Data Engineer interview questions & answers

Model answer

1. Tell me about a time you faced a significant challenge or difficult problem — at work, during an internship, in a research assignment, or on a project. How did you respond, what actions did you take, and what was the outcome?

Situation During my internship at a tech startup, I was tasked with optimizing the performance of a critical feature in our mobile app. The app was experiencing significant latency issues, which led to a poor user experience and negative feedback from our customers. As a junior developer, this was a daunting challenge, as the feature was complex and deeply integrated into the app.

Task My goal was to identify the root cause of the latency and implement a solution that would improve the app's performance by at least 30%, all within a tight two-week deadline. The key constraint was maintaining the app's existing functionality while making these improvements.

Action

• I began by conducting a thorough analysis of the app's performance metrics to pinpoint the specific areas causing delays. I used profiling tools to gather data on memory usage and processing time.
• After identifying that the main issue was inefficient data retrieval from the backend, I proposed a solution to implement data caching on the client side to reduce redundant network calls.
• I collaborated with the backend team to ensure that the data caching strategy would not compromise data consistency. We agreed on a cache invalidation policy that balanced performance with up-to-date information.
• I then implemented the caching mechanism and conducted extensive testing to ensure it worked seamlessly with the existing app features.
• Throughout the process, I communicated regularly with my manager and the team, providing updates and seeking feedback to ensure alignment with the project goals.

Result The optimization efforts resulted in a 40% reduction in app latency, exceeding the original target. This improvement led to a significant increase in user satisfaction, as reflected in positive customer reviews and a 15% increase in user retention over the following month. This experience taught me the importance of data-driven decision-making and cross-team collaboration in solving complex technical challenges.

2. Tell me about a time your team was struggling or blocked on execution. How did you motivate the team, align people on a plan, and help drive a solution?

Situation While working on a group project during my final year at university, our team faced a major roadblock. We were developing a web application for a class assignment, but we encountered a critical bug that prevented the app from functioning correctly. The deadline was approaching, and team morale was low due to the lack of progress.

Task As the team lead, my responsibility was to motivate the team, identify a clear path forward, and ensure we delivered a working solution by the deadline. The challenge was to keep everyone focused and aligned despite the frustration and time pressure.

Action

• I organized an emergency team meeting to openly discuss the issues and gather input from each member on potential solutions. This fostered a sense of ownership and collaboration.
• We decided to divide the tasks based on individual strengths, with some members focusing on debugging while others worked on documentation and testing.
• I took the lead on the debugging effort, systematically isolating the bug through a process of elimination and leveraging online resources and forums for additional insights.
• To keep the team motivated, I set up daily check-ins to track progress and celebrate small wins, which helped maintain momentum and morale.
• I also ensured that we communicated regularly with our professor to manage expectations and seek guidance when necessary.

Result We successfully identified and resolved the bug two days before the deadline, allowing us to complete the project on time. The final presentation was well-received, and we achieved a top grade for our work. This experience reinforced the value of clear communication, strategic task allocation, and maintaining a positive team environment under pressure.

3. Why do you want to join Amazon, and why is this role a good fit for your background and career goals?

Situation I have always admired Amazon's commitment to innovation and customer obsession, which aligns closely with my personal values and career aspirations. As a software development engineer intern, I am eager to contribute to projects that have a real-world impact and to learn from some of the best minds in the industry.

Task My goal is to leverage my technical skills and problem-solving abilities to contribute to Amazon's mission of being Earth's most customer-centric company. I am particularly drawn to the opportunity to work on scalable, high-impact projects that challenge me to grow and learn continuously.

Action

• I have prepared for this role by gaining experience in software development through internships and academic projects, where I honed my skills in coding, debugging, and collaborating with cross-functional teams.
• I have also familiarized myself with Amazon's Leadership Principles, which I believe are crucial for driving innovation and delivering results. I am committed to embodying these principles in my work.
• I am particularly excited about the prospect of working on projects that involve cutting-edge technologies and require innovative solutions, as I have a strong passion for learning and being curious about new advancements.

Result Joining Amazon would be a significant step in my career, providing me with the platform to apply my skills in a dynamic environment and to contribute to projects that make a difference. I am confident that this role will not only allow me to grow professionally but also enable me to add value to Amazon's teams and customers.

Model answer

Situation

In my role as a product manager at a tech company, I was responsible for launching a new social media feature aimed at increasing user engagement. The feature allowed users to create and share short video clips, similar to Instagram Reels. Given the competitive landscape, it was crucial to ensure the feature's success by closely monitoring its performance and impact on user engagement.

Task

My goal was to define success metrics for the feature, investigate any sudden drops in these metrics, and ensure that we could predict and address potential issues proactively. The challenge was to balance between immediate user engagement and long-term user retention.

Action

• Defined Success Metrics: I identified six key metrics: Daily Active Users (DAU), Average Session Duration, User Retention Rate, Number of Videos Created, Share Rate (how often videos were shared), and Feature Adoption Rate (percentage of users using the feature).
• Root-Cause Investigation Plan: If the primary metric, DAU, dropped, I planned to: 1. Analyze user feedback and reviews to identify any common complaints or issues. 2. Check for any recent changes or updates that might have affected user experience. 3. Review system logs for any technical issues or outages. 4. Conduct A/B testing to isolate potential causes. 5. Engage with customer support to gather insights from user interactions.
• Leading Metric: I chose the Number of Videos Created as a leading metric. A decline in this metric could predict a drop in DAU, as it directly reflects user engagement and content creation, which are critical for retaining users.
• Stakeholder Questions: Stakeholders might ask about the reasons for the metric drop, user satisfaction, and competitive positioning. I would answer by presenting data-driven insights, user feedback analysis, and competitive benchmarking to provide a comprehensive view.
• User Segments: I proposed segmenting users by demographics (age, location), engagement level (high, medium, low), and content creators vs. consumers. Each segment could reveal different user behaviors and preferences, helping tailor strategies to enhance engagement and retention.

Result

By implementing these strategies, we were able to maintain a steady increase in user engagement and quickly address any issues that arose. The feature's adoption rate exceeded initial projections by 20%, and user retention improved by 15% over six months. This experience reinforced the importance of proactive metric monitoring and a structured approach to problem-solving.

Model answer

Situation

During a pair-programming interview at a tech company, I was tasked with solving several SQL exercises in a shared editor. The interviewer emphasized collaboration and the use of a coding assistant. My role was to demonstrate effective teamwork and problem-solving skills while leveraging AI tools appropriately. This was crucial because it tested not only my technical abilities but also my capacity to work collaboratively and adaptively in a dynamic environment.

Task

My goal was to collaboratively construct accurate SQL queries to solve the given problems while maintaining open communication with the interviewer. I needed to balance the use of AI assistance without relying on it to do the thinking for me, ensuring that my reasoning and understanding were clear and correct.

