8 Most Important System Design Concepts You Should Know

Building scalable systems isn't just  about writing good code - it's about anticipating and solving problems  before they become critical. Today, we'll explore eight system design  challenges that every growing system faces, along with the solutions that  top companies use to tackle them. Every successful application eventually faces  the challenge of handling high read volumes.

Imagine a popular news website where  millions of readers view articles, but only a small team of  editors publishes new content. The mismatch between reads and writes  creates an interesting scaling problem. The solution is caching.

By implementing a fast cache layer, the system first checks for data there  before hitting the slower database. While this dramatically reduces database  load, caching has its challenges: keeping the cache in sync with the  database and managing cache expiration. Strategies like TTL on keys or write-through  caching can help maintain consistency.

Tools like Redis or Memcached make  implementing this pattern easier. Caching is especially effective for read-heavy, low-churn data, like static  pages or product listings. Some systems face the opposite challenge -  handling massive amounts of incoming writes.

Consider a logging system processing  millions of events per second, or a social media platform managing real-time user interactions. These systems need  different optimization strategies. We tackle this with two approaches.

First, asynchronous writes with message  queues and worker processes. Instead of processing writes immediately, the  system queues them for background handling. This gives users instant feedback while the  heavy processing happens in the background.

Second, we use LSM-Tree based  databases like Cassandra. These databases collect writes in memory and  periodically flush them to disk as sorted files. To maintain performance, they perform compaction: merging files to reduce the number  of lookups required during reads.

This makes writes very fast but reads become slower as they  may need to check multiple files. Handling high write loads is  just one part of the puzzle. Even the fastest system becomes  useless if it goes down.

An e-commerce platform with a single database server stops entirely on failure - no  searches, no purchases, no revenue. We solve this through redundancy and failover, implementing database replication  with primary and replica instances. While this increases availability, it introduces  complexity in consistency management.

We might choose synchronous replication to prevent  data loss and accept higher latency, or opt for asynchronous replication  that offers better performance but risks slight data loss during failures. Some systems even use quorum-based replication  to balance consistency and availability. Critical services like payment systems need true high availability.

This requires both load  balancing and replication working together. Load balancers distribute traffic across  server clusters and reroute around failures. For databases, a primary-replica  setup is standard: the primary handles writes while multiple replicas handle reads, and failover ensures a replica can  take over if the primary fails.

Multiple-primary replication is  another option for distributing writes geographically, though it comes  with more complex consistency trade-offs. Performance becomes even more  critical when serving users globally. Users in Australia shouldn't wait for  content to load from servers in Europe.

CDNs solve this by caching content closer  to users, dramatically reducing latency. Static content, like videos and  images, works perfectly with CDNs. For dynamic content, solutions like edge  computing can complement CDN caching.

Different types of content  need different cache-control headers - longer durations for media files, shorter for user profiles. Managing large amounts of data  brings its own challenges. Modern platforms use two types of  storage: block storage and object storage.

Block storage with its low latency and high IOPS is ideal for databases and  frequently accessed small files. Object storage on the other hand costs  less and is designed to handle large, static files like videos and backups at scale. Most platforms combine these:  user data goes into block storage, while media files are stored in object storage.

With all these systems running, we  need to monitor their performance. Modern monitoring tools like  Prometheus collect logs and metrics, while Grafana provides visualization. Distributed tracing tools like OpenTelemetry help  debug performance bottlenecks across components.

At scale, managing this flood of data is  challenging. The key is to sample routine events, keep detailed logs for critical operations, and  set up alerts that trigger only for real problems. One of the most common issues monitoring  reveals is slow database queries.

Indexing is the first line of defense. Without indexes, the database scans  every record to find what it needs. With indexes, it can quickly  jump to the right data.

Composite indexes, for multi-column  queries, can further optimize performance. But every index slows down writes slightly  since they need to be updated as data changes. Sometimes indexing alone isn't enough.

As a last resort, consider sharding - splitting  the database across multiple machines, using strategies like range-based  or hash-based distribution. While sharding can scale the system significantly, it adds substantial complexity  and can be challenging to reverse. If you like our video, you might like  our system design newsletter as well.

It covers topics and trends  in large-scale system design. Trusted by 1,000,000 readers. Subscribe at blog.

bytebytego. com Tools like Vitess simplify  sharding for databases like MySQL, but it’s a strategy to use sparingly  and only when absolutely necessary.