ARK-OS/arkos/arkrt/swift-metrics/CoreMetrics/Docs.docc/index.md

CoreMetrics

A Metrics API package for Swift.

Overview

Almost all production server software needs to emit metrics information for observability. Because it's unlikely that all parties can agree on one specific metrics backend implementation, this API is designed to establish a standard that can be implemented by various metrics libraries which then post the metrics data to backends like Prometheus, Graphite, publish over statsd, write to disk, and so on.

This is a community-driven open-source project that actively seeks contributions, be it code, documentation, or ideas. Apart from contributing to SwiftMetrics itself, the project needs metrics-compatible libraries which send the metrics over to backend systems such as the ones mentioned above. What SwiftMetrics provides today is covered in the API docs, and evolves with community input.

Getting started

If you have a server-side Swift application, or maybe a cross-platform (for example, Linux and macOS) application or library, and you would like to emit metrics, targeting this metrics API package is a great idea. Below you'll find all you need to know to get started.

Adding the dependency

To add a dependency on the metrics API package, declare it in your Package.swift:

// swift-metrics 1.x and 2.x are almost API compatible, so most clients should use
.package(url: "https://github.com/apple/swift-metrics.git", "1.0.0" ..< "3.0.0"),

and to your application/library target, add "Metrics" to your dependencies:

.target(
    name: "BestExampleApp",
    dependencies: [
        // ...
        .product(name: "Metrics", package: "swift-metrics"),
    ]
),

Emitting metrics information

// 1) Import the metrics API package.
import Metrics

// 2) Create a concrete metric object, the label works similarly to a `DispatchQueue` label.
let counter = Counter(label: "com.example.BestExampleApp.numberOfRequests")

// 3) Use the metric.
counter.increment()

Correct metrics usage

Create metric objects once with pre-defined labels and dimensions known at initialization time, and reuse them for the lifetime of the component. Creating new metric objects on every request or operation is an antipattern:

• It can lead to unbounded memory allocation if the labels or dimensions are unbounded (for example, per-request IDs), causing unbounded cardinality in the metrics backend.

• It is slow and can become a bottleneck for fast parallel execution, since metric creation typically involves factory synchronization and backend registration.

  // ❌ Creating metrics on demand — unbounded cardinality when dimensions vary per-request
  func handleRequest(requestID: String) {
  let counter = Counter(label: "requests", dimensions: [("request_id", requestID)])
  counter.increment()
  }
  
  // ✅ Create metrics once during setup with fixed dimensions and reuse them
  struct RequestHandler {
  let requestCounter = Counter(label: "requests")
  
  func handleRequest(requestID: String) {
      requestCounter.increment()
  }
  }
  

When you use a scoped factory override — such as withMetricsFactory(_:) for testing — the factory is only active for the duration of the closure. Any metrics created outside that scope do not see the overridden factory and fall back to the global one. If no global factory has been bootstrapped, such metrics fail to initialize, providing a safeguard against creating metrics outside of the designated setup scope.

struct UserService {
    let counter: Counter

    init() {
        // ✅ Created during init — picks up the task-local factory
        self.counter = Counter(label: "users.created")
    }

    func createUser(name: String) async throws -> User {
        // ❌ Created on demand — task-local factory is no longer in scope,
        //    falls back to global; fails if global is not bootstrapped
        let onDemandCounter = Counter(label: "users.created.on_demand")
        let user = User()
        self.counter.increment()
        return user
    }
}

@Test
func testUserCreation() async throws {
    let testMetrics = TestMetrics()

    // The task-local factory is only active inside this block
    let service = withMetricsFactory(testMetrics) {
        UserService()  // counter is created here — uses testMetrics
    }

    // service.createUser() runs outside the withMetricsFactory scope,
    // so onDemandCounter inside it will NOT use testMetrics
    _ = try await service.createUser(name: "Alice")

    #expect(try testMetrics.expectCounter("users.created").values == [1])
}

Selecting a metrics backend implementation (applications only)

Note: If you are building a library, you don't need to concern yourself with this section. It is the end users of your library (the applications) who decide which metrics backend to use. Libraries should never change the metrics implementation, as that choice is owned by the application.

SwiftMetrics only provides the metrics system API. As an application owner, you choose a metrics backend (such as the ones mentioned above) to make the metrics information useful.

Select a backend by adding a dependency on the desired backend client implementation and invoking the MetricsSystem.bootstrap function at the beginning of your program:

MetricsSystem.bootstrap(SelectedMetricsImplementation())

This instructs the MetricsSystem to install SelectedMetricsImplementation (the actual name will differ) as the metrics backend to use.

Tip: Refer to the project's README for an up-to-date list of backend implementations.