Action

• I began by discussing the requirements with the interviewer to ensure a clear understanding of the problem. I asked clarifying questions to confirm the data structure and the expected output.
• As we started constructing the SQL query, I proposed an initial approach based on my understanding. I explained my thought process to the interviewer, highlighting the logic behind my choices.
• I used the coding assistant to check syntax and suggest optimizations, ensuring that I understood each suggestion before implementing it. This allowed me to leverage AI for efficiency while maintaining control over the query logic.
• When the assistant suggested a solution that seemed incorrect, I communicated my concerns to the interviewer. We reviewed the suggestion together, discussing why it might not fit the problem requirements.
• After agreeing on a correct approach, I ran the query and verified the results. I explained the verification process to the interviewer, detailing how I checked for accuracy against the expected outcomes.
• When errors occurred, I took the initiative to debug by systematically reviewing each part of the query, discussing potential issues with the interviewer, and iterating on the solution.

Result

By the end of the session, we successfully completed the SQL exercises with accurate queries. The interviewer appreciated my collaborative approach and ability to use AI tools effectively without outsourcing my reasoning. This experience reinforced the importance of clear communication and critical thinking when working with both human and AI collaborators. It taught me how to balance technology use with personal expertise, ensuring that I remain an active participant in problem-solving processes.

Model answer

Situation

In my role as a data analyst at a mid-sized e-commerce company, I was tasked with creating a dashboard for our product management team. The company was experiencing a plateau in sales growth, and the stakeholders needed a comprehensive view of product performance to identify potential issues. The dashboard was crucial because it would provide insights into sales trends, customer behavior, and inventory levels, enabling data-driven decision-making.

Task

My primary goal was to design and implement a dashboard that would accurately reflect key performance metrics such as sales volume, conversion rates, and inventory turnover. A critical constraint was ensuring the data's accuracy and timeliness, as stakeholders needed real-time insights to make informed decisions.

Action

• I began by conducting interviews with product managers to understand their specific needs and the metrics that would be most valuable for them. This helped me tailor the dashboard to their requirements.
• I collaborated with the data engineering team to ensure data pipelines were optimized for real-time data ingestion. This involved setting up ETL processes that minimized latency and ensured data accuracy.
• To address data quality, I implemented validation checks that flagged anomalies and discrepancies in the data. This proactive approach helped maintain the integrity of the dashboard's insights.
• I used a BI tool to design the dashboard, focusing on user-friendly visualizations that highlighted key metrics. I iterated on the design based on feedback from initial user testing sessions with stakeholders.
• Once the dashboard was live, I conducted training sessions with the product team to ensure they could effectively interpret the data and leverage it for strategic decisions.

Result

The dashboard successfully highlighted a significant issue: certain products had high view rates but low conversion rates. This insight led us to investigate further, revealing that the product descriptions were not aligned with customer expectations. By updating the descriptions, we saw a 15% increase in conversion rates within two months.

Reflecting on the project, I learned the importance of continuous stakeholder engagement and iterative design. The feedback loop with stakeholders was invaluable in refining the dashboard to meet their evolving needs, ultimately enhancing its impact on the business.

Model answer

Situation

In my previous role as a software engineer at a mid-sized tech company, I was part of a team tasked with developing a new feature for our flagship product. This feature aimed to enhance user engagement by providing personalized content recommendations. The project was high-stakes because it directly impacted user retention and satisfaction, which were key performance indicators for our business.

Task

My specific responsibility was to lead the integration of machine learning algorithms into our existing system to enable personalized recommendations. The main challenge was to ensure that the integration was seamless and did not disrupt the current user experience.

Action

• I began by collaborating closely with our data scientist to understand the algorithms and the data requirements. We held several brainstorming sessions to align on the project goals and technical constraints.
• I took the initiative to design a modular architecture that allowed for easy integration of the machine learning models. This involved creating APIs that could fetch user data, process it, and return recommendations in real-time.
• To ensure smooth collaboration, we divided responsibilities based on our expertise. The data scientist focused on refining the algorithms, while I concentrated on the system integration and performance optimization.
• We encountered disagreements on the data processing pipeline's complexity. I proposed a compromise by implementing a phased approach, starting with a simpler model to validate the concept before scaling up.
• Throughout the project, I maintained open communication with the product manager and other stakeholders to keep them informed of our progress and any potential risks.

Result

The project was successfully completed within the deadline, and the new feature led to a 15% increase in user engagement within the first quarter of its launch. This success was a testament to our effective cross-functional collaboration and strategic planning. Reflecting on this experience, I learned the importance of flexibility and open communication in resolving technical disagreements and driving project success. If I were to do this project again, I would invest more time in user testing to gather early feedback and iterate on the solution more rapidly.

Model answer

Situation

In my previous role as a data engineer at a tech company, I led a project to develop a large-scale data processing system. The system was designed to handle and analyze data streams from millions of IoT devices in real-time. This project was critical as it aimed to enhance our data analytics capabilities, providing real-time insights to our clients, which was a key differentiator in our market.

Task

My primary responsibility was to architect and implement the data pipeline, ensuring it was scalable and reliable under high data throughput. A key constraint was the need to integrate with existing infrastructure without disrupting ongoing operations.

Action

• I began by organizing a series of brainstorming sessions with cross-functional teams, including data scientists, backend developers, and UX designers, to align on the project goals and technical requirements.
• To address scalability, I chose a distributed processing framework, Apache Kafka, for its robustness in handling high-throughput data streams. This decision was based on its ability to process large volumes of data with low latency.
• I implemented a fault-tolerant architecture by setting up multiple Kafka brokers and ensuring data replication across nodes. This setup minimized the risk of data loss and improved system reliability.
• During the development phase, I encountered a production data issue where data was not being processed in real-time due to a misconfiguration in the Kafka consumer settings. Under time pressure, I quickly diagnosed the issue by analyzing the consumer logs and identified a bottleneck in the data ingestion process.
• I resolved the issue by optimizing the consumer configurations and increasing the number of partitions, which improved data processing speed and throughput.
• To prevent recurrence, I established a monitoring system using Grafana and Prometheus to track data flow metrics and set up alerts for any anomalies.

Result

The project was completed successfully, and the system processed over a billion data points daily with minimal latency. This led to a 30% increase in client satisfaction due to improved data insights. The experience taught me the importance of proactive monitoring and the need for a flexible, scalable architecture. Additionally, it reinforced the value of cross-team collaboration in achieving complex project goals.

Adaptation to Stakeholder Changes

When stakeholders changed schedules last minute, I adapted by reprioritizing tasks and communicating the impact on timelines transparently. I reset expectations by providing a revised project plan and ensured all team members were aligned with the new schedule.

Motivation for Joining USDS

I am motivated to join a public-sector data engineering team like USDS because I am passionate about using technology to drive social impact. In my first 90 days, I aim to understand the existing data infrastructure, identify areas for improvement, and contribute to projects that enhance public services through data-driven solutions.

