If you are preparing for a System Design interview, you will likely encounter a question about designing a URL shortener, a file-sharing service, or a system similar to Pastebin. These types of problems are popular because they are simple to understand, relevant to real-world applications, and force candidates to address foundational challenges in scalable architecture.
Designing a paste-sharing system, where users can quickly upload and share snippets of text, is an excellent test of your ability to handle massive read traffic, manage data expiration, choose appropriate storage, and prevent abuse. The system needs to be highly available and serve content with low latency, often with a vastly disproportionate ratio of reads to writes.
The simplicity of the core feature—creating and retrieving a text snippet—allows the interviewer to delve deep into specific System Design aspects. You must quickly clarify functional and non-functional requirements before proposing a structured, scalable architecture. A well-articulated approach to a problem like Pastebin system design demonstrates a strong grasp of distributed systems principles.
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Clarifying requirements#
Before jumping into the architecture, we must define the scope by clarifying the functional and non-functional requirements. This is a critical step in any System Design interview.
Functional requirements#
The system must primarily support the following core features:
Create Paste: A user can submit a text snippet and receive a unique, short, and random URL (Paste ID).
Retrieve Paste: A user can access a paste using its unique ID.
Paste Expiration: Users can set an expiration time (e.g., ten minutes, one hour, one day, or never).
Visibility Control: Pastes can be public (accessible to anyone with the URL) or private (only accessible to the creator after logging in, though for simplicity, many designs initially focus on public links).
Syntax Highlighting: The system should render the paste content with proper syntax highlighting based on the language.
Non-functional requirements#
These requirements define the quality and performance characteristics of our system:
Scalability: The system must handle a high volume of traffic, especially read requests, and scale horizontally. We will target supporting tens of millions of new pastes per month.
Availability: The service should be highly available, minimizing downtime.
Latency: Pastes should be created and retrieved with low latency (e.g., in the low hundreds of milliseconds).
Durability: Once a paste is created, its content should not be lost.
Cost Efficiency: Since a significant number of pastes may never be read or only read once, the storage solution must be cost-efficient.
High-level system architecture#
A standard high-level architecture for a read-heavy, low-latency service like this includes several decoupled components.
The overall flow begins with the Client, which initiates a request to the API Layer. This layer acts as the entry point, handling request routing, authentication, and rate limiting. The requests are then passed to the Application Servers (also known as web servers or service handlers). These are stateless services responsible for the core business logic.
When a paste is created (write request), the application server first interacts with a Unique ID Generator service to get a new Paste ID. It then writes the Paste ID and its metadata to the Database and, if the content is very large, stores the raw content in Object Storage.
For read requests, the application server first checks the Cache for the Paste ID and content. If a cache hit occurs, the data is returned immediately. If there is a cache miss, the server queries the database or object storage.
Client: The user's browser or device interacting with the service.
API Layer: Handles load balancing, SSL termination, and basic input validation.
Application Servers: Stateless microservices that handle the core logic (create, retrieve, delete).
Databases: Stores the paste metadata (Paste ID, expiration, user ID, access control).
Cache: Stores frequently or recently accessed pastes to reduce database load and latency.
Object Storage: Used to store the raw content of very large pastes cost-effectively.
Data model and storage choices#
A robust data model is essential for efficiency, especially with a high volume of small write operations.
Schema design#
We need a schema that efficiently supports lookups by the Paste ID and allows for timely expiration checks.
A simplified paste metadata table might look like this:
Field Name | Data Type | Purpose |
paste_id | String/Char (unique) | The short, unique identifier used in the URL. |
user_id | Integer/String (nullable) | Creator's ID, if they are a registered user. |
content_url | String | URL to the raw content in Object Storage (if content is large). |
paste_content | Text | The raw text (for small pastes, stored directly in the DB). |
created_at | Timestamp | Time the paste was created. |
expires_at | Timestamp (index) | Time when the paste should be deleted. |
exposure | Enum (Public/Private) | Access control setting. |
lang_syntax | String | Language for syntax highlighting (e.g., 'python', 'java'). |
The expires_at field is crucial for index creation, allowing us to efficiently find expired pastes for deletion.
Choosing between SQL and NoSQL#
The choice between a relational database (SQL) and a non-relational database (NoSQL) is a classic System Design trade-off.
SQL (e.g., PostgreSQL, MySQL): Offers strong consistency, ACID properties, and flexible querying. However, scaling a single SQL instance for massive write loads can be complex, often requiring sharding.
NoSQL (e.g., Cassandra, DynamoDB): Excels at horizontal scaling and high-volume, low-latency reads and writes, often sacrificing strong consistency for availability and speed. The primary query pattern—lookup by Paste ID—is very simple, which suits NoSQL key-value stores well.
Given the simple access pattern (lookup by Paste ID) and the need for massive read and write scalability, a key-value NoSQL store is often the better choice for storing the metadata. The Paste ID serves as the primary key. If we require more complex, relational lookups (like fetching all pastes for a user), we might use a secondary, sharded SQL database or an eventually consistent NoSQL document store.
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Scaling the system#
Scaling is the central challenge for a service expected to handle millions of requests daily.
Read and write traffic patterns#
In a paste-sharing service, the traffic pattern is typically read-heavy. A paste might be created once (one write) but viewed hundreds or thousands of times (many reads). This massive imbalance dictates our design choices: we must prioritize read performance and capacity.
