Shadowing Practice: 8 Most Important System Design Concepts You Should Know - Learn English Speaking with Video
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Building scalable systems isn't just about writing good code, it's about anticipating and solving problems before they become critical.
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Today, we explore 8 system design challenges that every growing system faces, along with the solutions that top companies use to tackle them.
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Every successful application eventually faces the challenge of handling high read volumes.
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Imagine a popular news website, where millions of readers view articles, but only a small team of editors publishes new content.
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The mismatch between reads and writes creates an interesting scaling problem.
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The solution is caching.
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By implementing a fast cache layer, the system first checks for data there before hitting the slower database.
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While this dramatically reduces database load, caching has its challenges.
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Keeping the cache in sync with the database and managing cache expiration.
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Strategies like TTL on keys or write -through caching can help maintain consistency.
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Truths like Redis and Memcache make implementing this pattern easier.
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Caching is especially effective for read -heavy low -churn data like static pages or product listings.
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Some systems face the opposite challenge, handling massive amounts of incoming writes.
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Consider a logging system processing millions of events per second or a social media platform managing real -time user interactions.
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These systems need different optimization strategies.
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We tackle this with two approaches.
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First, asynchronous writes with message queues and worker processes.
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Instead of processing writes immediately, the system queues them for background handling.
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This gives user instant feedback while the heavy processing happens in the background.
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Second, we use LSM tree -based databases like Cassandra.
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These databases collect writes in memory and periodically flush them to disks as sorted files.
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To maintain performance, they perform compaction, merging files to reduce the number of lookups required during reads.
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This makes writes very fast, but reads become slower as they may need to check multiple files.
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Handling high write loads is just one part of the puzzle.
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Even the fastest system becomes useless if it goes down.
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An e -commerce platform with a single database server stops entirely on failure.
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No searches, no purchases, no revenue.
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We solve this through redundancy and failover, implementing database replication with primary and replica instances.
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While this increases availability, it introduces complexity in consistency management.
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We might choose synchronous replication to prevent data loss and accept higher latency,
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or opt for asynchronous replication that offers better performance but risks slight data loss during failures.
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Some systems even use core -based replication to balance consistency and availability.
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Critical services like payment systems need true high availability.
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This requires both load balancing and replication working together.
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Load balances distribute traffic across server clusters and reroute around failures.
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For databases, a primary replica setup is standard.
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The primary handles write, while multiple replicas handles reads, and failover ensures a replica can take over if the primary fails.
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Multiple primary replication is another option for distributing write geographically, though it comes with more complex consistency trade -offs.
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Performance becomes even more critical when serving users globally.
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Users in Australia shouldn't wait for content to load from servers in Europe.
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CDN solved this by caching content closer to users, dramatically reducing latency.
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Static content, live videos and images works perfectly with CDNs.
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For dynamic content, solutions like cache computing can complement CDN caching.
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Different types of content need different cache control headers, longer duration for media files, shorter for user profiles.
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Managing large amounts of data brings its own challenges.
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Modern platforms use two types of storage, block storage and object storage.
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Block storage with its low latency and high IOPS is ideal for databases and frequently accessed small files.
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Object storage on the other hand costs less and is designed to handle large static files like videos and backups at scale.
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Most platforms combine these.
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User data goes into block storage while media files are stored in object storage.
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With all these systems running we need to monitor their performance.
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Modern monitoring tools like Prometheus collect logs and metrics while Grafana provides visualization.
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Distributor tracing tools like OpenTelemetry help debug performance bottlenecks across components.
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At scale, managing this flood of data is challenging.
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The key is to sample routine events, keep detailed logs for critical operations, and set up alerts that trigger only for real problems.
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One of the most common issue monitoring reveals is slow database queries.
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Indexing is the first line of defense.
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Without indexes, the database scans every record to find what it needs.
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With indexes, it can quickly jump to the right data.
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Composite indexes for multi -column queries can further optimize performance.
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But every index slows down right slightly since they need to be updated for data changes.
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Sometimes, indexing alone isn't enough.
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As a last resort, consider sharding, splitting the database across multiple machines, using strategies like range -based or hash -based distribution.
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While sharding can scale the system significantly, it adds substantial complexity and can be challenging to reverse.
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Tools like Retest simplify sharding for databases like MySQL, but it's a strategy to use sparingly, and only when absolutely necessary.
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If you like our videos, you might like our system design newsletter as well.
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It covers topics and trends in large -scale system design, trusted by a million readers.
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