<A, E>(values: Iterable<A>): (
self: TxEnqueue<A, E>
) => Effect.Effect<Array<A>>
<A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<
Array<A>
>Offers multiple items to the queue, returning the items that were not accepted.
Details
Each item follows offer semantics: bounded queues retry while full, dropping queues reject new items when full, sliding queues evict old items to accept new items, and closing or done queues reject all items. This function mutates the original TxQueue by adding items according to the queue's strategy. It does not return a new TxQueue reference.
Example (Offering multiple values)
import { Effect, TxQueue } from "effect"
const program = Effect.gen(function*() {
const queue = yield* TxQueue.bounded<number>(10)
// Offer multiple items - returns rejected items as array
const rejected = yield* TxQueue.offerAll(queue, [1, 2, 3, 4, 5])
console.log(rejected) // [] if all accepted
console.log(rejected.length) // 0
})export const const offerAll: {
<A, E>(values: Iterable<A>): (
self: TxEnqueue<A, E>
) => Effect.Effect<Array<A>>
<A, E>(
self: TxEnqueue<A, E>,
values: Iterable<A>
): Effect.Effect<Array<A>>
}
Offers multiple items to the queue, returning the items that were not
accepted.
Details
Each item follows offer semantics: bounded queues retry while full, dropping queues reject new items when full, sliding queues evict old items to accept new items, and closing or done queues reject all items. This function mutates the original TxQueue by adding items according to the queue's strategy. It does not return a new TxQueue reference.
Example (Offering multiple values)
import { Effect, TxQueue } from "effect"
const program = Effect.gen(function*() {
const queue = yield* TxQueue.bounded<number>(10)
// Offer multiple items - returns rejected items as array
const rejected = yield* TxQueue.offerAll(queue, [1, 2, 3, 4, 5])
console.log(rejected) // [] if all accepted
console.log(rejected.length) // 0
})
offerAll: {
<function (type parameter) A in <A, E>(values: Iterable<A>): (self: TxEnqueue<A, E>) => Effect.Effect<Array<A>>A, function (type parameter) E in <A, E>(values: Iterable<A>): (self: TxEnqueue<A, E>) => Effect.Effect<Array<A>>E>(values: Iterable<A>values: interface Iterable<T, TReturn = any, TNext = any>Iterable<function (type parameter) A in <A, E>(values: Iterable<A>): (self: TxEnqueue<A, E>) => Effect.Effect<Array<A>>A>): (self: TxEnqueue<A, E>(parameter) self: {
strategy: "bounded" | "unbounded" | "dropping" | "sliding";
capacity: number;
items: TxChunk.TxChunk<any>;
stateRef: TxRef.TxRef<State<any, any>>;
toString: () => string;
toJSON: () => unknown;
}
interface TxEnqueue<in A, in E = never>Namespace containing type definitions for TxEnqueue variance annotations.
A TxEnqueue represents the write-only interface of a transactional queue, providing
operations for adding elements (enqueue operations) and inspecting queue state.
Example (Offering values through enqueue handles)
import { Effect, TxQueue } from "effect"
import type { Cause } from "effect"
const program = Effect.gen(function*() {
// Queue without error channel
const queue = yield* TxQueue.bounded<number>(10)
const accepted = yield* TxQueue.offer(queue, 42)
// Queue with error channel for completion signaling
const faultTolerantQueue = yield* TxQueue.bounded<number, string>(10)
yield* TxQueue.offerAll(faultTolerantQueue, [1, 2, 3])
yield* TxQueue.fail(faultTolerantQueue, "processing complete")
// Works with Done for clean completion
const completableQueue = yield* TxQueue.bounded<
string,
Cause.Done
>(5)
yield* TxQueue.offer(completableQueue, "task")
yield* TxQueue.end(completableQueue)
})
TxEnqueue<function (type parameter) A in <A, E>(values: Iterable<A>): (self: TxEnqueue<A, E>) => Effect.Effect<Array<A>>A, function (type parameter) E in <A, E>(values: Iterable<A>): (self: TxEnqueue<A, E>) => Effect.Effect<Array<A>>E>) => import EffectEffect.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<interface Array<T>Array<function (type parameter) A in <A, E>(values: Iterable<A>): (self: TxEnqueue<A, E>) => Effect.Effect<Array<A>>A>>
<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A, function (type parameter) E in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>E>(self: TxEnqueue<A, E>(parameter) self: {
strategy: "bounded" | "unbounded" | "dropping" | "sliding";
capacity: number;
items: TxChunk.TxChunk<any>;
stateRef: TxRef.TxRef<State<any, any>>;
toString: () => string;
toJSON: () => unknown;
}
interface TxEnqueue<in A, in E = never>Namespace containing type definitions for TxEnqueue variance annotations.
