Glean interview questions & answers

Model answer

Situation In my role as a data scientist at a tech company, I was tasked with evaluating the success of a newly launched feature within our mobile application. This feature was designed to enhance user engagement and retention, and its success was crucial for our product's growth phase. The stakes were high as the feature's performance would influence our strategic decisions for future development and marketing efforts.

Task My primary goal was to define and measure the success of this feature using data science and product analytics. This involved identifying key metrics and ensuring that the feature met its objectives without negatively impacting other aspects of the product.

Action

• I began by clarifying the product objective, which was to increase user engagement and retention. Given the feature's growth stage, success would be defined by its ability to attract and retain users effectively.
• I identified the primary success metric, or north-star metric, as the increase in daily active users (DAU) attributable to the feature.
• To support this, I tracked metrics across the user funnel, including acquisition (new user sign-ups), activation (first-time feature use), engagement (frequency of feature use), retention (repeat use over time), monetization (conversion to paid plans), and user satisfaction (NPS scores).
• I established guardrail metrics to ensure that improvements in engagement did not lead to negative outcomes, such as increased churn or decreased user satisfaction.
• To distinguish correlation from causal impact, I implemented A/B testing. This involved randomly assigning users to either a control group or a treatment group that had access to the new feature, allowing us to measure the feature's direct impact.
• I addressed potential confounding factors by controlling for seasonality and novelty effects through time-series analysis. I also segmented users to analyze heterogeneous effects, ensuring that the feature was beneficial across different user demographics.
• For decision-making, I used a data-driven framework that combined statistical significance from the A/B tests with business impact analysis. This helped in concluding whether the feature was successful and informed decisions on scaling or iterating the feature.

Result The analysis showed a significant increase in user engagement and retention, with a 15% rise in DAU and a 10% improvement in retention rates. The feature did not negatively impact other metrics, confirming its overall success. This evaluation provided valuable insights for future product iterations and helped prioritize features that align with user needs. I learned the importance of a holistic approach in evaluating product success, considering both direct impacts and broader business objectives.

Model answer

Situation In my previous role as a software developer at a tech startup, we faced a challenging situation where multiple high-priority projects coincided with tight deadlines. We were in the final stages of launching a new feature, while simultaneously preparing for a major client presentation. Both tasks were critical to our business objectives, and the stakes were high as they directly impacted our market position and client relationships.

Task My responsibility was to ensure the successful completion of the feature development while also preparing the technical components for the client presentation. The key challenge was managing these tasks effectively within the limited time available.

Action

• I began by reassessing the priorities of each task. I identified the most critical components that needed immediate attention and those that could be delegated or postponed without impacting the overall outcomes.
• I coordinated with my team to redistribute the workload. I delegated less critical tasks to team members who had the capacity to take on additional work, ensuring that everyone was aligned with the priorities.
• To maximize efficiency, I streamlined my workflow by minimizing distractions and focusing on one task at a time. I also extended my work hours temporarily to ensure that I could give each task the attention it required.
• I maintained clear and regular communication with stakeholders, providing updates on progress and any adjustments to timelines. This transparency helped manage expectations and allowed for quick adjustments if needed.
• I also sought assistance from other teams for specific tasks where their expertise could expedite the process, ensuring that we leveraged all available resources effectively.

Result Through these efforts, we successfully completed the feature development and delivered a compelling client presentation on time. The feature launch was well-received by users, and the client was impressed with our presentation, which strengthened our relationship. This experience taught me the importance of strategic prioritization, effective delegation, and clear communication in managing multiple tasks under tight deadlines.

Model answer

Situation In my previous role as a software developer at a mid-sized tech company, we were tasked with developing a new feature that required integrating a real-time data processing system. This was crucial for enhancing our product's capabilities and staying competitive in the market. However, our team had limited experience with real-time data processing technologies, and I was assigned to lead this initiative. The stakes were high as the feature was a key selling point for an upcoming product launch.

Task My specific goal was to quickly learn Apache Kafka, a technology we had identified as suitable for our needs, and implement it effectively within our existing infrastructure. The main constraint was the tight timeline, as the product launch was scheduled in just three months.

Action

• I began by immersing myself in learning Apache Kafka. I enrolled in an intensive online course that provided both theoretical knowledge and practical exercises.
• To accelerate my learning, I set up a small-scale Kafka environment on my local machine to experiment with different configurations and understand its core concepts.
• I reached out to a colleague who had some prior experience with Kafka and scheduled regular knowledge-sharing sessions to discuss best practices and potential pitfalls.
• Concurrently, I documented my learning process and findings, creating a guide for the team to ensure a smoother onboarding process for others who might need to work with Kafka in the future.
• I organized a series of workshops to share my knowledge with the team, ensuring everyone was aligned and could contribute effectively to the project.