API architecture

For the Swift on Server ecosystem, it's crucial to have a metrics API that can be adopted by anybody so a multitude of libraries from different parties can provide metrics information. More concretely, all the metrics events from all libraries should end up in the same place: one of the backends mentioned above or wherever the application owner chooses.

There are so many opinions over how exactly a metrics system should behave, how metrics should be aggregated and calculated, and where/how to persist them that it's not feasible to wait for one metrics package to support everything while still being simple enough to use and remain performant. For this reason, the problem is split into two parts:

1. a metrics API
2. a metrics backend implementation

This package only provides the metrics API, and therefore, SwiftMetrics is a "metrics API package." SwiftMetrics can be configured (using MetricsSystem.bootstrap) to use any compatible metrics backend implementation. This mechanism allows libraries to adopt the API and support the application choosing a compatible backend implementation without requiring any changes to the libraries.

This API was designed with the contributors to the Swift on Server community and approved by the SSWG (Swift Server Work Group) to the "sandbox level" of the SSWG's incubation process.

pitch | discussion | feedback

Transforming labels and dimensions

When integrating metrics from multiple libraries or subsystems, you may want to add common dimensions (such as service name or environment) or rename labels consistently. MappingMetricsFactory wraps an existing MetricsFactory and applies a transformation to the label and dimensions of every metric before forwarding it to the upstream factory:

// Add a "service" dimension to all metrics created through this factory
let factory = myMetricsImplementation.withLabelAndDimensionsMapping { label, dimensions in
    (label, dimensions + [("service", "checkout")])
}
let counter = Counter(label: "request_count", dimensions: [("method", "GET")], factory: factory)

Transforms can also be chained:

let factory = myMetricsImplementation
    .withLabelAndDimensionsMapping { label, dimensions in
        ("myapp.\(label)", dimensions)
    }
    .withLabelAndDimensionsMapping { label, dimensions in
        (label, dimensions + [("env", "production")])
    }

Implementing a metrics backend

Note: Unless you need to implement a custom metrics backend, everything in this section is likely not relevant, so feel free to skip it.

Each constructor for Counter, Gauge, Meter, Recorder, and Timer provides a metric object. This uncertainty obscures the selected metrics backend calling these constructors by design. Each application selects and configures its desired backend. Configure the metrics backend like this:

let metricsImplementation = MyFavoriteMetricsImplementation()
MetricsSystem.bootstrap(metricsImplementation)

This instructs the MetricsSystem to install MyFavoriteMetricsImplementation as the MetricsFactory to use. Do this only once at the beginning of your program.

An implementation of a metric backend needs to conform to MetricsFactory:

public protocol MetricsFactory {
    func makeCounter(label: String, dimensions: [(String, String)]) -> CounterHandler
    func makeMeter(label: String, dimensions: [(String, String)]) -> MeterHandler
    func makeRecorder(label: String, dimensions: [(String, String)], aggregate: Bool) -> RecorderHandler
    func makeTimer(label: String, dimensions: [(String, String)]) -> TimerHandler

    func destroyCounter(_ handler: CounterHandler)
    func destroyMeter(_ handler: MeterHandler)
    func destroyRecorder(_ handler: RecorderHandler)
    func destroyTimer(_ handler: TimerHandler)
}

The MetricsFactory is responsible for instantiating the concrete metrics classes that capture the metrics and perform aggregation and calculation of various quantiles as needed.

CounterHandler

public protocol CounterHandler: AnyObject {
    func increment(by: Int64)
    func reset()
}

MeterHandler

public protocol MeterHandler: AnyObject {
    func set(_ value: Int64)
    func set(_ value: Double)
    func increment(by: Double)
    func decrement(by: Double)
}

RecorderHandler

public protocol RecorderHandler: AnyObject {
    func record(_ value: Int64)
    func record(_ value: Double)
}

TimerHandler

public protocol TimerHandler: AnyObject {
    func recordNanoseconds(_ duration: Int64)
}

Dealing with overflows

Implementations of metric objects that deal with integers, like Counter and Timer, should be careful with overflow. The expected behavior is to cap at .max, and never crash the program due to overflow. For example:

class ExampleCounter: CounterHandler {
    var value: Int64 = 0
    func increment(by amount: Int64) {
        let result = self.value.addingReportingOverflow(amount)
        if result.overflow {
            self.value = Int64.max
        } else {
            self.value = result.partialValue
        }
    }
}

