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slashmo/opentelemetry-swift 0.1.1
OpenTelemetry Tracer built for Swift Distributed Tracing
⭐️ 20
🕓 9 weeks ago
.package(url: "https://github.com/slashmo/opentelemetry-swift.git", from: "0.1.1")

OpenTelemetry for Swift

Swift 5.3 Swift 5.4 Swift 5.5 CI

Made for Swift Distributed Tracing

An OpenTelemetry client implementation for Swift.

"OpenTelemetry Swift" builds on top of Swift Distributed Tracing by implementing its instrumentation & tracing API. This means that any library instrumented using Swift Distributed Tracing will automatically work with "OpenTelemetry Swift".

Getting Started

In this guide we'll create a service called "onboarding". It won't do anything other than starting a couple of spans and exporting them, but it highlights the key aspects of "OpenTelemetry Swift" and how to set it up. To wet your appetite, here are screenshots from both Jaeger & Zipkin displaying a trace created by "onboarding":

Our trace exported to Jaeger Our trace exported to Zipkin

You can find the source code of the "onboarding" example here.

Installation

To add "OpenTelemetry Swift" to our project, we first need to include it as a package dependency:

.package(url: "https://github.com/slashmo/opentelemetry-swift.git", from: "0.1.0"),

Then we add OpenTelemetry to our executable target:

.product(name: "OpenTelemetry", package: "opentelemetry-swift"),

Bootstrapping

Now that we installed "OpenTelemetry Swift", it's time to bootstrap the instrumentation system to use OpenTelemetry. Before we can retrieve a tracer we need to configure and start the main object OTel:

import NIO
import OpenTelemetry
import Tracing

let group = MultiThreadedEventLoopGroup(numberOfThreads: 1)
let otel = OTel(serviceName: "onboarding", eventLoopGroup: group)

try otel.start().wait()
InstrumentationSystem.bootstrap(otel.tracer())

We should also not forget to shutdown OTel and the EventLoopGroup:

try otel.shutdown().wait()
try group.syncShutdownGracefully()

⚠️ With this setup, ended spans will be ignored and not exported to a tracing backend. Read on to learn more about how to configure processing & exporting.

Configuring processing and exporting

To start processing and exporting spans, we must pass a processor to the OTel initializer. "OpenTelemetry Swift" comes with a number of built in processors and you can even build your own. Check out the "Span Processors" section to learn more.

For now, we're going to use the SimpleSpanProcessor. As the name suggests, this processor doesn't do much except for forwarding ended spans to an exporter one by one. This exporter must be injected when initializing the SimpleSpanProcessor.

Starting the collector

We want to export our spans to both Jaeger and Zipkin. The OpenTelemetry project provides the "OpenTelemetry Collector" which acts as a middleman between clients such as "OpenTelemetry Swift" and tracing backends such as Jaeger and Zipkin. We won't go into much detail on how to configure the collector in this guide, but instead focus on our "onboarding" service.

We use Docker to run the OTel collector, Jaeger, and Zipkin locally. Both docker-compose.yaml and collector-config.yaml are located in the "docker" folder of the "onboarding" example.

# In Examples/Onboarding
docker-compose -f docker/docker-compose.yaml up --build

Using OtlpGRPCSpanExporter

After a couple of seconds everything should be up-and-running. Let's go ahead and configure OTel to export to the collector. "OpenTelemetry Swift" contains a second library called "OtlpGRPCSpanExporting", providing the necessary span exporter. We need to also include it in our target in Package.swift:

.product(name: "OtlpGRPCSpanExporting", package: "opentelemetry-swift"),

On to the fun part - Configuring the OtlpGRPCSpanExporter:

let exporter = OtlpGRPCSpanExporter(
    config: OtlpGRPCSpanExporter.Config(
        eventLoopGroup: group
    )
)

As mentioned above we need to inject this exporter into a processor:

let processor = OTel.SimpleSpanProcessor(exportingTo: exporter)

The only thing left to do is to tell OTel to use this processor:

- let otel = OTel(serviceName: "onboarding", eventLoopGroup: group)
+ let otel = OTel(serviceName: "onboarding", eventLoopGroup: group, processor: processor)

Starting spans

Our demo application creates two spans: hello and world. To make things even more realistic we'll add an event to the hello span:

let rootSpan = InstrumentationSystem.tracer.startSpan("hello", baggage: .topLevel)

sleep(1)
rootSpan.addEvent(SpanEvent(
    name: "Discovered the meaning of life",
    attributes: ["meaning_of_life": 42]
))

let childSpan = InstrumentationSystem.tracer.startSpan("world", baggage: rootSpan.baggage)

sleep(1)
childSpan.end()

sleep(1)
rootSpan.end()

Note that we retrieve the the tracer through InstrumentationSystem.tracer instead of directly using otel.tracer(). This allows us to easily switch out the bootstrapped tracer in the future. It's also how frameworks/libraries implement tracing support without even knowing about OpenTelemetry.

Finally, because the demo app start shutting down right after the last span was ended, we should add another delay to give the exporter a chance to finish its work:

+ sleep(1)
try otel.shutdown().wait()
try group.syncShutdownGracefully()

Now, when running the app, the trace including both spans will automatically appear in both Jaeger & Zipkin 🎉 You can find them at http://localhost:16686 & http://localhost:9411 respectively.

