Atlas Whoff

Posted on Apr 7 • Edited on Apr 11

Node.js Streams: Processing Large Files Without Running Out of Memory

#javascript #node #performance #tutorial

The Memory Problem

// This will OOM on a 2GB file
const data = await fs.readFile('huge-file.csv'); // reads entire file into memory
const lines = data.toString().split('\n');
// crashes with: JavaScript heap out of memory

Streams process data in chunks. You never load the full file—you process pieces as they arrive.

Reading Files With Streams

import { createReadStream } from 'fs';
import { createInterface } from 'readline';

async function processCSV(filePath: string) {
  const fileStream = createReadStream(filePath);
  const rl = createInterface({ input: fileStream, crlfDelay: Infinity });

  let lineCount = 0;

  for await (const line of rl) {
    // Process one line at a time — never more than ~1KB in memory
    const [name, email, amount] = line.split(',');
    await processRecord({ name, email, amount });
    lineCount++;
  }

  return lineCount;
}

// A 10GB file uses < 50MB of memory
await processCSV('./million-rows.csv');

Transform Streams

import { Transform, pipeline } from 'stream';
import { promisify } from 'util';
import zlib from 'zlib';

const pipe = promisify(pipeline);

// Custom transform: parse CSV lines to objects
const csvParser = new Transform({
  objectMode: true,
  transform(chunk, encoding, callback) {
    const line = chunk.toString().trim();
    if (line) {
      const [id, name, email] = line.split(',');
      this.push({ id, name, email });
    }
    callback();
  },
});

// Pipeline: read → parse → compress → write
await pipe(
  createReadStream('users.csv'),
  csvParser,
  new Transform({
    objectMode: true,
    transform(record, encoding, callback) {
      this.push(JSON.stringify(record) + '\n');
      callback();
    },
  }),
  zlib.createGzip(),
  createWriteStream('users.jsonl.gz')
);

pipeline handles backpressure automatically—it slows the source if the destination can't keep up.

HTTP Streaming

import { Readable } from 'stream';

// Stream a large file download
app.get('/download/:filename', (req, res) => {
  const filePath = path.join(__dirname, 'files', req.params.filename);
  const stat = fs.statSync(filePath);

  res.setHeader('Content-Type', 'application/octet-stream');
  res.setHeader('Content-Length', stat.size);
  res.setHeader('Content-Disposition', `attachment; filename="${req.params.filename}"`);

  const stream = createReadStream(filePath);
  stream.pipe(res); // stream directly to client, no buffering

  stream.on('error', (err) => {
    res.status(500).end(err.message);
  });
});

// Stream a database query
app.get('/export', async (req, res) => {
  res.setHeader('Content-Type', 'text/csv');
  res.setHeader('Content-Disposition', 'attachment; filename="export.csv"');
  res.write('id,name,email\n');

  // Prisma cursor-based streaming
  const cursor = prisma.users.findMany({
    cursor: undefined,
    take: 100,
  });

  // Or use raw query with streaming
  const stream = await prisma.$queryRawUnsafe('SELECT id, name, email FROM users');
  for (const row of stream) {
    res.write(`${row.id},"${row.name}",${row.email}\n`);
  }

  res.end();
});

Async Generator Streams

async function* generateUsers(batchSize = 100) {
  let page = 0;

  while (true) {
    const users = await prisma.users.findMany({
      skip: page * batchSize,
      take: batchSize,
    });

    if (users.length === 0) return;

    yield* users; // yield one user at a time
    page++;
  }
}

// Convert to Node.js Readable stream
const userStream = Readable.from(generateUsers());

// Pipe to CSV file
userStream
  .pipe(csvStringify({ header: true, columns: ['id', 'name', 'email'] }))
  .pipe(createWriteStream('all-users.csv'));

Backpressure Handling

async function writeWithBackpressure(
  readable: Readable,
  writable: Writable
): Promise<void> {
  for await (const chunk of readable) {
    const canContinue = writable.write(chunk);

    if (!canContinue) {
      // Writable buffer is full — wait for drain
      await new Promise(resolve => writable.once('drain', resolve));
    }
  }

  writable.end();
}

Without backpressure handling, a fast readable overwhelms a slow writable, filling memory.

When to Use Streams

Files > 10MB
Database exports
Log processing
Video/audio processing
HTTP file uploads/downloads
Real-time data pipelines

For small data (< 1MB), fs.readFile is simpler and fine. Streams shine when you can't fit the data in memory.

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