A TxEnqueue represents the write-only interface of a transactional queue, providing
operations for adding elements (enqueue operations) and inspecting queue state.
Example (Offering values through enqueue handles)
import { Effect, TxQueue } from "effect"
import type { Cause } from "effect"
const program = Effect.gen(function*() {
// Queue without error channel
const queue = yield* TxQueue.bounded<number>(10)
const accepted = yield* TxQueue.offer(queue, 42)
// Queue with error channel for completion signaling
const faultTolerantQueue = yield* TxQueue.bounded<number, string>(10)
yield* TxQueue.offerAll(faultTolerantQueue, [1, 2, 3])
yield* TxQueue.fail(faultTolerantQueue, "processing complete")
// Works with Done for clean completion
const completableQueue = yield* TxQueue.bounded<
string,
Cause.Done
>(5)
yield* TxQueue.offer(completableQueue, "task")
yield* TxQueue.end(completableQueue)
})
TxEnqueue<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A, function (type parameter) E in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>E>, values: Iterable<A>values: interface Iterable<T, TReturn = any, TNext = any>Iterable<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A>): import EffectEffect.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<interface Array<T>Array<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A>>
} = dual<(...args: Array<any>) => any, <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>) => Effect.Effect<Array<A>>>(arity: 2, body: <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>) => Effect.Effect<Array<A>>): ((...args: Array<any>) => any) & (<A, E>(self: TxEnqueue<A, E>, values: Iterable<A>) => Effect.Effect<Array<A>>) (+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(
2,
<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A, function (type parameter) E in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>E>(self: TxEnqueue<A, E>(parameter) self: {
strategy: "bounded" | "unbounded" | "dropping" | "sliding";
capacity: number;
items: TxChunk.TxChunk<any>;
stateRef: TxRef.TxRef<State<any, any>>;
toString: () => string;
toJSON: () => unknown;
}
interface TxEnqueue<in A, in E = never>Namespace containing type definitions for TxEnqueue variance annotations.
A TxEnqueue represents the write-only interface of a transactional queue, providing
operations for adding elements (enqueue operations) and inspecting queue state.
