System Design Interview Sample Answers for Load Balancers and Caching

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System design interviews often include load balancers and caching as key components. Even if the question isn’t explicitly about these, they frequently form an important part of the solution.

In fact, it's rare to get a question purely on load balancing or caching – instead, you'll face a broad problem where these tools improve scalability and performance.

This guide provides answers to common interview questions on load balancers and caching.

We’ll break down how to approach such questions step by step, cover real-world scenarios for different strategies, discuss trade-offs and optimizations, highlight best practices, and point out common mistakes to avoid.

By the end, you’ll know how to confidently incorporate load balancing and caching in your system design interview answers. Let’s get in!

How to Approach System Design Questions Involving Load Balancing and Caching

A structured approach is crucial for any system design question.

Load balancers and caches usually come into play when discussing scaling and performance.

Here’s a step-by-step method to tackle these questions:

1. Clarify Requirements: Begin by understanding the system's goals. How many users or requests are we expecting? Is the traffic read-heavy, write-heavy, or mixed? What are the latency and throughput requirements? Clarifying the scope and constraints will inform if and how we use a load balancer or cache.
2. Outline High-Level Design: Sketch out the major components (clients, servers, databases, etc.) and how they interact. Identify where in the architecture a load balancer would sit (usually in front of a tier of servers) and where caching might help (between a slow data store and the application). At this stage, mention that you'll likely use a load balancer to distribute requests and a caching layer to speed up reads, to set the expectation.
3. Deep Dive into Key Components: Focus on the load balancing layer and caching layer in detail. Discuss how the load balancer will distribute traffic across multiple servers to prevent any single server from overloading. Also, explain what data to cache and where to place the cache (browser, CDN, application memory, database) for maximum effect . This is where you show your knowledge of different strategies.
4. Address Scalability and Reliability: Explain how your design scales. For load balancing, mention that adding more servers horizontally behind the LB is easy, and consider using multiple LBs if needed for high availability. For caching, talk about how caching reduces load on databases and improves throughput. Also cover failure scenarios: What if a cache node fails? What if the load balancer fails? Describe redundancy (multiple cache replicas, redundant load balancer instances) and data replication if applicable .
5. Discuss Trade-offs: Acknowledge any trade-offs in your design. For example, caching introduces data staleness versus fresh data; load balancers introduce a potential single point of failure (mitigated by redundancy) and added complexity. We’ll explore these more later, but showing awareness of trade-offs is key.
6. Summarize and Optimize: Conclude your answer by summarizing how load balancing and caching meet the requirements. Mention any optimizations: e.g., enabling health checks on the load balancer, using efficient cache eviction policies, or tuning TTL (time-to-live) for cached items to balance freshness and hit rate.

This general framework can be applied to many system design questions.

Next, let’s focus on load balancers and caching individually, with sample Q&A-style answers to illustrate how you might tackle them in an interview.

Understanding Load Balancers in System Design

Load balancing is all about distributing incoming requests across multiple servers to ensure no single machine is overwhelmed.

In system design, load balancers are critical for achieving horizontal scaling and high availability.

As one source puts it, “Load balancers are crucial for spreading incoming traffic across multiple servers, optimizing performance, and ensuring no single server becomes overwhelmed.”

They are key to allowing you to add more servers as load grows.

How Load Balancing Works

A load balancer sits between clients and your server fleet.

Clients send requests to the load balancer, and the balancer forwards each request to one of the backend servers.

This way, if you have N servers, the traffic is divided among them, preventing any single server from becoming a bottleneck.

If one server goes down, the load balancer can redirect traffic to the others, improving reliability.

Types of Load Balancers:

Layer 4 vs Layer 7: A Layer 4 (transport layer) load balancer operates at the network level (TCP/UDP), directing traffic based on IPs and ports without inspecting content. A Layer 7 (application layer) load balancer (like an HTTP reverse proxy) can make smarter decisions by looking at HTTP headers, URLs, etc., e.g. routing requests for static content to one set of servers and dynamic content to another. Layer 7 allows content-based routing but adds a bit more overhead (and terminates TLS if used, which can be CPU-intensive).
Hardware vs Software: Hardware load balancers are specialized appliances (with high performance, but expensive), while software load balancers (like HAProxy, Nginx, or cloud-managed LBs) run on standard servers. Software LBs are more flexible and cost-effective, though hardware can handle extreme loads with dedicated optimizations.
DNS Load Balancing: Using DNS round-robin is a simple form of load balancing at the domain level – the DNS rotates through a list of IPs for each request, effectively distributing traffic. This is often used for geo-distribution (directing users to different data centers) but offers less fine-grained control.

