This reduces some of the lock contention when writing to the cache.
When a new entry is created, it avoids an allocation. It also skips
a check to see if we need to sorted if we already know it needs to sorted.
Writing the snapshot would deduplicate the snapshot points
while still holding the engine write-lock. This can be expensive
under high load and cause writes to back up and OOM the server.
Instead, grab the snapshot under the lock and dedup it after releasing
the lock.
Possible fix for #5442
This commit enforces a limit on `RawMapper` so that it will not
produce more values than are specified by the LIMIT clause.
Previously the mapper would read up to the chunk size and the
values would be limited afterward.
We were closing the cursor when we read the last block which caused
the internal state to be cleared. In a group by query, we seeked multiple
times so depending on the group by interval and how the data was laid out
in the blocks, we woudl close the cursor and the last block would get skipped.
Fixes#5193
This may be causing slow restart times for systems with many large TSM files.
What I believe is happening at startup in these cases is that multiple goroutines
are started to load each TSM file concurrently. The kernel appears to serialize
mmap calls from the same process so all of the goroutines end up getting blocked
on the actual mmap system call. MAP_POPULATE instruct the kernel to pre-fault the
page table for the files and triggers read-ahead of the pages. For larger, 2GB files,
this makes the mmap call more expensive and slower. When there are many of these files
and calls it is possible to fill all available memory with pagecache. In this case,
the OS will end up pre-faulting pages from one file and have to remove pages that it just
loaded from another files causing slowness. MAP_POPULATE may also be cause much more data
to be pre-faulted than necessary. To load a file, we just need to scan the index at the end
of the file. MAP_POPULATE is likely causing the whole file to be loaded when it won't actually
be accessed for a while (or at all).
Might fix issue #5311.
Some data shapes would cause files to grow larger than the max size more
quickly which resulted in them getting skipped by the full compaction planner
at times. Some datasets that could make this happen are very large keys or
very large numbers of keys (10M). When this happened, multiple max sized
files would accumulate but the blocks would not be full. When the shard went
cold for writes, these files would get recompacted down to the optimal size, but
a lot of space would be wasted in the mean time.
This is contributing to some of the high memory usage on queries and possibly
some OOMs. This is slightly slower, but removing it allows some fairly large
count queries over 5M series to complete instead of crashing the process using
tsm1 engine.
Key() returned the key and the entries. We did not always need the
entries so they would be allocated and ignored. Added a KeyAt func
that just returns the key to avoid the unnecesary entries allocation.
Use sync.Pool for some temporary buffers used while encoding instead of
allocatin new ones each time. Also increased the default buffer size which
might be too small. Probably need to make this a config var.
Ben Johnson
Jonathan A. Sternberg
Gabriel Levine
Jason Wilder
Jason Wilder
INADA Naoki
Cory LaNou
Joe LeGasse
runner.mei
Paul Dix