This is still from my own fork as there are still critical PRs that
bmizerany hasn't responded to yet.
Change-Id: Ib46e68ec9ecbae9423b1bfe1311569426dfc7ba0
- Change Fingerprints to be simple uint64s.
- Deal sensibly with missing metric names.
- Enable finer-grained time resolution.
Merge this concurrently with the merge of the new storage backend into
prometheus/prometheus.
Change-Id: Idd82f137aa0c4286df422c53ce3c62e0de285360
computeApproximateRequestSize is run in a goroutine, but the
handlerFunc that runs in parallel may modify the URL, which is also
needed by computeApproximateRequestSize. So get the URL length
beforehand.
Change-Id: Idb84735845afe7be4ef79b3d642d5764f6d26a7c
Also, remove quotes from the Content-type header. It's not illegal to
have quotes there, but they are not needed, and at other places, we
are not using them. So fewer characters and more consistency.
Change-Id: If7a78bde85154163e4426daec493d973213e83e9
Since we prepare the whole content in a buf before sending, we can as
well set the Content-Length explicitly.
Change-Id: Ifd91764c90af53be49f93f0b33032138130b6f96
This is actually the intended behavior, and (as a nice side effect)
makes things cheaper to calculate.
Also, introduce a separator character to avoid hash collisions
(like label values {"ab","c"} vs {"a", "bc"}).
Apply the same principles to signature.go.
Change-Id: I607db544f278ed89684fe5fa11abdbc3e03d3061
Also, fix seconds to microseconds fot the http instrumentation to
match the metric name.
Fix Desc.String().
Simplify http error display.
Change-Id: Ib7397f4eac1eeed92b291e1c9cc88c080aee99ca
This rewrite had may backs and forths. In my git repository, it
consists of 35 commits which I cannot group or merge into reasonable
review buckets. Gerrit breaks fundamental git semantics, so I have to
squash the 35 commits into one for the review.
I'll push this not with refs/for/master, but with refs/for/next so
that we can transition after submission in a controlled fashion.
For the review, I recommend to start with looking at godoc and in
particular the many examples. After that, continue with a line-by-line
detailed review. (The big picture is hopefully as expected after
wrapping up the discussion earlier.)
Change-Id: Ib38cc46493a5139ca29d84020650929d94cac850
Most important here is the simple & flat text format, but while I'm on
it, I have also added the text representations for protobufs (which is
purely meant for debugging purposes). I hope my basic idea about
handling those various protocols (and the text package) becomes
clearer now.
Change-Id: I7299853eadc82a426101e907f2b3d4e37f9e4c71
The LabelsToSignature function is now used outside of the prometheus
package, too. Leaving it in the prometheuos package is misleading
design and will lead to circulat import chains soon.
Change-Id: If1ca442d4023b33b138cf79fee68e82ff2a355be
- Full support for UNTYPED type.
- Receptive support for timestamp_ms (i.e. the processor can process
it, but the client library cannot yet create it - which is kind of
intended as timestamps are meant for other things like federation,
which will need separate support anyway).
Change-Id: I5913164a80089943d49ad58bf86e465a843ab82b
The idea here is to always go via the protobufs if dealing with the
text format. That won't always be the most efficient way, but it
avoids the multiplicity of conversion routines required for direct
conversion (e.g. text format -> internal representation in the
Prometheus server). The loss of efficiency is acceptable because the
text format should not be used in high performance (high throughput,
low latency) situations anyway.
In that way, the text format stays perfectly isolated from other parts
of the code. To receive text format, just plug the conversion in
before the code path that normally reads protobufs. Correspondingly,
for sending text format, simply replace the WriteDelimited call by a
text.Create call.
Nevertheless, the conversion code itself is optimized for efficiency
and minimized memory churn (which was one of the reason for handcoding
the parser and not using a lexer/parser code generation tool).
Change-Id: Iee45ffe8aa421a844225d13a1f859becd8a3b066
These are all simple changes we should have caught a long time ago:
1. The hashing mechanism for fingerprint label sets should have not
allocated new objects for the actual hashing---at least not
egregiously. This simplifies the hash writing by just byte-
dumping the string stream into the hasher.
2. The hashing mechanism within the scope of a metric does not care
about the value of the label keys themselves but only of the label
values. The keys can be dropped from the calculation.
3. The locking mechanism for the metrics should not block on hash
computation but rather solely on the actual mutation or critical
section reads.
4. For scalar metrics (i.e., ones with niladic label signatures), we
should rely on a preallocated map versus requesting a new one
ad hoc.
This is tested with Go 1.1, so the results may yield other values
for us elsewhere:
BEFORE
BenchmarkLabelValuesToSignatureScalar 500000000 3.97 ns/op 0 B/op 0 allocs/op
BenchmarkLabelValuesToSignatureSingle 5000000 714 ns/op 74 B/op 4 allocs/op
BenchmarkLabelValuesToSignatureDouble 1000000 1153 ns/op 107 B/op 5 allocs/op
BenchmarkLabelValuesToSignatureTriple 1000000 1588 ns/op 138 B/op 6 allocs/op
BenchmarkLabelToSignatureScalar 500000000 3.91 ns/op 0 B/op 0 allocs/op
BenchmarkLabelToSignatureSingle 2000000 874 ns/op 92 B/op 5 allocs/op
BenchmarkLabelToSignatureDouble 1000000 1528 ns/op 139 B/op 7 allocs/op
BenchmarkLabelToSignatureTriple 1000000 2172 ns/op 186 B/op 9 allocs/op
AFTER
BenchmarkLabelValuesToSignatureScalar 500000000 4.36 ns/op 0 B/op 0 allocs/op
BenchmarkLabelValuesToSignatureSingle 5000000 378 ns/op 89 B/op 4 allocs/op
BenchmarkLabelValuesToSignatureDouble 5000000 574 ns/op 142 B/op 5 allocs/op
BenchmarkLabelValuesToSignatureTriple 5000000 758 ns/op 186 B/op 6 allocs/op
BenchmarkLabelToSignatureScalar 500000000 4.06 ns/op 0 B/op 0 allocs/op
BenchmarkLabelToSignatureSingle 5000000 472 ns/op 106 B/op 5 allocs/op
BenchmarkLabelToSignatureDouble 2000000 746 ns/op 174 B/op 7 allocs/op
BenchmarkLabelToSignatureTriple 1000000 1061 ns/op 235 B/op 9 allocs/op
In effect, a single metric mutation operation's lookup overhead will
move from Before::iBenchmarkLabelToSignature to
After::BenchmarkLabelValuesToSignature. This MINIMALLY reduces
1/2 the overhead. I would be hesitant in reading the memory
allocation statistics, for this was run with the GC still on and
thusly inaccurate per Go benchmarking documentation.
Before::BenchmarkLabelValuesToSignature never existed, so it is not
of any intrinsic value in itself. That said, the cases that still
rely on LabelToSignature experience consistently a 1/2 drop in time.
Change-Id: Ifc9e69f718af65a59f5be8117473518233258159
This hook is needed for the upcoming push gateway.
Also remove go vet warnings and add test for Handler().
Change-Id: If6c56676c7a0f10c16b4effae7285903f8267616
This also adds a check that forbids any user-supplied metrics to start
with the reserved label name prefix "__".
Change-Id: I2fe94c740b685ad05c4c670613cf2af7b9e1c1c0
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.
Change-Id: I253a467388774280c10400fda122369ff77c1730
Tobias Schmidt