Event Data Validation System: A Comprehensive Design

by Sebastian Müller 53 views

Introduction

Hey guys! We're diving deep into designing a comprehensive event data validation system for FreqSeek, our TypeScript events discovery platform. This is super crucial because we're pulling event data from all sorts of sources – think Ticketmaster, Eventbrite, Facebook, and more. To make sure our users get the best experience, we need to ensure the data is clean, accurate, and free of duplicates. This article will walk you through the technical specifications, database schema changes, API endpoint design, implementation timeline, and testing strategy. Let’s get started!

1. Technical Specification Document

1.1. Overview

The main goal of this event data validation system is to create a robust and scalable solution that ensures the quality and consistency of event data ingested into FreqSeek. We'll need to handle various data formats and potential inconsistencies from different sources. The system should be able to validate data against predefined schemas, detect duplicates, score data quality, and provide a mechanism for manual review of edge cases. This validation system will be a critical component in maintaining the integrity of our platform and providing a reliable user experience.

1.2. Requirements

  • Zod Validation Schemas: We’ll use Zod, a TypeScript-first schema declaration and validation library, to define schemas for our event data. This will allow us to ensure that incoming data conforms to our expected structure and types. Zod is perfect because it’s super type-safe and plays well with TypeScript, which is our jam.
  • Fuzzy Matching Algorithm for Duplicate Detection: To prevent duplicate events from cluttering our platform, we'll implement a fuzzy matching algorithm. This will help us identify events that are similar but not exact matches, accounting for variations in titles, descriptions, and venues. Think of it as a smart way to catch events that are the same but described slightly differently.
  • Data Quality Scoring System: We need a way to quantify the quality of each event record. This system will assign scores based on various factors, such as completeness of data, accuracy of information, and consistency across fields. This scoring system will help us prioritize which events to display and which ones might need manual review.
  • Manual Review Queue for Edge Cases: No system is perfect, so we'll set up a manual review queue for events that fail validation or are flagged by the fuzzy matching algorithm. This will allow our team to review and correct data as needed, ensuring that even tricky cases are handled correctly.

1.3. System Architecture

The system will consist of several key components:

  1. Data Ingestion Service: This service will be responsible for receiving event data from various sources. It will act as the entry point for all incoming event information.
  2. Validation Service: This is the heart of the system. It will use Zod schemas to validate data, apply the fuzzy matching algorithm for duplicate detection, and calculate data quality scores. This service will ensure that data meets our standards before it's stored.
  3. Database: We'll use PostgreSQL with PostGIS for storing event data, taking advantage of PostGIS for spatial queries and location-based features. PostgreSQL will serve as our reliable data store.
  4. Manual Review UI: A user interface will be built to allow manual review of events flagged for further inspection. This UI will enable our team to make necessary corrections and ensure data accuracy.
  5. API Endpoints: We’ll create API endpoints for submitting data for validation and retrieving validation results. These endpoints will be the communication channels between the services.

1.4. Technology Stack

  • Programming Language: TypeScript (because it’s awesome and we love it!)
  • Validation Library: Zod (for type-safe schema validation)
  • Fuzzy Matching Library: A library like fuzzy-wuzzy or string-similarity (for finding those near-duplicate events)
  • Database: PostgreSQL with PostGIS (for storing and querying event data)
  • Backend Framework: Express/Fastify (for building our API services)
  • Frontend Framework: Next.js (for the manual review UI)
  • Monorepo Tool: pnpm workspaces (to keep our codebase organized)

2. Database Schema Changes Required

2.1. Existing Schema

Let's assume our current events table looks something like this:

CREATE TABLE events (
    id UUID PRIMARY KEY,
    title VARCHAR(255) NOT NULL,
    description TEXT,
    venue VARCHAR(255),
    start_time TIMESTAMP WITH TIME ZONE,
    end_time TIMESTAMP WITH TIME ZONE,
    source VARCHAR(255), -- e.g., 'Ticketmaster', 'Eventbrite'
    source_id VARCHAR(255), -- Unique ID from the source
    created_at TIMESTAMP WITH TIME ZONE DEFAULT now(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT now()
);

2.2. Proposed Schema Changes

To support our data validation system, we’ll need to add a few columns:

  1. validation_score (INTEGER): To store the data quality score.
  2. is_duplicate (BOOLEAN): To flag potential duplicates.
  3. needs_review (BOOLEAN): To indicate if an event needs manual review.
  4. validation_errors (JSONB): To store specific validation errors.
  5. normalized_title (TEXT): To store a normalized version of the title for fuzzy matching.

