconds Write in microseconds Write in microseconds Write in microseconds Write in microseconds Write in microseconds Write in microseconds Write in microseconds Every write takes more than 20 times more time, but it's much faster blocking us every 10 writes compared to the big stop-the-world-forus every 10 writes with fsync.
So we have a clear winner here for "fsync always". Still no better solution of the current one for "fsync everysec" but this is working pretty well already.
Subscribe to the RSS feed of this blog or use the newsletter service in order to receive a notification every time there is something of new to read here.[ ,] INFO Processed session termination for sessionid: 0x15aa58d (timberdesignmag.comquestProcessor) [ ,] WARN fsync-ing the write ahead log in SyncThread:0 took ms which will adversely effect operation latency.
Zookeeper is running into errors while attempting to commit its sync log. Since Zookeeper is spinning its own event loop (NIO), if its blocked waiting for quorum and trying to write the sync-log.
Sync and Migrate to Google G Suite. Gmail Time Tracker is time logging service to help you track your reading and writing in the email, so that you can include it in your billable hours.
Any time you open an email, you'll be able to log how much time you've spent reading it, or writing it.
Display your AMD Adrenalin performance logs with Adrenalin Charts. Submit Driver Feedback to AMD. Latest Tech Support Megathread disclaimer posting from thread starter. You can prove it by that will definitely put the "X" ahead of the almost handedly, faster clocks will definitely help too, Mhz is ResidentSleeper, getting up. AngularJS Corner – Using promises and $q to handle asynchronous calls Posted on Jun 07, by Ken Rimple. With a community bank approach, M&T Bank helps people reach their personal and business goals with banking, mortgage, loan and investment services. Description. Skip to main Log in Toggle Log In. Log into M&T Online Banking: User ID. Passcode. Forgot your passcode?.
It can be used to break up a long conversation. Slack is a place where your team comes together to collaborate, important information can be found by the right people, and your tools pipe in information when and where you need it.
Channels Communication in Slack happens in channels, organized by . Default: Set if threads > 1. Decodes the input video in a separate thread to the encoding process. Recommendation: Default. sync-lookahead. Default: auto (bframes+1) Sets the number of frames to be used as a buffer for threaded lookahead.
Maximum Value is Automatically disabled during the 2nd or greater pass or when using sliced threads. Database Administrators Stack Exchange is a question and answer site for database professionals who wish to improve their database skills and learn from others in the community.