Streams: Processing Data Piece by Piece

When you're working with large files, network requests, or any substantial amount of data, loading everything into memory at once can slow down your application—or worse, crash it entirely. Streams solve this problem by letting you process data incrementally, piece by piece, as it becomes available. Instead of waiting for a massive file to load completely, you can start working with the first chunk while the rest is still being read.

Think of it like watching a video online. You don't wait for the entire movie to download before you start watching—the video streams to your device, and you begin watching within seconds while the rest continues to load in the background. That's the power of streams: immediate processing without memory overload.

What You Need to Know First

This is a foundational article about streams—you don't need prior experience with Node.js streams to understand it.

However, you should be comfortable with:

JavaScript basics: Variables, functions, and how to write basic code
Asynchronous JavaScript: Understanding callbacks, Promises, or async/await (we'll show examples of each)
Node.js environment: How to run JavaScript files using Node.js

If you're new to asynchronous JavaScript, we recommend getting familiar with Promises and async/await first, as streams work asynchronously.

What We'll Cover in This Article

By the end of this guide, you'll understand:

What streams are and why they're essential for efficient data processing
The real-world problem streams solve (memory efficiency)
The four types of streams in Node.js
When to use each type of stream
How streams differ from loading all data at once

What We'll Explain Along the Way

Don't worry if you're unfamiliar with these—we'll explain them as we go:

Chunks (small pieces of data)
Backpressure (when data flows too fast)
Piping (connecting streams together)
Buffers (temporary data storage)
Memory management concepts

What Is a Stream?

A stream is a sequence of data that flows continuously from one place to another. Instead of loading an entire file or dataset into memory all at once, streams let you work with data in small, manageable pieces called chunks.

The Core Problem Streams Solve

Let's say you need to process a 2GB video file. If you try to load the entire file into memory, here's what happens:

// ❌ Loading everything at once (Bad approach)
import { readFileSync } from "fs";

// This tries to load the ENTIRE 2GB file into memory
const hugeFile = readFileSync("video.mp4");

// Problems:
// 1. Your application might crash if it runs out of memory
// 2. User has to wait for the entire file to load before anything happens
// 3. If you only need to process part of the file, you've wasted time and memory
console.log("File size:", hugeFile.length); // 2,000,000,000 bytes in memory!

What went wrong?

Memory overload: The entire 2GB file is loaded into RAM
Long wait time: Nothing happens until the complete file is loaded
Inefficient: Even if you only need the first 5 minutes of the video, you loaded all 2 hours

Now, here's the stream approach:

// ✅ Using streams (Better approach)
import { createReadStream } from "fs";

// Create a stream that reads the file in small chunks
const fileStream = createReadStream("video.mp4");

// Process data as it arrives, chunk by chunk
fileStream.on("data", (chunk: Buffer) => {
  // Each chunk is typically 64KB (65,536 bytes)
  console.log(`Processing ${chunk.length} bytes`);

  // You can start working with this data immediately
  // while the rest of the file is still being read
});

// Benefits:
// 1. Memory usage stays low (only one chunk at a time)
// 2. Processing starts immediately
// 3. You can stop reading if you find what you need

What's better here?

Low memory footprint: Only a small chunk (typically 64KB) is in memory at any time
Immediate processing: Work begins as soon as the first chunk arrives
Flexibility: You can stop reading early if you've found what you need

Understanding Streams Through Analogy

Let's use a real-world analogy to make streams crystal clear.

The Water Tank Analogy

Imagine you need to transfer 10,000 liters of water from one tank to another:

Without Streams (All at Once):

Tank A (10,000L) ──→ [Trying to carry ALL water at once] ──→ Tank B

Problems:
- You'd need a massive container to hold all 10,000 liters
- You can't start filling Tank B until you've collected ALL the water
- If you drop the container, you lose everything
- Physically impossible and inefficient

With Streams (Piece by Piece):

Tank A ──→ [Hose flowing steadily] ──→ Tank B
         ↑
      Water flows continuously
      in a steady stream

Benefits:
- Tank B starts filling immediately
- You only need the hose (minimal resources)
- If something goes wrong, you've only lost a small amount
- You can control the flow rate
- Both tanks can operate simultaneously

This is exactly how data streams work:

Tank A = Source (file, network, database)
Hose = Stream (the connection)
Water flow = Data chunks moving continuously
Tank B = Destination (another file, network, database)