Common Load Balancing Algorithms: Once the load balancer receives a request, how does it pick a server?

Round Robin: cycles through the server list evenly. Great for equal workloads and simple setups.
Least Connections: sends the new request to the server with the fewest active connections, helping when some requests are heavier than others.
Weighted Round Robin: if some servers are more powerful, they get a higher weight to receive more traffic.
Consistent Hashing: maps requests (often based on client IP or a key like user ID) to the same server consistently. Moreover, consistent hashing is useful for session stickiness or caching locality (so that the same user or item hits the same server to utilize warm caches).

Learn more about load balancing algorithms.

Each algorithm has trade-offs.

For instance, Round Robin is simple but may overload a slower server; Least Connections tries to account for load but requires the balancer to track sessions; Consistent Hashing helps with sticky sessions but can become skewed if one server has many “sticky” clients.

Considerations: In using a load balancer, remember:

The load balancer itself can become a bottleneck or single point of failure. At very high scale, you might use multiple load balancers in parallel (and DNS-load-balance across them, or have a primary-secondary failover setup).
If user sessions are stored in memory on a specific server, you need to ensure subsequent requests from the same user go to that server (session persistence). Otherwise, consider storing session data in a shared store so any server can handle any request. Managing user sessions across servers can be complex unless session persistence is maintained.
Health checks are vital: the load balancer should regularly ping the backend servers and stop sending traffic to any server that isn't responding (and ideally automatically add it back when it recovers).

Now that we have the concepts in mind, let's look at a sample interview question about load balancing and how to answer it.

Sample Interview Question (Load Balancing):

“Design a scalable web architecture for a high-traffic website. How would you use load balancers?”

Sample Answer:

Clarify the scenario: “First, I'd clarify the traffic expectations and requirements. How many users or requests are we talking about – millions per day or per second? Are we dealing with mostly read requests (like fetching content) or writes (like uploads)? And what's the acceptable latency?”* This helps determine how many servers and what type of load balancer might be needed.
High-Level Design: “At a high level, I'd propose a multi-tier architecture. Clients connect to the system via a load balancer, which sits in front of a pool of web servers. The web servers handle the application logic and talk to backend services (databases, caches, etc.). The load balancer will ensure no single web server handles too much load. If we have data centers in multiple regions, we might have a DNS-based global load balancer to route users to the nearest region, each of which has its own local load balancer and server pool.”*
Why Load Balancer & How: “Using a load balancer means we can scale horizontally by adding more servers behind it. When traffic increases, instead of one super-powerful server, we use many commodity servers. The LB distributes incoming requests (for example, via Round Robin or a similar algorithm) so each server gets a fair share. This improves reliability too – if one server goes down, the LB will stop sending traffic to it (after detecting it via health checks) and the users won’t experience downtime.”
Load Balancer Strategy Details: “For this website, a Layer 7 load balancer (like an Application Load Balancer) is appropriate since we are dealing with HTTP traffic. It can make smart routing decisions – for instance, static content requests could be forwarded to a cache or a lightweight server pool, while API requests go to another pool optimized for logic. The LB will use a Least Connections algorithm if we expect variable request loads, so heavier requests don't overwhelm one server. We’ll also use sticky sessions if necessary – but preferably, we design the system to be stateless by storing session info in a distributed cache, so any server can handle any request.”
Avoid Single Point of Failure: “I'll ensure the load balancing tier is not a new single point of failure. In practice, that means having at least two load balancer instances (in active-passive or active-active mode). If one fails, the other takes over seamlessly. Cloud providers do this for you, but if we manage it ourselves, we'd set up a virtual IP that can switch to a backup LB if needed.”
Result and Evolution: “With this design, as our user base grows, we can keep adding web servers and the load balancer will keep distributing to them. We can even have multiple layers of load balancers if needed – for example, DNS load balancing at a global level, then an L4 TCP load balancer at each region’s entry, then L7 load balancers for different services. This ensures the system can handle millions of users. In summary, load balancers give us scalability (by spreading load) and reliability (by isolating failures), which are exactly what a high-traffic site needs.”

