The one-off keys have been replaced with ``model.LabelPair``, which is
indexable. The performance impact is negligible, but it represents
a cognitive simplification.
The reality is that if we ever try to encode a Protocol Buffer and it
fails, it's likely that such an error is ultimately not a runtime error
and should be fixed forthwith. Thusly, we should rename
``Encoder.Encode`` to ``Encoder.MustEncode`` and drop the error return
value.
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.
- only the data extracted in the last loop iteration of ExtractSamples() was
emitted as output
- if e.g. op interval < sample interval, there were situations where the same
sample was added multiple times to the output
This commit updates the documentation, Makefiles, formatting, and
code semantics to support the 1.1. runtime, which includes ...
1. ``make advice``,
2. ``make format``, and
3. ``go fix`` on various targets.
This commit simplifies the way that compactions across a database's
keyspace occur due to reading the LevelDB internals. Secondarily it
introduces the database size estimation mechanisms.
Include database health and help interfaces.
Add database statistics; remove status goroutines.
This commit kills the use of Go routines to expose status throughout
the web components of Prometheus. It also dumps raw LevelDB status
on a separate /databases endpoint.
This commit simplifies the way that compactions across a database's
keyspace occur due to reading the LevelDB internals. Secondarily it
introduces the database size estimation mechanisms.
This commit introduces the long-tail deletion mechanism, which will
automatically cull old sample values. It is an acceptable
hold-over until we get a resampling pipeline implemented.
Kill legacy OS X documentation, too.
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 commit extracts the model.Values truncation behavior into the actual
tiered storage, which uses it and behaves in a peculiar way—notably the
retention of previous elements if the chunk were to ever go empty. This is
done to enable interpolation between sparse sample values in the evaluation
cycle. Nothing necessarily new here—just an extraction.
Now, the model.Values TruncateBefore functionality would do what a user
would expect without any surprises, which is required for the
DeletionProcessor, which may decide to split a large chunk in two if it
determines that the chunk contains the cut-off time.
This commit introduces three background compactors, which compact
sparse samples together.
1. Older than five minutes is grouped together into chunks of 50 every 30
minutes.
2. Older than 60 minutes is grouped together into chunks of 250 every 50
minutes.
3. Older than one day is grouped together into chunks of 5000 every 70
minutes.
This commit drops the Storage interface and just replaces it with a
publicized TieredStorage type. Storage had been anticipated to be
used as a wrapper for testability but just was not used due to
practicality. Merely overengineered. My bad. Anyway, we will
eventually instantiate the TieredStorage dependencies in main.go and
pass them in for more intelligent lifecycle management.
These changes will pave the way for managing the curators without
Law of Demeter violations.
This commit employs explicit memory freeing for the in-memory storage
arenas. Secondarily, we take advantage of smaller channel buffer sizes
in the test.
The curator requires the existence of a curator remark table, which
stores the progress for a given curation policy. The tests for the
curator create an ad hoc table, but core Prometheus presently lacks
said table, which this commit adds.
Secondarily, the error handling for the LevelDB lifecycle functions
in the metric persistence have been wrapped into an UncertaintyGroup,
which mirrors some of the functions of sync.WaitGroup but adds error
capturing capability to the mix.
This commit introduces to Prometheus a batch database sample curator,
which corroborates the high watermarks for sample series against the
curation watermark table to see whether a curator of a given type
needs to be run.
The curator is an abstract executor, which runs various curation
strategies across the database. It remarks the progress for each
type of curation processor that runs for a given sample series.
A curation procesor is responsible for effectuating the underlying
batch changes that are request. In this commit, we introduce the
CompactionProcessor, which takes several bits of runtime metadata and
combine sparse sample entries in the database together to form larger
groups. For instance, for a given series it would be possible to
have the curator effectuate the following grouping:
- Samples Older than Two Weeks: Grouped into Bunches of 10000
- Samples Older than One Week: Grouped into Bunches of 1000
- Samples Older than One Day: Grouped into Bunches of 100
- Samples Older than One Hour: Grouped into Bunches of 10
The benefits hereof of such a compaction are 1. a smaller search
space in the database keyspace, 2. better employment of compression
for repetious values, and 3. reduced seek times.
