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.
We were buffering up the data to write into byte slices to reduce
IO calls but at larger sizes, this causes memory to spike. The TSMWriter
was switched to use a bufio.Writer internally so this byte slice buffering
is unnecessary and costly now.
The block count was an uint16 when incrementing the index location
which was an int32. This caused the value the uint16 value to overflow
before the index location was incremented causing the wrong location
to be read on the next iteration of the loop. This triggers the slice
out of range errors.
Added a test that recreates the panic seen in #5257 and possibly #5202 which
is older code.
Fixes#5257
This changes backup and restore to work for TSM. It breaks it for b1 and bz1, but since those are getting removed it's ok.
The backup runs against any host that is specified and can backup either the metasstore, a database, specific retention policy, or a specific shard. It can also take incremental backups with the `since` flag, which will only backup TSM files that have been created since that timestamp.
The backup is safe to run online. However, for shards that are still hot for writes, they won't be able to create new TSM files while the backup for that single shard runs. If the backup isn't too large and the write throughput isn't too high this shouldn't be a problem since the writes will just go into the WAL cache.
This has a few changes in it (unfortuantely). The main change is to run compactions
concurrently. While implementing this, a few query and performance bugs showed up that
are also fixed by this commit.
Ben Johnson
Jason Wilder
Paul Dix
Ady
Philip O'Toole