Real-world example: In one sample solution for designing an online bookstore, the use of a load balancer was highlighted: “Implement load balancing for front-end servers; cache product listings for faster access.” This shows how load balancing is paired with caching to handle scale. Many big websites (like e-commerce or social media) deploy load balancers to distribute user requests across hundreds of servers in multiple regions for both high availability and low latency.

Understanding Caching in System Design

Caching is a technique to store frequently accessed data in a fast storage layer so that future requests for that data can be served quicker.

In simple terms, caching keeps hot data closer to the user or the application.

This dramatically reduces latency and load on the primary data source. According to one guide, “Caching stores frequently accessed data closer to where it's used, significantly speeding up response times and reducing server load.”

In system design interviews, demonstrating where and how caching can be used to improve performance is often a key part of a good answer.

How Caching Works: When a request is made, the application first checks the cache:

If the data is found in the cache (a cache hit), it returns it directly from there (which is very fast, e.g. from memory).
If the data is not in the cache (a miss), the application fetches from the original source (say, a database), then often stores a copy in the cache for next time. This way, subsequent requests avoid hitting the slow database repeatedly.

The rationale behind caching is that in many systems, the same data is requested repeatedly (temporal locality) or nearby data is requested (spatial locality). By keeping a copy handy, we save time.

“Caching is an essential technique used in software engineering to improve system performance and user experience. It works by temporarily storing frequently accessed data in a cache, which is faster to access than the original source of the data.”

Types of Caches / Where to Cache:

Client-side Cache: e.g. Browser cache or mobile app cache. Stores responses (like images, HTML, API responses) on the user's device. Reduces repeat requests to the server. Good for static assets with versioning.
CDN (Content Delivery Network): A distributed network of servers that cache static content (images, videos, CSS/JS) at edge locations around the world. This brings content physically closer to users and offloads traffic from your origin servers. Use CDNs for large scale static content delivery (e.g., serving Netflix videos or website images globally).
Server-side In-Memory Cache: e.g. Redis or Memcached deployed as a layer between the application and database. These are key-value stores kept in RAM, used to cache database query results, computed values, or session data. This is common for caching expensive queries or frequently accessed records (like user profiles, feed posts, etc.) in web applications.
Database caching: Many databases have internal caches (e.g., query cache in MySQL) or you can use an in-memory index. Additionally, you might cache at the application level by storing results in something like Redis (which we already counted as server-side cache).
Application-level cache: Sometimes specific subsystems have caches, like an in-memory list of configuration settings, or an LRU cache in front of a service call. Even CPU L1/L2 caches are a form of caching at a hardware level – the concept recurs at many levels of a system.

Caching Strategies (When and What to Cache):

Cache-aside (Lazy loading): The application checks cache first, on miss fetches from DB and populates cache. This is simple and most commonly used. It loads data into cache on-demand.
Write-through: Every time the database is updated, also update the cache. This keeps cache and DB consistent, but writes are slower (because they go to two places). Read is fast because data is already in cache when needed.
Write-back (Write-behind): Write to cache first and allow the cache to asynchronously flush to the database. This can improve write performance but is complex and risks data loss if cache node fails before write completes.
TTL (Time-to-live): Set expiration times for cache entries. This ensures that eventually data is refreshed. The TTL is a trade-off between freshness and performance. Short TTL = fresher data but more frequent cache misses; long TTL = higher hit rate but possibly stale data.

Learn about read through vs. write through.

Eviction Policies: Caches have limited size, so we need policies to evict old entries when full:

LRU (Least Recently Used): Evict the item that hasn't been used in the longest time (a common good default, as it mirrors temporal locality assumptions).
LFU (Least Frequently Used): Evict the item with the fewest accesses.
FIFO (First In First Out): Evict the oldest item (regardless of usage).
Random or custom policies can also be used, but LRU is very popular in web caches.

