This was initially motivated by wanting to distribute the rule checker
tool under `tools/rule_checker`. However, this was not possible without
also distributing the LevelDB dynamic libraries because the tool
transitively depended on Levigo:
rule checker -> query layer -> tiered storage layer -> leveldb
This change separates external storage interfaces from the
implementation (tiered storage, leveldb storage, memory storage) by
putting them into separate packages:
- storage/metric: public, implementation-agnostic interfaces
- storage/metric/tiered: tiered storage implementation, including memory
and LevelDB storage.
I initially also considered splitting up the implementation into
separate packages for tiered storage, memory storage, and LevelDB
storage, but these are currently so intertwined that it would be another
major project in itself.
The query layers and most other parts of Prometheus now have notion of
the storage implementation anymore and just use whatever implementation
they get passed in via interfaces.
The rule_checker is now a static binary :)
Change-Id: I793bbf631a8648ca31790e7e772ecf9c2b92f7a0
There are four label-matching ops for selecting timeseries now:
- Equal: =
- NotEqual: !=
- RegexMatch: =~
- RegexNoMatch: !~
Instead of looking up labels by a simple clientmodel.LabelSet (basically
an equals op for every key/value pair in the set), timeseries
fingerprint selection is now done via a list of metric.LabelMatchers.
Change-Id: I510a83f761198e80946146770ebb64e4abc3bb96
- Mostly docstring fixed/additions.
(Please review these carefully, since most of them were missing, I
had to guess them from an outsider's perspective. (Which on the
other hand proves how desperately required many of these docstrings
are.))
- Removed all uses of new(...) to meet our own style guide (draft).
- Fixed all other 'go vet' and 'golint' issues (except those that are
not fixable (i.e. caused by bugs in or by design of 'go vet' and
'golint')).
- Some trivial refactorings, like reorder functions, minor renames, ...
- Some slightly less trivial refactoring, mostly to reduce code
duplication by embedding types instead of writing many explicit
forwarders.
- Cleaned up the interface structure a bit. (Most significant probably
the removal of the View-like methods from MetricPersistenc. Now they
are only in View and not duplicated anymore.)
- Removed dead code. (Probably not all of it, but it's a first
step...)
- Fixed a leftover in storage/metric/end_to_end_test.go (that made
some parts of the code never execute (incidentally, those parts
were broken (and I fixed them, too))).
Change-Id: Ibcac069940d118a88f783314f5b4595dce6641d5
So far we've been using Go's native time.Time for anything related to sample
timestamps. Since the range of time.Time is much bigger than what we need, this
has created two problems:
- there could be time.Time values which were out of the range/precision of the
time type that we persist to disk, therefore causing incorrectly ordered keys.
One bug caused by this was:
https://github.com/prometheus/prometheus/issues/367
It would be good to use a timestamp type that's more closely aligned with
what the underlying storage supports.
- sizeof(time.Time) is 192, while Prometheus should be ok with a single 64-bit
Unix timestamp (possibly even a 32-bit one). Since we store samples in large
numbers, this seriously affects memory usage. Furthermore, copying/working
with the data will be faster if it's smaller.
*MEMORY USAGE RESULTS*
Initial memory usage comparisons for a running Prometheus with 1 timeseries and
100,000 samples show roughly a 13% decrease in total (VIRT) memory usage. In my
tests, this advantage for some reason decreased a bit the more samples the
timeseries had (to 5-7% for millions of samples). This I can't fully explain,
but perhaps garbage collection issues were involved.
*WHEN TO USE THE NEW TIMESTAMP TYPE*
The new clientmodel.Timestamp type should be used whenever time
calculations are either directly or indirectly related to sample
timestamps.
For example:
- the timestamp of a sample itself
- all kinds of watermarks
- anything that may become or is compared to a sample timestamp (like the timestamp
passed into Target.Scrape()).
When to still use time.Time:
- for measuring durations/times not related to sample timestamps, like duration
telemetry exporting, timers that indicate how frequently to execute some
action, etc.
*NOTE ON OPERATOR OPTIMIZATION TESTS*
We don't use operator optimization code anymore, but it still lives in
the code as dead code. It still has tests, but I couldn't get all of them to
pass with the new timestamp format. I commented out the failing cases for now,
but we should probably remove the dead code soon. I just didn't want to do that
in the same change as this.
Change-Id: I821787414b0debe85c9fffaeb57abd453727af0f
AppendSample will be repcated with AppendSamples, which will take
advantage of bulks appends. This is a necessary step for indexing
pipeline decoupling.
Change-Id: Ia83811a87bcc89973d3b64d64b85a28710253ebc
The one-off keys have been replaced with ``model.LabelPair``, which is
indexable. The performance impact is negligible, but it represents
a cognitive simplification.
Some users of GetMetricForFingerprint() end up modifying the returned metric
labelset. Since the memory storage's implementation of
GetMetricForFingerprint() returned a pointer to the metric (and maps are
reference types anyways), the external mutation propagated back into the memory
storage.
The fix is to make a copy of the metric before returning it.
This does two things:
1) Make TieredStorage.AppendSamples() write directly to memory instead of
buffering to a channel first. This is needed in cases where a rule might
immediately need the data generated by a previous rule.
2) Replace the single storage mutex by two new ones:
- memoryMutex - needs to be locked at any time that two concurrent
goroutines could be accessing (via read or write) the
TieredStorage memoryArena.
- memoryDeleteMutex - used to prevent any deletion of samples from
memoryArena as long as renderView is running and
assembling data from it.
The LevelDB disk storage does not need to be protected by a mutex when
rendering a view since renderView works off a LevelDB snapshot.
The rationale against adding memoryMutex directly to the memory storage: taking
a mutex does come with a small inherent time cost, and taking it is only
required in few places. In fact, no locking is required for the memory storage
instance which is part of a view (and not the TieredStorage).
This makes the memory persistence the backing store for views and
adjusts the MetricPersistence interface accordingly. It also removes
unused Get* method implementations from the LevelDB persistence so they
don't need to be adapted to the new interface. In the future, we should
rethink these interfaces.
All staleness and interpolation handling is now removed from the storage
layer and will be handled only by the query layer in the future.
It is the case with the benchmark tool that we thought that we
generated multiple series and saved them to the disk as such, when
in reality, we overwrote the fields of the outgoing metrics via
Go map reference behavior. This was accidental. In the course of
diagnosing this, a few errors were found:
1. ``newSeriesFrontier`` should check to see if the candidate fingerprint is within the given domain of the ``diskFrontier``. If not, as the contract in the docstring stipulates, a ``nil`` ``seriesFrontier`` should be emitted.
2. In the interests of aiding debugging, the raw LevelDB ``levigoIterator`` type now includes a helpful forensics ``String()`` method.
This work produced additional cleanups:
1. ``Close() error`` with the storage stack is technically incorrect, since nowhere in the bowels of it does an error actually occur. The interface has been simplified to remove this for now.
Kill LevelDB watermarks due to redundancy.
General interface documentation has begun.
Creating custom types for the model to prevent errors down the
road.
Renaming of components for easier comprehension.
Exposition of interface in LevelDB.
Slew of simple refactorings.