Practical Example: Reading a Large Log File

Let's see a complete, real-world example:

// Scenario: You need to find a specific error in a 500MB log file
import { createReadStream } from "fs";
import { once } from "events";

// ❌ Bad approach: Load entire file
function findErrorWithoutStream(filepath: string): void {
  const fs = require("fs");

  console.time("Without stream");

  // Loads entire 500MB into memory
  const entireFile = fs.readFileSync(filepath, "utf-8");

  // Now search through it
  if (entireFile.includes("ERROR: Database connection failed")) {
    console.log("Found the error!");
  }

  console.timeEnd("Without stream");
  // Typical result: 2-3 seconds, 500MB memory used
}

// ✅ Good approach: Use stream
async function findErrorWithStream(filepath: string): Promise<void> {
  console.time("With stream");

  const stream = createReadStream(filepath, {
    encoding: "utf-8",
    highWaterMark: 64 * 1024, // Read 64KB at a time
  });

  let foundError = false;

  // Process each chunk as it arrives
  stream.on("data", (chunk: string) => {
    if (chunk.includes("ERROR: Database connection failed")) {
      console.log("Found the error!");
      foundError = true;

      // Stop reading the rest of the file
      stream.destroy();
    }
  });

  // Wait for stream to finish
  await once(stream, "end");

  console.timeEnd("With stream");
  // Typical result: 0.1-0.5 seconds, only ~64KB memory used
}

// Usage:
findErrorWithStream("./app.log");

// Why is the stream version faster?
// 1. Starts processing immediately (no wait for full file)
// 2. Stops as soon as error is found (doesn't read unnecessary data)
// 3. Uses minimal memory (only one 64KB chunk at a time)
// 4. The error might be near the beginning of the file

Breaking Down What Happens:

Stream starts: Opens the file and begins reading the first 64KB chunk
First chunk arrives: You start searching immediately (no waiting)
Error found: Let's say the error is in chunk 5 (320KB into the file)
Stream stops: You call stream.destroy() and stop reading
Result: You've only read 320KB instead of 500MB

Memory comparison:

Without stream: 500MB loaded into memory
With stream: 64KB in memory at any given time (7,812x less memory!)

The Four Types of Streams in Node.js

Node.js provides four distinct types of streams, each designed for specific use cases. Let's explore each one in detail.

1. Readable Streams: Getting Data From a Source

Readable streams let you read data from a source such as a file, HTTP request, or user input.

What They Do:

Pull data from somewhere (file system, network, database)
Deliver data to your application in chunks
You consume the data (read it)

Real-World Examples:

Reading a file from disk
Receiving data from an HTTP request
Reading user input from the terminal
Fetching data from a database cursor

import { createReadStream } from "fs";

// Example 1: Reading a text file
const fileReader = createReadStream("./document.txt", {
  encoding: "utf-8", // Convert bytes to text
  highWaterMark: 16 * 1024, // Read 16KB chunks
});

// Event: 'data' - Fires each time a chunk arrives
fileReader.on("data", (chunk: string) => {
  console.log("Received chunk:", chunk.substring(0, 50) + "...");
  console.log("Chunk size:", chunk.length, "characters");
});

// Event: 'end' - Fires when all data has been read
fileReader.on("end", () => {
  console.log("Finished reading file");
});

// Event: 'error' - Fires if something goes wrong
fileReader.on("error", (error: Error) => {
  console.error("Error reading file:", error.message);
});

// What happens behind the scenes:
// 1. Node.js opens the file
// 2. Reads the first 16KB → triggers 'data' event
// 3. Reads the next 16KB → triggers 'data' event again
// 4. Continues until file ends → triggers 'end' event
// 5. Closes the file automatically

Example 2: Receiving Data from HTTP Request

import http from "http";

const server = http.createServer((req, res) => {
  // req is a Readable stream!
  let bodyData = "";

  // Read the request body chunk by chunk
  req.on("data", (chunk: Buffer) => {
    // Convert buffer to string and accumulate
    bodyData += chunk.toString("utf-8");
    console.log(`Received ${chunk.length} bytes`);
  });

  req.on("end", () => {
    // All data received
    console.log("Complete request body:", bodyData);
    res.end("Received your data!");
  });
});

server.listen(3000);

// When a client sends data:
// POST /api/data
// Body: { "user": "John", "message": "Hello" }
//
// The stream receives it in chunks:
// Chunk 1: { "user": "J
// Chunk 2: ohn", "message
// Chunk 3: ": "Hello" }

2. Writable Streams: Sending Data to a Destination

Writable streams let you write data to a destination such as a file, HTTP response, or database.

