System Design Interview Guide for Beginners: Learn System Design Step by Step

Step 5: High-Level Architecture Design

Now that we know what we need to build (steps 1-3) and how data is handled (step 4), paint the high-level architecture. This is where you identify the major components and how they interact.

A common approach is to draw a block diagram (you can describe it in words in an interview, or use a whiteboard).

Start with ~5-6 main components, such as:

• Clients (users’ browsers or mobile apps that will use the service).

• Web/Application Servers that handle client requests, run the core application logic, and serve APIs. In a scalable setup, you will have multiple instances behind a Load Balancer. The load balancer distributes incoming requests among the app servers to ensure no single server is overwhelmed and to provide redundancy.

• Database(s) where persistent data lives (from step 4, e.g., the URL mapping database).

• Cache (if needed) to store frequently accessed data in memory for fast retrieval. For a URL shortener, you might cache the most popular URLs' mappings to reduce database hits on reads.

• Asynchronous Processing components if any – for instance, a message queue and worker services. (Our URL shortener may not need this, but many systems do for tasks like sending notifications, processing images, etc., that can be done outside the main request flow.)

• External Services or additional components as needed (for example, if designing a system like YouTube, you'd have a separate video encoding service, CDN for content delivery, etc. In a shortener, maybe an external monitoring service for analytics or a third-party for link previews – usually not, but consider what’s relevant to the problem).

Describe the interactions:

For example,

"Clients connect to the service via a load balancer, which routes to one of the application servers. The application server on a POST /create call will write a new entry to the database (and update cache). On a GET /<short> call, the server first checks the cache for the short code; if a cache miss, it queries the database, then returns the redirect response."

This is essentially narrating the request flow.

Be sure to mention how components communicate (REST calls from client to server, server to DB via SQL queries, etc., queue for async messaging if used).

If applicable, mention using CDNs for static content, or API gateway if designing a system with many microservices (though for a simpler system you might not need an API gateway explicitly).

At this stage, highlight any important design decisions for high-level:

• Will you use a monolithic service or break into microservices? (Often in interview designs, a monolith or simple service is fine unless the question explicitly expects multiple services.)

• How do you ensure high availability? (Multiple servers + load balancer, replicated database or a primary-secondary setup, etc.)

• Any data partitioning? (You might not partition immediately, but say "if traffic grows to X, we can shard the database by ...").

Step 6: Detailed Design of Key Components

After outlining the big picture, the interviewer may ask you to go deep into a few components. This is where you show more detailed thinking on the particularly challenging or important parts of the system.

Typically, you won't have time to detail everything, so focus on 1-2 areas that are most critical for this system’s success or that involve interesting trade-offs.

Often those areas are:

• Database and storage details: If using a relational DB, discuss the schema briefly, or how to partition (shard) the data when it grows. If using NoSQL, talk about the partition key choice, replication factor, etc. Also discuss how you'll handle indexes to optimize lookups (e.g., an index on the short code field for fast search, which is usually a given if it's primary key).

• Caching strategy: If cache is important, explain what data to cache and eviction policy. For example, "We'll use a Redis cache in front of the DB for read-mostly data like the URL mappings. Given Zipf's law, a small fraction of short links might get a large fraction of traffic (viral links). Caching those popular links will drastically reduce DB load. We'll set keys to expire in 24 hours or use an LRU eviction if memory fills up." Also address cache consistency: what if a URL mapping is updated or deleted? In our example, mappings might be mostly immutable, so cache consistency is easy – on create we add to cache, on access we populate cache if not present. If data can change, mention cache invalidation strategies (update or invalidate the cache on writes).

• Load balancer behavior: It's usually straightforward (round-robin or least connections routing). You can mention health checks (if a server is down, LB stops sending traffic to it).

• Component communication and protocols: e.g., maybe the service uses HTTP/HTTPS for communication, maybe uses gRPC for internal service calls (if microservices involved). For a beginner level, this may be too deep, but if the design had microservices, one could mention using REST or gRPC between them and why.

• Third-party services or special components: For instance, if designing something requiring search (like designing Twitter, you might have a search service using Elasticsearch), or for a chat system, detail how you'd implement real-time messaging (using long polling vs WebSocket, etc.). These are domain-specific deep dives.

• Task scheduling / cron jobs: If relevant, mention any periodic jobs. E.g., "We'll have a daily job to remove expired short links from the database."

For our URL shortener, a detailed component could be the unique key generation service. How do we generate short codes without collision?

• A simple scheme: use an auto-incrementing number from the database and convert it to a base-62 string (0-9, A-Z, a-z) for the short code. The database can give a unique incrementing ID, which is reliable but if extremely high scale, that could become a bottleneck. Alternatively, use a separate service or even something like Snowflake ID generator or UUID approach. But base-62 from an incrementing ID is nice because it produces short codes (like "abc123").