Result As a result of these efforts, we successfully integrated Apache Kafka into our system within the deadline. The new feature was launched on time and received positive feedback from users for its improved performance and reliability. This experience not only enhanced my technical skills but also demonstrated the importance of proactive learning and collaboration. The project was a success, and my ability to adapt quickly was recognized by both my team and management, leading to further opportunities for leadership roles in future projects.

Model answer

Situation

In my previous role as a software engineer at a tech company, I was part of a team tasked with developing a new feature for our application. One of our team members, whom I'll call Alex, was highly skilled but had a tendency to dominate discussions and dismiss others' ideas. This behavior led to tension within the team and hindered our collaborative efforts. The stakes were high as we were working under a tight deadline and needed to ensure a smooth team dynamic to meet our project goals.

Task

My responsibility was to ensure the project stayed on track while fostering a positive and collaborative team environment. It was crucial to address Alex's behavior without escalating the situation or impacting team morale negatively.

Action

• I first observed team meetings to understand the dynamics and pinpoint specific instances where Alex's behavior was disruptive.
• I scheduled a one-on-one meeting with Alex to discuss my observations. I approached the conversation with empathy, focusing on the team's shared goals rather than personal criticism.
• During our discussion, I acknowledged Alex's expertise and contributions but highlighted the importance of diverse perspectives and collaboration for the project's success.
• I proposed implementing a structured meeting format where each team member had an opportunity to voice their ideas, ensuring balanced participation.
• I also facilitated a team workshop to improve communication skills and foster mutual respect, emphasizing the value of each member's input.

Result

As a result of these actions, the team's dynamics improved significantly. Alex became more receptive to others' ideas, and the overall collaboration increased. We successfully completed the project on time, and the feature received positive feedback from users. This experience taught me the importance of addressing interpersonal issues proactively and the power of structured communication in enhancing team collaboration.

Model answer

function twoSum(nums, target) {
    // Create a map to store the difference and its index
    const numMap = new Map();

    // Iterate through the array
    for (let i = 0; i < nums.length; i++) {
        // Calculate the difference needed to reach the target
        const complement = target - nums[i];

        // Check if the complement is already in the map
        if (numMap.has(complement)) {
            // If found, return the indices of the two numbers
            return [numMap.get(complement), i];
        }

        // Store the current number and its index in the map
        numMap.set(nums[i], i);
    }

    // If no solution is found, return an empty array
    return [];
}

// Example usage:
// const indices = twoSum([2, 7, 11, 15], 9);
// console.log(indices); // Output: [0, 1]

• Approach:
• Use a hash map to store each number and its index as you iterate through the array.
• For each number, calculate the complement needed to reach the target.
• Check if this complement is already in the map.
• If it is, return the indices of the current number and the complement.
• If not, store the current number and its index in the map.
• Complexity:
• Time: O(n), where n is the number of elements in the array. Each element is processed at most once.
• Space: O(n), for storing elements in the hash map.

Model answer

function reverseString(str) {
  // Convert the string into an array of characters
  let charArray = str.split('');
  
  // Initialize two pointers, one at the start and one at the end of the array
  let left = 0;
  let right = charArray.length - 1;
  
  // Swap characters until the two pointers meet in the middle
  while (left < right) {
    // Swap the characters at the left and right pointers
    let temp = charArray[left];
    charArray[left] = charArray[right];
    charArray[right] = temp;
    
    // Move the pointers towards the center
    left++;
    right--;
  }
  
  // Join the array back into a string and return
  return charArray.join('');
}

// Example usage:
console.log(reverseString("hello")); // Output: "olleh"

• The function reverseString takes a string as input and reverses it using a two-pointer approach.
• It converts the string into an array of characters to facilitate swapping.
• Two pointers, left and right, are initialized at the start and end of the array, respectively.
• Characters at these pointers are swapped until they meet in the middle.
• Finally, the array is joined back into a string and returned.

Complexity:

• Time: O(n), where n is the length of the string. Each character is visited once.
• Space: O(n), due to the array created to hold the characters of the string.

Model answer

function isPalindrome(s) {
  // Convert the string to lowercase and remove non-alphanumeric characters
  const cleanedString = s.toLowerCase().replace(/[^a-z0-9]/g, '');

  // Initialize two pointers
  let left = 0;
  let right = cleanedString.length - 1;

  // Check characters from both ends towards the center
  while (left < right) {
    if (cleanedString[left] !== cleanedString[right]) {
      return false; // Characters do not match
    }
    left++;
    right--;
  }

  return true; // All characters matched
}

// Example usage:
console.log(isPalindrome("A man, a plan, a canal: Panama")); // true
console.log(isPalindrome("race a car")); // false

• Approach:
• Convert the input string to lowercase to ensure case insensitivity.
• Remove all non-alphanumeric characters using a regular expression.
• Use two pointers, starting from the beginning and end of the cleaned string.
• Compare characters at these pointers, moving towards the center.
• If all characters match, the string is a palindrome.
• Complexity:
• Time: O(n), where n is the length of the string, due to single pass for cleaning and another for palindrome check.
• Space: O(n), for storing the cleaned version of the string.