What They Do:

Accept data from your application
Write data to a destination (file, network, database)
You produce the data (write it)

Real-World Examples:

Writing to a file on disk
Sending an HTTP response to a client
Writing to a database
Logging to a file or console

import { createWriteStream } from "fs";

// Example 1: Writing to a file
const fileWriter = createWriteStream("./output.txt", {
  encoding: "utf-8",
});

// Write data to the stream
fileWriter.write("First line of text\n");
fileWriter.write("Second line of text\n");
fileWriter.write("Third line of text\n");

// Important: Signal that you're done writing
fileWriter.end("Final line of text\n");

// Event: 'finish' - Fires when all data has been written
fileWriter.on("finish", () => {
  console.log("All data written to file");
});

// Event: 'error' - Fires if writing fails
fileWriter.on("error", (error: Error) => {
  console.error("Error writing file:", error.message);
});

// What happens:
// 1. Node.js opens/creates the file
// 2. Writes "First line of text\n"
// 3. Writes "Second line of text\n"
// 4. Writes "Third line of text\n"
// 5. Writes "Final line of text\n" and closes the file
// 6. Triggers 'finish' event

Example 2: Streaming Data to HTTP Response

import http from "http";
import { createReadStream } from "fs";

const server = http.createServer((req, res) => {
  // res is a Writable stream!

  // Set headers
  res.writeHead(200, { "Content-Type": "text/plain" });

  // Send data in chunks
  res.write("Starting data transfer...\n");

  // Simulate processing and sending data over time
  let count = 0;
  const interval = setInterval(() => {
    res.write(`Chunk ${++count}\n`);

    if (count === 5) {
      clearInterval(interval);
      res.end("Transfer complete!\n");
    }
  }, 1000);
});

server.listen(3000);

// Client receives:
// Starting data transfer...
// Chunk 1  (after 1 second)
// Chunk 2  (after 2 seconds)
// Chunk 3  (after 3 seconds)
// Chunk 4  (after 4 seconds)
// Chunk 5  (after 5 seconds)
// Transfer complete!

3. Duplex Streams: Two-Way Communication

Duplex streams can both read and write data. They're like a telephone line—you can talk (write) and listen (read) at the same time.

What They Do:

Read data from a source AND write data to a destination
Both operations happen independently
Useful for bidirectional communication

Real-World Examples:

Network sockets (TCP connections)
WebSocket connections
Bidirectional IPC (Inter-Process Communication)

import { Duplex } from "stream";

// Example: Creating a custom Duplex stream
class SimpleTransform extends Duplex {
  private buffer: string = "";

  // Reading side: Provide data when requested
  _read(size: number): void {
    // Push some data to be read
    if (this.buffer) {
      this.push(this.buffer);
      this.buffer = "";
    } else {
      this.push(null); // Signal end of data
    }
  }

  // Writing side: Accept data being written
  _write(chunk: Buffer, encoding: string, callback: Function): void {
    // Store the written data
    this.buffer += chunk.toString().toUpperCase();
    console.log("Received:", chunk.toString());
    callback(); // Signal write completed successfully
  }
}

// Usage:
const duplexStream = new SimpleTransform();

// Write data to it
duplexStream.write("hello ");
duplexStream.write("world");
duplexStream.end();

// Read data from it
duplexStream.on("data", (chunk: Buffer) => {
  console.log("Reading:", chunk.toString());
  // Output: "HELLO WORLD"
});

// How it works:
// 1. You write "hello " → Stream stores it as "HELLO "
// 2. You write "world" → Stream stores it as "WORLD"
// 3. You call end() → Stream finishes writing
// 4. You read from it → Stream returns "HELLO WORLD"

Example 2: TCP Socket (Real Duplex Stream)

import net from "net";

// Create a TCP server
const server = net.createServer((socket) => {
  // socket is a Duplex stream!

  console.log("Client connected");

  // Reading from the socket
  socket.on("data", (data: Buffer) => {
    const message = data.toString();
    console.log("Received from client:", message);

