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 is an optimization of labelsToSignature to avoid excess allocations
when the label set is empty.
Change-Id: If2d59bbc3ae6d4457e2ded197b6f4e7c67e6a173
Bjoern Rabenstein