---

name: effect-patterns-streams-getting-started description: Effect-TS patterns for Streams Getting Started. Use when working with streams getting started in Effect-TS applications.

Effect-TS Patterns: Streams Getting Started

This skill provides 4 curated Effect-TS patterns for streams getting started. Use this skill when working on tasks related to:

• streams getting started
• Best practices in Effect-TS applications
• Real-world patterns and solutions

---

🟢 Beginner Patterns

Your First Stream

Rule: Use Stream to process sequences of data lazily and efficiently.

Good Example:

Copyimport { Effect, Stream } from "effect"

// Create a stream from explicit values
const numbers = Stream.make(1, 2, 3, 4, 5)

// Create a stream from an array
const fromArray = Stream.fromIterable([10, 20, 30])

// Create a single-value stream
const single = Stream.succeed("hello")

// Transform and run the stream
const program = numbers.pipe(
  Stream.map((n) => n * 2),           // Double each number
  Stream.filter((n) => n > 4),        // Keep only > 4
  Stream.runCollect                    // Collect results
)

Effect.runPromise(program).then((chunk) => {
  console.log([...chunk])  // [6, 8, 10]
})

Anti-Pattern:

Don't use regular arrays when you need lazy processing or async operations:

Copy// Anti-pattern: Eager processing, all in memory
const numbers = [1, 2, 3, 4, 5]
const doubled = numbers.map((n) => n * 2)
const filtered = doubled.filter((n) => n > 4)

This loads everything into memory immediately. Use Stream when:

• Data is large or potentially infinite
• Data arrives asynchronously
• You need backpressure or resource management

Rationale:

A Stream is a lazy sequence of values that can be processed one at a time. Create streams with Stream.make, Stream.fromIterable, or Stream.succeed.

---

Streams are Effect's answer to processing sequences of data. Unlike arrays which hold all values in memory at once, streams produce values on demand. This makes them ideal for:

1. Large datasets - Process millions of records without loading everything into memory
2. Async data - Handle data that arrives over time (files, APIs, events)
3. Composable pipelines - Chain transformations that work element by element

---

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Stream vs Effect - When to Use Which

Rule: Use Effect for single values, Stream for sequences of values.

Good Example:

Copyimport { Effect, Stream } from "effect"

// ============================================
// EFFECT: Single result operations
// ============================================

// Fetch one user - returns Effect<User>
const fetchUser = (id: string) =>
  Effect.tryPromise(() =>
    fetch(`/api/users/${id}`).then((r) => r.json())
  )

// Read entire config - returns Effect<Config>
const loadConfig = Effect.tryPromise(() =>
  fetch("/config.json").then((r) => r.json())
)

// ============================================
// STREAM: Multiple values operations
// ============================================

// Process file line by line - returns Stream<string>
const fileLines = Stream.fromIterable([
  "line 1",
  "line 2",
  "line 3",
])

// Generate events over time - returns Stream<Event>
const events = Stream.make(
  { type: "click", x: 10 },
  { type: "click", x: 20 },
  { type: "scroll", y: 100 },
)

// ============================================
// CONVERTING BETWEEN THEM
// ============================================

// Effect → Stream (single value becomes 1-element stream)
const effectToStream = Stream.fromEffect(fetchUser("123"))

// Stream → Effect (collect all values into array)
const streamToEffect = Stream.runCollect(fileLines)

// Stream → Effect (process each value for side effects)
const processAll = fileLines.pipe(
  Stream.runForEach((line) => Effect.log(`Processing: ${line}`))
)

// ============================================
// DECISION GUIDE
// ============================================

// Use Effect when:
// - Fetching a single resource
// - Computing a single result
// - Performing one action

// Use Stream when:
// - Reading files line by line
// - Processing paginated API results
// - Handling real-time events
// - Processing large datasets
// - Building data pipelines

Rationale:

Use Effect when your operation produces a single result. Use Stream when your operation produces multiple values over time.

---

Both Effect and Stream are lazy and composable, but they serve different purposes:

---

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Running and Collecting Stream Results

Rule: Choose the right Stream.run* method based on what you need from the results.

Good Example:

Copyimport { Effect, Stream, Option } from "effect"

const numbers = Stream.make(1, 2, 3, 4, 5)

// ============================================
// runCollect - Get all results as a Chunk
// ============================================

const collectAll = numbers.pipe(
  Stream.map((n) => n * 10),
  Stream.runCollect
)

Effect.runPromise(collectAll).then((chunk) => {
  console.log([...chunk])  // [10, 20, 30, 40, 50]
})

// ============================================
// runForEach - Process each item
// ============================================

const processEach = numbers.pipe(
  Stream.runForEach((n) =>
    Effect.log(`Processing: ${n}`)
  )
)

Effect.runPromise(processEach)
// Logs: Processing: 1, Processing: 2, etc.