Full example

Here is a full, but contrived, example of an in-memory implementation:

class SimpleMetricsLibrary: MetricsFactory {
    init() {}

    func makeCounter(label: String, dimensions: [(String, String)]) -> CounterHandler {
        return ExampleCounter(label, dimensions)
    }

    func makeMeter(label: String, dimensions: [(String, String)]) -> MeterHandler {
        return ExampleMeter(label, dimensions)
    }

    func makeRecorder(label: String, dimensions: [(String, String)], aggregate: Bool) -> RecorderHandler {
        return ExampleRecorder(label, dimensions, aggregate)
    }

    func makeTimer(label: String, dimensions: [(String, String)]) -> TimerHandler {
        return ExampleTimer(label, dimensions)
    }

    // implementation is stateless, so nothing to do on destroy calls
    func destroyCounter(_ handler: CounterHandler) {}
    func destroyMeter(_ handler: TimerHandler) {}
    func destroyRecorder(_ handler: RecorderHandler) {}
    func destroyTimer(_ handler: TimerHandler) {}

    private class ExampleCounter: CounterHandler {
        init(_: String, _: [(String, String)]) {}

        let lock = NSLock()
        var value: Int64 = 0
        func increment(by amount: Int64) {
            self.lock.withLock {
                self.value += amount
            }
        }

        func reset() {
            self.lock.withLock {
                self.value = 0
            }
        }
    }

    private class ExampleMeter: MeterHandler {
        init(_: String, _: [(String, String)]) {}

        let lock = NSLock()
        var _value: Double = 0

        func set(_ value: Int64) {
            self.set(Double(value))
        }

        func set(_ value: Double) {
            self.lock.withLock { _value = value }
        }

        func increment(by value: Double) {
            self.lock.withLock { self._value += value }
        }

        func decrement(by value: Double) {
            self.lock.withLock { self._value -= value }
        }
    }

    private class ExampleRecorder: RecorderHandler {
        init(_: String, _: [(String, String)], _: Bool) {}

        private let lock = NSLock()
        var values = [(Int64, Double)]()
        func record(_ value: Int64) {
            self.record(Double(value))
        }

        func record(_ value: Double) {
            // TODO: sliding window
            lock.withLock {
                values.append((Date().nanoSince1970, value))
                self._count += 1
                self._sum += value
                self._min = Swift.min(self._min, value)
                self._max = Swift.max(self._max, value)
            }
        }

        var _sum: Double = 0
        var sum: Double {
            return self.lock.withLock { _sum }
        }

        private var _count: Int = 0
        var count: Int {
            return self.lock.withLock { _count }
        }

        private var _min: Double = 0
        var min: Double {
            return self.lock.withLock { _min }
        }

        private var _max: Double = 0
        var max: Double {
            return self.lock.withLock { _max }
        }
    }

    private class ExampleTimer: TimerHandler {
        init(_: String, _: [(String, String)]) {}

        let lock = NSLock()
        var _value: Int64 = 0

        func recordNanoseconds(_ duration: Int64) {
            self.lock.withLock { _value = duration }
        }
    }
}

Related libraries

swift-system-metrics uses the Metrics API to emit system resource metrics such as CPU, memory, and file descriptors, providing insight into your application's resource consumption.

Topics

Metric types

Counter is a cumulative metric that represents a single monotonically increasing counter whose value can only increase or be reset to zero on restart. Use a counter to represent the number of requests served, tasks completed, or errors.

counter.increment(by: 100)

FloatingPointCounter is a variation of a Counter that records a floating point value instead of an integer.

floatingPointCounter.increment(by: 10.5)

Gauge is a metric that represents a single numerical value that can arbitrarily go up and down. Gauges are typically used for measured values like temperatures or current memory usage, but also "counts" that can go up and down, like the number of active threads. Gauges are modeled as a Recorder with a sample size of 1 that does not perform any aggregation.

gauge.record(100)

Meter is similar to Gauge — a metric that represents a single numerical value that can arbitrarily go up and down. Meters are typically used for measured values like temperatures or current memory usage, but also "counts" that can go up and down, like the number of active threads. Unlike Gauge, Meter also supports atomic increments and decrements.

meter.record(100)

Recorder collects observations within a time window (usually things like response sizes) and can provide aggregated information about the data sample, for example count, sum, min, max and various quantiles.

recorder.record(100)

Timer collects observations within a time window (usually things like request duration) and provides aggregated information about the data sample, for example min, max and various quantiles. It is similar to a Recorder but specialized for values that represent durations.

timer.recordMilliseconds(100)

• Counter
• FloatingPointCounter
• Meter
• Gauge
• Recorder
• Timer

Metrics Handlers

• CounterHandler
• FloatingPointCounterHandler
• MeterHandler
• RecorderHandler
• TimerHandler
• MultiplexMetricsHandler
• NOOPMetricsHandler

Bootstrapping

• MetricsFactory
• MetricsSystem
• MappingMetricsFactory

Supporting Types

• TimeUnit

Contribute to the project

• doc:Proposals