fp-ts Pipe and Flow Composition

Function composition is the heart of functional programming. fp-ts provides two powerful utilities for composing functions:

pipe

flow

Imports

import { pipe, flow, identity } from 'fp-ts/function'

Understanding Pipe vs Flow

pipe: Immediate Execution with a Starting Value

pipe

// pipe(value, fn1, fn2, fn3) === fn3(fn2(fn1(value)))

const result = pipe(
  5,
  n => n * 2,      // 10
  n => n + 1,      // 11
  n => `Result: ${n}` // "Result: 11"
)

Use pipe when:

• You have a value and want to transform it immediately
• Building one-off transformations
• Working with fp-ts data types (Option, Either, Task, etc.)
• You need readable, top-to-bottom data flow

flow: Creating Reusable Pipelines

flow

// flow(fn1, fn2, fn3) === (x) => fn3(fn2(fn1(x)))

const processNumber = flow(
  (n: number) => n * 2,
  n => n + 1,
  n => `Result: ${n}`
)

processNumber(5)  // "Result: 11"
processNumber(10) // "Result: 21"

Use flow when:

• Creating reusable transformations
• Defining functions to pass as callbacks
• Building composable utilities
• You don't have the input value yet

Quick Comparison

// These are equivalent:
const result1 = pipe(5, double, increment, toString)
const result2 = flow(double, increment, toString)(5)

Working with Different Arities

Unary Functions (Single Argument)

Most fp-ts operations return unary functions, making them ideal for composition.

import { pipe } from 'fp-ts/function'
import * as A from 'fp-ts/Array'

const numbers = [1, 2, 3, 4, 5]

const result = pipe(
  numbers,
  A.filter(n => n % 2 === 0),
  A.map(n => n * 10)
)
// [20, 40]

Handling Multi-Argument Functions

When you need to use functions with multiple arguments, use currying or partial application.

// Method 1: Inline arrow function
const add = (a: number, b: number) => a + b

pipe(
  5,
  n => add(n, 10), // Wrap in arrow function
  n => n * 2
)

// Method 2: Curried version
const addCurried = (b: number) => (a: number) => a + b

pipe(
  5,
  addCurried(10), // Clean composition
  n => n * 2
)

// Method 3: Using fp-ts curry utilities
import { curry2 } from 'fp-ts-std/Function'

const addC = curry2(add)

pipe(
  5,
  addC(10),
  n => n * 2
)

Data Transformation Pipelines

Array Transformations

import { pipe } from 'fp-ts/function'
import * as A from 'fp-ts/Array'
import * as NEA from 'fp-ts/NonEmptyArray'

interface User {
  id: number
  name: string
  age: number
  active: boolean
}

const users: User[] = [
  { id: 1, name: 'Alice', age: 30, active: true },
  { id: 2, name: 'Bob', age: 25, active: false },
  { id: 3, name: 'Charlie', age: 35, active: true },
]

// Complex transformation pipeline
const activeUserNames = pipe(
  users,
  A.filter(u => u.active),
  A.map(u => u.name),
  A.sort(S.Ord)
)
// ['Alice', 'Charlie']

// With grouping
import * as R from 'fp-ts/Record'
import * as S from 'fp-ts/string'

const usersByActiveStatus = pipe(
  users,
  A.groupBy(u => u.active ? 'active' : 'inactive')
)
// { active: [...], inactive: [...] }

Record/Object Transformations

import { pipe } from 'fp-ts/function'
import * as R from 'fp-ts/Record'

const scores: Record<string, number> = {
  alice: 85,
  bob: 92,
  charlie: 78
}

const adjustedScores = pipe(
  scores,
  R.map(score => score * 1.1),
  R.filter(score => score >= 85)
)

String Transformations

import { pipe, flow } from 'fp-ts/function'
import * as S from 'fp-ts/string'

const normalizeString = flow(
  S.trim,
  S.toLowerCase,
  s => s.replace(/\s+/g, '-')
)

const slug = normalizeString('  Hello World  ') // 'hello-world'

