<Input, State, E>(
metric: Metric.Metric<Input, State>,
f: (error: E) => Input
): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>
<State, E>(metric: Metric.Metric<NoInfer<E>, State>): <A, R>(
self: Effect<A, E, R>
) => Effect<A, E, R>
<A, E, R, Input, State>(
self: Effect<A, E, R>,
metric: Metric.Metric<Input, State>,
f: (error: E) => Input
): Effect<A, E, R>
<A, E, R, State>(
self: Effect<A, E, R>,
metric: Metric.Metric<NoInfer<E>, State>
): Effect<A, E, R>Updates the provided Metric every time the wrapped Effect fails with an
expected error.
Details
Also accepts an optional function which can be used to map the error value
of the Effect into a valid Input for the Metric.
Example (Counting expected failures)
import { Effect, Metric } from "effect"
const errorCounter = Metric.counter("errors").pipe(
Metric.withConstantInput(1)
)
const program = Effect.fail("Network timeout").pipe(
Effect.trackErrors(errorCounter)
)
Effect.runPromiseExit(program).then(() =>
Effect.runPromise(Metric.value(errorCounter)).then(console.log)
// Output: { count: 1, incremental: false }
)Example (Mapping errors before tracking)
import { Data, Effect, Metric } from "effect"
class ConnectionFailedError extends Data.TaggedError("ConnectionFailedError")<{}> {}
// Track error types using frequency metric
const errorTypeFrequency = Metric.frequency("error_types")
const program = Effect.fail(new ConnectionFailedError()).pipe(
Effect.trackErrors(errorTypeFrequency, (error: ConnectionFailedError) => error._tag)
)
Effect.runPromiseExit(program).then(() =>
Effect.runPromise(Metric.value(errorTypeFrequency)).then(console.log)
// Output: { occurrences: Map(1) { "ConnectionFailedError" => 1 } }
)export const const trackErrors: {
<Input, State, E>(
metric: Metric.Metric<Input, State>,
f: (error: E) => Input
): <A, R>(
self: Effect<A, E, R>
) => Effect<A, E, R>
<State, E>(
metric: Metric.Metric<NoInfer<E>, State>
): <A, R>(
self: Effect<A, E, R>
) => Effect<A, E, R>
<A, E, R, Input, State>(
self: Effect<A, E, R>,
metric: Metric.Metric<Input, State>,
f: (error: E) => Input
): Effect<A, E, R>
<A, E, R, State>(
self: Effect<A, E, R>,
metric: Metric.Metric<NoInfer<E>, State>
): Effect<A, E, R>
}
Updates the provided Metric every time the wrapped Effect fails with an
expected error.
Details
Also accepts an optional function which can be used to map the error value
of the Effect into a valid Input for the Metric.
Example (Counting expected failures)
import { Effect, Metric } from "effect"
const errorCounter = Metric.counter("errors").pipe(
Metric.withConstantInput(1)
)
const program = Effect.fail("Network timeout").pipe(
Effect.trackErrors(errorCounter)
)
Effect.runPromiseExit(program).then(() =>
Effect.runPromise(Metric.value(errorCounter)).then(console.log)
// Output: { count: 1, incremental: false }
)
Example (Mapping errors before tracking)
import { Data, Effect, Metric } from "effect"
class ConnectionFailedError extends Data.TaggedError("ConnectionFailedError")<{}> {}
// Track error types using frequency metric
const errorTypeFrequency = Metric.frequency("error_types")
const program = Effect.fail(new ConnectionFailedError()).pipe(
Effect.trackErrors(errorTypeFrequency, (error: ConnectionFailedError) => error._tag)
)
Effect.runPromiseExit(program).then(() =>
Effect.runPromise(Metric.value(errorTypeFrequency)).then(console.log)
// Output: { occurrences: Map(1) { "ConnectionFailedError" => 1 } }
)
trackErrors: {
<function (type parameter) Input in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>Input, function (type parameter) State in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>State, function (type parameter) E in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E>(
metric: Metric.Metric<Input, State>(parameter) metric: {
Input: Contravariant<Input>;
State: Covariant<State>;
id: string;
type: Metric.Type;
description: string | undefined;
attributes: Metric.AttributeSet | undefined;
valueUnsafe: (context: Context.Context<never>) => State;
updateUnsafe: (input: Input, context: Context.Context<never>) => void;
modifyUnsafe: (input: Input, context: Context.Context<never>) => void;
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
}
import MetricMetric.interface Metric<in Input, out State>A Metric<Input, State> represents a concurrent metric which accepts update
values of type Input and are aggregated to a value of type State.