Here’s the updated SQL schema:

ALTER TABLE events ADD COLUMN validation_score INTEGER;
ALTER TABLE events ADD COLUMN is_duplicate BOOLEAN DEFAULT FALSE;
ALTER TABLE events ADD COLUMN needs_review BOOLEAN DEFAULT FALSE;
ALTER TABLE events ADD COLUMN validation_errors JSONB;
ALTER TABLE events ADD COLUMN normalized_title TEXT;

-- Add an index for the normalized title for faster fuzzy matching
CREATE INDEX idx_events_normalized_title ON events (normalized_title);

-- Add an index for events needing review
CREATE INDEX idx_events_needs_review ON events (needs_review) WHERE needs_review = TRUE;

2.3. Rationale

  • validation_score: This column allows us to easily filter and sort events based on their quality.
  • is_duplicate: This flag helps us quickly identify and handle potential duplicates.
  • needs_review: This flag allows us to efficiently query for events that require manual intervention.
  • validation_errors: Storing errors as JSONB gives us flexibility in capturing various validation issues without needing to alter the schema.
  • normalized_title: Normalizing the title (e.g., lowercasing, removing punctuation) improves the accuracy of the fuzzy matching algorithm.

3. API Endpoint Design for Validation Service

3.1. Endpoints

We'll need a few key API endpoints for our validation service:

  1. POST /validate:

    • Purpose: Validates a single event or a batch of events.
    • Request Body:
    [
  {
    "title": "Event Title",
    "description": "Event Description",
    "venue": "Venue Name",
    "start_time": "2024-07-28T19:00:00Z",
    "end_time": "2024-07-28T22:00:00Z",
    "source": "Ticketmaster",
    "source_id": "12345"
  }
]
    • Response:
    {
  "results": [
    {
      "event": {
        "title": "Event Title",
        "description": "Event Description",
        "venue": "Venue Name",
        "start_time": "2024-07-28T19:00:00Z",
        "end_time": "2024-07-28T22:00:00Z",
        "source": "Ticketmaster",
        "source_id": "12345"
      },
      "validation_score": 90,
      "is_duplicate": false,
      "needs_review": false,
      "validation_errors": []
    }
  ]
}

  2. GET /events/:id/validation-results:

    • Purpose: Retrieves validation results for a specific event.
    • Response:
    {
  "validation_score": 90,
  "is_duplicate": false,
  "needs_review": false,
  "validation_errors": []
}

  3. GET /events/needs-review:

    • Purpose: Retrieves a list of events that need manual review.
    • Query Parameters: limit, offset (for pagination)
    • Response:
    [
  {
    "id": "uuid",
    "title": "Event Title",
    "description": "Event Description",
    "venue": "Venue Name",
    "start_time": "2024-07-28T19:00:00Z",
    "end_time": "2024-07-28T22:00:00Z",
    "source": "Ticketmaster",
    "source_id": "12345"
  }
]

3.2. Implementation Details

  • Framework: We’ll use Express or Fastify for building these API endpoints. Both are lightweight and performant Node.js frameworks that we’re comfortable with.
  • Middleware: We’ll implement middleware for request validation, error handling, and logging. This will help keep our code clean and maintainable.
  • Authentication/Authorization: Depending on our needs, we may add authentication and authorization to these endpoints to ensure only authorized services can access them.

4. Implementation Timeline and Task Breakdown

4.1. Timeline

We’re estimating a 4-6 week timeline for this project, broken down into the following phases:

  • Week 1:
    • Task 1: Set up the project repository and monorepo structure using pnpm workspaces. (2 days)
    • Task 2: Design and implement Zod validation schemas for event data. (3 days)
  • Week 2:
    • Task 3: Implement the fuzzy matching algorithm for duplicate detection. (3 days)
    • Task 4: Design and implement the data quality scoring system. (2 days)
  • Week 3:
    • Task 5: Implement the API endpoints for the validation service. (5 days)
  • Week 4:
    • Task 6: Implement the manual review queue and UI in Next.js. (5 days)
  • Week 5:
    • Task 7: Write unit and integration tests for the validation service. (3 days)
    • Task 8: Perform end-to-end testing and identify bugs. (2 days)
  • Week 6:
    • Task 9: Bug fixing and performance optimization. (3 days)
    • Task 10: Documentation and deployment. (2 days)