Model answer

class Node {
  constructor(key, value) {
    this.key = key;
    this.value = value;
    this.prev = null;
    this.next = null;
  }
}

class LRUCache {
  constructor(capacity) {
    this.capacity = capacity;
    this.map = new Map(); // To store key-node pairs
    this.head = new Node(null, null); // Dummy head
    this.tail = new Node(null, null); // Dummy tail
    this.head.next = this.tail;
    this.tail.prev = this.head;
  }

  // Method to get the value of the key if it exists, otherwise return -1
  get(key) {
    if (!this.map.has(key)) {
      return -1;
    }
    const node = this.map.get(key);
    this._remove(node);
    this._add(node);
    return node.value;
  }

  // Method to put a key-value pair in the cache
  put(key, value) {
    if (this.map.has(key)) {
      this._remove(this.map.get(key));
    }
    const newNode = new Node(key, value);
    this._add(newNode);
    this.map.set(key, newNode);

    if (this.map.size > this.capacity) {
      const lru = this.head.next;
      this._remove(lru);
      this.map.delete(lru.key);
    }
  }

  // Helper method to add a node right before the tail
  _add(node) {
    const prev = this.tail.prev;
    prev.next = node;
    node.prev = prev;
    node.next = this.tail;
    this.tail.prev = node;
  }

  // Helper method to remove a node from the linked list
  _remove(node) {
    const prev = node.prev;
    const next = node.next;
    prev.next = next;
    next.prev = prev;
  }
}

// Example usage:
// const cache = new LRUCache(2);
// cache.put(1, 1);
// cache.put(2, 2);
// console.log(cache.get(1)); // returns 1
// cache.put(3, 3);           // evicts key 2
// console.log(cache.get(2)); // returns -1 (not found)
// cache.put(4, 4);           // evicts key 1
// console.log(cache.get(1)); // returns -1 (not found)
// console.log(cache.get(3)); // returns 3
// console.log(cache.get(4)); // returns 4

• Approach:
• Use a doubly linked list to maintain the order of elements based on usage (most recently used at the tail).
• Use a hash map to store key-node pairs for O(1) access.
• On get, move the accessed node to the tail.
• On put, add the new node at the tail and remove the least recently used node if the capacity is exceeded.
• Complexity:
• Time: O(1) for both get and put operations.
• Space: O(n) where n is the capacity of the cache.

Model answer

Clarify & scope: The goal is to measure the success of Glean's new onboarding process. Assume the onboarding process aims to improve user activation and retention.

Define metric(s): Key metrics include:

• Activation Rate: Percentage of users completing key onboarding tasks.
• Time to Complete Onboarding: Average time taken to finish the onboarding process.
• User Retention Rate: Percentage of users returning after completing onboarding.

Break down (funnel):

funnel
    title Onboarding Funnel
    section Start
    New Users: 100%
    section Task Completion
    Task 1 Completed: 80%
    Task 2 Completed: 60%
    section Onboarding Completion
    Onboarding Completed: 50%

Ranked hypotheses:

1. Users find the onboarding process too lengthy.
2. Key tasks are not clearly defined.
3. Onboarding lacks engaging content.

How to investigate: Conduct user surveys and A/B tests to identify pain points. Analyze drop-off points in the onboarding funnel.

Decision & guardrails: Based on findings, streamline onboarding tasks and enhance content. Ensure changes do not negatively impact user retention or satisfaction.

Model answer

Product selection: Choose a product you are passionate about, such as a popular app or tool.

Clarify & scope: Describe the product briefly and its primary use case. Identify a specific area for improvement based on user feedback or personal experience.

User segments & pain points: Identify the target users and their pain points. For example, if the product is a task management app, users might struggle with complex task organization.

Goals & success metrics: Define goals such as improving user satisfaction or increasing feature adoption. Use metrics like task completion rate or user retention.

Solutions:

1. Simplified Interface: Redesign the UI for easier navigation and task management.
2. Enhanced Integration: Allow seamless integration with other tools users commonly use.
3. Customizable Features: Enable users to tailor the app to their specific workflow needs.

Recommendation: Implement a simplified interface to address immediate usability concerns.

Measurement & rollout: Track success through user satisfaction surveys and feature usage analytics. Iterate based on feedback to refine the improvements.

Model answer

Clarify & scope: The goal is to enhance Glean's search functionality specifically for enterprise users. Assume that enterprise users need efficient access to a vast amount of internal documents and data. The improvements should focus on speed, relevance, and user satisfaction.

User segments & pain points: Focus on enterprise knowledge workers who frequently search for internal documents. Their pain points include difficulty finding relevant information quickly and navigating through large volumes of data.