Model answer

// Problem 1: Count unique Morse code translations

function uniqueMorseRepresentations(words) {
    const morseCodeMap = [
        ".-", "-...", "-.-.", "-..", ".", "..-.", "--.", "....", "..", ".---", "-.-", ".-..", "--",
        "-.", "---", ".--.", "--.-", ".-.", "...", "-", "..-", "...-", ".--", "-..-", "-.--", "--.."
    ];
    
    const seen = new Set();
    
    for (let word of words) {
        let morseTranslation = '';
        for (let char of word) {
            morseTranslation += morseCodeMap[char.charCodeAt(0) - 'a'.charCodeAt(0)];
        }
        seen.add(morseTranslation);
    }
    
    return seen.size;
}

// Problem 2: Return all valid word-break sentences

function wordBreak(s, wordDict) {
    const wordSet = new Set(wordDict);
    const memo = new Map();
    
    function backtrack(start) {
        if (memo.has(start)) return memo.get(start);
        if (start === s.length) return [''];
        
        const sentences = [];
        
        for (let end = start + 1; end <= s.length; end++) {
            const word = s.substring(start, end);
            if (wordSet.has(word)) {
                const restOfSentences = backtrack(end);
                for (let sentence of restOfSentences) {
                    sentences.push(word + (sentence ? ' ' + sentence : ''));
                }
            }
        }
        
        memo.set(start, sentences);
        return sentences;
    }
    
    return backtrack(0);
}

// Approach for Problem 1:
// - Create a map of Morse code for each letter.
// - Use a set to store unique Morse translations of words.
// - For each word, translate it to Morse code and add to the set.
// - Return the size of the set as the count of unique translations.

// Approach for Problem 2:
// - Use a backtracking approach with memoization to explore all possible sentences.
// - For each starting index, check all substrings if they are in the word dictionary.
// - Recursively find valid sentences for the remaining string.
// - Memoize results to avoid redundant calculations.

// Complexity:
// - Problem 1: Time O(n * m), Space O(n), where n is the number of words and m is the average length of a word.
// - Problem 2: Time O(n^3), Space O(n^3), where n is the length of the string `s`.

Model answer

# Part 1: SQL Queries

# 1. Count good, not-yet-returned books
# This query counts the number of books that are in 'good' condition and have not been returned.
"""
SELECT COUNT(*)
FROM Loans
JOIN Books ON Loans.book_id = Books.book_id
WHERE Books.condition = 'good' AND Loans.returned_at IS NULL;
"""

# 2. Top 3 high-value books with many copies
# This query retrieves the top 3 books with more than 10 copies and the highest lifetime value.
"""
SELECT book_id, title
FROM Books
WHERE copies > 10
ORDER BY lifetime_value DESC, book_id ASC
LIMIT 3;
"""

# Part 2: Python Functions

# Task 1: Calculate the average lifetime value of books in 'good' condition
def average_lifetime_value_good_books(books):
    """
    Calculate the average lifetime value of books in 'good' condition.
    
    :param books: List of dictionaries, each representing a book with keys 'condition' and 'lifetime_value'.
    :return: Float representing the average lifetime value of books in 'good' condition.
    """
    total_value = 0
    count = 0
    
    for book in books:
        if book['condition'] == 'good':
            total_value += book['lifetime_value']
            count += 1
    
    return total_value / count if count > 0 else 0

# Task 2: Find members who have reserved more than a given number of copies
def members_with_reserved_copies(members, min_reserved):
    """
    Find members who have reserved more than a given number of copies.
    
    :param members: List of dictionaries, each representing a member with keys 'member_id' and 'reserved_copies'.
    :param min_reserved: Integer representing the minimum number of reserved copies.
    :return: List of member IDs who have reserved more than the given number of copies.
    """
    result = []
    
    for member in members:
        if member['reserved_copies'] > min_reserved:
            result.append(member['member_id'])
    
    return result

• SQL Query 1: Joins the Loans and Books tables to count books in 'good' condition that have not been returned (returned_at IS NULL).
• SQL Query 2: Selects books with more than 10 copies, ordering by lifetime_value descending and book_id ascending, limiting to the top 3.
• Python Function 1: Iterates over books, calculating the average lifetime_value for those in 'good' condition.
• Python Function 2: Filters members who have reserved more than a specified number of copies, returning their IDs.

Complexity:

• SQL Queries: Both queries have a time complexity of O(n) where n is the number of rows in the Books or Loans table.
• Python Functions: Both functions have a time complexity of O(n), where n is the number of books or members in the input list.

Model answer

Clarifying Questions

1. What are the constraints on the input size? This helps determine if a more efficient algorithm is necessary.
2. Can we assume that the input data is always valid, or do we need to handle invalid inputs?

Edge Cases

• Empty Input: Ensure the function handles cases where input is empty or null.
• Single Element: Consider how the function behaves with the smallest non-empty input.
• Large Numbers: Check if the function can handle very large numbers without overflow.
• Negative Numbers: If applicable, ensure the function correctly processes negative numbers.

Code Implementation

def find_maximum_subarray_sum(arr):
    # Initialize variables to store the maximum sum and current sum
    max_sum = float('-inf')
    current_sum = 0
    
    for num in arr:
        # Update the current sum by adding the current number
        current_sum += num
        
        # Update max_sum if current_sum is greater
        if current_sum > max_sum:
            max_sum = current_sum
        
        # Reset current_sum to zero if it becomes negative
        if current_sum < 0:
            current_sum = 0
    
    return max_sum

# Example usage:
# print(find_maximum_subarray_sum([1, -2, 3, 4, -1, 2, 1, -5, 4]))  # Output: 10

• Formatting Choices: The code is structured with clear variable names (max_sum, current_sum) and comments to enhance readability. The loop iterates through each element, maintaining a running sum and updating the maximum found so far.

Complexity

• Time Complexity: O(n), where n is the number of elements in the array. The algorithm iterates through the array once.
• Space Complexity: O(1), as it uses a constant amount of additional space regardless of input size.

Test Plan

1. Empty Array: find_maximum_subarray_sum([]) should return 0 or handle gracefully.
2. Single Element: find_maximum_subarray_sum([5]) should return 5.
3. All Negative Numbers: find_maximum_subarray_sum([-1, -2, -3]) should return -1.
4. Mixed Positive and Negative: find_maximum_subarray_sum([1, -2, 3, 4, -1, 2, 1, -5, 4]) should return 10.
5. Large Input: Test with a large array to ensure performance constraints are met.

This solution uses Kadane's algorithm to efficiently find the maximum subarray sum, balancing between functional and imperative programming for optimal performance.

Model answer

// Definition for singly-linked list.
function ListNode(val, next = null) {
  this.val = val;
  this.next = next;
}

function mergeTwoLists(l1, l2) {
  // Create a dummy node to act as the start of the merged list
  let dummy = new ListNode(-1);
  // This will be our current node in the new list
  let current = dummy;

  // Traverse both lists
  while (l1 !== null && l2 !== null) {
    // Compare the values in the current nodes of both lists
    if (l1.val < l2.val) {
      // If l1's value is smaller, attach it to the merged list
      current.next = l1;
      l1 = l1.next; // Move to the next node in l1
    } else {
      // If l2's value is smaller or equal, attach it to the merged list
      current.next = l2;
      l2 = l2.next; // Move to the next node in l2
    }
    // Move to the next node in the merged list
    current = current.next;
  }

  // If one of the lists is not empty, attach it to the end of the merged list
  if (l1 !== null) {
    current.next = l1;
  } else if (l2 !== null) {
    current.next = l2;
  }

  // Return the merged list, which starts at dummy.next
  return dummy.next;
}

• Approach:
• Use a dummy node to simplify edge cases and maintain a reference to the head of the merged list.
• Traverse both linked lists simultaneously, comparing the current nodes.
• Append the smaller node to the merged list and move the pointer of that list forward.
• Once one list is exhausted, append the remaining nodes of the other list.
• Complexity:
• Time: O(n + m), where n and m are the lengths of the two lists. Each node is processed exactly once.
• Space: O(1), as we are only using a few extra pointers and not creating new nodes except for the dummy node.