// With validation
import * as O from 'fp-ts/Option'

const safeSlug = flow(
  O.fromPredicate((s: string) => s.length > 0),
  O.map(normalizeString)
)

Composing with fp-ts Data Types

With Option

import { pipe } from 'fp-ts/function'
import * as O from 'fp-ts/Option'

interface Config {
  database?: {
    host?: string
    port?: number
  }
}

const getConnectionString = (config: Config): O.Option<string> =>
  pipe(
    O.fromNullable(config.database),
    O.flatMap(db => O.fromNullable(db.host)),
    O.map(host => `postgresql://${host}`)
  )

// Chaining multiple Option operations
const findUser = (id: number): O.Option<User> => { /* ... */ }
const getUserEmail = (user: User): O.Option<string> => { /* ... */ }
const validateEmail = (email: string): O.Option<string> => { /* ... */ }

const getValidatedEmail = (userId: number): O.Option<string> =>
  pipe(
    findUser(userId),
    O.flatMap(getUserEmail),
    O.flatMap(validateEmail)
  )

With Either

import { pipe } from 'fp-ts/function'
import * as E from 'fp-ts/Either'

type ValidationError = { type: 'validation'; message: string }
type NetworkError = { type: 'network'; message: string }
type AppError = ValidationError | NetworkError

const validateAge = (age: number): E.Either<ValidationError, number> =>
  age >= 0 && age <= 150
    ? E.right(age)
    : E.left({ type: 'validation', message: 'Invalid age' })

const validateName = (name: string): E.Either<ValidationError, string> =>
  name.length >= 2
    ? E.right(name)
    : E.left({ type: 'validation', message: 'Name too short' })

// Sequential validation (fail on first error)
const validateUser = (name: string, age: number) =>
  pipe(
    E.Do,
    E.bind('name', () => validateName(name)),
    E.bind('age', () => validateAge(age)),
    E.map(({ name, age }) => ({ name, age, createdAt: new Date() }))
  )

// Accumulating errors with Validation
import * as A from 'fp-ts/Apply'
import * as NEA from 'fp-ts/NonEmptyArray'

type ValidationErrors = NEA.NonEmptyArray<string>
type Validation<A> = E.Either<ValidationErrors, A>

const applicativeValidation = E.getApplicativeValidation(NEA.getSemigroup<string>())

const validateUserAll = (name: string, age: number) =>
  pipe(
    A.sequenceS(applicativeValidation)({
      name: validateName(name),
      age: validateAge(age)
    }),
    E.map(({ name, age }) => ({ name, age }))
  )

With Task and TaskEither

import { pipe } from 'fp-ts/function'
import * as T from 'fp-ts/Task'
import * as TE from 'fp-ts/TaskEither'

// Composing async operations
const fetchUser = (id: number): TE.TaskEither<Error, User> =>
  TE.tryCatch(
    () => fetch(`/api/users/${id}`).then(r => r.json()),
    (error) => new Error(String(error))
  )

const fetchUserPosts = (userId: number): TE.TaskEither<Error, Post[]> =>
  TE.tryCatch(
    () => fetch(`/api/users/${userId}/posts`).then(r => r.json()),
    (error) => new Error(String(error))
  )

const getUserWithPosts = (id: number): TE.TaskEither<Error, UserWithPosts> =>
  pipe(
    fetchUser(id),
    TE.flatMap(user =>
      pipe(
        fetchUserPosts(user.id),
        TE.map(posts => ({ ...user, posts }))
      )
    )
  )

// Parallel execution
import * as A from 'fp-ts/Array'

const fetchAllUsers = (ids: number[]): TE.TaskEither<Error, User[]> =>
  pipe(
    ids,
    A.map(fetchUser),
    A.sequence(TE.ApplicativePar) // Parallel execution
  )