Goals & success metrics: The North Star metric is the search satisfaction score. Guardrails include search speed (response time) and relevance (click-through rate on top results).

Solutions:

1. AI-Powered Search Suggestions: Implement AI to offer predictive search suggestions based on user behavior and past searches.
2. Advanced Filters and Facets: Allow users to filter search results by document type, date, or author to quickly narrow down results.
3. Personalized Search Results: Use machine learning to tailor search results based on the user's role and past interactions.

Recommendation: Implement AI-powered search suggestions as it directly addresses the speed and relevance issues.

graph TD;
A[User Initiates Search] --> B{AI Analyzes Query};
B --> C[Provide Predictive Suggestions];
C --> D[User Selects Suggestion];
D --> E[Display Results];

Prioritization & trade-offs: Using RICE, AI-powered suggestions score high on impact and reach but require significant effort. Advanced filters are easier to implement but may have less impact.

MVP, measurement & rollout: Launch AI-powered suggestions as an MVP. Measure impact using search satisfaction scores and iterate based on feedback.

Model answer

Clarify & scope: The goal is to prioritize feature requests for Glean's product roadmap. Assume a variety of requests from different user segments and stakeholders.

Criteria for prioritization:

• Impact: Potential effect on user satisfaction and business goals.
• Effort: Resources required for development and implementation.
• Alignment: Consistency with Glean's strategic objectives.
• Urgency: Time-sensitive needs or competitive pressures.

Prioritization framework: Use the RICE framework (Reach, Impact, Confidence, Effort) to score each feature request.

Recommendation: Focus on features with high impact and reach but moderate effort. Ensure alignment with strategic objectives and consider urgency where applicable.

Execution & measurement: Develop a prioritized list and review it periodically. Measure feature success post-launch using relevant metrics such as adoption rate and user feedback.

Model answer

1. Requirements & scale

Functional Requirements:

• Track login events using user_id.
• Compute Monthly Active Users (MAU) as the number of distinct user_id with at least one login in the last 30 days.
• Handle a one-time rehash of user_id on a specific date T.

Non-Functional Requirements:

• Ensure accuracy in MAU computation despite user_id rehashing.
• Maintain system scalability to handle large volumes of login data.

Estimates:

• Assume 1 million users with an average of 1 login per day.
• Daily login events: 1 million.
• Storage: If each event requires 100 bytes (including metadata), daily storage is approximately 100 MB.
• Over a 30-day window, this results in 3 GB of storage.

2. High-level architecture

flowchart TD
    subgraph Client
        A[User Device]
    end

    subgraph Edge/CDN
        B[CDN]
    end

    subgraph Load Balancer
        C[Load Balancer]
    end

    subgraph API / Services
        D[Login Service]
        E[MAU Calculation Service]
    end

    subgraph Datastores
        F["Event Store (NoSQL)"]
        G["User Mapping Store (SQL)"]
    end

    subgraph Cache
        H[Redis Cache]
    end

    subgraph Workers
        I[Batch Processor]
    end

    A -->|Login Event| B
    B --> C
    C --> D
    D -->|Store Event| F
    E -->|Fetch Events| F
    E -->|Fetch Mapping| G
    E -->|Cache Results| H
    I -->|Process Events| E

3. API design

• POST /login: Record a login event with user_id.
• GET /mau: Retrieve the MAU for a specified date range.

4. Data model & storage

Datastores:

• Event Store (NoSQL): Used for storing login events. Chosen for its scalability and ability to handle high write throughput.
• User Mapping Store (SQL): Stores the mapping between old and new user_id. Chosen for its strong consistency guarantees.

Key Tables:

• LoginEvents: {user_id, timestamp}
• UserMapping: {user_id_old, user_id_new}

Partition Key:

• LoginEvents partitioned by user_id to distribute load evenly.

5. Deep dive

The core challenge is ensuring accurate MAU computation across the user_id rehash. Without the mapping, distinct counts will be inflated for windows overlapping date T.

sequenceDiagram
    participant MAUService as MAU Calculation Service
    participant EventStore as Event Store
    participant MappingStore as User Mapping Store
    participant Cache as Redis Cache

    MAUService->>EventStore: Fetch login events for [d-29, d]
    MAUService->>MappingStore: Fetch user_id mapping for date range
    MAUService->>Cache: Check cached MAU
    alt Cache Hit
        Cache-->>MAUService: Return cached MAU
    else Cache Miss
        MAUService->>MAUService: Compute distinct user_id
        MAUService->>Cache: Cache computed MAU
    end

6. Scale, bottlenecks & trade-offs

Replication and Sharding:

• The Event Store should be sharded by user_id to handle large volumes of data efficiently.
• The User Mapping Store should be replicated across multiple nodes to ensure availability and fault tolerance.

Caching:

• Use Redis to cache computed MAU results to reduce computation overhead for frequently queried date ranges.

Single Points of Failure:

• Ensure load balancers and key services are redundant to prevent single points of failure.