Details
For example, a counter metric would have type Metric<number, number>,
representing the fact that the metric can be updated with numbers (the amount
to increment or decrement the counter by), and the state of the counter is a
number.
There are five primitive metric types supported by Effect:
- Counters
- Frequencies
- Gauges
- Histograms
- Summaries
Example (Using multiple metric types)
import { Data, Effect, Metric } from "effect"
class MetricExample extends Data.TaggedError("MetricExample")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter: Metric.Counter<number> = Metric.counter("requests", {
description: "Total requests processed"
})
const memoryGauge: Metric.Gauge<number> = Metric.gauge("memory_usage", {
description: "Current memory usage in MB"
})
const statusFrequency: Metric.Frequency = Metric.frequency("status_codes", {
description: "HTTP status code frequency"
})
// All metrics share the same interface for updates and reads
yield* Metric.update(requestCounter, 1)
yield* Metric.update(memoryGauge, 128)
yield* Metric.update(statusFrequency, "200")
// All metrics can be read with Metric.value
const counterState = yield* Metric.value(requestCounter)
const gaugeState = yield* Metric.value(memoryGauge)
const frequencyState = yield* Metric.value(statusFrequency)
// Metrics have common properties accessible through the interface:
// - id: unique identifier
// - type: metric type ("Counter", "Gauge", "Frequency", etc.)
// - description: optional human-readable description
// - attributes: optional key-value attributes for tagging
return {
counter: {
id: requestCounter.id,
type: requestCounter.type,
state: counterState
},
gauge: { id: memoryGauge.id, type: memoryGauge.type, state: gaugeState },
frequency: {
id: statusFrequency.id,
type: statusFrequency.type,
state: frequencyState
}
}
})
The Metric namespace provides a comprehensive system for collecting, aggregating, and observing
application metrics in Effect applications.
Example (Collecting application metrics)
import { Data, Effect, Metric } from "effect"
class MetricsError extends Data.TaggedError("MetricsError")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter = Metric.counter("http_requests_total")
const responseTimeHistogram = Metric.histogram("http_response_time", {
boundaries: Metric.linearBoundaries({ start: 0, width: 10, count: 10 })
})
const activeConnectionsGauge = Metric.gauge("active_connections")
const statusFrequency = Metric.frequency("http_status_codes")
// Update metrics
yield* Metric.update(requestCounter, 1)
yield* Metric.update(responseTimeHistogram, 45.2)
yield* Metric.update(activeConnectionsGauge, 12)
yield* Metric.update(statusFrequency, "200")
// Get metric values
const counterValue = yield* Metric.value(requestCounter)
const histogramValue = yield* Metric.value(responseTimeHistogram)
const gaugeValue = yield* Metric.value(activeConnectionsGauge)
const frequencyValue = yield* Metric.value(statusFrequency)
return {
counter: counterValue,
histogram: histogramValue,
gauge: gaugeValue,
frequency: frequencyValue
}
})
Metric<function (type parameter) Input in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>Input, function (type parameter) State in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>State>,
f: (error: E) => Inputf: (error: Eerror: function (type parameter) E in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E) => function (type parameter) Input in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>Input
): <function (type parameter) A in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>A, function (type parameter) R in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>R>(self: Effect<A, E, R>(parameter) self: {
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
toString: () => string;
toJSON: () => unknown;
}
interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>A, function (type parameter) E in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E, function (type parameter) R in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>R>) => interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>A, function (type parameter) E in <Input, State, E>(metric: Metric.Metric<Input, State>, f: (error: E) => Input): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E, function (type parameter) R in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>R>
