Histogram<Input>A Histogram metric that records observations in configurable buckets to analyze value distributions.
When to use
Use when histograms are ideal for measuring request durations, response sizes, and other continuous values where you need to understand the distribution of values rather than just aggregates.
Example (Using histogram metrics)
import { Data, Effect, Metric } from "effect"
class HistogramInterfaceError
extends Data.TaggedError("HistogramInterfaceError")<{
readonly operation: string
}>
{}
const program = Effect.gen(function*() {
// Create histograms with different boundary strategies
const responseTimeHistogram: Metric.Histogram<number> = Metric.histogram(
"http_response_time_ms",
{
description: "HTTP response time distribution in milliseconds",
boundaries: Metric.linearBoundaries({ start: 0, width: 50, count: 20 }) // 0, 50, 100, ..., 950
}
)
const fileSizeHistogram: Metric.Histogram<number> = Metric.histogram(
"file_size_bytes",
{
description: "File size distribution in bytes",
boundaries: Metric.exponentialBoundaries({
start: 1,
factor: 2,
count: 10
}) // 1, 2, 4, 8, ..., 512
}
)
// Record observations (values get placed into appropriate buckets)
yield* Metric.update(responseTimeHistogram, 125) // Goes into 100-150ms bucket
yield* Metric.update(responseTimeHistogram, 75) // Goes into 50-100ms bucket
yield* Metric.update(responseTimeHistogram, 200) // Goes into 150-200ms bucket
yield* Metric.update(responseTimeHistogram, 45) // Goes into 0-50ms bucket
yield* Metric.update(fileSizeHistogram, 3) // Goes into 2-4 bytes bucket
yield* Metric.update(fileSizeHistogram, 15) // Goes into 8-16 bytes bucket
yield* Metric.update(fileSizeHistogram, 100) // Goes into 64-128 bytes bucket
// Read histogram state
const responseTimeState: Metric.HistogramState = yield* Metric.value(
responseTimeHistogram
)
const fileSizeState: Metric.HistogramState = yield* Metric.value(
fileSizeHistogram
)
// Histogram state contains:
// - buckets: Array of [boundary, cumulativeCount] pairs
// - count: total number of observations
// - min: smallest observed value
// - max: largest observed value
// - sum: sum of all observed values
return {
responseTime: {
totalRequests: responseTimeState.count, // 4
fastestRequest: responseTimeState.min, // 45
slowestRequest: responseTimeState.max, // 200
totalTime: responseTimeState.sum, // 445
averageTime: responseTimeState.sum / responseTimeState.count // 111.25
},
fileSize: {
totalFiles: fileSizeState.count, // 3
smallestFile: fileSizeState.min, // 3
largestFile: fileSizeState.max, // 100
totalBytes: fileSizeState.sum // 118
}
}
})metrics
Source effect/Metric.ts:6441 lines
export interface interface Histogram<Input>A Histogram metric that records observations in configurable buckets to analyze value distributions.
When to use
Use when histograms are ideal for measuring request durations, response sizes, and other continuous values
where you need to understand the distribution of values rather than just aggregates.
Example (Using histogram metrics)
import { Data, Effect, Metric } from "effect"
class HistogramInterfaceError
extends Data.TaggedError("HistogramInterfaceError")<{
readonly operation: string
}>
{}
const program = Effect.gen(function*() {
// Create histograms with different boundary strategies
const responseTimeHistogram: Metric.Histogram<number> = Metric.histogram(
"http_response_time_ms",
{
description: "HTTP response time distribution in milliseconds",
boundaries: Metric.linearBoundaries({ start: 0, width: 50, count: 20 }) // 0, 50, 100, ..., 950
}
)
const fileSizeHistogram: Metric.Histogram<number> = Metric.histogram(
"file_size_bytes",
{
description: "File size distribution in bytes",
boundaries: Metric.exponentialBoundaries({
start: 1,
factor: 2,
count: 10
}) // 1, 2, 4, 8, ..., 512
}
)
// Record observations (values get placed into appropriate buckets)
yield* Metric.update(responseTimeHistogram, 125) // Goes into 100-150ms bucket
yield* Metric.update(responseTimeHistogram, 75) // Goes into 50-100ms bucket
yield* Metric.update(responseTimeHistogram, 200) // Goes into 150-200ms bucket
yield* Metric.update(responseTimeHistogram, 45) // Goes into 0-50ms bucket
yield* Metric.update(fileSizeHistogram, 3) // Goes into 2-4 bytes bucket
yield* Metric.update(fileSizeHistogram, 15) // Goes into 8-16 bytes bucket
yield* Metric.update(fileSizeHistogram, 100) // Goes into 64-128 bytes bucket
// Read histogram state
const responseTimeState: Metric.HistogramState = yield* Metric.value(
responseTimeHistogram
)
const fileSizeState: Metric.HistogramState = yield* Metric.value(
fileSizeHistogram
)
// Histogram state contains:
// - buckets: Array of [boundary, cumulativeCount] pairs
// - count: total number of observations
// - min: smallest observed value
// - max: largest observed value
// - sum: sum of all observed values
return {
responseTime: {
totalRequests: responseTimeState.count, // 4
fastestRequest: responseTimeState.min, // 45
slowestRequest: responseTimeState.max, // 200
totalTime: responseTimeState.sum, // 445
averageTime: responseTimeState.sum / responseTimeState.count // 111.25
},
fileSize: {
totalFiles: fileSizeState.count, // 3
smallestFile: fileSizeState.min, // 3
largestFile: fileSizeState.max, // 100
totalBytes: fileSizeState.sum // 118
}
}
})
Histogram<function (type parameter) Input in Histogram<Input>Input> extends 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 Histogram<Input>Input, HistogramState> {}Referenced by 3 symbols