File Sharing Service: DynamoDB, Terraform, Lambda Guide

by Alex Johnson 56 views

In this comprehensive guide, we will explore how to implement a robust and scalable file sharing service using a combination of cutting-edge technologies: DynamoDB, Terraform, and Lambda. This approach leverages the strengths of each service to create a cost-effective, efficient, and highly available solution. We'll delve into the intricacies of designing a single-table DynamoDB schema, provisioning infrastructure with Terraform, and building event-driven workers using Lambda functions. This guide will provide you with a step-by-step approach to building your own file-sharing service, covering everything from initial setup to advanced features like permission management and data cleanup.

Introduction to File Sharing Service Implementation

Implementing a file sharing service involves several key components working seamlessly together. At its core, we need a database to store file metadata, infrastructure to host our services, and functions to handle file uploads, downloads, and sharing permissions. DynamoDB provides a NoSQL database solution ideal for handling large volumes of data with high throughput and low latency. Terraform enables us to define and provision our infrastructure as code, ensuring consistency and repeatability. Lambda functions allow us to run serverless code in response to events, making them perfect for processing file uploads, managing permissions, and cleaning up stale data. By combining these technologies, we can create a file sharing service that is both scalable and cost-effective.

Key Components of the File Sharing Service

  1. DynamoDB Table (FileMetadata): A single-table schema to store file metadata, share grants, and view links.
  2. Terraform Infrastructure: Provisioning of DynamoDB table, Global Secondary Indexes (GSIs), streams, SQS/DLQ, Lambdas, IAM roles/policies, and CloudWatch log groups.
  3. Stream Processor Lambda (Rust): A Lambda function to process DynamoDB stream records and maintain VIEW_LINK entries.
  4. SQS Cleanup Worker Lambda (Rust): A Lambda function to process cleanup messages for revocations and delete VIEW_LINKs.
  5. API/Application Helpers & Docs: Helpers for permission validation, lazy VIEW_LINK creation, and query patterns.

Benefits of Using DynamoDB, Terraform, and Lambda

  • Scalability: DynamoDB and Lambda can automatically scale to handle increasing workloads, ensuring your service remains responsive.
  • Cost-Effectiveness: Serverless architecture with Lambda and DynamoDB's pay-per-use pricing model reduces operational costs.
  • Efficiency: Event-driven architecture with Lambda functions ensures timely processing of events and efficient resource utilization.
  • Consistency: Terraform enables consistent infrastructure provisioning, reducing the risk of errors and inconsistencies.
  • High Availability: DynamoDB's distributed nature and Lambda's built-in redundancy ensure high availability of the service.

1. DynamoDB Single-Table Schema Design

A well-designed DynamoDB schema is crucial for the performance and scalability of our file sharing service. The single-table design approach allows us to store different types of data items (files, grants, view links) in the same table, leveraging DynamoDB's powerful querying capabilities. This section will delve into the design of the FileMetadata table, including the primary key, sort key, and Global Secondary Indexes (GSIs). Understanding this schema is the foundation for building an efficient and scalable file sharing service.

Understanding the Single-Table Design

The single-table design pattern in DynamoDB involves using a single table to store multiple entity types. This approach can improve query efficiency and reduce costs compared to multi-table designs. In our file sharing service, we'll store file metadata, share grants, and view links in the FileMetadata table. To differentiate between these items, we'll use a composite primary key consisting of a partition key (PK) and a sort key (SK). The ItemType attribute will further help in identifying the type of item.

FileMetadata Table Schema

  • Primary Key (PK): USER#<UserID> for files and grants, USER#<UserID> for view links.
  • Sort Key (SK): FILE#<FilePath> for files, GRANT#<RecipientID>#<GrantID> for grants, VIEWLINK#<OwnerID>#<FileID> for view links.
  • ItemType: FILE, SHARE_GRANT, VIEW_LINK.
  • Attributes:
    • FileID: Unique identifier for the file.
    • OwnerID: User ID of the file owner.
    • FileName: Name of the file.
    • FolderPrefix: Folder path of the file.
    • CreatedDate: Timestamp of file creation.
    • MediaType: MIME type of the file.
    • S3Key: S3 key for the file.
    • Size: File size in bytes.
    • ContentType: Content type of the file.
    • MediaMetadata: Metadata for media files (images, videos).
    • GrantID: Unique identifier for the share grant.
    • RecipientID: User ID of the recipient.
    • Permissions: Permissions granted to the recipient (READ, WRITE).
    • Prefix: Shared folder prefix.

