Callers of this function log the returned error and exit.
Let's log the error with the path to the filename and call stack
inside the function. This simplifies the code at callers' side
without reducing the level of debuggability.
This commit changes background merge algorithm, so it becomes compatible with Windows file semantics.
The previous algorithm for background merge:
1. Merge source parts into a destination part inside tmp directory.
2. Create a file in txn directory with instructions on how to atomically
swap source parts with the destination part.
3. Perform instructions from the file.
4. Delete the file with instructions.
This algorithm guarantees that either source parts or destination part
is visible in the partition after unclean shutdown at any step above,
since the remaining files with instructions is replayed on the next restart,
after that the remaining contents of the tmp directory is deleted.
Unfortunately this algorithm doesn't work under Windows because
it disallows removing and moving files, which are in use.
So the new algorithm for background merge has been implemented:
1. Merge source parts into a destination part inside the partition directory itself.
E.g. now the partition directory may contain both complete and incomplete parts.
2. Atomically update the parts.json file with the new list of parts after the merge,
e.g. remove the source parts from the list and add the destination part to the list
before storing it to parts.json file.
3. Remove the source parts from disk when they are no longer used.
This algorithm guarantees that either source parts or destination part
is visible in the partition after unclean shutdown at any step above,
since incomplete partitions from step 1 or old source parts from step 3 are removed
on the next startup by inspecting parts.json file.
This algorithm should work under Windows, since it doesn't remove or move files in use.
This algorithm has also the following benefits:
- It should work better for NFS.
- It fits object storage semantics.
The new algorithm changes data storage format, so it is impossible to downgrade
to the previous versions of VictoriaMetrics after upgrading to this algorithm.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
Previously these caches could exceed limits set via `-memory.allowedPercent` and/or `-memory.allowedBytes`,
since limits were set independently per each data part. If the number of data parts was big, then limits could be exceeded,
which could result to out of memory errors.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
The vm_cache_size_max_bytes metric can be used for determining caches which reach their capacity via the following query:
vm_cache_size_bytes / vm_cache_size_max_bytes > 0.9
One minute cache timeout result in slower queries in some production workloads where the interval
between query execution is in the range 1 minute - 2 minutes.
This should reduce memory usage on a system with high number of active time series and a high churn rate.
One minute is enough for caching the blocks needed for repeated queries (e.g. alerting rules, recording rules and dashboard refreshes).
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.
Previously such blocks were cleaned after they weren't accessed during 10 minutes.
Now they are cleaned after one minute of missing access. This should reduce memory usage in general case.