// ============================================
// runDrain - Run for side effects only
// ============================================

const withSideEffects = numbers.pipe(
  Stream.tap((n) => Effect.log(`Saw: ${n}`)),
  Stream.runDrain  // Discard values, just run
)

// ============================================
// runHead - Get first value only
// ============================================

const getFirst = numbers.pipe(
  Stream.runHead
)

Effect.runPromise(getFirst).then((option) => {
  if (Option.isSome(option)) {
    console.log(`First: ${option.value}`)  // First: 1
  }
})

// ============================================
// runLast - Get last value only
// ============================================

const getLast = numbers.pipe(
  Stream.runLast
)

Effect.runPromise(getLast).then((option) => {
  if (Option.isSome(option)) {
    console.log(`Last: ${option.value}`)  // Last: 5
  }
})

// ============================================
// runFold - Accumulate into single result
// ============================================

const sum = numbers.pipe(
  Stream.runFold(0, (acc, n) => acc + n)
)

Effect.runPromise(sum).then((total) => {
  console.log(`Sum: ${total}`)  // Sum: 15
})

// ============================================
// runCount - Count elements
// ============================================

const count = numbers.pipe(Stream.runCount)

Effect.runPromise(count).then((n) => {
  console.log(`Count: ${n}`)  // Count: 5
})

Rationale:

Streams are lazy - nothing happens until you run them. Choose your run method based on what you need: all results, per-item effects, or just completion.

---

Effect provides several ways to consume a stream, each optimized for different use cases:

---

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Take and Drop Stream Elements

Rule: Use take/drop to control stream size, takeWhile/dropWhile for conditional limits.

Good Example:

Copyimport { Effect, Stream } from "effect"

const numbers = Stream.make(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

// ============================================
// take - Get first N elements
// ============================================

const firstThree = numbers.pipe(
  Stream.take(3),
  Stream.runCollect
)

Effect.runPromise(firstThree).then((chunk) => {
  console.log([...chunk])  // [1, 2, 3]
})

// ============================================
// drop - Skip first N elements
// ============================================

const skipThree = numbers.pipe(
  Stream.drop(3),
  Stream.runCollect
)

Effect.runPromise(skipThree).then((chunk) => {
  console.log([...chunk])  // [4, 5, 6, 7, 8, 9, 10]
})

// ============================================
// Combine for pagination (skip + limit)
// ============================================

const page2 = numbers.pipe(
  Stream.drop(3),   // Skip first page
  Stream.take(3),   // Take second page
  Stream.runCollect
)

Effect.runPromise(page2).then((chunk) => {
  console.log([...chunk])  // [4, 5, 6]
})

// ============================================
// takeWhile - Take while condition is true
// ============================================

const untilFive = numbers.pipe(
  Stream.takeWhile((n) => n < 5),
  Stream.runCollect
)

Effect.runPromise(untilFive).then((chunk) => {
  console.log([...chunk])  // [1, 2, 3, 4]
})

// ============================================
// dropWhile - Skip while condition is true
// ============================================

const afterFive = numbers.pipe(
  Stream.dropWhile((n) => n < 5),
  Stream.runCollect
)

Effect.runPromise(afterFive).then((chunk) => {
  console.log([...chunk])  // [5, 6, 7, 8, 9, 10]
})

// ============================================
// takeUntil - Take until condition becomes true
// ============================================

const untilSix = numbers.pipe(
  Stream.takeUntil((n) => n === 6),
  Stream.runCollect
)

Effect.runPromise(untilSix).then((chunk) => {
  console.log([...chunk])  // [1, 2, 3, 4, 5, 6]
})

// ============================================
// Practical: Process file with header
// ============================================

const fileLines = Stream.make(
  "# Header",
  "# Comment",
  "data1",
  "data2",
  "data3"
)

const dataOnly = fileLines.pipe(
  Stream.dropWhile((line) => line.startsWith("#")),
  Stream.runCollect
)

Effect.runPromise(dataOnly).then((chunk) => {
  console.log([...chunk])  // ["data1", "data2", "data3"]
})

Rationale:

Use take to limit how many elements to process. Use drop to skip elements. Add While variants for condition-based limits.

---

Streams can be infinite or very large. These operators let you:

1. Limit processing - Only take what you need
2. Skip headers - Drop first N elements
3. Conditional limits - Take/drop based on predicates
4. Pagination - Implement skip/limit patterns

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