Example (Offering values through enqueue handles)
import { Effect, TxQueue } from "effect"
import type { Cause } from "effect"
const program = Effect.gen(function*() {
// Queue without error channel
const queue = yield* TxQueue.bounded<number>(10)
const accepted = yield* TxQueue.offer(queue, 42)
// Queue with error channel for completion signaling
const faultTolerantQueue = yield* TxQueue.bounded<number, string>(10)
yield* TxQueue.offerAll(faultTolerantQueue, [1, 2, 3])
yield* TxQueue.fail(faultTolerantQueue, "processing complete")
// Works with Done for clean completion
const completableQueue = yield* TxQueue.bounded<
string,
Cause.Done
>(5)
yield* TxQueue.offer(completableQueue, "task")
yield* TxQueue.end(completableQueue)
})
TxEnqueue<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A, function (type parameter) E in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>E>, values: Iterable<A>values: interface Iterable<T, TReturn = any, TNext = any>Iterable<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A>): import EffectEffect.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<interface Array<T>Array<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A>> =>
import EffectEffect.const gen: {
<Eff extends Effect<any, any, any>, AEff>(
f: () => Generator<Eff, AEff, never>
): Effect<
AEff,
[Eff] extends [never]
? never
: [Eff] extends [
Effect<infer _A, infer E, infer _R>
]
? E
: never,
[Eff] extends [never]
? never
: [Eff] extends [
Effect<infer _A, infer _E, infer R>
]
? R
: never
>
<Self, Eff extends Effect<any, any, any>, AEff>(
options: { readonly self: Self },
f: (this: Self) => Generator<Eff, AEff, never>
): Effect<
AEff,
[Eff] extends [never]
? never
: [Eff] extends [
Effect<infer _A, infer E, infer _R>
]
? E
: never,
[Eff] extends [never]
? never
: [Eff] extends [
Effect<infer _A, infer _E, infer R>
]
? R
: never
>
}
const rejected: A[]rejected: interface Array<T>Array<function (type parameter) A in <A, E>(self: TxEnqueue<A, E>, values: Iterable<A>): Effect.Effect<Array<A>>A> = []
for (const const value: Avalue of values: Iterable<A>values) {
const const accepted: booleanaccepted = yield* const offer: {
<A, E>(value: A): (
self: TxEnqueue<A, E>
) => Effect.Effect<boolean>
<A, E>(
self: TxEnqueue<A, E>,
value: A
): Effect.Effect<boolean>
}
self: TxEnqueue<A, E>(parameter) self: {
strategy: "bounded" | "unbounded" | "dropping" | "sliding";
capacity: number;
items: TxChunk.TxChunk<any>;
stateRef: TxRef.TxRef<State<any, any>>;
toString: () => string;
toJSON: () => unknown;
}
const value: Avalue)
if (!const accepted: booleanaccepted) {
const rejected: A[]rejected.Array<A>.push(...items: A[]): numberAppends new elements to the end of an array, and returns the new length of the array.
push(const value: Avalue)
}
}
return const rejected: A[]rejected
}).Pipeable.pipe<Effect.Effect<A[], never, never>, Effect.Effect<A[], never, never>>(this: Effect.Effect<A[], never, never>, ab: (_: Effect.Effect<A[], never, never>) => Effect.Effect<A[], never, never>): Effect.Effect<A[], never, never> (+21 overloads)pipe(import EffectEffect.const tx: <A, E, R>(
effect: Effect<A, E, R>
) => Effect<A, E, Exclude<R, Transaction>>
Defines a transaction boundary. Transactions are "all or nothing" with respect to changes
made to transactional values (i.e. TxRef) that occur within the transaction body.
Details
If called inside an active transaction, tx composes with the current transaction and reuses
its journal and retry state instead of creating a nested boundary.
Effect transactions are optimistic with retry. A transaction is retried when
its body explicitly calls Effect.txRetry and any accessed transactional
value changes, or when any accessed transactional value changes because a
different transaction commits before the current one.
The outermost tx call creates the transaction boundary and commits or rolls back the full
composed transaction.
Example (Running a transaction)
import { Effect, TxRef } from "effect"
const program = Effect.gen(function*() {
const ref1 = yield* TxRef.make(0)
const ref2 = yield* TxRef.make(0)
// Nested tx calls compose into the same transaction
yield* Effect.tx(Effect.gen(function*() {
yield* TxRef.set(ref1, 10)
yield* Effect.tx(TxRef.set(ref2, 20))
const sum = (yield* TxRef.get(ref1)) + (yield* TxRef.get(ref2))
console.log(`Transaction sum: ${sum}`)
}))
console.log(`Final ref1: ${yield* TxRef.get(ref1)}`) // 10
console.log(`Final ref2: ${yield* TxRef.get(ref2)}`) // 20
})
tx)
)