<function (type parameter) State in <State, E>(metric: Metric.Metric<NoInfer<E>, State>): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>State, function (type parameter) E in <State, E>(metric: Metric.Metric<NoInfer<E>, State>): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E>(
metric: Metric.Metric<NoInfer<E>, State>(parameter) metric: {
Input: Contravariant<Input>;
State: Covariant<State>;
id: string;
type: Metric.Type;
description: string | undefined;
attributes: Metric.AttributeSet | undefined;
valueUnsafe: (context: Context.Context<never>) => State;
updateUnsafe: (input: Input, context: Context.Context<never>) => void;
modifyUnsafe: (input: Input, context: Context.Context<never>) => void;
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
}
import MetricMetric.interface Metric<in Input, out State>A Metric<Input, State> represents a concurrent metric which accepts update
values of type Input and are aggregated to a value of type State.
Details
For example, a counter metric would have type Metric<number, number>,
representing the fact that the metric can be updated with numbers (the amount
to increment or decrement the counter by), and the state of the counter is a
number.
There are five primitive metric types supported by Effect:
- Counters
- Frequencies
- Gauges
- Histograms
- Summaries
Example (Using multiple metric types)
import { Data, Effect, Metric } from "effect"
class MetricExample extends Data.TaggedError("MetricExample")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter: Metric.Counter<number> = Metric.counter("requests", {
description: "Total requests processed"
})
const memoryGauge: Metric.Gauge<number> = Metric.gauge("memory_usage", {
description: "Current memory usage in MB"
})
const statusFrequency: Metric.Frequency = Metric.frequency("status_codes", {
description: "HTTP status code frequency"
})
// All metrics share the same interface for updates and reads
yield* Metric.update(requestCounter, 1)
yield* Metric.update(memoryGauge, 128)
yield* Metric.update(statusFrequency, "200")
// All metrics can be read with Metric.value
const counterState = yield* Metric.value(requestCounter)
const gaugeState = yield* Metric.value(memoryGauge)
const frequencyState = yield* Metric.value(statusFrequency)
// Metrics have common properties accessible through the interface:
// - id: unique identifier
// - type: metric type ("Counter", "Gauge", "Frequency", etc.)
// - description: optional human-readable description
// - attributes: optional key-value attributes for tagging
return {
counter: {
id: requestCounter.id,
type: requestCounter.type,
state: counterState
},
gauge: { id: memoryGauge.id, type: memoryGauge.type, state: gaugeState },
frequency: {
id: statusFrequency.id,
type: statusFrequency.type,
state: frequencyState
}
}
})
The Metric namespace provides a comprehensive system for collecting, aggregating, and observing
application metrics in Effect applications.
Example (Collecting application metrics)
import { Data, Effect, Metric } from "effect"
class MetricsError extends Data.TaggedError("MetricsError")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter = Metric.counter("http_requests_total")
const responseTimeHistogram = Metric.histogram("http_response_time", {
boundaries: Metric.linearBoundaries({ start: 0, width: 10, count: 10 })
})
const activeConnectionsGauge = Metric.gauge("active_connections")
const statusFrequency = Metric.frequency("http_status_codes")
// Update metrics
yield* Metric.update(requestCounter, 1)
yield* Metric.update(responseTimeHistogram, 45.2)
yield* Metric.update(activeConnectionsGauge, 12)
yield* Metric.update(statusFrequency, "200")
// Get metric values
const counterValue = yield* Metric.value(requestCounter)
const histogramValue = yield* Metric.value(responseTimeHistogram)
const gaugeValue = yield* Metric.value(activeConnectionsGauge)
const frequencyValue = yield* Metric.value(statusFrequency)
return {
counter: counterValue,
histogram: histogramValue,
gauge: gaugeValue,
frequency: frequencyValue
}
})
Metric<type NoInfer<A> = [A][A extends any ? 0 : never]Prevents TypeScript from inferring a type parameter from a specific
position.