Considerations/Trade-offs with Caching:

Consistency vs Freshness: A cache might return stale data if the underlying data changed. Mitigate this with short TTLs or cache invalidation (e.g., purge or update the cache when data is updated). In some systems, slightly stale data is acceptable (e.g., a news feed 1 minute out of date) in exchange for speed, but in others (bank balances) it is not.
Cache Miss Penalty: The first request (cache miss) is as slow as without cache, sometimes slightly slower if the system also then writes to cache. For infrequently accessed data, a cache might not help much (worse, it could evict something useful to make space for a one-time query).
Cache Warming: Sometimes after a restart, the cache is empty (cold cache) and many misses happen. Systems might "warm up" caches by preloading popular data. A sudden traffic spike on cold cache can hammer the database.
Distributed Caches: If using a cluster of cache servers (to scale the cache itself), consider how to distribute data (consistent hashing can map keys to cache nodes). Also handle what happens if a cache node goes down (keys on that node are gone, causing cache misses that fall back to DB – this surge is called a cache stampede; one mitigation is to stagger cache re-population or use a lock so only one request rebuilds missing cache entries).
When Not to Cache: If data is almost never reused or changes extremely often, caching might give little benefit but still add complexity. Also, very small datasets that are quick to query might not need an extra caching layer.

Read about Caching Patterns and Policies.

Now let’s apply these concepts in a sample interview question about caching.

Sample Interview Question (Caching):

“Our service is getting slow due to frequent database reads. How would you design a caching solution to speed it up?”

Sample Answer:

Identify what to cache: “First, I'd profile which data is read frequently and causing load. Let's assume we have certain queries (like fetching user profiles or top trending items) that are very frequent. Those are prime candidates for caching. Also, if the data doesn't change every second, it's cacheable. I would not cache data that is user-specific and one-off, but focus on common, repetitive reads.”
Choose cache storage and placement: “I would introduce a distributed in-memory cache (for example, a Redis cluster) between the application and the database. The application will check Redis before hitting the database. Since Redis stores data in memory, reads are extremely fast (microseconds). If we have multiple application servers, they all communicate with this cache cluster. This way, if any app server fetches an item from DB, it can store it in Redis, and all other servers will benefit from that cached result.” (Alternatively, if using a CDN for static content, mention that for things like images or static files, a CDN should cache them to reduce load on origin servers.)
Caching strategy (cache-aside): “I’ll use a cache-aside strategy. That means: when a request comes in, the service will try to get the data from cache (Redis). If it's a hit, great – we return quickly. If it's a miss, we'll query the database, get the data, then store a copy in Redis with an appropriate key. This ensures subsequent requests for the same item are fast.” We should also decide a reasonable TTL for each cache entry. For example, user profile data might be cached for 5 minutes (since profiles don’t change often), while a trending list might update every minute.
Maintain consistency: “To keep cached data from being stale for too long, we'll use expiration times (TTL). If data is updated, we can also choose to proactively invalidate or update the cache. For instance, if a user updates their profile, we can either invalidate the cache entry for that user or update it with the new data in cache (write-through strategy for that operation). Cache invalidation is one of the trickier parts, but a simple approach is often fine: e.g., expire the cache after X seconds so it eventually refreshes.” We accept that there is a slight window where data might be stale, but for our use case (speeding up reads), that trade-off is acceptable. If not (say we needed strong consistency), we might avoid caching certain data or implement a more complex invalidation on writes.
Size and eviction policy: “We'll need to set an eviction policy for the cache. I'll likely use LRU eviction, meaning if the cache is full, the least recently used item gets evicted to make space. This tends to keep more useful data in cache. We also size the cache appropriately (maybe enough to hold our most frequently accessed 10k records) to get a high cache hit rate. Monitoring cache hit/miss rates is important to tune this.”
Result: “With this caching layer in place, most reads will hit the fast cache and relieve load on the database. That reduces latency for users and allows the system to handle more traffic with the same DB. For example, if 80% of requests can be served from cache with sub-millisecond latency, the database only handles 20% of the traffic, which drastically improves overall throughput.”
Follow-up (if needed on client-side or CDN): “Additionally, for static content (images, scripts), I'd use browser caching and a CDN so that those assets rarely hit our servers after the first time. But for database query caching, the described Redis layer is the core solution.”

Real-world example: A classic example is how social networks cache content. Facebook’s newsfeed system, for instance, caches the latest posts for each user so that retrieving a feed is fast and doesn’t always hit the primary database . Similarly, Stack Overflow once detailed how they cached hot questions to handle spikes in traffic . These examples show that caching frequently viewed data is essential for performance in real systems.

