We would overscan when hitting a value directly, interspersed with
samples in between timestamps. Apparently, that happens rarely enough
that it was only noticed recently.
This can happen in the situation where the system scales up the number of shards massively (to deal with some backlog), then scales it down again as the number of samples sent during the time period is less than the number received.
* Compress remote storage requests and responses with unframed/raw snappy, for compatibility with other languages.
* Remove backwards compatibility code from remote_storage_adapter, update example_write_adapter
* Add /documentation/examples/remote_storage/example_write_adapter/example_writer_adapter to .gitignore
* Use request.Context() instead of a global map of contexts.
* Add some basic opentracing instrumentation on the query path.
* Remove tracehandler endpoint.
- checkpointSeriesMapAndHeads accepts a context now to allow
cancelling.
- If a shutdown is initiated, cancel the ongoing checkpoint. (We will
create a final checkpoint anyway.)
- Always wait for at least as long as the last checkpoint took before
starting the next checkpoint (to cap the time spending checkpointing
at 50%).
- If an error has occurred during checkpointing, don't bother to sync
the write.
- Make sure the temporary checkpoint file is deleted, even if an error
has occurred.
- Clean up the checkpoint loop a bit. (The concurrent Timer.Reset(0)
call might have cause a race.)
Fixes#2480. For certain definition of "fixes".
This is something that should never happen. Sadly, it does happen,
albeit extremely rarely. This could be some weird cornercase we
haven't covered yet. Or it happens as a consequesnce of data
corruption or a crash recovery gone bad.
This is not a "real" fix as we don't know the root cause of the
incident reported in #2480. However, this makes sure the server does
not crash, but deals gracefully with the problem: The series in
question is quarantined, which even makes it available for forensics.
An unopenable archived_fingerprint_to_timerange is simply deleted and
will be rebuilt during crash recovery (wich can then take quite some time).
An unopenable archived_fingerprint_to_metric is not deleted but
instructions to the user are logged. A deletion has to be done by the
user explicitly as it means losing all archived series (and a repair
with a 3rd party tool might still be possible).
Sadly, we have a number of places where we use varint encoding for
numbers that cannot be negative. We could have saved a bit by using
uvarint encoding. On the bright side, we now have a 50% chance to
detect data corruption. :-/
Fixes#1800 and #2492.
This is in line with the v1.5 change in paradigm to not keep
chunk.Descs without chunks around after a series maintenance.
It's mainly motivated by avoiding excessive amounts of RAM usage
during crash recovery.
The code avoids to create memory time series with zero chunk.Descs as
that is prone to trigger weird effects. (Series maintenance would
archive series with zero chunk.Descs, but we cannot do that here
because the archive indices still have to be checked.)
The fpIter was kind of cumbersome to use and required a lock for each
iteration (which wasn't even needed for the iteration at startup after
loading the checkpoint).
The new implementation here has an obvious penalty in memory, but it's
only 8 byte per series, so 80MiB for a beefy server with 10M memory
time series (which would probably need ~100GiB RAM, so the memory
penalty is only 0.1% of the total memory need).
The big advantage is that now series maintenance happens in order,
which leads to the time between two maintenances of the same series
being less random. Ideally, after each maintenance, the next
maintenance would tackle the series with the largest number of
non-persisted chunks. That would be quite an effort to find out or
track, but with the approach here, the next maintenance will tackle
the series whose previous maintenance is longest ago, which is a good
approximation.
While this commit won't change the _average_ number of chunks
persisted per maintenance, it will reduce the mean time a given chunk
has to wait for its persistence and thus reduce the steady-state
number of chunks waiting for persistence.
Also, the map iteration in Go is non-deterministic but not truly
random. In practice, the iteration appears to be somewhat "bucketed".
You can often observe a bunch of series with similar duration since
their last maintenance, i.e. you see batches of series with similar
number of chunks persisted per maintenance. If that batch is
relatively young, a whole lot of series are maintained with very few
chunks to persist. (See screenshot in PR for a better explanation.)