Global Secondary Indexes (GSIs)

To support various query patterns, we'll create two GSIs:

  • ShareAccessIndex (GSI1):
    • Partition Key (GSI1-PK): ACCESS#<RecipientID>
    • Sort Key (GSI1-SK): GRANT#<OwnerID>#<Prefix>
    • This index is used to query grants for a specific recipient and prefix.
  • MergedFolderViewIndex (GSI2):
    • Partition Key (GSI2-PK): VIEWER#<UserID>#FOLDER#<FolderPrefix>
    • Sort Key (GSI2-SK): <MediaType>#<CreatedDate>#<FileID>
    • This index is used to query view links for a specific user and folder.

Example Data

The following JSON snippet shows sample data entries in the FileMetadata table:

[
  {
    "PK": "USER#Sheldon",
    "SK": "FILE#media/Project Docs/DSCN0010.jpg",
    "ItemType": "FILE",
    "FileID": "R102",
    "OwnerID": "Sheldon",
    "FileName": "DSCN0010.jpg",
    "FolderPrefix": "media/Project Docs/",
    "CreatedDate": 1224685719000,
    "MediaType": "image/jpeg",
    "S3Key": "Sheldon/media/Project Docs/DSCN0010.jpg",
    "Size": 161713,
    "ContentType": "image/jpeg",
    "MediaMetadata": {
      "type": "image",
      "width": 640,
      "height": 480,
      "exif": {
        "FocalLengthIn35mmFilm": "112",
        "GPSSatellites": "06",
        "ExposureMode": "auto exposure",
        "Model": "COOLPIX P6000",
        "PixelXDimension": "640",
        "GPSLatitudeRef": "N",
        "GainControl": "none",
        "ImageDescription": "",
        "DateTimeOriginal": "2008-10-22 16:28:39",
        "DateTimeDigitized": "2008-10-22 16:28:39",
        "XResolution": "72",
        "ExposureTime": "1/75",
        "GPSAltitudeRef": "above sea level"
      },
      "gps": {
        "latitude": 43.46744833333334,
        "longitude": 11.885126666663888,
        "altitude": null
      }
    }
  },
  {
    "PK": "USER#Sheldon",
    "SK": "GRANT#Justin#G-a1b2c3d4-5e6f-7g8h-9i0j-k1l2m3n4o5p6",
    "ItemType": "SHARE_GRANT",
    "GrantID": "G-a1b2c3d4-5e6f-7g8h-9i0j-k1l2m3n4o5p6",
    "OwnerID": "Sheldon",
    "RecipientID": "Justin",
    "Permissions": "READ",
    "Prefix": "media/Project Docs/",
    "CreatedDate": 1224685700000,
    "GSI1-PK": "ACCESS#Justin",
    "GSI1-SK": "GRANT#Sheldon#media/Project Docs/"
  },
  {
    "PK": "USER#Justin",
    "SK": "VIEWLINK#Sheldon#R102",
    "ItemType": "VIEW_LINK",
    "FileID": "R102",
    "OwnerID": "Sheldon",
    "GrantID": "G-a1b2c3d4-5e6f-7g8h-9i0j-k1l2m3n4o5p6",
    "CreatedDate": 1224685719000,
    "FolderName": "Project Docs/",
    "MediaType": "image/jpeg",
    "GSI2-PK": "VIEWER#Justin#FOLDER#Project Docs/",
    "GSI2-SK": "image/jpeg#1224685719000#R102"
  }
]

This single-table schema design allows for efficient querying of files, grants, and view links, making it a robust foundation for our file sharing service. By understanding the schema and GSIs, you can effectively retrieve and manage data within your application.

