7efd34a6f8
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 |
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| documentation | ||
| examples | ||
| extraction | ||
| model | ||
| prometheus | ||
| test | ||
| vendor/goautoneg | ||
| .gitignore | ||
| .travis.yml | ||
| LICENSE | ||
| Makefile | ||
| README.md | ||
| TODO | ||
README.md
Overview
These Go packages are an extraction of pieces of instrumentation code I whipped-up for a personal project that a friend of mine and I are working on. We were in need for some rudimentary statistics to observe behaviors of the server's various components, so this was written.
The code here is not a verbatim copy thereof but rather a thoughtful re-implementation should other folks need to consume and analyze such telemetry.
N.B. --- I have spent a bit of time working through the model in my head and probably haven't elucidated my ideas as clearly as I need to. If you examine examples/{simple,uniform_random}/main.go and registry.go, you'll find several examples of what types of potential instrumentation use cases this package addresses. There are probably numerous Go language idiomatic changes that need to be made, but this task has been deferred for now.
Continuous Integration
Documentation
Please read the generated documentation for the project's documentation from source code.
Basic Overview
Metrics
A metric is a measurement mechanism.
Gauge
A Gauge is a metric that exposes merely an instantaneous value or some snapshot thereof.
Counter
A Counter is a metric that exposes merely a sum or tally of things.
Histogram
A Histogram is a metric that captures events or samples into Buckets. It exposes its values via percentile estimations.
Buckets
A Bucket is a generic container that collects samples and their values. It prescribes no behavior on its own aside from merely accepting a value, leaving it up to the concrete implementation to what to do with the injected values.
Accumulating Bucket
An Accumulating Bucket is a Bucket that appends the new sample to a queue such that the eldest values are evicted according to a given policy.
Eviction Policies
Once an Accumulating Bucket reaches capacity, its eviction policy is invoked. This reaps the oldest N objects subject to certain behavior.
####### Remove Oldest This merely removes the oldest N items without performing some aggregation replacement operation on them.
####### Aggregate Oldest This removes the oldest N items while performing some summary aggregation operation thereupon, which is then appended to the list in the former values' place.
Tallying Bucket
A Tallying Bucket differs from an Accumulating Bucket in that it never stores any of the values emitted into it but rather exposes a simplied summary representation thereof. For instance, if a values therein is requested, it may situationally emit a minimum, maximum, an average, or any other reduction mechanism requested.
Getting Started
- The source code is periodically indexed: Go Exposition Client.
- All of the core developers are accessible via the Prometheus Developers Mailinglist.
Testing
This package employs gocheck for testing. Please ensure that all tests pass by running the following from the project root:
$ go test ./...
The use of gocheck is summarily being phased out; however, old tests that use it still exist.