In Google’s technical interviews, no question is truly “simple.” Even if a problem appears to be just a basic data structure implementation, it often conceals deeper evaluations of algorithm optimization, engineering implementation, and system design. Many candidates believe that extensive LeetCode practice guarantees success, only to realize that Google interviews are far more complex than expected.

Today, we’re sharing a real interview experience of a candidate who encountered a data structure design problem at Google. CSOAHELP Remote Interview Assistance played a crucial role throughout the process, enabling the candidate to flawlessly deliver a high-quality answer and advance to the next round.

The interviewer first introduced the business scenario for Google Shopping and presented the following data structure design problem:

// Adds offer_id, with the specified price, for product_id.
void AddOffer(int64 offer_id, int64 product_id, double price);

// The offer is not valid anymore.
void RemoveOffer(int64 offer_id);

// Returns the id of an offer corresponding to the specified product, which has
// its price closest to the query parameter.
int64 QueryClosestOffer(int64 product_id, double price);

The interviewer explained that each product (product_id) has multiple offers (offer_id), each with a corresponding price. The required data structure must efficiently support the following:

• Adding new offers
• Removing invalid offers
• Quickly finding the closest price for a given product

The candidate initially considered using an unordered_map, mapping product_id to a list of offer_id and price. However, this approach requires iterating through all offers in QueryClosestOffer, resulting in O(n) time complexity, which is inefficient.

At this moment, CSOAHELP Remote Interview Assistance promptly provided complete textual guidance:

“A better data structure is an ordered map (std::map), which allows storing offers in sorted order by price, reducing query complexity to O(log n).”

The candidate immediately restated this idea and implemented the solution using the code template provided by CSOAHELP.

With CSOAHELP’s detailed code example guidance, the candidate implemented the following solution:

#include <iostream>
#include <map>
#include <unordered_map>

using namespace std;

unordered_map<int64_t, map<double, int64_t>> productOffers;  // Each product_id maps to an ordered map
unordered_map<int64_t, pair<int64_t, double>> offerDetails;  // Stores offer_id -> (product_id, price)

void AddOffer(int64_t offer_id, int64_t product_id, double price) {
    productOffers[product_id][price] = offer_id;
    offerDetails[offer_id] = {product_id, price};
}

void RemoveOffer(int64_t offer_id) {
    if (offerDetails.find(offer_id) == offerDetails.end()) return;
    auto [product_id, price] = offerDetails[offer_id];
    productOffers[product_id].erase(price);
    offerDetails.erase(offer_id);
}

int64_t QueryClosestOffer(int64_t product_id, double price) {
    if (productOffers.find(product_id) == productOffers.end() || productOffers[product_id].empty()) {
        return -1;  // No offers available
    }
    
    auto it = productOffers[product_id].lower_bound(price);
    if (it == productOffers[product_id].begin()) return it->second;
    if (it == productOffers[product_id].end()) return prev(it)->second;

    double leftDiff = price - prev(it)->first;
    double rightDiff = it->first - price;
    return leftDiff <= rightDiff ? prev(it)->second : it->second;
}

During the interview, the candidate read the code line by line and explained key points:

• std::map maintains offers in sorted order, enabling efficient closest price lookup using lower_bound(), reducing query complexity to O(log n).
• unordered_map stores offer details, ensuring O(1) deletion.
• Overall, insertion, deletion, and queries run in O(log n) time, making it suitable for large-scale data.

The interviewer acknowledged the approach and commented on the candidate’s clear thought process and well-structured code.

The interviewer then introduced a more challenging scenario:

“What if the number of product_ids grows significantly—reaching millions—and each product has tens of thousands of offers? How would you optimize this?”

The candidate hesitated, unsure how to improve scalability. CSOAHELP Remote Assistance immediately provided a fully structured response, which the candidate repeated:

“For large-scale data, we can use a Skip List instead of std::map, as Skip Lists perform better in concurrent read-write scenarios than Red-Black trees. Additionally, we can integrate Redis Sorted Sets for distributed storage, enabling efficient lookups at scale. Finally, leveraging an LRU Cache for high-frequency queries reduces database load.”

The candidate successfully delivered this expanded answer, and the interviewer appeared satisfied.

The interviewer then pushed further:

“What if some product_ids have a disproportionately large number of offers while others have very few? How would you optimize storage?”

CSOAHELP Remote Assistance immediately provided a refined explanation, which the candidate reiterated:

• Sharding (hash partitioning) distributes product_ids across different servers to balance load.
• LRU Caching ensures frequently queried offers are readily available, minimizing database queries.
• Periodic data cleanup removes inactive offers to optimize storage usage.

The interviewer nodded in agreement, acknowledging that the candidate had thoroughly addressed the large-scale data optimization challenge.

This interview did not simply test a data structure implementation but also evaluated the candidate’s algorithmic skills, system optimization knowledge, and ability to articulate ideas clearly. Without support, the candidate might have struggled with the optimization discussions, impacting their performance. However, CSOAHELP Remote Assistance provided:
✔ Complete code templates, allowing the candidate to read and explain confidently.
✔ Structured responses, ensuring smooth and precise answers to complex follow-up questions.
✔ Scalability insights, enabling the candidate to address advanced topics successfully.

Ultimately, the candidate passed the interview and advanced to the next round.

In top-tier tech company interviews, solving LeetCode problems alone is not enough. The real challenge lies in maintaining clarity under pressure, responding effectively to interviewers’ follow-up questions, and applying engineering principles to optimize algorithms.

If you don’t want to risk losing an opportunity due to nervousness or lack of structured responses, CSOAHELP can provide real-time precision support, ensuring you succeed in landing your dream job at top companies like Google, Meta, Amazon, and more!