Trade-offs:

• Consistency vs. Availability: Opt for eventual consistency in the Event Store to improve availability and write throughput.
• Push vs. Pull: Use a pull-based approach for MAU computation to allow flexibility in handling data rehash scenarios.

By implementing a robust user mapping mechanism and leveraging caching, the system can accurately compute MAU even in the presence of user_id rehashing, ensuring minimal bias and operational resilience.

Model answer

1. Requirements & scale

Functional Requirements:

• Users can create, read, update, and delete notes.
• Notes are stored persistently and can be retrieved by users.
• Users can organize notes into categories or tags.
• Basic user authentication to ensure note privacy.

Non-Functional Requirements:

• High availability and reliability.
• Low latency for note operations.
• Scalability to handle increasing numbers of users and notes.
• Secure storage and access to notes.

Estimates:

• Assume 1 million users, each creating an average of 10 notes.
• Each note is approximately 1 KB in size.
• Total storage required: \(1 \text{ million users} \times 10 \text{ notes/user} \times 1 \text{ KB/note} = 10 \text{ GB}\).
• Assume 1000 active users at peak, each making 1 request per minute.
• Queries Per Second (QPS): \(1000 \text{ users} \times \frac{1 \text{ request}}{60 \text{ seconds}} \approx 17 \text{ QPS}\).

2. High-level architecture

flowchart TD
    subgraph Client
        A[User Device]
    end

    subgraph Edge/CDN
        B[CDN]
    end

    subgraph Load Balancer
        C[Load Balancer]
    end

    subgraph API / Services
        D[API Gateway]
        E[Auth Service]
        F[Notes Service]
    end

    subgraph Cache
        G[Redis Cache]
    end

    subgraph Datastores
        H["SQL Database (PostgreSQL)"]
    end

    A -->|HTTP Requests| B
    B -->|Forward Requests| C
    C -->|Route to Service| D
    D -->|Auth Requests| E
    D -->|Note CRUD Requests| F
    F -->|Read/Write| G
    F -->|Read/Write| H

3. API design

• POST /api/notes: Create a new note.
• GET /api/notes/{noteId}: Retrieve a specific note.
• PUT /api/notes/{noteId}: Update an existing note.
• DELETE /api/notes/{noteId}: Delete a note.
• GET /api/notes: List all notes for a user.

4. Data model & storage

Datastore Choice:

• SQL Database (PostgreSQL): Chosen for its ACID properties, which ensure data consistency and integrity, crucial for note-taking applications.

Key Tables:

• Users Table: Stores user credentials and metadata.
• user_id (Primary Key)
• username
• password_hash
• Notes Table: Stores note data.
• note_id (Primary Key)
• user_id (Foreign Key)
• title
• content
• created_at
• updated_at

Partition/Sharding Key:

• Use user_id as the partition key to distribute notes across database shards, ensuring even load distribution.

5. Deep dive

The core functionality of this application is the CRUD operations on notes. The following sequence diagram illustrates the flow for creating a new note:

sequenceDiagram
    participant User
    participant API Gateway
    participant Auth Service
    participant Notes Service
    participant SQL Database

    User->>API Gateway: POST /api/notes
    API Gateway->>Auth Service: Validate Token
    Auth Service-->>API Gateway: Token Valid
    API Gateway->>Notes Service: Create Note Request
    Notes Service->>SQL Database: Insert Note
    SQL Database-->>Notes Service: Insert Success
    Notes Service-->>API Gateway: Note Created
    API Gateway-->>User: Note Created Response

6. Scale, bottlenecks & trade-offs

Scaling:

• Horizontal Scaling: Add more instances of the Notes Service and database shards to handle increased load.
• Caching: Use Redis to cache frequently accessed notes to reduce database load and improve response times.

Bottlenecks:

• Database: As the number of users and notes grows, database performance may degrade. Sharding and indexing can alleviate this.
• Cache Consistency: Ensuring cache consistency with the database is crucial, especially after updates or deletes.

Trade-offs:

• Consistency vs. Availability (CAP Theorem): Prioritize consistency to ensure users always see the most recent version of their notes.
• SQL vs. NoSQL: SQL is chosen for its strong consistency and relational capabilities, which are suitable for structured data like notes.

Failure Modes:

• Single Points of Failure: Use redundant instances and failover strategies for critical components like the database and load balancer.
• Network Failures: Implement retry logic with exponential backoff for network requests to handle transient failures gracefully.

Model answer

1. Requirements & scale

Functional Requirements:

• Index documents for efficient search.
• Allow users to search documents by keywords.
• Return search results ranked by relevance.

Non-Functional Requirements:

• Low latency for search queries.
• High availability and reliability.
• Scalability to handle increasing data and user load.