2. Terraform Infrastructure Provisioning

Terraform is a powerful Infrastructure as Code (IaC) tool that allows us to define and provision our infrastructure in a consistent and repeatable manner. In this section, we'll explore how to use Terraform to provision the necessary resources for our file sharing service, including the DynamoDB table, GSIs, streams, SQS/DLQ, Lambdas, IAM roles/policies, and CloudWatch log groups. By using Terraform, we can ensure that our infrastructure is provisioned correctly and efficiently.

Key Terraform Resources

  1. aws_dynamodb_table: Defines the DynamoDB table with PK, SK, GSIs, streams, TTL, and PITR.
  2. aws_sqs_queue: Creates SQS queue and DLQ with redrive policy.
  3. aws_lambda_function: Provisions Lambda functions for stream processor and cleanup worker.
  4. aws_lambda_event_source_mapping: Configures event source mappings for DynamoDB streams and SQS.
  5. aws_iam_role and aws_iam_policy: Defines IAM roles and policies with least privilege for DynamoDB, streams, SQS, and CloudWatch logs.
  6. aws_cloudwatch_log_group: Creates CloudWatch log groups with retention policies.

Terraform Modules

To organize our Terraform code, we'll use modules for each component:

  • dynamodb_table: Provisions the DynamoDB table and GSIs.
  • sqs_queue: Creates SQS queue and DLQ.
  • lambda_function: Provisions Lambda functions and event source mappings.
  • iam_roles: Defines IAM roles and policies.
  • cloudwatch_logs: Creates CloudWatch log groups.

Example Terraform Code

Here's an example of how to define the DynamoDB table using Terraform:

resource "aws_dynamodb_table" "file_metadata" {
  name           = "FileMetadata"
  billing_mode   = "PAY_PER_REQUEST"
  hash_key       = "PK"
  range_key      = "SK"
  stream_enabled = true
  stream_view_type = "NEW_AND_OLD_IMAGES"

  attribute {
    name = "PK"
    type = "S"
  }

  attribute {
    name = "SK"
    type = "S"
  }

  attribute {
    name = "GSI1-PK"
    type = "S"
  }

  attribute {
    name = "GSI1-SK"
    type = "S"
  }

  attribute {
    name = "GSI2-PK"
    type = "S"
  }

  attribute {
    name = "GSI2-SK"
    type = "S"
  }

  global_secondary_index {
    name            = "ShareAccessIndex"
    hash_key        = "GSI1-PK"
    range_key       = "GSI1-SK"
    projection_type = "ALL"
  }

  global_secondary_index {
    name            = "MergedFolderViewIndex"
    hash_key        = "GSI2-PK"
    range_key       = "GSI2-SK"
    projection_type = "ALL"
  }

  ttl {
    attribute_name = "TimeToExist"
    enabled        = true
  }

  tags = {
    Name = "FileMetadataTable"
  }
}

output "dynamodb_table_name" {
  value = aws_dynamodb_table.file_metadata.name
}

output "dynamodb_table_arn" {
  value = aws_dynamodb_table.file_metadata.arn
}

output "dynamodb_table_stream_arn" {
  value = aws_dynamodb_table.file_metadata.stream_arn
}

This Terraform code defines the FileMetadata table with the primary key attributes (PK and SK), global secondary indexes (ShareAccessIndex and MergedFolderViewIndex), and other settings like billing mode, stream configuration, and TTL. The outputs provide the table name, ARN, and stream ARN for use in other modules.

Benefits of Using Terraform

  • Infrastructure as Code: Define infrastructure in code, ensuring consistency and repeatability.
  • Version Control: Track infrastructure changes using version control systems like Git.
  • Automation: Automate infrastructure provisioning and management.
  • Collaboration: Collaborate on infrastructure changes with a team.
  • Modularity: Organize infrastructure code into reusable modules.