    // Writing to the socket
    socket.write(`Server received: ${message}\n`);
  });

  socket.on("end", () => {
    console.log("Client disconnected");
  });
});

server.listen(8080, () => {
  console.log("Server listening on port 8080");
});

// Client side:
const client = net.connect({ port: 8080 }, () => {
  console.log("Connected to server");

  // Write to server (socket is Duplex)
  client.write("Hello, server!");
});

// Read from server
client.on("data", (data: Buffer) => {
  console.log("Received from server:", data.toString());
});

// Communication flow:
// Client → Write "Hello, server!" → Server
// Client ← Read "Server received: Hello, server!" ← Server

4. Transform Streams: Modifying Data as It Flows

Transform streams are a special type of Duplex stream where the output is a transformation of the input. They read data, modify it, and write the modified data.

What They Do:

Read data from a source
Modify/transform the data
Write the transformed data to a destination

Real-World Examples:

Compressing files (ZIP, GZIP)
Encrypting/decrypting data
Converting data formats (CSV to JSON)
Image resizing
Data validation and sanitization

import { Transform } from "stream";

// Example 1: Converting text to uppercase
class UppercaseTransform extends Transform {
  _transform(chunk: Buffer, encoding: string, callback: Function): void {
    // Step 1: Get the input data
    const input = chunk.toString();

    // Step 2: Transform it
    const output = input.toUpperCase();

    // Step 3: Push the transformed data
    this.push(output);

    // Step 4: Signal we're done with this chunk
    callback();
  }
}

// Usage:
const uppercase = new UppercaseTransform();

// Write data to the transform stream
uppercase.write("hello ");
uppercase.write("world");
uppercase.end("!");

// Read transformed data
uppercase.on("data", (chunk: Buffer) => {
  console.log(chunk.toString());
});

// Output:
// HELLO
// WORLD
// !

// Data flow:
// Input: "hello " → Transform → Output: "HELLO "
// Input: "world"  → Transform → Output: "WORLD"
// Input: "!"      → Transform → Output: "!"

Example 2: Compressing Data

import { createReadStream, createWriteStream } from "fs";
import { createGzip } from "zlib";
import { pipeline } from "stream/promises";

// Compress a file using a Transform stream
async function compressFile(input: string, output: string): Promise<void> {
  try {
    // Create streams
    const source = createReadStream(input); // Readable
    const gzip = createGzip(); // Transform
    const destination = createWriteStream(output); // Writable

    // Connect them together
    await pipeline(source, gzip, destination);

    console.log("File compressed successfully");
  } catch (error) {
    console.error("Compression failed:", error);
  }
}

// Usage:
await compressFile("./large-file.txt", "./large-file.txt.gz");

// What happens:
// 1. Read chunk from large-file.txt
// 2. Pass chunk to gzip (Transform stream)
// 3. gzip compresses the chunk
// 4. Write compressed chunk to large-file.txt.gz
// 5. Repeat until entire file is processed

// If the file is 100MB:
// - Without streams: Load 100MB → compress → write (uses 200MB+ memory)
// - With streams: Process in 64KB chunks → uses only ~64KB memory

Example 3: Data Validation Transform

import { Transform } from "stream";

// Transform stream that validates and filters JSON objects
class JSONValidator extends Transform {
  constructor() {
    super({ objectMode: true }); // Work with objects, not buffers
  }

  _transform(chunk: any, encoding: string, callback: Function): void {
    try {
      // Validation rules
      if (typeof chunk !== "object") {
        // Invalid: Not an object, skip it
        return callback();
      }

      if (!chunk.id || !chunk.name) {
        // Invalid: Missing required fields, skip it
        return callback();
      }

      if (chunk.age && chunk.age < 0) {
        // Invalid: Negative age, skip it
        return callback();
      }

      // Valid: Pass it through
      this.push(chunk);
      callback();
    } catch (error) {
      callback(error);
    }
  }
}

// Usage:
const validator = new JSONValidator();

// Input data (some valid, some invalid)
const data = [
  { id: 1, name: "Alice", age: 30 }, // ✅ Valid
  { id: 2, name: "Bob", age: -5 }, // ❌ Invalid age
  { id: 3 }, // ❌ Missing name
  { id: 4, name: "Charlie", age: 25 }, // ✅ Valid
  "invalid data", // ❌ Not an object
  { id: 5, name: "David" }, // ✅ Valid (age optional)
];