Trade-Offs and Optimizations for Load Balancing and Caching

Every design decision in system design comes with trade-offs. Let’s summarize the key trade-offs and optimizations for load balancers and caching solutions:

Load Balancer Trade-offs:

Single vs Multiple Load Balancers: One load balancer is simple but a single point of failure. Multiple load balancers improve reliability but add complexity in synchronization or require DNS-level balancing. Optimization: Use managed load balancer services or VRRP/Failover mechanisms to run active-passive LBs for redundancy.
Layer 4 vs Layer 7: L4 load balancing (e.g., AWS Network LB) is very fast and operates at network level but can’t make decisions based on content. L7 (e.g., HTTP LB) is more flexible (can route by URL, handle SSL, etc.) but slightly slower and consumes more resources (terminating SSL, parsing HTTP). Optimization: Sometimes a combination is used – L4 for basic distribution, L7 for specific routing needs.
Algorithm Choices: A simple Round Robin might not account for backend load differences, while Least Connections is better for varying load but needs realtime info. Consistent Hashing helps with cache locality but can lead to uneven distribution if keys are skewed. Optimization: Many systems use a hybrid approach (e.g., “power of two choices” algorithm picks two servers at random and then the least loaded of those for better balancing with low overhead).
Overhead: The load balancer adds a tiny overhead (especially L7 which might decrypt SSL). This is usually negligible compared to the benefit, but it’s a consideration. Hardware LBs are very fast; software LBs need CPU but on modern hardware that’s fine for thousands of requests per second. Optimization: Offload SSL/TLS at the load balancer (TLS termination) to reduce backend server load, and use HTTP/2 or keep-alive connections to make routing efficient.
Geo-distribution: If you have users globally, you might need geo-aware load balancing (via DNS or Anycast). This introduces the complexity of directing users to the nearest region and maybe replicating content across regions. Optimization: Use CDNs and Anycast IP for global routing, or DNS policies for routing traffic geographically.

Caching Trade-offs:

Freshness vs Performance: Caching inherently trades freshness of data for speed. Stale data can be a problem in some applications. We mitigate this with shorter TTLs or cache invalidation on writes, but that adds complexity. Choose TTLs wisely based on how fresh data needs to be. Optimization: If strong consistency is needed, consider strategies like write-through (update cache on every write) at the cost of write latency.
Memory vs Storage: In-memory caches (fast but limited capacity) vs on-disk caches (slower but larger). If the working set doesn’t fit in memory, you might cache to SSDs (e.g., using something like RocksDB-based cache). But that’s slower than RAM. Optimization: Use a tiered cache: most hot items in memory, less frequent items on SSD cache.
Cache Size vs Hit Rate: A small cache might have a low hit rate (many misses), giving limited benefit. A very large cache reduces misses but costs more and might have diminishing returns. Optimization: Identify the “hot set” of data that gives 80% of benefits and size cache for that (Pareto principle). Monitor and adjust.
Eviction Policy: The default (LRU) might not always be optimal. E.g., if you have cyclic access patterns, LRU might evict something that will be needed soon. In some cases, LFU could be better if certain items are consistently popular. Optimization: Some systems use adaptive policies or multiple tiers (LFU for tiny hot items, LRU for larger items).
Distributed Cache Consistency: In a distributed cache cluster, keeping consistency can be hard (if the same data exists on multiple nodes or sharding changes). Usually we solve this with a single authority per key (sharding) and accept eventual consistency if a node dies. Optimization: Use consistent hashing so that when a cache node is added/removed, minimal keys move around (prevent massive cache invalidation).
Cache Stampede: When a popular item expires, many requests may flood the database (cache miss storm). Optimization: Use techniques like lock caching (only one thread recomputes missing data while others wait), or slightly stagger expirations for similar keys, or pre-fetch data before TTL expires.

Understanding these trade-offs helps you justify your choices in an interview. Always explain why a certain caching policy or load balancing setup is chosen and what the potential downsides are, along with how to mitigate them.

Check out cache invalidation strategies.