By using Terraform, we can provision our infrastructure in a reliable and efficient manner, ensuring that our file sharing service has the resources it needs to operate effectively. Terraform's ability to manage complex infrastructure configurations makes it an invaluable tool for building scalable and resilient applications.

3. Stream Processor Lambda (Rust)

The Stream Processor Lambda is a crucial component of our file sharing service, responsible for processing DynamoDB stream records and maintaining VIEW_LINK entries. This Lambda function is triggered by events in the DynamoDB table, such as file creation, deletion, and share grant modifications. We'll implement this Lambda in Rust for performance and safety, leveraging its ability to handle concurrent operations efficiently. Understanding how this Lambda works is essential for ensuring that file sharing permissions are correctly managed and that the service behaves as expected.

Functionality of the Stream Processor Lambda

The Stream Processor Lambda performs the following key functions:

  1. Parse DynamoDB Stream Records: Parse the stream records to identify the type of event (FILE, SHARE_GRANT) and the operation (INSERT, MODIFY, REMOVE).
  2. Handle FILE Events:
    • On FILE INSERT: Query grants (ShareAccessIndex) for matching prefixes and batch_create VIEW_LINKs for the owner and recipients. Respect the 25-item batch limit and retry unprocessed items with backoff.
    • On FILE REMOVE: Delete owner + recipient VIEW_LINKs for that file.
  3. Handle SHARE_GRANT Events:
    • On SHARE_GRANT INSERT: Ensure lazy creation semantics and publish cleanup messages to SQS for revocation flows when appropriate.
    • On SHARE_GRANT REMOVE: Publish cleanup messages to SQS for revocation flows.
  4. Implement Idempotent Writes/Deletes: Ensure that writes and deletes are idempotent to handle duplicate events.
  5. Structured Logging: Log events and errors in a structured format for easy debugging and monitoring.
  6. Metrics: Emit metrics for monitoring performance and identifying issues.

Rust Implementation

We'll use Rust for the Stream Processor Lambda due to its performance, safety, and ability to handle concurrent operations efficiently. The implementation will involve the following steps:

  1. Set up the Rust Project:
    • Use cargo to create a new Rust project.
    • Add necessary dependencies, such as aws-sdk-dynamodb, aws-lambda-events, serde, and tokio.
  2. Define Data Structures:
    • Define structs for DynamoDB items (File, ShareGrant, ViewLink) and stream events.
  3. Implement Event Handling:
    • Implement the main Lambda function handler that parses DynamoDB stream records.
    • Implement functions to handle FILE and SHARE_GRANT events.
  4. Implement VIEW_LINK Creation and Deletion:
    • Implement functions to query grants, batch_create VIEW_LINKs, and delete VIEW_LINKs.
  5. Implement Idempotency:
    • Use conditional writes and deletes to ensure idempotency.
  6. Implement Logging and Metrics:
    • Use structured logging to log events and errors.
    • Emit metrics using CloudWatch metrics.

Example Rust Code Snippet

Here's an example of how to handle a DynamoDB stream event in Rust:

use aws_lambda_events::dynamodb::Event;
use lambda_runtime::{handler_fn, Context, Error};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Error> {
  lambda_runtime::run(handler_fn(stream_processor)).await?;
  Ok(())
}

async fn stream_processor(event: Event, _context: Context) -> Result<serde_json::Value, Error> {
  for record in event.records {
    match record.event_name.as_deref() {
      Some("INSERT") => {
        // Handle INSERT event
        println!("INSERT event: {:?}", record);
      }
      Some("MODIFY") => {
        // Handle MODIFY event
        println!("MODIFY event: {:?}", record);
      }
      Some("REMOVE") => {
        // Handle REMOVE event
        println!("REMOVE event: {:?}", record);
      }
      _ => {
        println!("Unhandled event: {:?}", record);
      }
    }
  }

  Ok(json!({ "message": "Stream processing complete" }))
}

This Rust code snippet shows how to set up a Lambda function handler for DynamoDB stream events. The stream_processor function iterates through the stream records and handles INSERT, MODIFY, and REMOVE events. You would need to implement the specific logic for creating and deleting VIEW_LINKs based on the event type and data.

