Cost optimization (read/write reduction) in Firebase - Time & Space Complexity

Time Complexity: Cost optimization (read/write reduction)

O(n)

Understanding Time Complexity

When using Firebase, each read or write operation costs money and time.

We want to understand how these costs grow as we do more operations.

Scenario Under Consideration

Analyze the time complexity of the following operation sequence.

const db = firebase.firestore();

async function fetchUsers(userIds) {
  const users = [];
  for (const id of userIds) {
    const doc = await db.collection('users').doc(id).get();
    users.push(doc.data());
  }
  return users;
}

This code fetches user data for each user ID by reading documents one by one.

Identify Repeating Operations

Identify the API calls, resource provisioning, data transfers that repeat.

• Primary operation: Reading a user document from Firestore.
• How many times: Once for each user ID in the input list.

How Execution Grows With Input

Each user ID causes one read operation, so the total reads grow directly with the number of users.

Input Size (n)	Approx. API Calls/Operations
10	10 reads
100	100 reads
1000	1000 reads

Pattern observation: The number of reads grows linearly as the input size increases.

Final Time Complexity

Time Complexity: O(n)

This means the cost and time increase directly in proportion to how many user IDs we fetch.

Common Mistake

[X] Wrong: "Fetching multiple users one by one is just as cheap as fetching them all at once."

[OK] Correct: Each separate read counts as a separate operation and adds cost and time, so doing many single reads is more expensive than batching.

Interview Connect

Understanding how costs grow with reads and writes helps you design efficient Firebase apps that save money and run faster.

Self-Check

"What if we changed the code to fetch all user documents in a single batch request? How would the time complexity change?"

Practice

1. Which practice helps reduce Firebase costs by minimizing the number of document reads?

easy

A. Use separate writes for each field update

B. Write data multiple times to ensure consistency

C. Read data once instead of using continuous listeners

D. Always overwrite entire documents on update

Solution

1. Step 1: Understand Firebase read costs

   Firebase charges for each document read. Continuous listeners keep reading data repeatedly, increasing costs.

2. Step 2: Choose cost-saving read method

   Reading data once fetches the document a single time, reducing read operations and costs.

3. Final Answer:

   Read data once instead of using continuous listeners -> Option C

4. Quick Check:

   Minimize reads = Read once [OK]

Hint: Read once, avoid continuous listeners to save costs [OK]

Common Mistakes:

• Thinking continuous listeners cost less
• Assuming multiple writes reduce costs
• Overwriting entire documents unnecessarily

2. Which Firebase syntax correctly updates only specific fields without overwriting the entire document?

easy

A. db.collection('users').doc('abc').set({age: 30}, {merge: true})

B. db.collection('users').doc('abc').set({age: 30})

C. db.collection('users').doc('abc').update({age: 30}, {merge: true})

D. db.collection('users').doc('abc').set({age: 30}, {overwrite: false})

Solution

1. Step 1: Identify update methods in Firebase

   Using set() with {merge: true} updates only specified fields without overwriting the whole document.

2. Step 2: Check options for correctness

   db.collection('users').doc('abc').set({age: 30}, {merge: true}) uses set() with merge: true, which is the correct syntax for partial updates.

3. Final Answer:

   db.collection('users').doc('abc').set({age: 30}, {merge: true}) -> Option A

4. Quick Check:

   Partial update = set with merge true [OK]

Hint: Use set with {merge: true} to update fields only [OK]

Common Mistakes:

• Using set without merge overwrites entire document
• Using update() with invalid merge option
• Using invalid option {overwrite: false}

3. What is the effect of this Firebase code snippet on write operations?

const batch = db.batch();
batch.set(db.collection('users').doc('a'), {name: 'Alice'});
batch.set(db.collection('users').doc('b'), {name: 'Bob'});
batch.commit();

medium

A. Two separate write operations are charged

B. One combined write operation is charged

C. No write operations are charged until batch.commit() is called

D. Writes are charged only if documents exist

Solution

1. Step 1: Understand batch writes in Firebase

   Batch writes group multiple writes to execute atomically but each write counts as a separate operation for billing.

2. Step 2: Count write operations in the snippet

   Two set calls inside the batch mean two write operations charged when commit() runs.

3. Final Answer:

   Two separate write operations are charged -> Option A

4. Quick Check:

   Batch writes count each write separately [OK]

Hint: Batch groups writes but charges per write [OK]

Common Mistakes:

• Thinking batch commit charges only once
• Assuming writes are free before commit
• Believing writes charge only if doc exists

4. You want to update a user's email without overwriting other fields, but your code overwrites the entire document. What is the error?

db.collection('users').doc('user1').set({email: '[e-mail]'});

medium

A. Using update() instead of set()

B. Email field name is invalid

C. Document path is incorrect

D. Missing {merge: true} option in set()

Solution

1. Step 1: Identify why entire document is overwritten

   Using set() without {merge: true} replaces the whole document, causing data loss.

2. Step 2: Fix the code to update only one field

   Add {merge: true} to set() to update only the email field without overwriting others.

3. Final Answer:

   Missing {merge: true} option in set() -> Option D

4. Quick Check:

   Partial update needs merge true [OK]

Hint: Add {merge: true} to set() to avoid overwriting [OK]

Common Mistakes:

• Using set() without merge option
• Confusing update() and set() usage
• Assuming field names cause overwrite

5. You want to reduce Firebase costs by minimizing writes when updating user profiles. Which approach is best to update multiple fields efficiently without overwriting existing data?

hard

A. Use multiple separate set() calls without merge option

B. Use a batch write with set() and merge: true for all updates

C. Use update() for each field separately in different calls

D. Overwrite entire document with set() each time

Solution

1. Step 1: Understand cost impact of writes

   Each write operation costs money. Multiple separate writes increase cost and risk overwriting data.

2. Step 2: Choose efficient update method

   Batch writes combine multiple updates into one commit, reducing network overhead. Using set() with {merge: true} updates only needed fields without overwriting.

3. Final Answer:

   Use a batch write with set() and merge: true for all updates -> Option B

4. Quick Check:

   Batch + merge = efficient partial updates [OK]

Hint: Batch writes with merge true save cost and data [OK]

Common Mistakes:

• Overwriting entire documents unnecessarily
• Using many separate update calls
• Ignoring batch writes for multiple updates