Model answer

import sqlite3
from datetime import datetime, timedelta

def calculate_utilization_rate(date_str):
    # Connect to the SQLite database
    conn = sqlite3.connect('car_rental.db')
    cursor = conn.cursor()

    # Convert date_str to datetime object
    date_d = datetime.strptime(date_str, '%Y-%m-%d')
    next_day = date_d + timedelta(days=1)

    # Query to get rented cars count for each (city, car_size)
    rented_cars_query = """
    SELECT loc.city, car.car_size, COUNT(DISTINCT car.car_id) AS rented_cars
    FROM fct_rental AS rental
    JOIN car ON rental.car_id = car.car_id
    JOIN location AS loc ON rental.pickup_location_id = loc.location_id
    WHERE rental.pickup_ts < ?
      AND (rental.dropoff_ts IS NULL OR rental.dropoff_ts > ?)
    GROUP BY loc.city, car.car_size
    """

    # Execute the query for rented cars
    cursor.execute(rented_cars_query, (next_day, date_d))
    rented_cars_data = cursor.fetchall()

    # Query to get inventory cars count for each (city, car_size)
    inventory_cars_query = """
    SELECT loc.city, car.car_size, COUNT(car.car_id) AS inventory_cars
    FROM car
    JOIN location AS loc ON car.location_id = loc.location_id
    WHERE car.is_active = 1
    GROUP BY loc.city, car.car_size
    """

    # Execute the query for inventory cars
    cursor.execute(inventory_cars_query)
    inventory_cars_data = cursor.fetchall()

    # Convert the results into dictionaries for easy lookup
    rented_cars_dict = {(city, car_size): rented_cars for city, car_size, rented_cars in rented_cars_data}
    inventory_cars_dict = {(city, car_size): inventory_cars for city, car_size, inventory_cars in inventory_cars_data}

    # Prepare the final result
    result = []
    for (city, car_size), inventory_cars in inventory_cars_dict.items():
        rented_cars = rented_cars_dict.get((city, car_size), 0)
        utilization_rate = rented_cars / inventory_cars if inventory_cars > 0 else None
        result.append((city, car_size, rented_cars, inventory_cars, utilization_rate))

    # Close the database connection
    conn.close()

    return result

# Example usage
date = '2025-01-15'
utilization_data = calculate_utilization_rate(date)
for data in utilization_data:
    print(f"City: {data[0]}, Car Size: {data[1]}, Rented Cars: {data[2]}, Inventory Cars: {data[3]}, Utilization Rate: {data[4]}")

• Approach:
• Connect to the SQLite database and set up the date range for the given date D.
• Query the fct_rental and car tables to count distinct rented cars for each (city, car_size) where the rental overlaps date D.
• Query the car and location tables to count active inventory cars for each (city, car_size).
• Calculate the utilization rate as rented_cars / inventory_cars, handling division by zero by setting the rate to None.
• Return results grouped by city and car_size.
• Complexity:
• Time: The queries involve joins and aggregations, making the complexity approximately O(n + m), where n and m are the number of rows in fct_rental and car tables, respectively.
• Space: O(k), where k is the number of unique (city, car_size) combinations, due to storage of intermediate results in dictionaries.

Model answer

1. Requirements & scale

Functional Requirements:

• Provide an API to create a short URL for a given long URL.
• Provide an API to redirect a short code back to the original long URL.
• Ensure the mapping is globally unique and reversible.

Non-functional Requirements:

• High availability and high QPS, with reads/redirects outnumbering writes.
• Support horizontal scaling across multiple instances/regions.
• Short codes should be 6–10 characters, URL-safe.
• Low redirect latency (p95 < 50 ms).
• Tolerate partial failures.

Assumptions:

• We expect around 100 million URLs to be shortened initially, with a growth rate of 10% per year.
• Read-heavy workload with a 100:1 read-to-write ratio.
• Estimate QPS: 1,000 writes and 100,000 reads per second.
• Each URL mapping requires approximately 500 bytes of storage.

2. High-level architecture

flowchart TD
    subgraph Client
        A[User]
    end

    subgraph Edge/CDN
        B[CDN]
    end

    subgraph Load Balancer
        C[Load Balancer]
    end

    subgraph API / Services
        D[URL Shortening Service]
    end

    subgraph Cache
        E[Redis Cache]
    end

    subgraph Datastores
        F[SQL Database]
        G[NoSQL Database]
    end

    subgraph Message Queue
        H[Kafka]
    end

    subgraph Workers
        I[Background Workers]
    end

    A -->|HTTP Request| B
    B -->|HTTP Request| C
    C -->|API Call| D
    D -->|Read/Write| E
    E -->|Cache Miss| F
    D -->|Write| G
    D -->|Publish| H
    H -->|Consume| I
    I -->|Write| G

3. API design

• Create Short URL
• Method: POST
• Path: /api/v1/shorten
• Request Body: { "long_url": "https://example.com/very/long/url" }
• Response: { "short_code": "abc123" }
• Status Codes: 201 Created, 400 Bad Request
• Redirect Short URL
• Method: GET
• Path: /r/{short_code}
• Response: HTTP 301 Redirect to the original URL
• Status Codes: 301 Moved Permanently, 404 Not Found

4. Data model & storage

Datastores:

• SQL Database: Used for transactional operations and ensuring data integrity.
• NoSQL Database (e.g., DynamoDB): Used for high-speed reads and writes, particularly for redirect operations.

Data Model:

• Table: URL_Mappings
• short_code (Primary Key)
• long_url
• created_at
• expiry
• owner_id

Partition Key: short_code for NoSQL to distribute load evenly.

5. Deep dive

Short-code Generation Strategy:

• Option B: Use a globally unique ID generation strategy (e.g., Snowflake) and Base62-encode the ID to create a short code.
• Uniqueness and Performance: Snowflake ensures unique IDs across distributed systems. Base62 encoding reduces the length of the ID while maintaining URL safety.

sequenceDiagram
    participant User
    participant URLService
    participant SQLDB
    participant NoSQLDB
    participant Cache

    User->>URLService: POST /api/v1/shorten
    URLService->>SQLDB: Insert URL Mapping
    SQLDB-->>URLService: Success
    URLService->>Cache: Cache Short Code
    URLService-->>User: 201 Created (short_code)
    User->>URLService: GET /r/abc123
    URLService->>Cache: Check Cache for Short Code
    Cache-->>URLService: Cache Miss
    URLService->>NoSQLDB: Retrieve Long URL
    NoSQLDB-->>URLService: Long URL
    URLService->>Cache: Cache Long URL
    URLService-->>User: 301 Redirect

6. Scale, bottlenecks & trade-offs

Scalability:

• Replication: Use database replication to ensure high availability and disaster recovery.
• Sharding: NoSQL database sharding based on short_code to distribute load.
• Caching: Use Redis for caching frequently accessed short codes to reduce database load.