When to use
Use when a function parameter must match an inferred type without becoming
an inference source.
Details
The parameter using NoInfer must still match the inferred type.
Example (Controlling inference)
import type { Types } from "effect"
declare function withDefault<T>(value: T, fallback: Types.NoInfer<T>): T
// T is inferred as "a" | "b" from the first argument only
const result = withDefault<"a" | "b">("a", "b")
NoInfer<function (type parameter) E in <State, E>(metric: Metric.Metric<NoInfer<E>, State>): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E>, function (type parameter) State in <State, E>(metric: Metric.Metric<NoInfer<E>, State>): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>State>
): <function (type parameter) A in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>A, function (type parameter) R in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>R>(self: Effect<A, E, R>(parameter) self: {
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
toString: () => string;
toJSON: () => unknown;
}
interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>A, function (type parameter) E in <State, E>(metric: Metric.Metric<NoInfer<E>, State>): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E, function (type parameter) R in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>R>) => interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>A, function (type parameter) E in <State, E>(metric: Metric.Metric<NoInfer<E>, State>): <A, R>(self: Effect<A, E, R>) => Effect<A, E, R>E, function (type parameter) R in <A, R>(self: Effect<A, E, R>): Effect<A, E, R>R>
<function (type parameter) A in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>A, function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>E, function (type parameter) R in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>R, function (type parameter) Input in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>Input, function (type parameter) State in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>State>(
self: Effect<A, E, R>(parameter) self: {
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
toString: () => string;
toJSON: () => unknown;
}
interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>A, function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>E, function (type parameter) R in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>R>,
metric: Metric.Metric<Input, State>(parameter) metric: {
Input: Contravariant<Input>;
State: Covariant<State>;
id: string;
type: Metric.Type;
description: string | undefined;
attributes: Metric.AttributeSet | undefined;
valueUnsafe: (context: Context.Context<never>) => State;
updateUnsafe: (input: Input, context: Context.Context<never>) => void;
modifyUnsafe: (input: Input, context: Context.Context<never>) => void;
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
}
import MetricMetric.interface Metric<in Input, out State>A Metric<Input, State> represents a concurrent metric which accepts update
values of type Input and are aggregated to a value of type State.
Details
For example, a counter metric would have type Metric<number, number>,
representing the fact that the metric can be updated with numbers (the amount
to increment or decrement the counter by), and the state of the counter is a
number.
There are five primitive metric types supported by Effect:
- Counters
- Frequencies
- Gauges
- Histograms
- Summaries
Example (Using multiple metric types)
import { Data, Effect, Metric } from "effect"
class MetricExample extends Data.TaggedError("MetricExample")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter: Metric.Counter<number> = Metric.counter("requests", {
description: "Total requests processed"
})
const memoryGauge: Metric.Gauge<number> = Metric.gauge("memory_usage", {
description: "Current memory usage in MB"
})
const statusFrequency: Metric.Frequency = Metric.frequency("status_codes", {
description: "HTTP status code frequency"
})
// All metrics share the same interface for updates and reads
yield* Metric.update(requestCounter, 1)
yield* Metric.update(memoryGauge, 128)
yield* Metric.update(statusFrequency, "200")
// All metrics can be read with Metric.value
const counterState = yield* Metric.value(requestCounter)
const gaugeState = yield* Metric.value(memoryGauge)
const frequencyState = yield* Metric.value(statusFrequency)
// Metrics have common properties accessible through the interface:
// - id: unique identifier
// - type: metric type ("Counter", "Gauge", "Frequency", etc.)