Estimates:

• Query Per Second (QPS): Assume 1000 users with an average of 5 searches per day, leading to approximately 0.06 QPS.
• Storage: If each document averages 10 KB and we have 1 million documents, total storage is around 10 GB.
• Bandwidth: Assuming each search result returns 10 KB of data, and with 0.06 QPS, bandwidth requirement is minimal.

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[Search Service]
    end

    subgraph "Cache"
        E[Cache (Redis)]
    end

    subgraph "Datastores"
        F[Document Store (S3)]
        G[Index Store (Elasticsearch)]
    end

    subgraph "Workers"
        H[Indexing Worker]
    end

    A -->|Search Request| B
    B -->|Search Request| C
    C -->|Search Request| D
    D -->|Check Cache| E
    E -->|Cache Miss| G
    G -->|Search Results| D
    D -->|Search Results| C
    C -->|Search Results| B
    B -->|Search Results| A
    F -->|New Document| H
    H -->|Index Update| G

3. API design

• GET /search?query={keywords}: Retrieve documents matching the keywords.
• POST /documents: Add a new document to the system for indexing.

4. Data model & storage

Datastores:

• Document Store (S3): Used for storing raw documents. Chosen for its scalability and cost-effectiveness.
• Index Store (Elasticsearch): Used for indexing and searching documents. Elasticsearch is chosen for its full-text search capabilities and support for complex queries.

Key Tables/Indexes:

• Elasticsearch Index:
• Document ID: Unique identifier for each document.
• Content: Full text of the document.
• Metadata: Additional information like author, creation date.

Partitioning:

• Elasticsearch Index Sharding: Use document ID as the shard key to distribute the load evenly across nodes.

5. Deep dive

The core of this design is the search functionality using Elasticsearch, which provides powerful full-text search capabilities. When a user submits a search query, the system first checks the cache (Redis) for recent results to reduce load on Elasticsearch. If not found, the query is executed against the Elasticsearch index.

sequenceDiagram
    participant User
    participant UI
    participant SearchService
    participant Cache
    participant Elasticsearch

    User->>UI: Enter search query
    UI->>SearchService: Send search request
    SearchService->>Cache: Check cache for results
    alt Cache hit
        Cache-->>SearchService: Return cached results
    else Cache miss
        SearchService->>Elasticsearch: Execute search query
        Elasticsearch-->>SearchService: Return search results
        SearchService->>Cache: Store results in cache
    end
    SearchService-->>UI: Return search results
    UI-->>User: Display search results

6. Scale, bottlenecks & trade-offs

Scalability:

• Elasticsearch Scaling: Elasticsearch can be scaled horizontally by adding more nodes. Sharding and replication ensure data is distributed and available.
• Cache Layer: Redis can be scaled by partitioning the cache across multiple instances.

Bottlenecks:

• Elasticsearch Load: High query volume can overwhelm Elasticsearch. Mitigation includes caching and optimizing query performance.
• Network Latency: Use of CDN and edge caching can reduce latency for users distributed globally.

Trade-offs:

• Consistency vs. Availability (CAP Theorem): Elasticsearch is eventually consistent. This means there might be a delay in reflecting the latest changes in search results.
• Push vs. Pull: The system uses a pull model for search queries, which is suitable for user-driven search operations.
• SQL vs. NoSQL: Elasticsearch, a NoSQL database, is chosen for its text search capabilities, which are not as efficient in traditional SQL databases.

This design provides a robust and scalable solution for a search feature in a knowledge management tool, balancing performance, scalability, and complexity.

Model answer

1. Requirements & scale

Functional Requirements:

• Provide a unified search interface across multiple data sources.
• Support real-time indexing and retrieval of documents.
• Offer personalized search results based on user context and preferences.
• Ensure secure access to data with authentication and authorization mechanisms.

Non-Functional Requirements:

• High availability and low latency for search queries.
• Scalability to handle increasing data volumes and user queries.
• Strong consistency for search results, especially after updates.
• Robust security and data privacy measures.

Estimates:

• Query Per Second (QPS): Assume 1000 active users with an average of 5 queries per minute, leading to approximately 83 QPS.
• Storage: If each document is around 1KB and we index 1 million documents, we need around 1TB of storage, considering metadata and indexing overhead.
• Bandwidth: Assuming each search result is about 10KB, the bandwidth requirement would be 830KB/s for query responses.

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[API Gateway]
        E[Search Service]
        F[Auth Service]
    end

    subgraph Cache
        G[Redis Cache]
    end

    subgraph Datastores
        H["Search Index (Elasticsearch)"]
        I["User Data (SQL)"]
    end

    subgraph Message Queue
        J[Kafka]
    end

    subgraph Workers
        K[Indexing Worker]
    end

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

3. API design

• GET /search?query={query}: Retrieve search results based on the user's query.
• POST /index: Add or update documents in the search index.
• GET /user/preferences: Fetch user-specific search preferences.
• POST /auth/login: Authenticate users and issue tokens.

