This fixes a bug where an interval op might advance too far past the end
of the currently extracted chunk, effectively skipping over relevant
(to-be-extracted) values in the subsequent chunk. The result: missing
samples at chunk boundaries in the resulting view.
Change-Id: Iebf5d086293a277d330039c69f78e1eaf084b3c8
- Most of this is the actual regression test in tiered_test.go.
- Working on that regression tests uncovered problems in
tiered_test.go that are fixed in this commit.
- The 'op.consumed = false' line added to freelist.go was actually not
fixing a bug. Instead, there was no bug at all. So this commit
removes that line again, but adds a regression test to make sure
that the assumed bug is indeed not there (cf. freelist_test.go).
- Removed more code duplication in operation.go (following the same
approach as before, i.e. embedding op type A into op type B if
everything in A is the same as in B with the exception of String()
and ExtractSample()). (This change make struct literals for ops more
clunky, but that only affects tests. No code change whatsoever was
necessary in the actual code after this refactoring.)
- Fix another op leak in tiered.go.
Change-Id: Ia165c52e33290ad4f6aba9c83d92318d4f583517
Currently, rendering a view is capable of handling multiple ops for
the same fingerprint efficiently. However, this capability requires a
lot of complexity in the code, which we are not using at all because
the way we assemble a viewRequest will never have more than one
operation per fingerprint.
This commit weeds out the said capability, along with all the code
needed for it. It is still possible to have more than one operation
for the same fingerprint, it will just be handled in a less efficient
way (as proven by the unit tests).
As a result, scanjob.go could be removed entirely.
This commit also contains a few related refactorings and removals of
dead code in operation.go, view,go, and freelist.go. Also, the
docstrings received some love.
Change-Id: I032b976e0880151c3f3fdb3234fb65e484f0e2e5
- 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
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.
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.
- 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
