windowCount - Split Observable by Specified Count

The windowCount operator divides emitted values into new Observables for each specified count. While bufferCount returns an array, windowCount returns an Observable<T>, allowing additional operators to be applied to each window.

🔰 Basic Syntax and Usage

import { interval } from 'rxjs';
import { windowCount, mergeAll } from 'rxjs';

// Emit values every 100ms
const source$ = interval(100);

source$.pipe(
  windowCount(5),
  mergeAll() // Flatten each window
).subscribe(value => {
  console.log('Value in window:', value);
});

// Output:
// Value in window: 0
// Value in window: 1
// Value in window: 2
// Value in window: 3
// Value in window: 4
// (New window starts)
// Value in window: 5
// ...

• A new window (Observable) is created for every 5 values.
• It is unique in that it divides on a count basis.

🌐 RxJS Official Documentation - windowCount

💡 Typical Usage Patterns

• Aggregate processing for each fixed count
• Batch transmission of data (different processing for each window)
• Pagination processing
• Calculate statistics per window

🔍 Difference from bufferCount

Operator	Output	Use Case
bufferCount	Array (T[])	Process grouped values together
windowCount	Observable<T>	Different stream processing for each group

import { interval } from 'rxjs';
import { bufferCount, windowCount, mergeAll } from 'rxjs';

const source$ = interval(100);

// bufferCount - Output as array
source$.pipe(
  bufferCount(5)
).subscribe(values => {
  console.log('Buffer (array):', values);
  // Output: Buffer (array): [0, 1, 2, 3, 4]
});

// windowCount - Output as Observable
source$.pipe(
  windowCount(5)
).subscribe(window$ => {
  console.log('Window (Observable):', window$);
  window$.subscribe(value => {
    console.log('  Value in window:', value);
  });
});

🧠 Practical Code Example 1: Sum Per Window

This is an example of calculating the sum of every 5 values.

import { interval } from 'rxjs';
import { windowCount, map, mergeAll, reduce } from 'rxjs';

// Create output area
const output = document.createElement('div');
output.innerHTML = '<h3>Sum Every 5 Values</h3>';
document.body.appendChild(output);

const source$ = interval(200);

let windowNumber = 0;

source$.pipe(
  windowCount(5),
  map(window$ => {
    const current = ++windowNumber;
    console.log(`Window ${current} started`);

    // Calculate sum for each window
    return window$.pipe(
      reduce((sum, value) => sum + value, 0),
      map(sum => ({ windowNum: current, sum }))  // Include window number
    );
  }),
  mergeAll()
).subscribe(result => {
  const div = document.createElement('div');
  div.textContent = `Window ${result.windowNum} sum: ${result.sum}`;
  output.appendChild(div);
});

// Output:
// Window 1 sum: 10  (0+1+2+3+4)
// Window 2 sum: 35  (5+6+7+8+9)
// Window 3 sum: 60  (10+11+12+13+14)

🎯 Practical Code Example 2: Specifying Start Index

You can specify a starting index with the second argument. This creates overlap windows.

import { range } from 'rxjs';
import { windowCount, mergeMap, toArray } from 'rxjs';

// Emit values from 0 to 9
range(0, 10).pipe(
  windowCount(3, 2), // 3 items each, start shifted by 2
  mergeMap(window$ => window$.pipe(toArray()))
).subscribe(values => {
  console.log('Window:', values);
});

// Output:
// Window: [0, 1, 2]
// Window: [2, 3, 4]    ← Started shifted by 2 (from 2)
// Window: [4, 5, 6]    ← Started shifted by 2 (from 4)
// Window: [6, 7, 8]
// Window: [8, 9]       ← Last 2 items

Start Index Operation Patterns

// windowCount(bufferSize, startBufferEvery)

windowCount(3, 3) // Continuous (default): [0,1,2], [3,4,5], [6,7,8]
windowCount(3, 2) // Overlap: [0,1,2], [2,3,4], [4,5,6]
windowCount(3, 4) // With gap: [0,1,2], [4,5,6], [8,9,10]