Our design should focus on:
Sharding the Database: Distributing the metadata across multiple database servers based on the Paste ID to prevent a single server from becoming a bottleneck.
Heavy Caching: Reducing the load on the database by serving content from memory.
Caching strategy#
Caching is the most effective way to handle the high read traffic.
Cache Layers: We can employ a multi-layer caching strategy: a small, fast in-memory cache on the application servers for the hottest data, and a distributed cache cluster (like Redis or Memcached) for broader coverage.
Data to Cache: We should cache the paste content and its critical metadata (expiration time, access level).
Cache Invalidation: We use a Write-Through or Write-Back strategy for writes. For invalidation, the simplest and most effective approach is to use Time-To-Live (TTL). When a paste is created, it is written to the cache with the exact expiration time specified by the user. When the TTL expires, the entry is automatically evicted, simplifying cache management.
Load balancing and horizontal scaling#
The Application Servers should be stateless. This means they do not store any user or session information locally. All necessary data must be retrieved from the cache or database. Being stateless allows us to easily add or remove application servers (horizontal scaling) behind a Load Balancer. The Load Balancer distributes incoming traffic evenly, preventing any single server from being overwhelmed and ensuring high availability. Common load balancing algorithms include Round Robin and Least Connections.
Handling large pastes and expiration#
The system must efficiently manage both the size of the content and its time-bound nature.
Object storage for large content#
While small text snippets can be stored directly in the database for low latency, very large pastes (e.g., several megabytes) should be handled differently. Storing large binary objects in a database is inefficient and costly.
We use Object Storage (e.g., Amazon S3, Google Cloud Storage) for large content. When a large paste is created, the raw text is uploaded to Object Storage, and the database only stores the URL or object key (content_url) needed to retrieve it. Object storage is highly durable, scalable, and significantly more cost-effective for large files than a high-performance database.
TTL-based deletion#
For pastes with an expiration time, the primary mechanism for removal is a built-in TTL feature.
Database/Cache TTL: Many NoSQL databases (e.g., Redis, DynamoDB) support an automatic TTL feature. When the expires_at time is reached, the entry is automatically deleted from the cache or data store.
Background Cleanup Jobs: For databases that don't support automatic TTL, or for cleaning up orphaned object storage files, we run background worker services that periodically scan the metadata table for entries where expires_at is less than the current time. These jobs then delete the corresponding entries from the database and Object Storage. These jobs are low-priority to avoid impacting the main read/write path.
Reliability, security, and abuse prevention#
A public-facing service must have robust measures to ensure reliability, security, and to combat abuse.
Rate Limiting: To prevent denial-of-service (DoS) attacks and abuse, we implement rate limiting at the API Layer. This limits the number of pastes a single IP address or registered user can create within a given time frame (e.g., ten pastes per minute).
Access Control: For private pastes, the application server verifies the user's authentication token and checks the user_id against the one stored in the metadata before serving the content.
Data Retention: Ensure a clear policy is in place. Pastes marked "never expire" must be highly durable, while expired pastes are permanently removed to comply with cost efficiency and data hygiene standards.
Basic Moderation Strategies: While full-scale content moderation is complex, basic checks can be performed upon creation, such as scanning for known malicious URLs or content matching a blacklist of known spam patterns.
Bottlenecks and trade-offs#
A strong design recognizes its limitations and the inherent trade-offs made.
Hot Keys: A few popular pastes can become "hot keys," leading to excessive requests to a single cache or database shard. This is mitigated by aggressive caching and potentially replicating the hottest data across multiple caches/shards.
Cache Invalidation: Relying on TTL simplifies invalidation. However, if a user manually deletes a non-expired paste, we must ensure a synchronous deletion/invalidation call is made to the database and the cache.
Storage Cost vs. Performance: Storing large pastes in Object Storage is cheaper but adds latency (an extra network hop) compared to storing small pastes directly in a high-speed database. This is a deliberate trade-off where cost efficiency is prioritized for the bulk of the data, while performance is prioritized for smaller, more frequent requests.
Consistency vs. Availability: In the search for high availability and low latency, we often lean toward Eventual Consistency for the metadata. For instance, the background cleanup job might take a few minutes to delete an expired paste, meaning it could still be available briefly after its expiry time. This minor inconsistency is acceptable for a paste-sharing service, where immediate deletion is not strictly mission-critical.
How this problem is evaluated in interviews#
Interviewers use the paste-sharing problem to assess your structured thinking and communication skills. They look for the following:
Structured Approach: Did you start by clarifying requirements before jumping into design?
Scalability Mindset: Did you prioritize scaling reads through caching and horizontal scaling?
Component Selection: Did you justify your choice of database (e.g., NoSQL for key-value lookups) and object storage?
Trade-off Analysis: Did you clearly articulate the trade-offs, such as consistency versus availability or cost versus latency? For example, understanding that a system built for massive read loads, like in pastebin system design, will inherently have different storage priorities than a purely write-heavy financial system.
A common mistake is spending too much time on the unique ID generation algorithm and not enough time on the overall architecture, caching, and scaling. Focus your effort on the bottlenecks and the rationale behind your design decisions.
Final takeaway#
Designing a paste-sharing service is a fundamental exercise that tests your mastery of core System Design concepts: sharding, caching strategies, stateless services, and choosing the right storage for different data types (metadata vs. large content). It demonstrates your ability to build a highly available and scalable system tailored to a read-heavy workload. A structured approach to these problems is the key to success in any System Design interview.
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