Benefits of Using Rust

  • Performance: Rust provides excellent performance, making it suitable for high-throughput applications.
  • Safety: Rust's ownership and borrowing system ensures memory safety and prevents common errors.
  • Concurrency: Rust's async/await syntax makes it easy to handle concurrent operations efficiently.
  • Ecosystem: Rust has a growing ecosystem of libraries and tools for AWS development.

By implementing the Stream Processor Lambda in Rust, we can ensure that our file sharing service can efficiently handle DynamoDB stream events and maintain VIEW_LINK entries, providing a seamless experience for users.

4. SQS Cleanup Worker Lambda (Rust)

The SQS Cleanup Worker Lambda is another critical component of our file sharing service. This Lambda function is responsible for processing cleanup messages from SQS and deleting VIEW_LINKs when share grants are revoked. Similar to the Stream Processor Lambda, we'll implement this Lambda in Rust for performance and reliability. This section will explore the functionality of the Cleanup Worker Lambda and how it ensures that file sharing permissions are correctly revoked and that stale data is removed from the system.

Functionality of the Cleanup Worker Lambda

The Cleanup Worker Lambda performs the following key functions:

  1. Consume SQS Messages: Consume SQS messages containing DELETE_VIEW_LINKS actions.
  2. Delete VIEW_LINKs: Delete VIEW_LINKs for the recipient for the matching owner/prefix.
  3. Query Owner Files by Prefix: Query owner files by prefix (respect batch/delete limits).
  4. Handle Retries and Backoff: Implement retries and exponential backoff for failed deletes.
  5. Implement Idempotent Deletes: Ensure that deletes are idempotent to handle duplicate messages.

Rust Implementation

We'll use Rust for the Cleanup Worker Lambda for the same reasons as the Stream Processor Lambda: performance, safety, and concurrency. The implementation will involve the following steps:

  1. Set up the Rust Project:
    • Use cargo to create a new Rust project.
    • Add necessary dependencies, such as aws-sdk-dynamodb, aws-sdk-sqs, aws-lambda-events, serde, and tokio.
  2. Define Data Structures:
    • Define structs for SQS messages and DynamoDB items.
  3. Implement Message Handling:
    • Implement the main Lambda function handler that consumes SQS messages.
    • Implement functions to parse SQS messages and extract the necessary information.
  4. Implement VIEW_LINK Deletion:
    • Implement functions to query and delete VIEW_LINKs from DynamoDB.
    • Respect batch delete limits and implement exponential backoff for unprocessed items.
  5. Implement Idempotency:
    • Use conditional deletes to ensure idempotency.
  6. Implement Logging and Metrics:
    • Use structured logging to log events and errors.
    • Emit metrics using CloudWatch metrics.

Example Rust Code Snippet

Here's an example of how to handle an SQS message in Rust:

use aws_lambda_events::sqs::SqsEvent;
use lambda_runtime::{handler_fn, Context, Error};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Error> {
  lambda_runtime::run(handler_fn(cleanup_worker)).await?;
  Ok(())
}

async fn cleanup_worker(event: SqsEvent, _context: Context) -> Result<serde_json::Value, Error> {
  for record in event.records {
    // Handle SQS message
    println!("Received message: {:?}", record);
    // Implement VIEW_LINK deletion logic here
  }

  Ok(json!({ "message": "Cleanup worker complete" }))
}

This Rust code snippet shows how to set up a Lambda function handler for SQS messages. The cleanup_worker function iterates through the SQS messages and implements the logic for deleting VIEW_LINKs from DynamoDB. You would need to parse the SQS message body to extract the owner, prefix, and recipient information and then query and delete the corresponding VIEW_LINKs.

Benefits of Using SQS and Lambda

  • Decoupling: SQS decouples the stream processor from the cleanup worker, improving system resilience.
  • Scalability: SQS and Lambda can automatically scale to handle varying workloads.
  • Reliability: SQS provides message durability and ensures that messages are processed at least once.
  • Cost-Effectiveness: Lambda's pay-per-use pricing model reduces operational costs.