Bottlenecks:

• Database Load: Mitigated by caching and using NoSQL for high read/write throughput.
• Concurrency: Snowflake ID generation handles concurrency without collisions.

Trade-offs:

• Consistency vs. Availability: Prioritize availability (AP in CAP theorem) given the read-heavy nature.
• Push vs. Pull: Use async processing for background tasks like analytics.
• SQL vs. NoSQL: SQL for integrity, NoSQL for speed and scalability.

This design ensures a robust, scalable URL shortening service with minimal latency and high availability, capable of handling millions of requests efficiently.

Model answer

1. Requirements & scale

Functional Requirements:

• Users can set their accounts to private.
• Non-followers must send follow requests to view private accounts.
• Account owners can approve or deny follow requests.
• Only approved followers can view posts, stories, reels, etc.

Non-Functional Requirements:

• High availability and low latency for follow request processing.
• Strong consistency for follow relationship data.
• Scalability to handle millions of users and requests.

Estimates:

• Assume 1 billion users, with 10% using private accounts.
• Average of 1 follow request per user per day: 100 million requests/day.
• Peak QPS for follow requests: ~1,200 QPS.
• Storage for follow relationships: Assuming 1 KB per relationship, ~100 TB for 100 billion relationships.

2. High-level architecture

flowchart TD
    subgraph Client
        A[User App]
    end
    subgraph Edge/CDN
        B[CDN]
    end
    subgraph Load Balancer
        C[Load Balancer]
    end
    subgraph API / Services
        D[Follow Service]
        E[Approval Service]
        F[Content Service]
    end
    subgraph Cache
        G[Redis Cache]
    end
    subgraph Datastores
        H["SQL DB (Follow Relationships)"]
        I["NoSQL DB (User Profiles)"]
        J["Blob Storage (Media)"]
    end
    subgraph Message Queue
        K[Follow Request Queue]
    end
    subgraph Workers
        L[Approval Worker]
    end

    A -->|Follow Request| B
    B --> C
    C --> D
    D -->|Check Follow Status| G
    G -->|Cache Miss| H
    D -->|Send Request| K
    K --> L
    L --> E
    E -->|Approve/Deny| H
    A -->|View Content| F
    F -->|Fetch Content| J
    F -->|Check Access| G
    G -->|Cache Miss| H

3. API design

• POST /follow-request: Send a follow request to a private account.
• POST /approve-request: Approve a follow request.
• GET /content: Retrieve content for viewing, checking access permissions.
• PATCH /account/privacy: Change account privacy settings.

4. Data model & storage

Datastores:

• SQL Database for follow relationships due to ACID properties needed for consistency.
• NoSQL Database for user profiles to handle high read/write throughput.
• Blob Storage for media content due to large size and infrequent updates.

Key Tables:

• FollowRelationships: user_id, follower_id, status (pending/approved/denied), timestamp.
• UserProfiles: user_id, privacy_state, profile_data.
• Content: content_id, user_id, content_type, storage_location.

Partition Key:

• For FollowRelationships, use user_id to distribute load evenly.

5. Deep dive

The core of this feature is the follow request and approval mechanism. When a user sends a follow request, it is queued and processed asynchronously to ensure the system remains responsive.

sequenceDiagram
    participant User
    participant FollowService
    participant Queue
    participant ApprovalWorker
    participant SQLDB

    User->>FollowService: Send Follow Request
    FollowService->>Queue: Enqueue Request
    Queue->>ApprovalWorker: Dequeue Request
    ApprovalWorker->>SQLDB: Update Follow Status
    SQLDB-->>ApprovalWorker: Acknowledge
    ApprovalWorker-->>User: Notify Approval/Deny

6. Scale, bottlenecks & trade-offs

Scaling:

• Use horizontal scaling for the Follow Service and Approval Worker to handle increased load.
• Implement read replicas for the SQL database to distribute read traffic.

Bottlenecks:

• The SQL database could become a bottleneck due to high write throughput; consider sharding if necessary.
• Cache misses in Redis could lead to increased database load; optimize cache hit rates.

Trade-offs:

• Consistency vs. Availability: Prioritize consistency for follow relationships to ensure correct access control.
• Push vs. Pull: Use asynchronous processing for follow requests to improve responsiveness.
• SQL vs. NoSQL: SQL is chosen for follow relationships due to the need for strong consistency, while NoSQL is used for user profiles for scalability.

By addressing these aspects, the system can efficiently manage private accounts, ensuring both user privacy and system performance.

Model answer

1. Requirements & scale

Functional Requirements:

• Ensure data quality by addressing incorrect, missing, and duplicated records.
• Optimize performance for ETL/ELT pipelines and query execution.
• Reduce compute costs while maintaining or improving performance.

Non-Functional Requirements:

• High availability and reliability of data processing.
• Scalability to handle increasing data volumes and user queries.
• Security and compliance with data governance standards.

Back-of-the-Envelope Estimates:

• Data Volume: Assume 1 TB of data ingested daily.
• Query Load: Assume 1000 queries per second (QPS) at peak.
• Storage: Assuming data retention for 1 year, total storage required is approximately 365 TB.
• Bandwidth: If each query processes 10 MB of data, bandwidth required is 10 GB/s.

2. High-level architecture

flowchart TD
    subgraph Client
        A[User]
    end

    subgraph Edge/CDN
        B[CDN]
    end

    subgraph Load Balancer
        C[Load Balancer]
    end

    subgraph API / Services
        D[API Gateway]
        E[Data Quality Service]
        F[Performance Monitoring Service]
    end

    subgraph Cache
        G[Redis Cache]
    end

    subgraph Datastores
        H["Data Lake (S3)"]
        I["Data Warehouse (Redshift)"]
    end

    subgraph Message Queue
        J[Kafka]
    end

    subgraph Workers
        K[ETL Workers]
        L[Data Validation Workers]
    end

    A --> B --> C --> D
    D --> E
    D --> F
    E --> J
    J --> K
    K --> H
    H --> I
    I --> G
    G --> A
    F --> L
    L --> I

3. API design

• POST /data/ingest: Ingest new data into the system.
• GET /data/query: Execute queries against the data warehouse.
• POST /data/validate: Trigger data validation processes.
• GET /performance/metrics: Retrieve performance metrics for monitoring.

4. Data model & storage

Datastores:

• Data Lake (S3): For raw data storage, allowing schema-on-read flexibility.
• Data Warehouse (Redshift): For structured data storage and fast query execution.

Key Tables:

• Raw Data Table: Stores ingested data with minimal transformation.
• Validated Data Table: Stores data post-validation, ensuring quality.
• Metrics Table: Stores performance metrics for monitoring and analysis.

Partitioning Strategy:

• Partition data by date and source to optimize query performance and manageability.

