This checks for the basic behaviour of GetFingerprintsForLabelMatchers, that is, whether the different matcher types filter the correct fingerprints and intersections are correct.
The capacity is basically how many persisted head chunks we will count
at most while doing other things, in particular checkpointing. To
limit the amount of already counted head chunks, keep this number low,
otherwise we will easily checkpoint too often if checkpoints take long
anyway.
In that commit, the 'maintainSeries' call was accidentally removed.
This commit refactors things a bit so that there is now a clean
'maintainMemorySeries' and a 'maintainArchivedSeries' call.
Straighten the nomenclature a bit (consistently use 'drop' for
chunks and 'purge' for series/metrics).
Remove the annoying 'Completed maintenance sweep through archived
fingerprints' message if there were no archived fingerprints to do
maintenance on.
This is done by bucketing chunks by fingerprint. If the persisting to
disk falls behind, more and more chunks are in the queue. As soon as
there are "double hits", we will now persist both chunks in one go,
doubling the disk throughput (assuming it is limited by disk
seeks). Should even more pile up so that we end wit "triple hits", we
will persist those first, and so on.
Even if we have millions of time series, this will still help,
assuming not all of them are growing with the same speed. Series that
get many samples and/or are not very compressable will accumulate
chunks faster, and they will soon get double- or triple-writes.
To improve the chance of double writes,
-storage.local.persistence-queue-capacity could be set to a higher
value. However, that will slow down shutdown a lot (as the queue has
to be worked through). So we leave it to the user to set it to a
really high value. A more fundamental solution would be to checkpoint
not only head chunks, but also chunks still in the persist queue. That
would be quite complicated for a rather limited use-case (running many
time series with high ingestion rate on slow spinning disks).
Starting a goroutine takes 1-2µs on my laptop. From the "numbers every
Go programmer should know", I had 300ns for a channel send in my
mind. Turns out, on my laptop, it takes only 60ns. That's fast enough
to warrant the machinery of yet another channel with a fixed set of
worker goroutines feeding from it. The number chosen (8 for now) is
low enough to not really afflict a measurable overhead (a big
Prometheus server has >1000 goroutines running), but high enough to
not make sample ingestion a bottleneck.
- Parallelize AppendSamples as much as possible without breaking the
contract about temporal order.
- Allocate more fingerprint locker slots.
- Do not run early checkpoints if we are behind on chunk persistence.
- Increase fpMinWaitDuration to give the disk more time for more
important things.
Also, switch math.MaxInt64 and math.MinInt64 to the new constants.
Also, set a much higher default value.
Chunk persist requests can be quite spiky. If you collect a large
number of time series that are very similar, they will tend to finish
up a chunk at about the same time. There is no reason we need to back
up scraping just because of that. The rationale of the new default
value is "1/8 of the chunks in memory".
persistence.go is way too long anyway, and a lot of code is just crash
recovery, which is not important to understand the normal operation.
Also, remove unused `exists` function.
Previously, it would return an error instead. Now we can distinguish
the cases 'error while deleting known key' vs. 'key not in index'
without testing for leveldb-internal kinds of errors.
If queries are still running when the shutdown is initiated, they will
finish _during_ the shutdown. In that case, they might request chunk
eviction upon unpinning their pinned chunks. That might completely
fill the evict request queue _after_ draining it during storage
shutdown. If that ever happens (which is the case if there are _many_
queries still running during shutdown), the affected queries will be
stuck while keeping a fingerprint locked. The checkpointing can then
not process that fingerprint (or one that shares the same lock). And
then we are deadlocked.
- Move CONTRIBUTORS.md to the more common AUTHORS.
- Added the required NOTICE file.
- Changed "Prometheus Team" to "The Prometheus Authors".
- Reverted the erroneous changes to the Apache License.
This mimics the locking leveldb is performing anyway. Advantages of
doing it separately:
- Should we ever replace the leveldb implementation by one without
double-start protection, we are still good.
- In contrast to leveldb, the new code creates a meaningful error
message.
Usually, if you unarchive a series, it is to add something to it,
which will create a new head chunk. However, if a series in
unarchived, and before anything is added to it, it is handled by the
maintenance loop, it will be archived again. In that case, we have to
load the chunkDescs to know the lastTime of the series to be
archived. Usually, this case will happen only rarely (as a race, has
never happened so far, possibly because the locking around unarchiving
and the subsequent sample append is smart enough). However, during
crash recovery, we sometimes treat series as "freshly unarchived"
without directly appending a sample. We might add more cases of that
type later, so better deal with archiving properly and load chunkDescs
if required.
- Documented checkpoint file format.
- High-level description of series sanitation.
- Replace fp.LoadFromString panic with an error.
(Change in client_golang already submitted.)
- Introduced checks for series file size where appropriate.
- Removed two Law of Demeter violations.
Change-Id: I555d97a2c8f4769820c2fc8bf5d6f4e160222abc
- Delete unneeded file view_adapter.go.
- Assessed that we still need the fingerprints in nodes
(to create iterators).
- Turned numMemChunkDescs into a metric.
Change-Id: I29be963c795a075ec00c095f76bf26405535609d
Now only purge if there is something to purge.
Also, set savedFirstTime and archived time range appropriately.
(Which is needed for the optimization.)
Change-Id: Idcd33319a84def3ce0318d886f10c6800369e7f9
beorn7