By implementing the SQS Cleanup Worker Lambda in Rust, we can ensure that our file sharing service correctly revokes permissions and removes stale data, maintaining data integrity and security.

5. API/Application Helpers & Documentation

To make our file sharing service user-friendly and maintainable, we need to provide API/Application Helpers and comprehensive Documentation. This section will cover the necessary helpers for permission validation, lazy VIEW_LINK creation, and query patterns. Additionally, we'll discuss the importance of clear and concise documentation for developers and users. These elements are crucial for the successful adoption and operation of the file sharing service.

API/Application Helpers

  1. Permission Validation Helpers:
    • Provide functions to query the ShareAccessIndex to validate user permissions.
    • Guidance to not rely on VIEW_LINK presence for permission checks.
  2. Lazy VIEW_LINK Creation Flow:
    • Implement a flow to spawn a background task to create VIEW_LINKs when a user first opens a shared folder.
  3. Example Query Patterns:
    • Provide examples for owners and viewers using base table and GSI2 queries.
  4. Pagination Examples:
    • Implement pagination for large result sets.

Example Helper Functions

Here are some example helper functions:

import boto3

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('FileMetadata')

def validate_permission(recipient_id, owner_id, prefix):
    response = table.query(
        IndexName='ShareAccessIndex',
        KeyConditionExpression='GSI1-PK = :pk and GSI1-SK = :sk',
        ExpressionAttributeValues={
            ':pk': f'ACCESS#{recipient_id}',
            ':sk': f'GRANT#{owner_id}#{prefix}'
        }
    )
    return response['Items']

def create_view_links_lazy(user_id, folder_prefix):
    # Spawn a background task to create VIEW_LINKs
    pass

def query_files_by_owner(owner_id, limit, start_key=None):
    if start_key:
      response = table.query(
          KeyConditionExpression='PK = :pk and begins_with(SK, :sk)',
          ExpressionAttributeValues={
              ':pk': f'USER#{owner_id}',
              ':sk': 'FILE#'
          },
          Limit=limit,
          ExclusiveStartKey=start_key
      )
    else:
      response = table.query(
          KeyConditionExpression='PK = :pk and begins_with(SK, :sk)',
          ExpressionAttributeValues={
              ':pk': f'USER#{owner_id}',
              ':sk': 'FILE#'
          },
          Limit=limit,
      )
    return response




def query_view_links_by_viewer_and_folder(viewer_id, folder_prefix, limit, start_key=None):
    if start_key:
      response = table.query(
          IndexName='MergedFolderViewIndex',
          KeyConditionExpression='GSI2-PK = :pk',
          ExpressionAttributeValues={
              ':pk': f'VIEWER#{viewer_id}#FOLDER#{folder_prefix}'
          },
          Limit=limit,
          ExclusiveStartKey=start_key
      )
    else:
      response = table.query(
          IndexName='MergedFolderViewIndex',
          KeyConditionExpression='GSI2-PK = :pk',
          ExpressionAttributeValues={
              ':pk': f'VIEWER#{viewer_id}#FOLDER#{folder_prefix}'
          },
          Limit=limit,
      )
    return response

These Python code snippets demonstrate how to implement helper functions for permission validation and querying files and view links. You would need to adapt these functions to your specific application logic and data structures.

Documentation

The documentation should include:

  1. Data Model: Describe the DynamoDB schema, including PK, SK, GSIs, and attributes.
  2. Query Patterns: Provide examples of common query patterns using the base table and GSIs.
  3. Operational Notes:
    • Cost considerations and scaling guidance.
    • Runbook for troubleshooting and DLQ handling.
  4. Architecture Flow Diagram: Visual representation of the system architecture.

Benefits of Good Documentation

  • Easy Onboarding: New developers can quickly understand the system and start contributing.
  • Maintainability: Clear documentation makes it easier to maintain and update the system.
  • Troubleshooting: Documentation helps in identifying and resolving issues quickly.
  • Scalability: Understanding the system architecture and operational notes ensures that the system can scale effectively.

