If a large series contains a point that is overwritten, the compactor
would load the whole series into RAM during a full compaction. If
the series was large, it could cause very large RAM spikes and OOMs.
The change reworks the compactor to merge blocks more incrementally
similar to the fix done in #6556.
Fixes#6557
The list of field keys in the index may have differed from the field
keys in the actual shard. Fixing `SHOW FIELD KEYS` so it relies only on
the shard rather than the index.
Fixes#6659.
Casting syntax is done with the PostgreSQL syntax `field1::float` to
specify which type should be used when selecting a field. You can also
do `field1::field` or `tag1::tag` to specify that a field or tag should
be selected.
This makes it possible to select a tag when a field key and a tag key
conflict with each other in a measurement. It also means it's possible
to choose a field with a specific type if multiple shards disagree. If
no types are given, the same ordering for how a type is chosen is used
to determine which type to return.
The FieldDimensions method has been updated to return the data type for
the fields that get returned. The SeriesKeys function has also been
removed since it is no longer needed. SeriesKeys was originally used for
the fill iterator, but then expanded to be used by auxiliary iterators
for determining the channel iterator types. The fill iterator doesn't
need it anymore and the auxiliary types are better served by
FieldDimensions implementing that functionality, so SeriesKeys is no
longer needed.
Fixes#6519.
Drop database was closing and deleting each shard dir individually and
serially. It would then delete the empty database dirs.
This changes drop database to close all shards in parallel and run
one os.RemoveAll to remove everything under the db dir which is more
efficient.
This also reworked the locking to avoid locking the tsdb.Store for
long periods of time. That can cause queries and writes for other
databases to block as well.
On data sets with many series and potentially large series keys,
the cost of parsing the key and re-indexing can be high.
Loading the TSM keys into the index was being done repeatedly for
series that were already index by an earlier TSM file. This was
wasted worked and slows down shard loading.
Parsing the key was also innefficient and allocated a new string
slice. This was simplified to remove that allocation.
This commit changes the `tsm1.Engine` to create individual series
iterators in batches so that it can be parallelized. Iterators
are combined at the end so they can be redistributed to the
parallelized merge iterator.
Before #6038 was merged, we needed to filter "name" so that it didn't
accidentally hit the code path that used "name" to check the name of a
measurement. This was changed to "_name" to avoid a conflict with a
legitimate tag that used "name" as the key.
SHOW TAG VALUES was never modified to remove the code that filtered out
"name". This removes that line of code so a condition with "name"
doesn't get removed erroneously.
Example:
SHOW TAG VALUES WITH KEY = host WHERE "name" = 'jsternberg'
Fixes#6581.
If a large series contains a point that is overwritten, the compactor
would load the whole series into RAM during a full compaction. If
the series was large, it could cause very large RAM spikes and OOMs.
The change reworks the compactor to merge blocks more incrementally
similar to the fix done in #6556.
This commit moves the `CallIterator` to wrap the individual series
instead of wrapping a shard. This allows individual points to be
aggregated before being merged.
This will cause a small increase in memory usuage per series but
it shows a 20% decrease in query time when there are a moderate
number of points per series.
In some query scenarios, if there are a lot of points on disk spread
across many blocks in TSM files and a point is overwritten near the
begginning of the shard's timerange, the full series could be loaded
into RAM triggering OOMs and huge allocations.
The issue was that the KeyCursor code that handles overwriting points
had a simple implementation that just deduped the whole series in this
case. This falls over when the series is quite large.
Instead, the KeyCursor has been changed to only decode blocks with
updated points. It then keeps track of what section of the blocks
have been read so they are not re-read when the later points are
decoded.
Since the points in a block are always sorted, the code was also changed
to remove the Deduplicate calls since they end up
reallocating the slice. Instead, we do a sorted merge and re-use
the slice as much as we can.
The cursors were returning the wrong value in the case when points
existed in both the cache and tsm files with the same timestamp. The
cache value should have been returned, but the tsm value was returned
incorrectly.
Fixes#6439
This commit changes the `SeriesIterator` to process one measurement
at a time and uses a `floatFastDedupeIterator` to avoid point
encoding during deduplication.
If a shard is empty for a specific field and the field type is something
other than a float, a nil iterator would get returned from one of the
empty shards and cause the combined iterators to be cast to the float
type and all other iterator types to be discarded (or for integers, to
be cast).
This is rare since most aggregates don't accept strings or booleans, but
for queries like:
SELECT distinct(string) FROM mydata
It would result in nothing getting returned if one of the shards didn't
have a value for `string`.
This change modifies the query engine to return nil for the shards
instead of a fake iterator and then to only use the fake iterator if the
final aggregate iterator is nil (meaning that no iterators could be
constructed for the field from any shard).
Fixes#6495.
If multiple tombstone entries happen to exist for the same key in a
tombstone file, it was possible to panic. The first application
would remove all index entries and the second time around the code
still assumed entries would exist and would index into the nil slice.
Also fixes a case where the range of time would fully delete all index
entries, but it did not align with math.MinInt64 and math.MaxInt64. This
would cause the index locations to still exist in the offset slice. This
is inefficient because the BlockIterator would still scan and decode the block
only to discover that all the values are deleted. We now just remove it from
the offsets slice in this case since the range of values are deleted.
When a large tombstone file existed on disk, this code was slow since
it would apply each tombstone to the index one at a time causing the
index to be scanned for each key.
Instead, we group all the tombstones together by timestamp and apply
in bulk so that the index in scan once for each set of tombstones.
If we change to immuntable tombstone files, it might be better to just
write a file where all the keys have the same tombstone so we can re-apply
them efficiently.
This was the wrong fix. The real issue was the tombstones were
being read incorrectly and also applied incorrectly at times. This
code is slower and not necessary so reverting it.
Each iteration of the loop was incrementing the position by 4 incorrectly.
The position should start at four since the header is 4 bytes. This
caused tombstones at the end of the file to not be read because the counter
was out of sync with the actual file position which cause the loop to exit early.
Probably better to refactor this to check for io.EOF instead of using the counter.
Jason Wilder
Jonathan A. Sternberg
Edd Robinson
Joe LeGasse
Jason Wilder
Ben Johnson
Cory LaNou
thbourlove