• If we expect a distributed system with multiple servers generating IDs, we need to avoid collisions: we could dedicate one server for ID generation, or use database sequences, or partition the ID space among servers.

• It's good to mention the trade-off: a single ID generator is a single point of failure, but it's simple. There are known algorithms for decentralized unique ID generation (Twitter's Snowflake algorithm gives each server a unique ID space by encoding server ID + timestamp). For a beginner-friendly answer, acknowledging this concern is usually enough.

By drilling into such specifics, you demonstrate an ability to handle the complex internals of system components, not just the surface-level diagram.

Use this step to also touch on any bottleneck solutions (which leads into the next step) and to show familiarity with technology choices (databases, caches, etc.) relevant to the component.

Step 7: Identify Bottlenecks and Discuss Trade-offs & Improvements

Finally, it's wise to proactively discuss how your design handles failures or extreme cases, and what trade-offs were made.

No design is perfect – interviewers actually appreciate when you can critique your own design and suggest improvements, because it shows pragmatism and foresight.

Key points to consider:

• Single Points of Failure: Check each component in your design – if it fails, does the system still work? For instance, if we had only one database server, that's a single point of failure. The improvement would be to have a primary-secondary setup or a cluster. If the cache goes down, the system should still function (just slower by hitting the DB).

• Bottlenecks in scale: What component is likely to struggle as traffic grows? In many designs, a single database can become a bottleneck for both capacity and throughput. Solutions include:

  • Adding read replicas (for read-heavy workloads) to offload reads from the master.

  • Sharding the database when it gets too large (partition the data by some key so each DB instance handles a subset of traffic).

  • Using a distributed database or NoSQL store that is built to scale horizontally from the start.

  • Introducing message queues to buffer bursts of writes or tasks that can be processed asynchronously, which smooths out load.

  • Using microservices to split a monolithic system if certain parts need to scale independently (e.g., separating the image service from the text handling service in a social network design).

• Latency issues: If certain operations are slow, how to optimize? Maybe the geolocation service call in your design of Uber is slow; you might cache results or do it asynchronously.

• Cost considerations: Sometimes a design can meet all requirements but be extremely expensive (e.g., using very high-end hardware or too many resources). A good design balances performance with cost. Mention if your design is cost-efficient or how you might reduce costs (e.g., using cloud auto-scaling to add resources only during peak times).

• Consistency vs Availability trade-offs: Revisit the CAP aspect – if you chose eventual consistency somewhere (like caching or a multi-region database), mention what the impact is and why it's acceptable (e.g., "users might not see a newly posted comment for a few seconds due to replication lag, but the system remains available even if one region goes down. This trade-off favors availability which is important for a global social network.").

• Security and Privacy: Identify any potential security weaknesses. For instance, "We should ensure all communication is over HTTPS to prevent snooping. Rate-limiting should be in place on the URL shortener API to prevent someone from spamming millions of create requests (which could be a DoS or could fill our database). Also, we should sanitize inputs to prevent any SQL injection (if using SQL) or other injection attacks."

It’s helpful to present bottleneck solutions as future enhancements: "Our initial design meets the requirements, but if we needed to support 10x traffic, we’d likely shard the database and add a distributed cache layer.

We might also consider using a content delivery network (CDN) if we were serving large static assets, though in a URL shortener that’s not needed." This shows that you are designing for current requirements but aware of how to evolve.

By going through these steps methodically, you demonstrate a comprehensive approach to system design. Now, to solidify understanding, let's apply this framework to a concrete example.

Case Study: Designing a URL Shortener (TinyURL / Bit.ly)

To illustrate the thought process, let's walk through a simplified design of a URL Shortener (like TinyURL or Bitly) using the steps above.

Imagine we’re designing a simple URL shortener service (like bit.ly). It’s a classic system design example for beginners and touches on all the key ideas we’ve discussed.

Our URL shortener must allow users to enter a long URL and get back a short URL, which when visited, redirects to the original long URL.

This is a classic example often used in system design practice for beginners – it's simpler than something like designing YouTube or Facebook, but still covers many core ideas.

Sounds simple, right?

The challenge is doing this for millions of users and billions of links, efficiently and reliably.

Requirements:

1. Convert a long URL into a short, unique URL.

2. Redirect short URLs to the original long URL quickly.

3. Handle a large number of requests (both creating new short links and retrieving existing ones).

4. Ensure the system is reliable (minimal downtime, no data loss of URL mappings).

5. Requirements Clarification:

   • Functional: Users enter a long URL and get a shortened URL. When that short link is visited, the service redirects to the original long URL. We should allow a high volume of redirects (read) and also creation (write) operations. If specified, perhaps users can pick a custom short code instead of a random one (optional feature). Analytics (like click counts per link) might be another extension, but let's keep core first.

