1. Introduction

1.1 Tutorial's Goal

This tutorial aims to provide a comprehensive guide on how to optimize Google's Firestore for large web applications. Firestore is a NoSQL document database for mobile, web, and server development from Firebase and Google Cloud.

1.2 What the User Will Learn

At the end of this tutorial, you'll have the knowledge to:
- Understand Firestore's structure and how to use it optimally
- Implement best practices to optimize Firestore performance
- Use Firestore features to speed up your large scale applications

1.3 Prerequisites

Before proceeding with this tutorial, you should have:
- Basic knowledge of Firestore and Firebase
- Basic understanding of JavaScript and Node.js
- Firebase project with Firestore enabled

2. Step-by-Step Guide

2.1 Firestore Structure

Firestore organizes data as documents (which are key-value pairs) and collections (which are containers for documents). Understanding this structure is key to optimizing performance.

2.2 Indexing

Firestore automatically creates indexes for all fields in your documents, which allows for quick querying. However, this also means additional storage and cost. Therefore, it's crucial to only index what you need.

2.3 Denormalization

In Firestore, it's often more efficient to duplicate data (denormalization) than to perform complex queries. This is because Firestore charges per read, write, and delete operation.

2.4 Batch Operations

Batch operations allow you to perform multiple operations in a single transaction. This is more efficient than performing each operation individually.

3. Code Examples

3.1 Batch Operations

Below is a code snippet demonstrating how to perform batch operations in Firestore:

let batch = db.batch();

let nycRef = db.collection('cities').doc('NYC');
batch.set(nycRef, {name: 'New York City'});

let sfRef = db.collection('cities').doc('SF');
batch.update(sfRef, {population: 1000000});

let laRef = db.collection('cities').doc('LA');
batch.delete(laRef);

batch.commit().then(function () {
  // ...
});

In this code:
- We first create a write batch
- We then add operations (set, update, delete) to the batch
- We commit the batch, which will then atomically apply all changes

3.2 Denormalization

Here's a code snippet showing how you might denormalize data:

let cityRef = db.collection('cities').doc('LA');
let getDoc = cityRef.get()
  .then(doc => {
    if (!doc.exists) {
      console.log('No such document!');
    } else {
      let data = doc.data();
      // Copy the data to a 'citiesSummary' collection
      db.collection('citiesSummary').doc('LA').set(data);
    }
  })
  .catch(err => {
    console.log('Error getting document', err);
  });

In this code:
- We're fetching a document from the cities collection
- If the document exists, we're copying its data to another collection

4. Summary

In this tutorial, we've covered:
- The basic structure of Firestore
- The importance of indexing and denormalization
- Using batch operations to improve performance
- Code examples for each of the above points

5. Practice Exercises

To further cement your understanding, try out these exercises:
1. Create a Firestore collection and populate it with some documents. Then, write a function to index only specific fields in the documents.
2. Write a function to copy data from one collection to another (denormalization).
3. Write a function to perform multiple operations (create, update, delete) in a single batch.

Remember, practice is key to mastering any new concept. Happy coding!