By providing API/Application Helpers and comprehensive Documentation, we can ensure that our file sharing service is user-friendly, maintainable, and scalable. Good documentation is essential for the long-term success of any software project.

6. Example Data & Tests

To ensure that our file sharing service functions correctly, we need to provide Example Data and implement comprehensive Tests. This section will cover how to insert the provided sample dataset and provide guidance on unit and integration testing. Testing is a critical part of the development process, ensuring that our service meets the required functionality and performance criteria.

Example Data

We'll use the provided sample dataset to seed our DynamoDB table. This dataset includes files, share grants, and view links for several users. We can use a script or Terraform/CLI example to insert this data into the table.

Example Data Insertion Script (Python)

import boto3
import json

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('FileMetadata')

with open('sample_dataset.json', 'r') as f:
    data = json.load(f)

with table.batch_writer() as batch:
    for item in data:
        batch.put_item(Item=item)

print('Sample dataset inserted successfully')

This Python script reads the sample dataset from a JSON file and inserts it into the DynamoDB table using the batch_writer for efficient batch operations. Make sure to replace 'FileMetadata' with the actual name of your DynamoDB table.

Unit/Integration Test Guidance

  1. Simulate Stream Events:
    • Create test cases to simulate DynamoDB stream events (INSERT, MODIFY, REMOVE).
    • Verify that the Stream Processor Lambda correctly creates and deletes VIEW_LINKs.
  2. Verify VIEW_LINK Creation/Deletion:
    • Write tests to verify that VIEW_LINK entries are created and deleted as expected.
    • Ensure that the SQS Cleanup Worker Lambda correctly deletes VIEW_LINKs when grants are revoked.
  3. Query Testing:
    • Test various query patterns using the base table and GSIs.
    • Verify that the queries return the expected results for the sample dataset.
  4. Idempotency Testing:
    • Write tests to ensure that operations are idempotent and handle duplicate events correctly.
  5. Batch Limits and Retries:
    • Test the handling of DynamoDB batch limits and retry behavior.

Example Test Cases

Here are some example test cases:

  • Test Case 1:
    • Insert a new file.
    • Verify that VIEW_LINK entries are created for the owner.
  • Test Case 2:
    • Create a share grant.
    • Verify that VIEW_LINK entries are created for the recipient.
  • Test Case 3:
    • Revoke a share grant.
    • Verify that VIEW_LINK entries for the recipient are deleted.
  • Test Case 4:
    • Query files by owner.
    • Verify that the correct files are returned.
  • Test Case 5:
    • Query view links by viewer and folder.
    • Verify that the correct view links are returned.

Benefits of Testing

  • Correctness: Ensure that the service functions correctly and meets the requirements.
  • Reliability: Identify and fix bugs early in the development process.
  • Maintainability: Tests make it easier to maintain and update the service.
  • Scalability: Tests help in identifying performance bottlenecks and ensure that the service can scale effectively.

By providing Example Data and implementing comprehensive Tests, we can ensure that our file sharing service is reliable, maintainable, and performs as expected. Testing is an essential part of the development lifecycle and contributes to the overall quality of the service.

Conclusion

Implementing a file sharing service using DynamoDB, Terraform, and Lambda offers a scalable, cost-effective, and efficient solution. By following the steps outlined in this guide, you can build a robust file sharing service that meets your specific needs. From designing the DynamoDB schema to provisioning infrastructure with Terraform and implementing event-driven workers with Lambda, each component plays a crucial role in the overall functionality and performance of the service. Remember to focus on best practices such as using a single-table design in DynamoDB, implementing least-privilege IAM policies, and ensuring idempotent operations. Proper testing and documentation are also key to the long-term success and maintainability of the service. With careful planning and execution, you can create a file sharing service that is both user-friendly and scalable.

For further reading and deeper understanding of the concepts discussed, visit the AWS Documentation.