In recent years, Snowflake has emerged as a leading company in data storage and computing, attracting numerous developers. However, its interview process is known to be highly challenging. System design questions, in particular, test a candidate's architectural skills and require them to optimize, scale, and ensure high availability. If candidates fail to navigate these questions fluently, they risk being eliminated.

Today, we share a real interview experience of a candidate who faced the Quota Service design question at Snowflake. Despite having solid development experience, he struggled under the pressure of the interview. Fortunately, CSOAHELP’s remote interview assistance provided him with complete answers and code samples, allowing him to confidently handle every follow-up question and ultimately secure an offer from Snowflake.

The interview began with a brief introduction from the interviewer, followed by a direct technical question.

Quota Service Explanation
Companies like Google, Apple, Microsoft, etc. offer various services, like drive, photos, documents, mail, etc. but typically they will offer you a single quota. For example, you buy the 100GB storage plan, and as you use each of these systems, they will all consume from your same bucket of quota.

Can you design a Quota Service for Snowflake, ensuring that multiple services can consume from a shared quota while maintaining efficiency and consistency?

Hearing this question, the candidate immediately felt the pressure. Designing a high-concurrency, highly available distributed quota management system on the spot while addressing the interviewer’s follow-up questions was an immense challenge. CSOAHELP’s remote assistance team promptly provided a structured answer framework and code samples, enabling the candidate to respond smoothly.

The candidate began answering according to CSOAHELP’s guidance. He first analyzed the requirements, explaining that the core goal of the Quota Service was to manage storage quotas across multiple services while ensuring correctness under high concurrency. Additionally, it needed an efficient querying and updating mechanism to prevent quota overuse. He then described the system architecture, explaining that it consisted of three main components: the Quota Service as the quota management system responsible for querying and updating quotas; storage services managing specific data storage and calling the Quota Service; and a database and cache (PostgreSQL + Redis) for storing quota data and improving query speed.

The interviewer nodded in approval but quickly followed up with, “If multiple storage services request quotas simultaneously, how do you prevent over-allocation?”

CSOAHELP provided an answer: using a distributed counter to ensure correct quota deductions across services under high concurrency, combined with optimistic locking or distributed transactions to maintain data consistency. The candidate successfully repeated the response. The interviewer seemed satisfied but then pushed further, “If the Redis cache fails or the database crashes, how do you ensure system availability?”

To make the response more convincing, CSOAHELP provided a complete code sample.

import redis
import psycopg2

# Connect to Redis
redis_client = redis.StrictRedis(host='localhost', port=6379, db=0)

# Connect to PostgreSQL
conn = psycopg2.connect(database="quota_db", user="admin", password="password", host="localhost", port="5432")
cursor = conn.cursor()

def get_quota(user_id):
    quota = redis_client.get(f"quota:{user_id}")
    if quota is None:
        cursor.execute("SELECT quota FROM quota_table WHERE user_id=%s", (user_id,))
        quota = cursor.fetchone()
        redis_client.set(f"quota:{user_id}", quota)  # Cache the result
    return int(quota)

def consume_quota(user_id, amount):
    current_quota = get_quota(user_id)
    if current_quota >= amount:
        redis_client.decr(f"quota:{user_id}", amount)
        cursor.execute("UPDATE quota_table SET quota = quota - %s WHERE user_id = %s", (amount, user_id))
        conn.commit()
        return True
    return False

After hearing the implementation, the interviewer continued, “What happens if 10 million users request quotas simultaneously?”

CSOAHELP guided the candidate’s response: using Redis as a distributed cache, most queries would be served directly from the cache, reducing database load. Additionally, using Kafka message queues would asynchronously process storage service updates, preventing excessive database writes. The candidate fully repeated the response, and the interviewer finally showed a satisfied expression.

To further assess the candidate’s ability to scale the system, the interviewer asked, “If we need to support global users, how do we ensure scalability?”

CSOAHELP suggested multi-region deployment, where multiple Quota Service instances are deployed across different regions with consistent hashing for traffic distribution. User ID-based sharding ensures balanced database load. Only active users’ quota data is stored in Redis, while historical data is stored in NoSQL to improve query efficiency. The candidate repeated CSOAHELP’s guidance, and the interviewer nodded, “Good, this effectively improves system scalability.”

The interviewer then posed the final question: “If one of the storage services crashes, will it cause inconsistencies in the Quota Service data?”

CSOAHELP provided three solutions: using an event-driven architecture to log all quota changes, allowing event replay in case of failures; implementing health check mechanisms to periodically monitor storage services and prevent invalid requests; and applying a rollback mechanism to revert quota changes in case of failure. The candidate successfully reiterated these points, and the interviewer smiled, saying, “Great, this prevents inconsistencies due to storage service failures.”

After 45 minutes of intense discussion, the interviewer concluded, “Your answers were very comprehensive, your reasoning was clear, and your code implementation was solid.”

If you’re worried about struggling with high-concurrency system designs, nervous about rapid-fire follow-up questions, or prone to making mistakes under pressure, CSOAHELP’s remote interview assistance can help. We provide complete answers and code samples—you only need to repeat them fluently. Let CSOAHELP help you secure your dream job at top tech companies! Contact us now and start your journey to interview success.

经过csoahelp的面试辅助，候选人获取了良好的面试表现。如果您需要面试辅助或面试代面服务，帮助您进入梦想中的大厂，请随时联系我。

If you need more interview support or interview proxy practice, feel free to contact us. We offer comprehensive interview support services to help you successfully land a job at your dream company.