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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 |
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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.