8 Common System Design Problems and How to Solve Them
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In this newsletter, we'll walk through 8 common system design challenges faced by developers and engineers and explore practical, real-world solutions to build better systems.
Building highly scalable, reliable, and efficient systems is like constructing the foundation of a skyscraper. One weak component, and the whole structure could collapse. From database bottlenecks to high latency and availability issues, engineers encounter system design problems every day. The good news? For every problem, there’s a solution!
1. Slow Reads: Use Caching for Faster Reads
When users experience slow response times, it often stems from repeated trips to the database. In high-traffic systems, fetching data directly from the database for every request can bog down the system and degrade performance. Enter caching—a proven technique for boosting read performance.
Solution:
Use a caching layer, like Redis or Memcached, to store frequently requested data in memory. When a request is made, the system checks the cache first before querying the database. This reduces the load on the database, speeds up response times, and improves the overall user experience.
Real-World Example:
Netflix uses caching to ensure smooth playback for millions of users worldwide. By caching frequently accessed content, they prevent overload on their primary databases and reduce buffering times for users.
2. High-Write Traffic: Use Asynchronous Writes
A system bombarded with write operations can quickly become overwhelmed, causing slowdowns, especially in real-time applications. For instance, an e-commerce platform that logs thousands of transactions per second may experience lag if all writes are processed synchronously.
Solution:
To handle high-write traffic, implement asynchronous writing mechanisms using message queues like Kafka, RabbitMQ, or AWS SQS. Asynchronous writes allow the system to process and queue write operations without blocking the main thread, ensuring the system remains responsive.
Real-World Example:
WhatsApp uses asynchronous writing for message delivery. When a user sends a message, it’s queued and processed in the background, allowing the app to remain fast even during high traffic.
3. Handling Large Files: Use Distributed Storage
When dealing with large files—such as videos, high-resolution images, or data backups—traditional storage systems can struggle to keep up with the demands. As the volume of data grows, both in size and quantity, storing everything in a single location not only slows down access but also increases the risk of system failure.
Solution: Use Distributed Storage
Distributed storage systems, such as Amazon S3, Google Cloud Storage, or Hadoop Distributed File System (HDFS), are designed to handle massive datasets by distributing files across multiple servers. This ensures that data is not only stored efficiently but can also be retrieved quickly, even under heavy load.
Real-World Example:
YouTube relies on distributed storage to manage the vast amount of video content uploaded by millions of users daily. Each video is broken into chunks and distributed across a global network of data centers. This enables fast access, efficient storage, and the ability to stream content without interruptions, regardless of the file size.
4. Single Point of Failure: Implement Redundancy and Failover
Having a Single Point of Failure (SPOF) is like putting all your eggs in one basket. If that component fails, the entire system comes crashing down, leading to downtime and lost revenue.
Solution:
Implement redundancy and failover strategies to ensure system reliability. This involves replicating critical components (servers, databases, etc.) and using failover mechanisms to automatically switch to backups in case of failure. Tools like Zookeeper, AWS RDS Multi-AZ, and Consul can help automate failover processes.
Real-World Example:
Amazon Web Services (AWS) offers multi-AZ deployments for databases, which automatically fail over to a secondary zone if the primary zone goes down, ensuring that downtime is minimised.
5. Read-Heavy Systems: Use Proper Indexing
In read-heavy systems, querying large databases without proper indexing can lead to slow response times. Without indexes, the database has to scan the entire table for the relevant data, which can be time-consuming and inefficient, especially in large datasets
Solution:
To enhance query performance, design proper indexes on columns that are frequently queried. Indexes allow the database to quickly locate the data, bypassing the need for a full table scan. Be mindful of which fields to index, as over-indexing can also affect write performance.
Real-World Example:
In MySQL or PostgreSQL databases, indexing the `user_id` or `email` fields in a large user table dramatically reduces the query time for login or user lookup operations.
6. High Availability : Use Sharding
As data grows, the capacity of a single database server can be quickly exceeded. Instead of scaling vertically (adding more resources to a single server), which has limitations, horizontal scaling via sharding can distribute the load across multiple servers.
Solution:
Sharding breaks your database into smaller, more manageable pieces (shards), each responsible for a subset of data. This allows for distributed query execution and increases scalability. Many NoSQL databases like MongoDB and Cassandra support sharding out of the box.
Real-World Example:
Twitter implemented database sharding to handle the billions of tweets generated by users globally. By distributing tweets across multiple database shards, they were able to scale efficiently and keep performance optimal.
7. High Latency: Use a Content Delivery Network (CDN)
Latency, especially for globally distributed systems, is a significant issue. When users from different parts of the world access a central server, the physical distance can cause delays, leading to high latency and poor user experience.
Solution:
Content Delivery Networks (CDNs), such as Cloudflare or Akamai, distribute copies of your content to servers located closer to users. By caching content like images, CSS, and JavaScript files on edge servers worldwide, the CDN reduces the distance data has to travel, cutting down on latency.
Real-World Example:
YouTube uses a CDN to store video files in edge locations across the globe, ensuring that users can stream videos with minimal latency, regardless of their location.
8. Monitoring and Alerting : Centralised Logging and Monitoring
Without a proper logging system, identifying performance bottlenecks and slow issues becomes a daunting task.
Solution:
Set up a centralised logging solution like the ELK stack (Elasticsearch, Logstash, Kibana) or Grafana and Prometheus for monitoring. This allows you to collect, analyse, and visualise logs in real time.
Real-World Example:
Uber uses centralised logging with the ELK stack to monitor real-time traffic and database performance, ensuring smooth ride-hailing operations for millions of users.
Conclusion: Building Resilient Systems, One Solution at a Time
System design isn’t just about building something that works; it’s about building something that scales, performs well under pressure, and recovers from failure gracefully. From implementing caching to reduce latency, using asynchronous writes for high traffic, or deploying CDNs for global distribution, these solutions are critical for tackling the most common system design challenges.
Every problem has a solution, and as engineers, it's our job to find the right one. By applying these strategies to your systems, you can enhance performance, scalability, and reliability, ensuring your application stands the test of time.
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