5. Deep dive

To address data quality issues, implement a robust data validation pipeline. This involves:

1. Data Ingestion: Use Kafka to handle high-throughput data ingestion, ensuring durability and backpressure management.
2. Data Validation: Employ Data Validation Workers to check for duplicates, missing values, and consistency against predefined schemas.
3. Storage: Store validated data in the Data Warehouse for efficient querying.

sequenceDiagram
    participant User
    participant API Gateway
    participant Data Quality Service
    participant Kafka
    participant Data Validation Workers
    participant Data Warehouse

    User->>API Gateway: POST /data/ingest
    API Gateway->>Data Quality Service: Validate Data
    Data Quality Service->>Kafka: Publish Validated Data
    Kafka->>Data Validation Workers: Consume Data
    Data Validation Workers->>Data Warehouse: Store Validated Data

6. Scale, bottlenecks & trade-offs

Replication and Sharding:

• Replication: Use database replicas to ensure high availability and load distribution.
• Sharding: Implement sharding in the Data Warehouse to handle large data volumes efficiently.

Caching:

• Use Redis to cache frequently accessed query results, reducing load on the Data Warehouse and improving response times.

Single Points of Failure:

• Ensure redundancy in the Load Balancer and API Gateway to prevent single points of failure.

Trade-offs:

• Consistency vs. Availability: Opt for eventual consistency in the Data Lake to improve availability and performance.
• Push vs. Pull: Use a pull-based approach for data validation to allow for batch processing and reduced latency.
• SQL vs. NoSQL: Use SQL for structured data and complex queries, while leveraging NoSQL for flexible schema requirements in the Data Lake.

By addressing these aspects, we can enhance data quality and performance, ensuring the system is robust, scalable, and cost-effective.

Model answer

1. Requirements & scale

Functional Requirements:

• Collect GPU telemetry data at two resolutions: 30-second and 1-second intervals.
• Provide a dashboard for operators to view three-month trends.
• Allow operators to drill down into per-GPU, per-second telemetry data.
• Support a natural-language SQL assistant that queries data securely and efficiently.

Non-Functional Requirements:

• High throughput and low-latency data ingestion.
• Scalable storage to handle large volumes of time-series data.
• Secure access control for data queries.
• Reliable and fault-tolerant system design.

Scale Estimates:

• Assume 10,000 GPUs, each generating telemetry data every second.
• Data size per telemetry record: 1 KB.
• Ingestion rate: 10,000 records/second = 10 MB/second.
• Monthly data storage: 10 MB/second 86,400 seconds/day 30 days ≈ 25.9 TB/month.
• Three-month storage requirement: 77.7 TB.

2. High-level architecture

flowchart TD
    subgraph Client
        A[Dashboard]
        B[Natural Language SQL Assistant]
    end

    subgraph Edge/CDN
        C[CDN]
    end

    subgraph Load Balancer
        D[Load Balancer]
    end

    subgraph API / Services
        E[Telemetry Ingestion API]
        F[Analytics Service]
        G[Auth Service]
    end

    subgraph Cache
        H[In-memory Cache]
    end

    subgraph Datastores
        I[Time-series DB]
        J[SQL DB]
    end

    subgraph Message Queue
        K[Message Queue]
    end

    subgraph Workers
        L[Stream Processor]
    end

    A --> C
    B --> C
    C --> D
    D --> E
    E --> K
    K --> L
    L --> I
    F --> H
    H --> J
    G --> F
    F --> A
    B --> G

3. API design

• POST /telemetry: Ingest telemetry data from GPUs.
• GET /analytics/trends: Retrieve high-level trend analytics for the dashboard.
• GET /analytics/drilldown: Fetch detailed per-GPU telemetry data.
• POST /auth: Authenticate users for secure access.
• POST /query: Execute natural-language SQL queries with access control.

4. Data model & storage

Datastores:

• Time-series Database (e.g., InfluxDB, TimescaleDB): Chosen for efficient storage and querying of time-series data. Supports high write throughput and time-based queries.
• SQL Database (e.g., PostgreSQL): Used for metadata and access control data, supporting complex queries and transactions.

Key Tables:

• TelemetryData: (timestamp, gpu_id, metrics)
• UserAccess: (user_id, permissions)

Partitioning Strategy:

• TelemetryData: Partitioned by time (e.g., daily) and GPU ID for efficient querying and storage management.

5. Deep dive

The core of this design is the telemetry ingestion and processing pipeline. The system must handle high-throughput data ingestion while ensuring data reliability and low latency for real-time analytics.

sequenceDiagram
    participant GPU as GPU Device
    participant E as Telemetry Ingestion API
    participant K as Message Queue
    participant L as Stream Processor
    participant I as Time-series DB

    GPU->>E: Send telemetry data
    E->>K: Publish to message queue
    K->>L: Stream data for processing
    L->>I: Write processed data to time-series DB

Key Considerations:

• Backpressure Handling: The message queue decouples ingestion from processing, allowing the system to handle spikes in data volume.
• Stream Processing: The stream processor aggregates and enriches telemetry data, ensuring it is ready for both real-time and historical analysis.

6. Scale, bottlenecks & trade-offs

Replication and Sharding:

• Time-series DB: Use replication for high availability and sharding to distribute load across multiple nodes.
• Message Queue: Scale horizontally to handle increased data ingestion rates.

Caching:

• In-memory Cache: Store frequently accessed trend data to reduce query latency and load on the database.

Single Points of Failure:

• Load Balancer and Message Queue: Ensure redundancy and failover mechanisms to prevent service disruption.

Trade-offs:

• Consistency vs. Availability: Prioritize availability using eventual consistency for telemetry data, as real-time insights are more critical than immediate consistency.
• Push vs. Pull: Use a push-based model for telemetry ingestion to reduce latency and ensure timely data processing.

This design balances the need for real-time analytics with the scalability and reliability required to handle large volumes of telemetry data.

Model answer

1. Requirements & scale

Functional Requirements:

• Continuously ingest order lifecycle events.
• Provide operational metrics within five minutes.
• Generate a reproducible daily table for the finance team.
• Handle duplicated, delayed, and out-of-order events.
• Support historical corrections and event-schema changes.

Non-Functional Requirements:

• Low-latency processing for near real-time metrics.
• High reliability and fault tolerance.
• Scalability to handle increasing event volumes.
• Data consistency for financial reporting.

Estimates:

• Assume 10,000 orders per second (QPS) at peak.
• Each event is approximately 1 KB, leading to 10 MB/s of incoming data.
• Daily data storage: 10 MB/s * 86,400 seconds/day ≈ 864 GB/day.

2. High-level architecture

flowchart TD
    subgraph Client
        A[Order Event Producers]
    end

    subgraph Edge/CDN
        B[Event Gateway]
    end

    subgraph Load Balancer
        C[Load Balancer]
    end

    subgraph API / Services
        D[Stream Processor]
        E[Batch Processor]
    end

    subgraph Cache
        F[In-memory Cache]
    end

    subgraph Datastores
        G[Real-time Metrics DB]
        H[Data Lake]
    end

    subgraph Message Queue
        I[Event Queue]
    end

    subgraph Workers
        J[Data Transformation Workers]
    end

    A --> B["Order Events"]
    B --> C["Order Events"]
    C --> I["Order Events"]
    I --> D["Stream Processing"]
    D --> F["Processed Metrics"]
    F --> G["Real-time Metrics"]
    I --> E["Batch Processing"]
    E --> J["Data Transformation"]
    J --> H["Daily Table"]

3. API design

• POST /events: Ingest order lifecycle events into the system.
• GET /metrics: Retrieve real-time operational metrics.
• GET /daily-report: Access the daily financial report.