// - description: optional human-readable description
// - attributes: optional key-value attributes for tagging
return {
counter: {
id: requestCounter.id,
type: requestCounter.type,
state: counterState
},
gauge: { id: memoryGauge.id, type: memoryGauge.type, state: gaugeState },
frequency: {
id: statusFrequency.id,
type: statusFrequency.type,
state: frequencyState
}
}
})
The Metric namespace provides a comprehensive system for collecting, aggregating, and observing
application metrics in Effect applications.
Example (Collecting application metrics)
import { Data, Effect, Metric } from "effect"
class MetricsError extends Data.TaggedError("MetricsError")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter = Metric.counter("http_requests_total")
const responseTimeHistogram = Metric.histogram("http_response_time", {
boundaries: Metric.linearBoundaries({ start: 0, width: 10, count: 10 })
})
const activeConnectionsGauge = Metric.gauge("active_connections")
const statusFrequency = Metric.frequency("http_status_codes")
// Update metrics
yield* Metric.update(requestCounter, 1)
yield* Metric.update(responseTimeHistogram, 45.2)
yield* Metric.update(activeConnectionsGauge, 12)
yield* Metric.update(statusFrequency, "200")
// Get metric values
const counterValue = yield* Metric.value(requestCounter)
const histogramValue = yield* Metric.value(responseTimeHistogram)
const gaugeValue = yield* Metric.value(activeConnectionsGauge)
const frequencyValue = yield* Metric.value(statusFrequency)
return {
counter: counterValue,
histogram: histogramValue,
gauge: gaugeValue,
frequency: frequencyValue
}
})
Metric<function (type parameter) Input in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>Input, function (type parameter) State in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>State>,
f: (error: E) => Inputf: (error: Eerror: function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>E) => function (type parameter) Input in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>Input
): interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>A, function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>E, function (type parameter) R in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: (error: E) => Input): Effect<A, E, R>R>
<function (type parameter) A in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>A, function (type parameter) E in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>E, function (type parameter) R in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>R, function (type parameter) State in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>State>(
self: Effect<A, E, R>(parameter) self: {
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
toString: () => string;
toJSON: () => unknown;
}
interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>A, function (type parameter) E in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>E, function (type parameter) R in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>R>,
metric: Metric.Metric<NoInfer<E>, State>(parameter) metric: {
Input: Contravariant<Input>;
State: Covariant<State>;
id: string;
type: Metric.Type;
description: string | undefined;
attributes: Metric.AttributeSet | undefined;
valueUnsafe: (context: Context.Context<never>) => State;
updateUnsafe: (input: Input, context: Context.Context<never>) => void;
modifyUnsafe: (input: Input, context: Context.Context<never>) => void;
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
}
import MetricMetric.interface Metric<in Input, out State>A Metric<Input, State> represents a concurrent metric which accepts update
values of type Input and are aggregated to a value of type State.
Details
For example, a counter metric would have type Metric<number, number>,
representing the fact that the metric can be updated with numbers (the amount
to increment or decrement the counter by), and the state of the counter is a
number.
There are five primitive metric types supported by Effect:
- Counters
- Frequencies
- Gauges
- Histograms
- Summaries
Example (Using multiple metric types)
import { Data, Effect, Metric } from "effect"
class MetricExample extends Data.TaggedError("MetricExample")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter: Metric.Counter<number> = Metric.counter("requests", {
description: "Total requests processed"
})
const memoryGauge: Metric.Gauge<number> = Metric.gauge("memory_usage", {
description: "Current memory usage in MB"
})
const statusFrequency: Metric.Frequency = Metric.frequency("status_codes", {
description: "HTTP status code frequency"
})
// All metrics share the same interface for updates and reads
yield* Metric.update(requestCounter, 1)
yield* Metric.update(memoryGauge, 128)
yield* Metric.update(statusFrequency, "200")
// All metrics can be read with Metric.value
const counterState = yield* Metric.value(requestCounter)
const gaugeState = yield* Metric.value(memoryGauge)
const frequencyState = yield* Metric.value(statusFrequency)
// Metrics have common properties accessible through the interface:
// - id: unique identifier
// - type: metric type ("Counter", "Gauge", "Frequency", etc.)