4. Data model & storage

Datastores:

• Search Index (Elasticsearch): Chosen for its full-text search capabilities and scalability. It supports complex queries and near real-time search.
• User Data (SQL): Stores user profiles and preferences. SQL is chosen for its ACID properties and structured data handling.

Key Tables:

• Documents Table: Stores metadata and pointers to document locations.
• User Preferences Table: Stores user-specific settings and search history.

Partitioning:

• Elasticsearch: Use document IDs as shard keys to distribute load evenly.
• SQL: Partition user data by user ID to optimize retrieval and updates.

5. Deep dive

The core of the Glean product is its search functionality, which involves indexing and querying documents efficiently.

sequenceDiagram
    participant UI as User Interface
    participant API as API Gateway
    participant SS as Search Service
    participant ES as Elasticsearch
    participant Cache as Redis Cache

    UI->>API: GET /search?query={query}
    API->>SS: Forward request
    SS->>Cache: Check cache for query results
    alt Cache hit
        Cache-->>SS: Return cached results
    else Cache miss
        SS->>ES: Query Elasticsearch
        ES-->>SS: Return search results
        SS->>Cache: Store results in cache
    end
    SS-->>API: Return search results
    API-->>UI: Display results

6. Scale, bottlenecks & trade-offs

Scaling:

• Elasticsearch Clusters: Scale horizontally by adding more nodes to handle increased indexing and query loads.
• Redis Cache: Use sharding to distribute cache load across multiple instances.

Bottlenecks:

• Write Latency: Write-through caching ensures consistency but introduces latency. Mitigate by optimizing database writes and using asynchronous processing where possible.
• Search Index Updates: Frequent updates can lead to high load on Elasticsearch. Use a message queue (Kafka) to batch updates and reduce load.

Trade-offs:

• Consistency vs. Availability: Prioritize consistency in search results, accepting higher latency for updates.
• Push vs. Pull: Use a push model for updates to ensure the search index is always current.
• SQL vs. NoSQL: SQL is used for structured user data, while Elasticsearch handles unstructured document data, balancing consistency and performance needs.

By addressing these aspects, the Glean architecture ensures efficient, scalable, and reliable search capabilities across diverse data sources.

Model answer

Purpose of a Hash Table

1. Efficient Data Retrieval: A hash table is a data structure that provides efficient data retrieval. It allows for average-case constant time complexity, O(1), for both insertions and lookups, making it ideal for applications where quick access to data is crucial.
2. Key-Value Storage: Hash tables store data in key-value pairs, enabling fast access to values when the corresponding key is known. This makes them suitable for implementing associative arrays or dictionaries.
3. Dynamic Size: Hash tables can dynamically resize themselves to handle varying amounts of data, maintaining efficient operations even as the dataset grows.

Handling Collisions

Collisions occur when two different keys hash to the same index in a hash table. There are several standard methods to handle collisions:

1. Chaining: This technique involves storing all elements that hash to the same index in a linked list or another data structure. When a collision occurs, the new element is simply added to the list at that index.

• Pros: Simple to implement and handles collisions effectively.
• Cons: Can degrade performance to O(n) in the worst case if many elements hash to the same index.

1. Open Addressing: This method involves finding another open slot within the hash table when a collision occurs. Common strategies include: - Linear Probing: Check the next slot sequentially until an empty one is found. - Quadratic Probing: Check slots at intervals that are quadratic in nature (e.g., 1, 4, 9, ...). - Double Hashing: Use a secondary hash function to determine the step size for finding the next slot.

• Pros: Avoids the use of additional data structures like linked lists.
• Cons: Can lead to clustering and requires careful handling of deletion.

1. Resizing: When the load factor (ratio of number of elements to the table size) exceeds a certain threshold, the hash table can be resized. This involves creating a new larger table and rehashing all existing elements into it.

• Pros: Helps maintain efficient operations by keeping the load factor low.
• Cons: Resizing is an expensive operation and can temporarily degrade performance.

By using these techniques, hash tables can efficiently manage collisions and maintain their performance characteristics.

Model answer

Expectation and Variance of X + Y

To compute the expectation and variance of the sum of two random variables \(X\) and \(Y\), we use the following formulas:

1. Expectation of X + Y:

The expectation (or mean) of the sum of two random variables is simply the sum of their individual expectations. This is given by the formula:

\[ E(X + Y) = E(X) + E(Y) \]

• Explanation: Expectation is a linear operator, which means it distributes over addition. Thus, regardless of whether \(X\) and \(Y\) are independent or not, the expectation of their sum is always the sum of their expectations.

