opentelemetry-go/sdk/metric/doc.go

// Copyright 2019, OpenTelemetry Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/*
Package metric implements the OpenTelemetry metric.Meter API.  The SDK
supports configurable metrics export behavior through a collection of
export interfaces that support various export strategies, described below.

The metric.Meter API consists of methods for constructing each of the basic
kinds of metric instrument.  There are six types of instrument available to
the end user, comprised of three basic kinds of metric instrument (Counter,
Measure, Observer) crossed with two kinds of number (int64, float64).

The API assists the SDK by consolidating the variety of metric instruments
into a narrower interface, allowing the SDK to avoid repetition of
boilerplate.  The API and SDK are separated such that an event reaching
the SDK has a uniform structure: an instrument, a label set, and a
numerical value.

To this end, the API uses a core.Number type to represent either an int64
or a float64, depending on the instrument's definition.  A single
implementation interface is used for counter and measure instruments,
metric.InstrumentImpl, and a single implementation interface is used for
their handles, metric.HandleImpl. For observers, the API defines
interfaces, for which the SDK provides an implementation.

There are four entry points for events in the Metrics API - three for
synchronous instruments (counters and measures) and one for asynchronous
instruments (observers). The entry points for synchronous instruments are:
via instrument handles, via direct instrument calls, and via BatchRecord.
The SDK is designed with handles as the primary entry point, the other two
entry points are implemented in terms of short-lived handles.  For example,
the implementation of a direct call allocates a handle, operates on the
handle, and releases the handle. Similarly, the implementation of
RecordBatch uses a short-lived handle for each measurement in the batch.
The entry point for asynchronous instruments is via observer callbacks.
Observer callbacks behave like a set of instrument handles - one for each
observation for a distinct label set.  The observer handles are alive as
long as they are used.  If the callback stops reporting values for a
certain label set, the associated handle is dropped.

Internal Structure

The SDK is designed with minimal use of locking, to avoid adding
contention for user-level code.  For each handle, whether it is held by
user-level code or a short-lived device, there exists an internal record
managed by the SDK.  Each internal record corresponds to a specific
instrument and label set combination.

Each observer also has its own kind of record stored in the SDK. This
record contains a set of recorders for every specific label set used in the
callback.

A sync.Map maintains the mapping of current instruments and label sets to
internal records.  To create a new handle, the SDK consults the Map to
locate an existing record, otherwise it constructs a new record.  The SDK
maintains a count of the number of references to each record, ensuring
that records are not reclaimed from the Map while they are still active
from the user's perspective.

Metric collection is performed via a single-threaded call to Collect that
sweeps through all records in the SDK, checkpointing their state.  When a
record is discovered that has no references and has not been updated since
the prior collection pass, it is removed from the Map.

The SDK maintains a current epoch number, corresponding to the number of
completed collections.  Each recorder of an observer record contains the
last epoch during which it was updated.  This variable allows the collection
code path to detect stale recorders and remove them.

Each record of a handle and recorder of an observer has an associated
aggregator, which maintains the current state resulting from all metric
events since its last checkpoint.  Aggregators may be lock-free or they may
use locking, but they should expect to be called concurrently.  Aggregators
must be capable of merging with another aggregator of the same type.

Export Pipeline

While the SDK serves to maintain a current set of records and
coordinate collection, the behavior of a metrics export pipeline is
configured through the export types in
go.opentelemetry.io/otel/sdk/export/metric.  It is important to keep
in mind the context these interfaces are called from.  There are two
contexts, instrumentation context, where a user-level goroutine that
enters the SDK resulting in a new record, and collection context,
where a system-level thread performs a collection pass through the
SDK.

Descriptor is a struct that describes the metric instrument to the export
pipeline, containing the name, recommended aggregation keys, units,
description, metric kind (counter or measure), number kind (int64 or
float64), and whether the instrument has alternate semantics or not (i.e.,
monotonic=false counter, absolute=false measure).  A Descriptor accompanies
metric data as it passes through the export pipeline.

The AggregationSelector interface supports choosing the method of
aggregation to apply to a particular instrument.  Given the Descriptor,
this AggregatorFor method returns an implementation of Aggregator.  If this
interface returns nil, the metric will be disabled.  The aggregator should
be matched to the capabilities of the exporter.  Selecting the aggregator
for counter instruments is relatively straightforward, but for measure and
observer instruments there are numerous choices with different cost and
quality tradeoffs.

Aggregator is an interface which implements a concrete strategy for
aggregating metric updates.  Several Aggregator implementations are
provided by the SDK.  Aggregators may be lock-free or use locking,
depending on their structure and semantics.  Aggregators implement an
Update method, called in instrumentation context, to receive a single
metric event.  Aggregators implement a Checkpoint method, called in
collection context, to save a checkpoint of the current state.
Aggregators implement a Merge method, also called in collection
context, that combines state from two aggregators into one.  Each SDK
record has an associated aggregator.

Batcher is an interface which sits between the SDK and an exporter.
The Batcher embeds an AggregationSelector, used by the SDK to assign
new Aggregators.  The Batcher supports a Process() API for submitting
checkpointed aggregators to the batcher, and a CheckpointSet() API
for producing a complete checkpoint for the exporter.  Two default
Batcher implementations are provided, the "defaultkeys" Batcher groups
aggregate metrics by their recommended Descriptor.Keys(), the
"ungrouped" Batcher aggregates metrics at full dimensionality.

LabelEncoder is an optional optimization that allows an exporter to
provide the serialization logic for labels.  This allows avoiding
duplicate serialization of labels, once as a unique key in the SDK (or
Batcher) and once in the exporter.

CheckpointSet is an interface between the Batcher and the Exporter.
After completing a collection pass, the Batcher.CheckpointSet() method
returns a CheckpointSet, which the Exporter uses to iterate over all
the updated metrics.

Record is a struct containing the state of an individual exported
metric.  This is the result of one collection interface for one
instrument and one label set.

Labels is a struct containing an ordered set of labels, the
corresponding unique encoding, and the encoder that produced it.

Exporter is the final stage of an export pipeline.  It is called with
a CheckpointSet capable of enumerating all the updated metrics.

Controller is not an export interface per se, but it orchestrates the
export pipeline.  For example, a "push" controller will establish a
periodic timer to regularly collect and export metrics.  A "pull"
controller will await a pull request before initiating metric
collection.  Either way, the job of the controller is to call the SDK
Collect() method, then read the checkpoint, then invoke the exporter.
Controllers are expected to implement the public metric.MeterProvider
API, meaning they can be installed as the global Meter provider.

*/
package metric // import "go.opentelemetry.io/otel/sdk/metric"