// - description: optional human-readable description
// - attributes: optional key-value attributes for tagging
return {
counter: {
id: requestCounter.id,
type: requestCounter.type,
state: counterState
},
gauge: { id: memoryGauge.id, type: memoryGauge.type, state: gaugeState },
frequency: {
id: statusFrequency.id,
type: statusFrequency.type,
state: frequencyState
}
}
})
The Metric namespace provides a comprehensive system for collecting, aggregating, and observing
application metrics in Effect applications.
Example (Collecting application metrics)
import { Data, Effect, Metric } from "effect"
class MetricsError extends Data.TaggedError("MetricsError")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter = Metric.counter("http_requests_total")
const responseTimeHistogram = Metric.histogram("http_response_time", {
boundaries: Metric.linearBoundaries({ start: 0, width: 10, count: 10 })
})
const activeConnectionsGauge = Metric.gauge("active_connections")
const statusFrequency = Metric.frequency("http_status_codes")
// Update metrics
yield* Metric.update(requestCounter, 1)
yield* Metric.update(responseTimeHistogram, 45.2)
yield* Metric.update(activeConnectionsGauge, 12)
yield* Metric.update(statusFrequency, "200")
// Get metric values
const counterValue = yield* Metric.value(requestCounter)
const histogramValue = yield* Metric.value(responseTimeHistogram)
const gaugeValue = yield* Metric.value(activeConnectionsGauge)
const frequencyValue = yield* Metric.value(statusFrequency)
return {
counter: counterValue,
histogram: histogramValue,
gauge: gaugeValue,
frequency: frequencyValue
}
})
Metric<type NoInfer<A> = [A][A extends any ? 0 : never]Prevents TypeScript from inferring a type parameter from a specific
position.
When to use
Use when a function parameter must match an inferred type without becoming
an inference source.
Details
The parameter using NoInfer must still match the inferred type.
Example (Controlling inference)
import type { Types } from "effect"
declare function withDefault<T>(value: T, fallback: Types.NoInfer<T>): T
// T is inferred as "a" | "b" from the first argument only
const result = withDefault<"a" | "b">("a", "b")
NoInfer<function (type parameter) E in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>E>, function (type parameter) State in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>State>
): interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>A, function (type parameter) E in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>E, function (type parameter) R in <A, E, R, State>(self: Effect<A, E, R>, metric: Metric.Metric<NoInfer<E>, State>): Effect<A, E, R>R>
} = dual<(...args: Array<any>) => any, <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined) => Effect<A, E, R>>(isDataFirst: (args: IArguments) => boolean, body: <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined) => Effect<A, E, R>): ((...args: Array<any>) => any) & (<A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined) => Effect<A, E, R>) (+1 overload)Creates a function that can be called in data-first style or data-last
(pipe-friendly) style.
When to use
Use to expose one implementation through both direct and pipe-friendly
call styles.
Details
Pass either the arity of the uncurried function or a predicate that decides
whether the current call is data-first. Arity is the common case. Use a
predicate when optional arguments make arity ambiguous.