With Reader and ReaderTaskEither

import { pipe } from 'fp-ts/function'
import * as RTE from 'fp-ts/ReaderTaskEither'

interface Dependencies {
  userRepo: UserRepository
  emailService: EmailService
  logger: Logger
}

const getUser = (id: number): RTE.ReaderTaskEither<Dependencies, Error, User> =>
  pipe(
    RTE.ask<Dependencies>(),
    RTE.flatMapTaskEither(deps => deps.userRepo.findById(id))
  )

const sendWelcomeEmail = (user: User): RTE.ReaderTaskEither<Dependencies, Error, void> =>
  pipe(
    RTE.ask<Dependencies>(),
    RTE.flatMapTaskEither(deps => deps.emailService.send(user.email, 'Welcome!'))
  )

const onboardUser = (id: number): RTE.ReaderTaskEither<Dependencies, Error, User> =>
  pipe(
    getUser(id),
    RTE.tap(sendWelcomeEmail),
    RTE.tap(user =>
      RTE.fromTask(deps => deps.logger.info(`Onboarded: ${user.name}`))
    )
  )

Best Practices for Readable Pipelines

1. Keep Functions Small and Focused

// Good: Each step does one thing
const processOrder = pipe(
  order,
  validateOrder,
  calculateTotals,
  applyDiscounts,
  formatForDisplay
)

// Avoid: Large inline functions
const processOrder = pipe(
  order,
  o => {
    // 50 lines of validation, calculation, and formatting
  }
)

2. Extract Named Functions for Clarity

// Good: Named functions explain intent
const isAdult = (user: User) => user.age >= 18
const formatName = (user: User) => `${user.firstName} ${user.lastName}`

const adultNames = pipe(
  users,
  A.filter(isAdult),
  A.map(formatName)
)

// Less clear: Anonymous functions inline
const adultNames = pipe(
  users,
  A.filter(u => u.age >= 18),
  A.map(u => `${u.firstName} ${u.lastName}`)
)

3. Use flow for Reusable Transformations

// Define reusable pipelines
const normalizeEmail = flow(
  S.trim,
  S.toLowerCase
)

const validateEmailFormat = flow(
  O.fromPredicate((s: string) => s.includes('@')),
  O.filter(s => s.length >= 5)
)

// Compose them
const processEmail = flow(
  normalizeEmail,
  validateEmailFormat
)

4. Group Related Operations

// Good: Logical grouping with comments
const processUsers = pipe(
  users,
  // Filter
  A.filter(isActive),
  A.filter(isVerified),
  // Transform
  A.map(enrichWithMetadata),
  A.map(formatForAPI),
  // Sort
  A.sort(byCreatedAt)
)

5. Handle Errors at Appropriate Levels

// Good: Handle errors where you can meaningfully respond
const getUserSafely = (id: number) =>
  pipe(
    fetchUser(id),
    TE.mapLeft(toAppError),
    TE.orElse(error =>
      error.type === 'not_found'
        ? TE.right(defaultUser)
        : TE.left(error)
    )
  )

6. Use Do Notation for Complex Dependencies

// Good: Clear when steps depend on previous results
const createOrder = pipe(
  E.Do,
  E.bind('user', () => validateUser(userData)),
  E.bind('items', () => validateItems(itemsData)),
  E.bind('shipping', ({ user }) => calculateShipping(user.address)),
  E.bind('total', ({ items, shipping }) => calculateTotal(items, shipping)),
  E.map(({ user, items, total }) => ({ user, items, total }))
)

Common Composition Patterns

Pattern 1: Transform and Validate

const processInput = flow(
  S.trim,
  O.fromPredicate(s => s.length > 0),
  O.map(S.toLowerCase),
  O.filter(isValidFormat)
)

Pattern 2: Fetch, Transform, Persist

const syncUser = (id: number) =>
  pipe(
    fetchExternalUser(id),
    TE.map(transformToInternalUser),
    TE.flatMap(saveUser),
    TE.map(user => ({ success: true, user }))
  )