🎯 Practical Example: Different Processing for Each Window

import { interval } from 'rxjs';
import { windowCount, map, mergeAll, take } from 'rxjs';

const source$ = interval(100);
let windowNumber = 0;

source$.pipe(
  windowCount(5),
  map(window$ => {
    const current = ++windowNumber;

    if (current % 2 === 0) {
      // Even windows: Get only first 2 items
      console.log(`Window ${current}: Get first 2 items`);
      return window$.pipe(take(2));
    } else {
      // Odd windows: Get all
      console.log(`Window ${current}: Get all`);
      return window$;
    }
  }),
  mergeAll()
).subscribe(value => {
  console.log(`Value: ${value} (Window ${windowNumber})`);
});

🧠 Practical Code Example 3: Pagination-like Processing

import { from } from 'rxjs';
import { windowCount, mergeMap, toArray, map } from 'rxjs';

// Data from 1-20
const data$ = from(Array.from({ length: 20 }, (_, i) => i + 1));

// Paginate by 5 items
data$.pipe(
  windowCount(5),
  mergeMap((window$, index) => {
    const pageNumber = index + 1;
    return window$.pipe(
      toArray(),
      map(items => ({ page: pageNumber, items }))
    );
  })
).subscribe(page => {
  console.log(`Page ${page.page}:`, page.items);
});

// Output:
// Page 1: [1, 2, 3, 4, 5]
// Page 2: [6, 7, 8, 9, 10]
// Page 3: [11, 12, 13, 14, 15]
// Page 4: [16, 17, 18, 19, 20]

⚠️ Notes

1. Window Subscription Management

Each window is an independent Observable and must be explicitly subscribed to.

source$.pipe(
  windowCount(5)
).subscribe(window$ => {
  // Values won't flow unless you subscribe to the window itself
  window$.subscribe(value => {
    console.log('Value:', value);
  });
});

Or use mergeAll(), concatAll(), switchAll(), etc. to flatten.

2. Last Window

On completion of the source Observable, the last window is output even if it contains fewer than the specified number of items.

import { of } from 'rxjs';
import { windowCount, mergeMap, toArray } from 'rxjs';

of(1, 2, 3, 4, 5, 6, 7).pipe(
  windowCount(3),
  mergeMap(window$ => window$.pipe(toArray()))
).subscribe(values => {
  console.log('Window:', values);
});

// Output:
// Window: [1, 2, 3]
// Window: [4, 5, 6]
// Window: [7]  ← Only 1 item

3. Memory Usage by Start Index

If startBufferEvery is less than bufferSize (overlap), multiple windows will be active at the same time, increasing memory usage.

// Overlap: Maximum 2 windows active simultaneously
windowCount(5, 3)

// Countermeasure: Limit with take() if necessary
source$.pipe(
  take(100), // Maximum 100 items
  windowCount(5, 3)
)

🆚 Comparison of window Operators

Operator	Timing of Delimiter	Use Case
window	Another Observable emits	Event-driven partitioning
windowTime	Fixed time interval	Time-based partitioning
windowCount	Fixed count	Count-based partitioning
windowToggle	Start and end Observables	Dynamic start/end control
windowWhen	Dynamic closing condition	Different end condition per window

📚 Related Operators

• bufferCount - Collect values as array (array version of windowCount)
• window - Split window at different Observable timings
• windowTime - Time-based window splitting
• windowToggle - Window control with start and end Observables
• windowWhen - Window splitting with dynamic closing conditions

Summary

The windowCount operator is a useful tool for partitioning streams on a count basis and treating each group as an independent Observable.

• ✅ Ideal for aggregation and processing by a fixed count
• ✅ Different processing can be applied to each window
• ✅ Can be overlapped by start index
• ⚠️ Requires subscription management
• ⚠️ Be aware of memory usage when overlapping