Example (Selecting data-first or data-last style by arity)
import { Function, pipe } from "effect"
const sum = Function.dual<
(that: number) => (self: number) => number,
(self: number, that: number) => number
>(2, (self, that) => self + that)
console.log(sum(2, 3)) // 5
console.log(pipe(2, sum(3))) // 5
Example (Defining overloads with call signatures)
import { Function, pipe } from "effect"
const sum: {
(that: number): (self: number) => number
(self: number, that: number): number
} = Function.dual(2, (self: number, that: number): number => self + that)
console.log(sum(2, 3)) // 5
console.log(pipe(2, sum(3))) // 5
Example (Selecting data-first or data-last style with a predicate)
import { Function, pipe } from "effect"
const sum = Function.dual<
(that: number) => (self: number) => number,
(self: number, that: number) => number
>(
(args) => args.length === 2,
(self, that) => self + that
)
console.log(sum(2, 3)) // 5
console.log(pipe(2, sum(3))) // 5
dual(
(args: IArgumentsargs) => const isEffect: (
u: unknown
) => u is Effect<any, any, any>
Checks whether a value is an Effect.
Example (Checking whether a value is an Effect)
import { Effect } from "effect"
console.log(Effect.isEffect(Effect.succeed(1))) // true
console.log(Effect.isEffect("hello")) // false
isEffect(args: IArgumentsargs[0]),
<function (type parameter) A in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>A, function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>E, function (type parameter) R in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>R, function (type parameter) Input in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>Input, function (type parameter) State in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>State>(
self: Effect<A, E, R>(parameter) self: {
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
toString: () => string;
toJSON: () => unknown;
}
interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>A, function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>E, function (type parameter) R in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>R>,
metric: Metric.Metric<Input, State>(parameter) metric: {
Input: Contravariant<Input>;
State: Covariant<State>;
id: string;
type: Metric.Type;
description: string | undefined;
attributes: Metric.AttributeSet | undefined;
valueUnsafe: (context: Context.Context<never>) => State;
updateUnsafe: (input: Input, context: Context.Context<never>) => void;
modifyUnsafe: (input: Input, context: Context.Context<never>) => void;
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
}
import MetricMetric.interface Metric<in Input, out State>A Metric<Input, State> represents a concurrent metric which accepts update
values of type Input and are aggregated to a value of type State.
Details
For example, a counter metric would have type Metric<number, number>,
representing the fact that the metric can be updated with numbers (the amount
to increment or decrement the counter by), and the state of the counter is a
number.
There are five primitive metric types supported by Effect:
- Counters
- Frequencies
- Gauges
- Histograms
- Summaries
Example (Using multiple metric types)
import { Data, Effect, Metric } from "effect"
class MetricExample extends Data.TaggedError("MetricExample")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter: Metric.Counter<number> = Metric.counter("requests", {
description: "Total requests processed"
})
const memoryGauge: Metric.Gauge<number> = Metric.gauge("memory_usage", {
description: "Current memory usage in MB"
})
const statusFrequency: Metric.Frequency = Metric.frequency("status_codes", {
description: "HTTP status code frequency"
})
// All metrics share the same interface for updates and reads
yield* Metric.update(requestCounter, 1)
yield* Metric.update(memoryGauge, 128)
yield* Metric.update(statusFrequency, "200")
// All metrics can be read with Metric.value
const counterState = yield* Metric.value(requestCounter)
const gaugeState = yield* Metric.value(memoryGauge)
const frequencyState = yield* Metric.value(statusFrequency)
// Metrics have common properties accessible through the interface:
// - id: unique identifier
// - type: metric type ("Counter", "Gauge", "Frequency", etc.)
// - description: optional human-readable description
// - attributes: optional key-value attributes for tagging
return {
counter: {
id: requestCounter.id,
type: requestCounter.type,
state: counterState
},
gauge: { id: memoryGauge.id, type: memoryGauge.type, state: gaugeState },
frequency: {
id: statusFrequency.id,
type: statusFrequency.type,
state: frequencyState
}
}
})
The Metric namespace provides a comprehensive system for collecting, aggregating, and observing
application metrics in Effect applications.