1. Variance of X + Y:

The variance of the sum of two random variables is given by:

\[ \text{Var}(X + Y) = \text{Var}(X) + \text{Var}(Y) + 2 \cdot \text{Cov}(X, Y) \]

• Role of Covariance: Covariance measures how much two random variables change together. If \(X\) and \(Y\) are positively correlated, the covariance is positive, and it increases the variance of the sum. Conversely, if they are negatively correlated, the covariance is negative, reducing the variance of the sum.
• Special Case - Independence: If \(X\) and \(Y\) are independent, their covariance is zero because independent variables do not influence each other. In this case, the formula simplifies to:

\[ \text{Var}(X + Y) = \text{Var}(X) + \text{Var}(Y) \]

• Explanation: When \(X\) and \(Y\) are independent, their joint variability does not contribute to the variance of their sum, hence the absence of the covariance term.

These formulas are fundamental in probability and statistics, providing insights into how random variables combine and how their distributions affect each other. Understanding the role of covariance is crucial, especially in scenarios where variables are not independent, as it directly impacts the variability of their sum.

Model answer

Advantages of Using a Document-Based Database like MongoDB for Glean's Data Storage

1. Schema Flexibility - MongoDB allows for a flexible schema design, which is beneficial for applications like Glean that may need to evolve their data models over time. This flexibility supports rapid development and iteration without the need for costly schema migrations.
2. Handling Unstructured Data - Document-based databases are well-suited for storing unstructured or semi-structured data. This is advantageous for Glean if it deals with diverse data types that do not fit neatly into a tabular format, such as JSON-like documents.
3. Horizontal Scalability - MongoDB provides horizontal scalability through sharding, which allows Glean to distribute data across multiple servers. This is crucial for handling large volumes of data and ensuring high availability and performance as the application scales.
4. Efficient Read and Write Operations - MongoDB is optimized for high read and write throughput, making it suitable for applications with heavy data access patterns. This can be particularly beneficial for Glean if it experiences high traffic and requires quick data retrieval and update capabilities.
5. Rich Query Language - MongoDB offers a powerful query language that supports complex queries, indexing, and aggregation. This enables Glean to perform sophisticated data retrieval operations efficiently, which is essential for analytics and reporting features.
6. Built-in Replication and High Availability - MongoDB's replication features ensure data redundancy and high availability. This is critical for Glean to maintain data integrity and uptime, even in the event of server failures.
7. Support for Geospatial Data - If Glean requires geospatial data handling, MongoDB provides built-in support for geospatial queries, making it easier to implement location-based features.
8. Community and Ecosystem - MongoDB has a large community and a rich ecosystem of tools and libraries, which can accelerate development and provide robust support for Glean's engineering team.

Complexity:

• Time Complexity: MongoDB's indexing capabilities can optimize query performance, often reducing time complexity for read operations from O(n) to O(log n) or better, depending on the index structure.
• Space Complexity: Document-based storage can be more space-efficient for certain data types, as it avoids the overhead of rigid schemas and can store data in a more compact format. However, the actual space complexity will depend on the specific data and indexing strategies used.

Model answer

Approach to Debugging a Performance Issue in a Web Application

1. Identify Symptoms and Gather Data - Start by identifying the specific symptoms of the performance issue. This could be slow page loads, high server response times, or increased error rates. - Gather data using monitoring tools like New Relic, Datadog, or Google Analytics to understand the scope and frequency of the problem.
2. Analyze Client-Side Performance - Use browser developer tools to analyze client-side performance. Focus on metrics like Time to First Byte (TTFB), First Contentful Paint (FCP), and Largest Contentful Paint (LCP). - Check for resource-heavy scripts, large images, or inefficient CSS that might be slowing down the rendering process.
3. Examine Network Performance - Investigate network latency and bandwidth issues. Use tools like Chrome DevTools to inspect network requests and identify slow or failed requests. - Look for opportunities to optimize assets through compression (e.g., gzip), minification, or using a Content Delivery Network (CDN) to reduce latency.
4. Inspect Server-Side Performance - Analyze server logs to identify bottlenecks. Check for slow database queries, inefficient algorithms, or resource-intensive operations. - Use profiling tools to monitor CPU and memory usage on the server. Identify any processes that are consuming excessive resources.
5. Evaluate Database Performance - Check for slow or inefficient database queries. Use query profiling tools to identify queries that need optimization. - Consider indexing strategies, query optimization, and database sharding if necessary to improve performance.
6. Implement Backpressure Mechanisms - If the issue involves a mismatch between producer and consumer speeds, implement backpressure mechanisms. This can prevent unbounded queue growth and manage overload effectively. - Use bounded queues or explicit flow control signals to ensure that the system can handle the load without degrading performance.
7. Test and Validate Solutions - After implementing changes, test the application to ensure that the performance issues have been resolved. - Use load testing tools to simulate high traffic and validate that the application can handle the expected load.
8. Monitor and Iterate - Continuously monitor the application’s performance post-deployment to catch any new issues early. - Be prepared to iterate on your solutions as new data and performance metrics become available.

By following these steps, you can systematically identify and resolve performance issues in a web application, ensuring a smooth and efficient user experience.