There are too many parameters to constructing a LevelDB storage
instance for a construction method, so I've opted to take an
idiomatic approach of embedding them in a struct for easier
mediation and versioning.
When samples get flushed to disk, they lose sub-second precision anyways. By
already dropping sub-second precision, data fetched from memory vs. disk will
behave the same. Later, we should consider also storing a more compact
representation than time.Time in memory if we're not going to use its full
precision.
Current series always get watermarks written out upon append now. This
drops support for old series without any watermarks by always reporting
them as too old (stale) during queries.
This also short-circuits optimize() for now, since it is complex to implement
for the new operator, and ops generated by the query layer already fulfill the
needed invariants. We should still investigate later whether to completely
delete operator optimization code or extend it to support
getValueRangeAtIntervalOp operators.
An design question was open for me in the beginning was whether to
serialize other types to disk, but Protocol Buffers quickly won out,
which allows us to drop support for other types. This is a good
start to cleaning up a lot of cruft in the storage stack and
can let us eventually decouple the various moving parts into
separate subsystems for easier reasoning.
This commit is not strictly required, but it is a start to making
the rest a lot more enjoyable to interact with.
This adds timers around several query-relevant code blocks. For now, the
query timer stats are only logged for queries initiated through the UI.
In other cases (rule evaluations), the stats are simply thrown away.
My hope is that this helps us understand where queries spend time,
especially in cases where they sometimes hang for unusual amounts of
time.
This commit conditionalizes the creation of the diskFrontier and
seriesFrontier along with the iterator such that they are provisioned
once something is actually required from disk.
This is mainly a small performance improvement, since we skip past the last
extracted time immediately if it was also the last sample in the chunk, instead
of trying to extract non-existent values before the chunk end again and again
and only gradually approaching the end of the chunk.