Example (Collecting application metrics)
import { Data, Effect, Metric } from "effect"
class MetricsError extends Data.TaggedError("MetricsError")<{
readonly operation: string
}> {}
const program = Effect.gen(function*() {
// Create different types of metrics
const requestCounter = Metric.counter("http_requests_total")
const responseTimeHistogram = Metric.histogram("http_response_time", {
boundaries: Metric.linearBoundaries({ start: 0, width: 10, count: 10 })
})
const activeConnectionsGauge = Metric.gauge("active_connections")
const statusFrequency = Metric.frequency("http_status_codes")
// Update metrics
yield* Metric.update(requestCounter, 1)
yield* Metric.update(responseTimeHistogram, 45.2)
yield* Metric.update(activeConnectionsGauge, 12)
yield* Metric.update(statusFrequency, "200")
// Get metric values
const counterValue = yield* Metric.value(requestCounter)
const histogramValue = yield* Metric.value(responseTimeHistogram)
const gaugeValue = yield* Metric.value(activeConnectionsGauge)
const frequencyValue = yield* Metric.value(statusFrequency)
return {
counter: counterValue,
histogram: histogramValue,
gauge: gaugeValue,
frequency: frequencyValue
}
})
Metric<function (type parameter) Input in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>Input, function (type parameter) State in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>State>,
f: ((error: E) => Input) | undefinedf: ((error: Eerror: function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>E) => function (type parameter) Input in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>Input) | undefined
): interface Effect<out A, out E = never, out R = never>The Effect interface defines a value that lazily describes a workflow or
job. The workflow requires some context R, and may fail with an error of
type E, or succeed with a value of type A.
When to use
Use when you need to represent a lazy, composable workflow that can require
services, fail with a typed error, or succeed with a typed value.
Details
Effect values model resourceful interaction with the outside world,
including synchronous, asynchronous, concurrent, and parallel interaction.
They use a fiber-based concurrency model, with built-in support for
scheduling, fine-grained interruption, structured concurrency, and high
scalability.
To run an Effect value, you need a Runtime, which is a type that is
capable of executing Effect values.
Effect<function (type parameter) A in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>A, function (type parameter) E in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>E, function (type parameter) R in <A, E, R, Input, State>(self: Effect<A, E, R>, metric: Metric.Metric<Input, State>, f: ((error: E) => Input) | undefined): Effect<A, E, R>R> =>
const tapError: {
<E, X, E2, R2>(
f: (e: NoInfer<E>) => Effect<X, E2, R2>
): <A, R>(
self: Effect<A, E, R>
) => Effect<A, E | E2, R2 | R>
<A, E, R, X, E2, R2>(
self: Effect<A, E, R>,
f: (e: E) => Effect<X, E2, R2>
): Effect<A, E | E2, R | R2>
}
self: Effect<A, E, R>(parameter) self: {
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
toString: () => string;
toJSON: () => unknown;
}
error: Eerror) => {
const const input: E | Inputinput = f: ((error: E) => Input) | undefinedf === var undefinedundefined ? error: Eerror : internalCall<Input>(body: () => Input): InputinternalCall(() => f: (error: E) => Inputf(error: Eerror))
return import MetricMetric.const update: {
<Input>(input: Input): <State>(
self: Metric<Input, State>
) => Effect<void>
<Input, State>(
self: Metric<Input, State>,
input: Input
): Effect<void>
}
metric: Metric.Metric<Input, State>(parameter) metric: {
Input: Contravariant<Input>;
State: Covariant<State>;
id: string;
type: Metric.Type;
description: string | undefined;
attributes: Metric.AttributeSet | undefined;
valueUnsafe: (context: Context.Context<never>) => State;
updateUnsafe: (input: Input, context: Context.Context<never>) => void;
modifyUnsafe: (input: Input, context: Context.Context<never>) => void;
pipe: { <A>(this: A): A; <A, B = never>(this: A, ab: (_: A) => B): B; <A, B = never, C = never>(this: A, ab: (_: A) => B, bc: (_: B) => C): C; <A, B = never, C = never, D = never>(this: A, ab: (_: A) => B, bc: (_: B) => C, cd: (_: C) => D): D; <…;
}
const input: E | Inputinput as any)
})
)