This should significantly reduce the number of open ReaderAt files
on VictoriaMetrics and VictoriaLogs startup.
The open files can be tracked via vm_fs_readers metric
Remove temporary file before closing it in order to signal the OS that it shouldn't
store the file contents from page cache to disk when the file is closed.
Gracefully handle the case when the file cannot be removed before being closed -
in this case remove the file after closing it. This allows working on Windows.
Also remove superflouos opening of temporary file for reading - re-use already opened file handle for writing.
This is a follow-up for 9b1e002287
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/4020
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
- Add 'BUG:' prefix to error messages related to programming errors aka bugs.
- Consistently log the path to the file in all the messages in order to improve debuggability.
* fixes windows compilation,
adds signal impl for windows,
adds free space usage for windows,
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1036
NOTE victoria metrics database still CANNOT work under windows system,
only vmagent is supported.
To completly port victoria metrics, you have to fix issues with separators,
parsing and posix file removall
* rollback separator
* Adds windows setInformation api,
it must behave like unix, need to test it.
changes procutil
* check for invlaid param
* Fixes posix delete semantic
* refactored a bit
* fixes openbsd build
* removed windows api call
* Fixes code after windows add
* Update lib/procutil/signal_windows.go
Co-authored-by: Aliaksandr Valialkin <valyala@gmail.com>
All the callers for fs.OpenReaderAt expect that the file will be opened.
So it is better to log fatal error inside fs.MustOpenReaderAt instead of leaving this to the caller.
This eliminates the need for storing block data into temporary files on a single-node VictoriaMetrics
during heavy queries, which touch big number of time series over long time ranges.
This improves single-node VM performance on heavy queries by up to 2x.
