- 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 fixes part 1) of https://github.com/prometheus/prometheus/issues/367 (the
storing of samples with the wrong fingerprint into a compacted chunk, thus
corrupting it).
Change-Id: I4c36d0d2e508e37a0aba90b8ca2ecc78ee03e3f1
This commit fixes a critique of the old storage API design, whereby
the input parameters were always as raw bytes and never Protocol
Buffer messages that encapsulated the data, meaning every place a
read or mutation was conducted needed to manually perform said
translations on its own. This is taxing.
Change-Id: I4786938d0d207cefb7782bd2bd96a517eead186f
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 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 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 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.