Pattern 3: Parallel with Aggregation

const fetchDashboardData = pipe(
  TE.Do,
  TE.apS('users', fetchUsers()),
  TE.apS('orders', fetchOrders()),
  TE.apS('metrics', fetchMetrics()),
  TE.map(({ users, orders, metrics }) =>
    buildDashboard(users, orders, metrics)
  )
)

Pattern 4: Sequential with Early Exit

const processPayment = (paymentData: PaymentData) =>
  pipe(
    validatePayment(paymentData),
    TE.flatMap(checkFunds),
    TE.flatMap(reserveFunds),
    TE.flatMap(processTransaction),
    TE.flatMap(sendConfirmation)
  )

Pattern 5: Fallback Chain

const getConfig = pipe(
  getEnvConfig(),
  O.alt(() => getFileConfig()),
  O.alt(() => getDefaultConfig()),
  O.getOrElse(() => hardcodedDefaults)
)

Pattern 6: Conditional Branching

const processUser = (user: User) =>
  pipe(
    user,
    O.fromPredicate(isAdmin),
    O.match(
      () => processRegularUser(user),
      () => processAdminUser(user)
    )
  )

Pattern 7: Accumulate Results

const processAll = (items: Item[]) =>
  pipe(
    items,
    A.map(processItem),
    A.separate, // Split into { left: errors[], right: successes[] }
    ({ left: errors, right: successes }) => ({
      successes,
      errors,
      successRate: successes.length / items.length
    })
  )

Type Inference Tips

Let TypeScript Infer When Possible

// Good: Types flow through
const result = pipe(
  users,
  A.filter(u => u.active), // TypeScript knows u is User
  A.map(u => u.name)       // TypeScript knows result is string[]
)

// Only annotate when necessary
const processNumber = flow(
  (n: number) => n * 2,  // First function needs annotation
  n => n + 1,            // Rest are inferred
  String
)

Use Explicit Types for Public APIs

// Good: Clear contract for public function
const processOrder: (order: Order) => E.Either<OrderError, ProcessedOrder> =
  flow(
    validateOrder,
    E.flatMap(calculateTotals),
    E.map(formatOrder)
  )

Performance Considerations

pipe vs flow Performance

• pipe

  has minimal overhead - essentially just function calls

• flow

  creates a new function, slight overhead on creation but not on execution
• For hot paths with known values,

  pipe

  is marginally faster
• For reusable functions,

  flow

  avoids recreation

Avoiding Unnecessary Intermediate Arrays

// Less efficient: Multiple array iterations
const result = pipe(
  largeArray,
  A.filter(predicate1),
  A.filter(predicate2),
  A.map(transform)
)

// More efficient: Combine predicates
const result = pipe(
  largeArray,
  A.filter(x => predicate1(x) && predicate2(x)),
  A.map(transform)
)

// Or use filterMap for filter + map
const result = pipe(
  largeArray,
  A.filterMap(x =>
    predicate1(x) && predicate2(x)
      ? O.some(transform(x))
      : O.none
  )
)

Debugging Pipelines

Using tap for Side Effects

import { tap } from 'fp-ts/function'

const debugPipeline = pipe(
  data,
  tap(x => console.log('Step 1:', x)),
  transform1,
  tap(x => console.log('Step 2:', x)),
  transform2
)

Using trace Helper

const trace = <A>(label: string) => (a: A): A => {
  console.log(label, a)
  return a
}

const result = pipe(
  data,
  trace('input'),
  transform1,
  trace('after transform1'),
  transform2,
  trace('final')
)

Summary

pipe

flow

Remember:

• pipe(value, f, g)

  executes immediately

• flow(f, g)

  returns a function for later
• Keep pipeline steps small and focused
• Extract named functions for clarity
• Use Do notation for complex dependencies
• Let TypeScript infer types when possible