Diving deeper 🤿


Customization

"OpenTelemetry Swift" is designed to be easily customizable. This sections goes over the different moving parts that may be switched out with other non-default implementations.

Generating trace & span ids

When starting spans, the OTel Tracer will generate IDs uniquely identifying each trace/span. Creating a root span generates both trace and span ID. Creating a child span re-uses the parent's trace ID and only generates a new span ID.

A "W3C TraceContext" compatible RandomIDGenerator is used for this by default. As the name suggests, it generates completely random IDs.

Some tracing systems require IDs in a slightly different format. XRayIDGenerator from the X-Ray compatibility library e.g. will include the current timestamp at the start of each generated trace ID.

To create your own ID generator you need to implement the OTelIDGenerator protocol.

Using a custom ID generator

Simply pass a different ID generator when initializing OTel like this:

let otel = OTel(
    serviceName: "service",
    eventLoopGroup: group,
    idGenerator: MyAwesomeIDGenerator()
)

Resources 🔗

Sampling

If your application creates a large amount of spans you might want to look into sampling out certain spans. By default, "OpenTelemetry Swift" ships with a "parent-based" sampler, configured to always sample root spans using a "constant sampler". Parent-based means that this sampler takes into account whether the parent span was sampled.

To create your own sampler you need to implement the OTelSampler protocol.

Using a custom sampler

The OTel initializer allows you to inject a sampler:

let otel = OTel(
    serviceName: "service",
    eventLoopGroup: group,
    sampler: ConstantSampler(isOn: false)
)

The above configuration would sample out each span, i.e. no span would ever be exported.

Resources 🔗

Processing ended spans

Span processors get passed read-only ended spans. The most common use-case of this is to forward the ended span to an exporter. The built-in SimpleSpanProcessor forwards them immediately one-by-one.

To create your own span processor you need to implement the OTelSpanProcessor protocol.

Using a custom span processor

To configure which span processor should be used, pass it along to the OTel initializer:

let otel = OTel(
    serviceName: "service",
    eventLoopGroup: group,
    processor: MyAwesomeSpanProcessor()
)

Resources 🔗

Exporting processed spans

To actually send span data to a tracing backend like Jaeger, spans need to be "exported". OtlpGRPCSpanExporting, which is a library included in this package implements exporting using the OpenTelemetry protocol (OTLP) by sending span data via gRPC to the OpenTelemetry collector. The collector can then be configured to forward received spans to tracing backends.

To create your own span exporter you need to implement the OTelSpanExporter protocol.

Using a custom span exporter

Instead of passing the exporter directly to OTel, you need to wrap it inside a span processor:

let otel = OTel(
    serviceName: "service",
    eventLoopGroup: group,
    processor: SimpleSpanProcessor(
        exportingTo: MyAwesomeSpanExporter()
    )
)

Resources 🔗

Propagating span context

OpenTelemetry uses the W3C TraceContext format to propagate span context across HTTP requests by default. Some tracing backends may not fully support this standard and need to use a custom propagator. X-Ray e.g. propagates using the X-Amzn-Trace-Id header. Support for this header is implemented in the X-Ray support library.

To create your own propagator you need to implement the OTelPropagator protocol.

Using a custom propagator

Pass your propagator of choice to the OTel initializer like this:

let otel = OTel(
    serviceName: "service",
    eventLoopGroup: group,
    propagator: MyAwesomePropagator()
)

Resources 🔗

Detecting resource information

When investigating traces it is often helpful to not only see insights about your application but also about the system (resource) it's running on. One option of including such information would be to set a bunch of span attributes on every span. But this would be cumbersome and inefficient. Therefore, OpenTelemetry has the concept of resource detection. Resource detectors run once on start-up, detect some attributes collected in a Resource and hand them off to OTel. From then on, the resulting Resource will be passed along to span exporters for them to include these attributes.

"OpenTelemetry Swift" comes with two built-in resource detectors which are enabled by default:

ProcessResourceDetector

This detector collects information such as the process ID and executable name.

EnvironmentResourceDetector

This detector allows you to specify resource attributes through an environment variable. This comes in handy for attributes that you don't know yet at built-time.

To create your own resource detector you need to implement the OTelResourceDetector protocol.

Using a custom resource detector

There are three possible settings for resource detection represented by the OTelResourceDetection enum:

// 1. Automatic, the default
OTel.ResourceDetection.automatic(
    additionalDetectors: [MyAwesomeDetector()]
)

// 2. Manual
OTel.ResourceDetection.manual(
    OTel.Resource(attributes: ["key": "value"])
)

// 3. None, i.e. disabled
OTel.ResourceDetection.none

Resources 🔗

Development

Formatting

To ensure a consistent code style we use SwiftFormat. To automatically run it before you push to GitHub, you may define a pre-push Git hook executing the soundness script:

echo './scripts/soundness.sh' > .git/hooks/pre-push
chmod +x .git/hooks/pre-push

GitHub

link
Stars: 20
Last commit: 7 weeks ago
jonrohan Something's broken? Yell at me @ptrpavlik. Praise and feedback (and money) is also welcome.

Release Notes

OpenTelemetry Swift 0.1.1
9 weeks ago

This release explicitly depends on the last version of swift-distributed-tracing-baggage that comes with LoggingContext. In the next minor release of "OpenTelemetry Swift", we'll be upgrading to the new task-local-based Tracing APIs.

Swiftpack is being maintained by Petr Pavlik | @ptrpavlik | @swiftpackco | API | Analytics