4. Data model & storage

Datastores:

• Real-time Metrics DB: A NoSQL database like Apache Cassandra for fast writes and reads, supporting real-time metrics.
• Data Lake: A distributed file system like Amazon S3 or Hadoop HDFS for storing raw and processed data, supporting batch processing.

Key Tables:

• OrderEvents: Partitioned by event timestamp for efficient querying.
• Metrics: Sharded by metric type and timestamp for real-time access.
• DailyReports: Stored as Parquet files for efficient batch processing and querying.

5. Deep dive

The core of this design is the hybrid processing approach that combines stream and batch processing to meet both low-latency and high-reliability requirements.

sequenceDiagram
    participant A as Event Gateway
    participant B as Event Queue
    participant C as Stream Processor
    participant D as Batch Processor
    participant E as Real-time Metrics DB
    participant F as Data Lake

    A->>B: Ingest Order Events
    B->>C: Stream Processing
    C->>E: Update Real-time Metrics
    B->>D: Batch Processing
    D->>F: Store Daily Table

Stream Processing:

• Utilizes a stream processing framework like Apache Kafka Streams or Apache Flink.
• Processes events in real-time to update operational metrics.
• Handles event deduplication and ordering using event timestamps and unique identifiers.

Batch Processing:

• Uses a batch processing framework like Apache Spark.
• Processes accumulated events overnight to generate the daily financial table.
• Supports schema evolution and historical data corrections.

6. Scale, bottlenecks & trade-offs

Scalability:

• Stream processing scales horizontally by adding more processing nodes.
• Batch processing can be scaled by increasing the cluster size or using more powerful instances.

Bottlenecks:

• The event queue could become a bottleneck if not properly scaled; use partitioning to distribute load.
• Real-time metrics DB must handle high write throughput; consider using a distributed NoSQL database.

Trade-offs:

• Consistency vs. Availability: Prioritize availability in stream processing for real-time metrics, while ensuring eventual consistency for daily reports.
• Latency vs. Accuracy: Real-time metrics may sacrifice some accuracy for speed, while daily reports focus on accuracy.
• Push vs. Pull: Stream processing uses a push model for low latency, whereas batch processing uses a pull model for comprehensive data analysis.

By leveraging both stream and batch processing, this architecture effectively balances the need for low-latency metrics and reliable, auditable daily reports, accommodating the dynamic nature of order events in an online marketplace.

Model answer

1. Requirements & scale

Functional Requirements:

• Track and analyze server creation, views, joins, and messages.
• Compute average weekly server views for a given year.
• Calculate week-over-week change in server views over the past 12 months.
• Handle missing weeks in time series data.
• Provide all-time counts of server views, joins, and messages per server.

Non-Functional Requirements:

• High throughput for event ingestion.
• Durability and reliability of stored data.
• Scalable architecture to handle growth in data volume.
• Efficient querying for analytics.

Scale Estimates:

• Assume 1 million servers, each with 1000 views, 500 joins, and 10,000 messages per month.
• Total events per month: \(1M \times (1000 + 500 + 10,000) = 11.5 \text{ billion events}\).
• Average event size: 100 bytes.
• Monthly storage: \(11.5 \text{ billion} \times 100 \text{ bytes} = 1.15 \text{ TB}\).
• Queries per second (QPS): Assuming 1000 concurrent analytics queries, each taking 1 second, QPS = 1000.

2. High-level architecture

flowchart TD
    subgraph Client
        A[User Interface]
    end

    subgraph Edge/CDN
        B[CDN]
    end

    subgraph Load Balancer
        C[Load Balancer]
    end

    subgraph API / Services
        D[Analytics Service]
    end

    subgraph Cache
        E[Redis Cache]
    end

    subgraph Datastores
        F[Fact Table (SQL)]
        G[Dimension Tables (SQL)]
        H[Time-series DB]
    end

    subgraph Message Queue
        I[Kafka]
    end

    subgraph Workers
        J[Stream Processing]
    end

    A --> B --> C --> D
    D --> E
    D --> I
    I --> J
    J --> F
    J --> G
    J --> H
    D --> F
    D --> G
    D --> H

3. API design

• GET /analytics/average-weekly-views: Compute average weekly server views for a given year.
• GET /analytics/week-over-week-views: Calculate week-over-week change in server views over the past 12 months.
• GET /analytics/all-time-counts: Return all-time counts of server views, joins, and messages per server.

4. Data model & storage

Datastores:

• Fact Table (SQL): Stores events with high write throughput.
• Dimension Tables (SQL): For metadata about servers, users, etc.
• Time-series DB: Efficient storage and querying of time-series data.

Data Model:

• Fact Table: events(event_id, server_id, user_id, event_type, timestamp)
• Dimension Tables:
• servers(server_id, creator_id, created_at)
• users(user_id, joined_at)

Partitioning Strategy:

• Fact table partitioned by timestamp for efficient time-based queries.

5. Deep dive

To compute the average weekly number of server_view events during 2020, we can use SQL aggregation functions:

SELECT 
    AVG(weekly_views) AS average_weekly_views
FROM (
    SELECT 
        DATE_TRUNC('week', viewed_at) AS week_start,
        COUNT(*) AS weekly_views
    FROM server_view
    WHERE viewed_at BETWEEN '2020-01-01' AND '2020-12-31'
    GROUP BY week_start
) AS weekly_data;

For week-over-week change:

WITH weekly_views AS (
    SELECT 
        DATE_TRUNC('week', viewed_at) AS week_start,
        COUNT(*) AS weekly_views
    FROM server_view
    WHERE viewed_at >= NOW() - INTERVAL '12 months'
    GROUP BY week_start
),
week_over_week AS (
    SELECT 
        week_start,
        weekly_views,
        LAG(weekly_views) OVER (ORDER BY week_start) AS previous_week_views
    FROM weekly_views
)
SELECT 
    week_start,
    (weekly_views - previous_week_views) / previous_week_views::float * 100 AS week_over_week_change
FROM week_over_week
WHERE previous_week_views IS NOT NULL;

Handling missing weeks involves generating a complete series of weeks and left joining it with the actual data.

6. Scale, bottlenecks & trade-offs

Replication and Sharding:

• Use sharding for the fact table based on timestamp to distribute load.
• Replicate dimension tables across nodes for read availability.

Caching:

• Use Redis for caching frequently accessed analytics results.

Bottlenecks:

• High write throughput to the fact table can be a bottleneck; use stream processing to batch writes.
• Query performance can degrade with large datasets; optimize with indexing and partitioning.

Trade-offs:

• Consistency vs. Availability: Prioritize availability for analytics queries; eventual consistency is acceptable.
• SQL vs. NoSQL: SQL chosen for its strong querying capabilities and support for complex joins and aggregations.
• Push vs. Pull: Use a pull model for analytics queries to allow flexible querying by users.