4.2. Task Breakdown

Here's a more detailed breakdown of the key tasks:

  1. Set up Project Repository and Monorepo:
    • Initialize a new Git repository.
    • Set up pnpm workspaces for managing multiple packages.
    • Configure CI/CD pipelines.
  2. Design and Implement Zod Validation Schemas:
    • Define schemas for various event data fields (title, description, venue, etc.).
    • Implement validation logic for data types, formats, and constraints.
    • Handle edge cases and potential data inconsistencies.
  3. Implement Fuzzy Matching Algorithm:
    • Choose a suitable fuzzy matching library (e.g., fuzzy-wuzzy, string-similarity).
    • Implement the algorithm to compare event titles and descriptions.
    • Tune the algorithm for optimal performance and accuracy.
  4. Design and Implement Data Quality Scoring System:
    • Define factors that contribute to data quality (completeness, accuracy, consistency).
    • Assign weights to each factor.
    • Implement the scoring logic.
  5. Implement API Endpoints:
    • Set up Express/Fastify routes for /validate, /events/:id/validation-results, and /events/needs-review.
    • Implement request validation and error handling.
    • Integrate with the Zod schemas, fuzzy matching algorithm, and data quality scoring system.
  6. Implement Manual Review Queue and UI:
    • Build a Next.js UI for reviewing events.
    • Implement features for approving, rejecting, and editing events.
    • Integrate with the API endpoints.
  7. Write Unit and Integration Tests:
    • Write tests for individual components (Zod schemas, fuzzy matching, scoring system).
    • Write integration tests for the API endpoints.
    • Ensure adequate test coverage.
  8. Perform End-to-End Testing:
    • Test the entire system with real-world data.
    • Identify and fix bugs.
  9. Bug Fixing and Performance Optimization:
    • Address any bugs identified during testing.
    • Optimize the performance of the validation service.
  10. Documentation and Deployment:
    • Write documentation for the system.
    • Deploy the service to our production environment.

5. Testing Strategy for Data Quality Metrics

5.1. Key Metrics

To ensure our data validation system is working effectively, we’ll track the following key metrics:

  1. Validation Rate: The percentage of events that pass validation without errors.
  2. Duplicate Detection Rate: The percentage of duplicate events successfully identified.
  3. False Positive Rate: The percentage of non-duplicate events incorrectly flagged as duplicates.
  4. Data Quality Score Distribution: The distribution of data quality scores across all events.
  5. Manual Review Rate: The percentage of events that require manual review.

5.2. Testing Methods

We’ll use a combination of testing methods to evaluate these metrics:

  1. Unit Tests: To test individual components of the system, such as Zod schemas and the fuzzy matching algorithm. Unit tests are crucial for ensuring the correctness of individual units of code.
  2. Integration Tests: To test the interaction between different components, such as the API endpoints and the database. Integration tests help verify that different parts of the system work together as expected.
  3. End-to-End Tests: To test the entire system from end to end, simulating real-world scenarios. End-to-end tests ensure that the entire system functions correctly under realistic conditions.
  4. Data Quality Audits: To manually review a sample of events and assess their quality. Data quality audits provide a human perspective on data accuracy and completeness.
  5. A/B Testing: To compare the performance of the validation system before and after changes. A/B testing can help us understand the impact of new features or optimizations.

5.3. Test Data

We’ll create a diverse set of test data to cover various scenarios:

  • Valid Events: Events that conform to our schemas and should pass validation.
  • Invalid Events: Events with missing or incorrect data that should fail validation.
  • Duplicate Events: Pairs or groups of events that are similar and should be flagged as duplicates.
  • Edge Cases: Events with unusual data or formatting that might cause issues.

5.4. Monitoring and Reporting

We’ll set up monitoring and reporting to track our key metrics over time. This will allow us to identify trends, detect issues, and ensure that our validation system continues to perform effectively. Regular reports will help us stay informed about the health of our data.

Conclusion

So there you have it, a comprehensive plan for designing an event data validation system for FreqSeek! This system will be vital in ensuring the quality and reliability of our event data, which in turn will provide a better experience for our users. By implementing Zod schemas, a fuzzy matching algorithm, a data quality scoring system, and a manual review queue, we’ll be well-equipped to handle the complexities of event data from multiple sources. Plus, with a solid testing strategy in place, we can continuously monitor and improve the system's performance. Let's get this done, team!