// Write each item to the validator
data.forEach((item) => validator.write(item));
validator.end();

// Read validated data
validator.on("data", (validItem: any) => {
  console.log("Valid:", validItem);
});

// Output (only valid items):
// Valid: { id: 1, name: 'Alice', age: 30 }
// Valid: { id: 4, name: 'Charlie', age: 25 }
// Valid: { id: 5, name: 'David' }

Comparing Stream Types: Side by Side

Here's a quick reference to help you choose the right stream type:

Stream Type	Can Read?	Can Write?	Transforms Data?	Common Examples
Readable	✅ Yes	❌ No	❌ No	Files (read), HTTP requests, Database queries
Writable	❌ No	✅ Yes	❌ No	Files (write), HTTP responses, Database inserts
Duplex	✅ Yes	✅ Yes	❌ No	TCP sockets, WebSockets, Network connections
Transform	✅ Yes	✅ Yes	✅ Yes	Compression, Encryption, Data parsing

When to Use Each Stream Type

Use Readable Streams When:

You need to read data from a source
Processing large files that don't fit in memory
Receiving data over a network
Reading database results row by row
Monitoring log files in real-time

Use Writable Streams When:

You need to write data to a destination
Creating or appending to files
Sending HTTP responses
Writing to databases
Logging data continuously

Use Duplex Streams When:

You need bidirectional communication
Working with network sockets
Building real-time chat applications
Implementing custom protocols
Inter-process communication

Use Transform Streams When:

You need to modify data as it flows
Compressing or encrypting files
Converting between formats
Validating or filtering data
Modifying images or videos
Text processing (uppercase, lowercase, replacements)

Practical Benefits of Streams

Let's quantify the real-world benefits with concrete examples:

1. Memory Efficiency

// Scenario: Processing a 1GB CSV file

// ❌ Without streams: Load everything into memory
import { readFileSync } from "fs";

const entireFile = readFileSync("data.csv", "utf-8"); // Uses 1GB RAM
const lines = entireFile.split("\n");
// Memory usage: 1GB + (array overhead)

// ✅ With streams: Process line by line
import { createReadStream } from "fs";
import { createInterface } from "readline";

const stream = createReadStream("data.csv");
const rl = createInterface({ input: stream });

rl.on("line", (line: string) => {
  // Process one line at a time
  // Memory usage: ~few KB per line
});

// Result:
// Without streams: 1GB+ memory
// With streams: ~64KB memory (buffer size)
// That's a 16,000x reduction!

2. Time to First Byte (Performance)

// Scenario: Serving a large video file to a user

// ❌ Without streams: Load then send
app.get("/video", (req, res) => {
  const video = fs.readFileSync("movie.mp4"); // Wait for 2GB to load
  res.send(video); // User waits ~10 seconds
});

// ✅ With streams: Start sending immediately
app.get("/video", (req, res) => {
  const stream = fs.createReadStream("movie.mp4");
  stream.pipe(res); // User sees video in ~0.1 seconds
});

// Result:
// Without streams: 10+ second wait
// With streams: Video starts playing almost immediately

3. Scalability

// Scenario: Server handling multiple large file requests

// ❌ Without streams
// 10 users × 100MB per file = 1GB memory
// 100 users × 100MB per file = 10GB memory (server crashes!)

// ✅ With streams
// 10 users × 64KB buffer = 640KB memory
// 100 users × 64KB buffer = 6.4MB memory
// 1000 users × 64KB buffer = 64MB memory (no problem!)

// Result: Handle 100x more concurrent users with streams

Common Pitfalls and Solutions

Pitfall 1: Forgetting to Handle Errors

// ❌ Bad: No error handling
const stream = createReadStream("file.txt");
stream.on("data", (chunk) => {
  console.log(chunk);
});
// If file doesn't exist, your application crashes!

// ✅ Good: Always handle errors
const stream = createReadStream("file.txt");

stream.on("data", (chunk) => {
  console.log(chunk);
});

stream.on("error", (error) => {
  console.error("Stream error:", error.message);
  // Your app continues running
});

Pitfall 2: Not Ending Writable Streams

// ❌ Bad: Forgot to call end()
const writer = createWriteStream("output.txt");
writer.write("Some data");
// File might not be written completely!