Best Practices for Load Balancing and Caching in System Design

To excel in system design (and impress your interviewer), keep these best practices in mind:

Design for Redundancy: For load balancers, always have a fallback. For caches, have a strategy if the cache fails (the system should still function, even if slower). This avoids single points of failure.
Keep it Simple (Avoid Over-engineering): Use straightforward load balancing algorithms and caching strategies unless the scenario clearly needs something fancy. A well-understood strategy (like cache-aside with Redis, or round-robin LB) implemented correctly is better than an overly complex solution that’s hard to maintain.
Monitoring and Metrics: Use metrics to guide optimizations. Monitor cache hit rates, latency, evictions, as well as server load and response times behind a load balancer. This data will tell you if your caching is effective or if the load balancer is distributing traffic evenly.
Health Checks and Timeouts: Configure health checks for your backends so the load balancer can detect failures quickly. Similarly, caches should handle timeouts – if the cache is unreachable, the app should fall back to the database rather than hang.
Consistency and Invalidation: For caching, implement clear rules for invalidation. Document them so you and your team know when the cache is updated or flushed. For example: "user profile cache invalidated on profile update event, otherwise expires after 24 hours." This prevents stale data issues.
Use CDN for Static Content: This is an often-mentioned best practice: offload images/videos and other static files to a CDN. It’s a form of caching (edge caching) that drastically cuts down load on your servers and reduces latency for users worldwide.
Graceful Degradation: If the cache layer is down, the system should still serve content (even if slower) by directly querying the database. Similarly, if one data center or load balancer goes down, DNS or failover should route users to a backup. Designing for partial failures is key in system architecture.

Common Mistakes to Avoid

Be mindful of these common pitfalls when designing or discussing load balancers and caching:

Forgetting Redundancy: A classic mistake is to introduce a load balancer but make it a single instance without failover. This shifts the single point of failure from a server to the LB. Always mention a redundant pair or some failover mechanism for the load balancer.
Caching Everything Blindly: Not all data should be cached. Caching data that is rarely used or that changes too often can waste memory and even hurt performance (due to overhead of cache maintenance). Always choose what to cache based on access patterns.
No Cache Invalidation Plan: A cache that never updates can serve stale data indefinitely. Simply saying "we'll cache it" without explaining when it expires or updates is a red flag. Avoid infinite TTLs unless data truly never changes. Have a clear invalidation strategy to prevent serving outdated information.
Ignoring Cache Warming/Stampede: If your system relies heavily on cache, an empty cache after a restart or an expired popular item can overwhelm the database. A mistake is not handling this – e.g., not using mutex locks or request coalescing for regenerating cache. Mention briefly how to mitigate a cache stampede (even something simple like "add a small random jitter to expirations" can show awareness).
Stateful Sessions without Stickiness: If your web servers store user sessions in memory (stateful) and you use a load balancer, you must ensure session stickiness or the user might bounce between servers and lose their session. A common error is to overlook this. Either use sticky sessions on the LB or, better, store sessions in a shared cache so they are stateful to the user but stateless to the web servers (no matter which server handles the request, it can fetch the session data).
Overcomplicating the Design: Talking about multiple layers of caches or load balancers that aren’t justified by the scenario can confuse your answer. For instance, don’t introduce a global CDN for a system that’s clearly local, or a sharded caching tier for a small-scale service. Stick to the requirements – show you can scale as needed, but don’t add components with no clear reason.

Avoiding these mistakes will make your system design answer more realistic and robust.

Conclusion

Mastering the use of load balancers and caching in system design interviews will significantly improve your ability to design scalable, high-performance systems.

In your answers, always start with the fundamentals – clarify the needs, then confidently incorporate a load balancer for distribution and caching for speeding up reads. Explain your choices, cover trade-offs, and demonstrate best practices.

With clear reasoning and a structured approach, you’ll show the interviewer that you can balance load and cache data effectively in any system design.

For more in-depth learning and practice on system design (including load balancing, caching, and beyond), consider these highly regarded resources on DesignGurus.io:

Grokking System Design Fundamentals: Great for beginners to learn core concepts like load balancing, caching, databases, etc., from the ground up.
Grokking the System Design Interview: A comprehensive course featuring common system design interview questions and step-by-step solutions. It covers how and when to use components like load balancers and caches in real interview scenarios.
Grokking the Advanced System Design Interview: Perfect for going beyond the basics into advanced topics (scaling to millions of users, advanced caching patterns, multi-region load balancing, etc.) to truly stand out in interviews.

By studying these resources and practicing the sample questions and answers, you’ll be well-prepared to ace your system design interview, confidently handling questions about load balancers, caching, and much more.