Model answer

-- Part 1: First share time per creator
WITH non_creator_views AS (
  SELECT fv.file_id, fv.user_id, fv.viewed_at, f.creator_id
  FROM file_views fv
  JOIN files f ON fv.file_id = f.file_id
  WHERE CAST(fv.user_id AS VARCHAR) != f.creator_id
),
first_share_times AS (
  SELECT creator_id, MIN(viewed_at) AS first_shared_at
  FROM non_creator_views
  GROUP BY creator_id
)
SELECT creator_id, first_shared_at
FROM first_share_times;

-- Part 2: Closest collaborator model
WITH first_non_creator_view AS (
  SELECT fv.file_id, fv.user_id, fv.viewed_at, f.creator_id,
         ROW_NUMBER() OVER (PARTITION BY fv.file_id ORDER BY fv.viewed_at, fv.user_id) AS rn
  FROM file_views fv
  JOIN files f ON fv.file_id = f.file_id
  WHERE CAST(fv.user_id AS VARCHAR) != f.creator_id
),
first_collaborators AS (
  SELECT creator_id, user_id AS collaborator_user_id, COUNT(*) AS collaborator_value
  FROM first_non_creator_view
  WHERE rn = 1
  GROUP BY creator_id, user_id
),
ranked_collaborators AS (
  SELECT creator_id, collaborator_user_id, collaborator_value,
         ROW_NUMBER() OVER (PARTITION BY creator_id ORDER BY collaborator_value DESC, MIN(viewed_at), collaborator_user_id) AS rn
  FROM first_non_creator_view fncv
  JOIN first_collaborators fc ON fncv.creator_id = fc.creator_id AND fncv.user_id = fc.collaborator_user_id
  WHERE fncv.rn = 1
  GROUP BY creator_id, collaborator_user_id, collaborator_value
)
SELECT creator_id, collaborator_user_id AS closest_collaborator_user_id, collaborator_value
FROM ranked_collaborators
WHERE rn = 1;

• First share time per creator:
• Use a CTE non_creator_views to filter views by non-creators.
• Group by creator_id to find the earliest viewed_at for each creator.
• Closest collaborator model:
• Use a CTE first_non_creator_view to determine the first non-creator viewer per file.
• Use ROW_NUMBER() to rank these views and select the first.
• Aggregate to find the collaborator with the highest count of first views.
• Use ROW_NUMBER() again to handle tie-breaking by collaborator_value, viewed_at, and user_id.

Complexity:

• Time: O(N log N) due to sorting operations in window functions.
• Space: O(N) for storing intermediate CTE results.

Model answer

To address the practical questions about operating production data pipelines in a senior data engineering role, let's break down each aspect with a focus on mechanics, trade-offs, and operational checks:

1. Ensuring Idempotency in Data Ingestion: - Use unique identifiers for each data record (e.g., UUIDs) to prevent duplicate processing. - Implement deduplication logic at the ingestion layer, possibly using a hash or checksum. - Store processed record IDs in a database or cache to track what has been processed.
2. Tuning a Slow or Expensive Spark Job: - Resource Allocation: Increase executor memory and cores if under-resourced. - Data Skew Handling: Use techniques like salting or repartitioning to distribute data evenly. - Optimize Shuffles: Reduce the number of shuffles by optimizing transformations. - Caching: Cache intermediate RDDs/DataFrames to avoid recomputation.
3. Understanding Spark Partitions and Handling Data Skew: - Partitions: Determine the number of partitions based on data size and cluster resources. - Data Skew: Identify skewed keys and redistribute data using techniques like salting or custom partitioners.
4. Planning and Executing a Backfill: - Plan: Identify the data range and dependencies for the backfill. - Execution: Use a separate cluster or schedule during off-peak hours to minimize impact. - Validation: Validate data integrity post-backfill using checksums or sample comparisons.
5. Optimizing a Costly Recurring Job: - Spot Instances: Use spot instances for cost savings if job timing is flexible. - Batch Processing: Consolidate smaller jobs into larger batches to reduce overhead. - Efficient Storage: Compress data and use columnar storage formats like Parquet.
6. Debugging Incorrect Downstream Results: - Data Lineage: Trace data flow from source to sink to identify transformation errors. - Logging and Metrics: Use detailed logs and metrics to pinpoint where the anomaly occurs. - Version Control: Verify code changes and dependencies that might have introduced errors.
7. Role of Kinesis, Prometheus, and Grafana in an Observability Pipeline: - Kinesis: Acts as a data ingestion and streaming service, handling real-time data flow. - Prometheus: Collects and stores metrics for monitoring system performance. - Grafana: Visualizes metrics and logs, providing dashboards for real-time observability.

Trade-offs and Operational Checks:

• Idempotency: Balancing between performance and the overhead of maintaining state.
• Spark Tuning: Trade-offs between resource usage and job completion time.
• Data Skew: Complexity of implementing custom partitioning vs. performance gains.
• Backfill: Risk of data inconsistency vs. the need for historical data accuracy.
• Cost Optimization: Balancing between cost savings and potential delays in job execution.
• Debugging: Time spent on tracing and logging vs. the need for quick resolution.

By understanding these components and their interactions, you can effectively manage and optimize data pipelines in a production environment, ensuring reliability, efficiency, and cost-effectiveness.

Model answer

-- 1) Who always lived in CA as of 2021-01-01?
SELECT ui.id, ui.shard, ui.name
FROM user_info ui
LEFT JOIN user_info_edit_log uel
ON ui.id = uel.id AND ui.shard = uel.shard AND uel.table_name = 'user_info' AND uel.field_name = 'state'
WHERE ui.state = 'CA'
  AND (uel.id IS NULL OR uel.created_date >= '2021-01-01 00:00:00');

-- 2) Who moved to CA before 2021-01-01 and lived in CA as of 2021-01-01?
SELECT DISTINCT ui.id, ui.shard, ui.name
FROM user_info ui
JOIN user_info_edit_log uel
ON ui.id = uel.id AND ui.shard = uel.shard AND uel.table_name = 'user_info' AND uel.field_name = 'state'
WHERE ui.state = 'CA'
  AND uel.new_value = 'CA'
  AND uel.old_value != 'CA'
  AND uel.created_date < '2021-01-01 00:00:00';

-- 3) Who might have moved out of CA after 2021-01-01?
SELECT DISTINCT ui.id, ui.shard, ui.name
FROM user_info ui
JOIN user_info_edit_log uel
ON ui.id = uel.id AND ui.shard = uel.shard AND uel.table_name = 'user_info' AND uel.field_name = 'state'
WHERE ui.state = 'CA'
  AND uel.old_value = 'CA'
  AND uel.new_value != 'CA'
  AND uel.created_date > '2021-01-01 00:00:00';

• Query 1: Select users whose current state is CA and have no edit logs indicating a non-CA state before 2021-01-01.
• Query 2: Select users whose current state is CA, with a log entry showing a change from a non-CA state to CA before 2021-01-01.
• Query 3: Select users whose current state is CA, with a log entry showing a change from CA to a non-CA state after 2021-01-01.

Complexity:

• Time: O(N + M) for each query, where N is the number of rows in user_info and M is the number of rows in user_info_edit_log.
• Space: O(N + M) due to the join operations.