// ✅ Good: Always call end()
const writer = createWriteStream("output.txt");
writer.write("Some data");
writer.end(); // Ensures data is flushed and file is closed

writer.on("finish", () => {
  console.log("File written successfully");
});

Pitfall 3: Ignoring Backpressure

// ❌ Bad: Writing without checking if the stream can handle it
const writer = createWriteStream("output.txt");

for (let i = 0; i < 1000000; i++) {
  writer.write(`Line ${i}\n`); // Might overwhelm the stream
}

// ✅ Good: Respect backpressure
const writer = createWriteStream("output.txt");

function writeData(i: number): void {
  let ok = true;

  while (i < 1000000 && ok) {
    ok = writer.write(`Line ${i}\n`);
    i++;
  }

  if (i < 1000000) {
    // Stream buffer is full, wait for it to drain
    writer.once("drain", () => {
      writeData(i);
    });
  } else {
    writer.end();
  }
}

writeData(0);

// Explanation:
// - writer.write() returns false when buffer is full
// - Wait for 'drain' event before writing more
// - This prevents memory overflow

Summary: Key Takeaways

Let's recap what you've learned about streams:

Streams process data incrementally instead of loading everything into memory at once
Memory efficiency: Streams use minimal memory (typically 64KB) regardless of file size
Immediate processing: Start working with data as soon as the first chunk arrives
Four types of streams:
- Readable: Read from a source (files, HTTP requests)
- Writable: Write to a destination (files, HTTP responses)
- Duplex: Both read and write (network sockets)
- Transform: Modify data as it flows (compression, encryption)
Real-world benefits: Handle 100x more concurrent users, 16,000x less memory, 100x faster time-to-first-byte
Always handle errors to prevent crashes
Call .end() on writable streams to ensure data is written
Respect backpressure to avoid memory overflow

What's Next?

Now that you understand the fundamentals of streams, you're ready to dive deeper:

Node.js Readable Streams: Learn how to create custom readable streams and control data flow
Node.js Writable Streams: Master writing data efficiently with backpressure handling
Stream Piping: Connect streams together to create data processing pipelines
Transform Streams: Build custom data transformers for your specific needs
Stream Best Practices: Advanced patterns for production applications

Streams are one of the most powerful features in Node.js for building efficient, scalable applications. The concepts you learned here will serve as the foundation for working with files, networks, and data processing throughout your development career.

What You Need to Know First​

What We'll Cover in This Article​

What We'll Explain Along the Way​

What Is a Stream?​

The Core Problem Streams Solve​

Understanding Streams Through Analogy​

The Water Tank Analogy​

Practical Example: Reading a Large Log File​

The Four Types of Streams in Node.js​

1. Readable Streams: Getting Data From a Source​

2. Writable Streams: Sending Data to a Destination​

3. Duplex Streams: Two-Way Communication​

4. Transform Streams: Modifying Data as It Flows​

Comparing Stream Types: Side by Side​

When to Use Each Stream Type​

Use Readable Streams When:​

Use Writable Streams When:​

Use Duplex Streams When:​

Use Transform Streams When:​

Practical Benefits of Streams​

1. Memory Efficiency​

2. Time to First Byte (Performance)​

3. Scalability​

Common Pitfalls and Solutions​

Pitfall 1: Forgetting to Handle Errors​

Pitfall 2: Not Ending Writable Streams​

Pitfall 3: Ignoring Backpressure​

Summary: Key Takeaways​

What's Next?​

What You Need to Know First

What We'll Cover in This Article

What We'll Explain Along the Way

What Is a Stream?

The Core Problem Streams Solve

Understanding Streams Through Analogy

The Water Tank Analogy

Practical Example: Reading a Large Log File

The Four Types of Streams in Node.js

1. Readable Streams: Getting Data From a Source

2. Writable Streams: Sending Data to a Destination

3. Duplex Streams: Two-Way Communication

4. Transform Streams: Modifying Data as It Flows

Comparing Stream Types: Side by Side

When to Use Each Stream Type

Use Readable Streams When:

Use Writable Streams When:

Use Duplex Streams When:

Use Transform Streams When:

Practical Benefits of Streams

1. Memory Efficiency

2. Time to First Byte (Performance)

3. Scalability

Common Pitfalls and Solutions

Pitfall 1: Forgetting to Handle Errors

Pitfall 2: Not Ending Writable Streams

Pitfall 3: Ignoring Backpressure

Summary: Key Takeaways

What's Next?