   • Non-Functional: The service should be highly available (once a link is created, the redirect should almost never fail), have low latency (redirects should be fast, <100ms overhead ideally), and be able to scale to millions of links and requests. Consistency isn't a big issue here – if a link is created, eventually everyone should reach the right destination (strong consistency on writes is fine because it's usually single data center). Partition tolerance: yes, we want the system to keep working even if parts fail.

   • Constraints: Short links are often ~7-8 characters long. We need to ensure we don't run out of combinations. With 62 characters (a-z, A-Z, 0-9) and length 7, that's 62^7 (~3.5e12) possibilities, which is ample. If custom aliases are allowed, we must handle duplicates by rejecting or name-spacing them per user. Also, should links expire? Let's assume not in the basic version (or maybe after a very long time).

6. Back-of-the-envelope Estimates:

   • Suppose we anticipate 100 million new short URLs per year (which is about 273k per day, ~3 per second on average; peaks maybe 100 per second in bursts). Redirects could be much higher – if each short URL is used 20 times on average, that's 2 billion redirects per year (~63 per second on average, peaks maybe a few thousand/sec).

   • Storage: 100 million records with original URL (let's average 100 bytes) + short code (say 10 bytes) + some overhead ≈ 110 bytes per record. 100e6 * 110 bytes = ~11e9 bytes, about 11 GB. That fits on one decent database server. If we replicate, it's double or triple that. So not insane.

   • Bandwidth: For redirects, each is a small HTTP response (a 301 redirect with a "Location" header). Very minimal, maybe a few hundred bytes per redirect. 2 billion * 200 bytes = 400 billion bytes/year ~ 12.7 bytes/sec on average, trivial. The bigger bandwidth is actually end users fetching the final content (which is outside our system's scope). For create requests, negligible.

   • QPS: Peak might be say 5,000 redirects/sec and 100 creates/sec during some viral event. We need to design to handle that kind of burst.

7. These numbers suggest one modern SQL database could handle writes (100/sec) easily and reads (5000/sec) with some help (like caching or replicas). The network and storage load are not crazy for a well-equipped machine, but we'll definitely use multiple app servers and a cache.

8. APIs:

   • POST /api/shorten – Request JSON: { "url": "https://verylongurl.com/..." , "customAlias": "optional-string" }. Response JSON: { "shortUrl": "http://sho.rt/abc123" }. (In a real design, the domain could be configurable or multiple domains.) If custom alias is taken, response might be an error or suggestion.

   • GET /<shortCode> – This is the redirect request. Not exactly an API returning JSON; rather, a user hitting http://sho.rt/abc123 should result in an HTTP 301/302 redirect to the stored long URL. If not found, perhaps a 404 page is shown.

   • (Optional) GET /api/info/<shortCode> – returns info like original URL, creation date, click count. This would be used if we have a UI or admin panel to show link analytics.

9. These define how clients interact. If we had a front-end, it would call the POST API to create links and simply rely on browser hitting the GET for redirects.

10. Data Model:

    • One primary table or collection: URL_Map. Fields: shortCode (primary key, string), originalURL (text), createdAt (datetime), clickCount (int, default 0 or updated on each redirect), maybe createdBy (user id if user accounts exist), customAliasFlag (bool if this was custom).

    • For a SQL approach: shortCode is primary key (unique). We can generate it as discussed (base-62 of an auto-increment ID or by using a random generator and checking for collision). The table size ~100M rows which is big but with proper indexing on primary key it's fine. We may want an index on createdBy if we need to query links by user, or maintain separate table for user->links mapping.

    • For a NoSQL approach: we could use a key-value store where key is shortCode and value is the original URL (plus metadata). This is effectively how a cache would treat it. Many interviewers are fine with SQL here, but mentioning a key-value store like DynamoDB or Cassandra for the mapping is also acceptable, especially if emphasizing horizontal scale. A key-value store is a natural fit since it's basically a big hash map of code -> URL. However, then you need a separate mechanism to generate unique keys (not provided by the DB as in auto-increment).

    • If we consider analytics beyond a simple counter, it might be a lot of data (each click event with timestamp, etc.). That could be a separate logging system or table, because writing an entry for each click might be too heavy for the main system. Perhaps a separate service or offline batch handles detailed analytics. But for simplicity, a clickCount that increments in the main DB on each redirect is easy but could become a write bottleneck at very high read traffic (a lot of locking on that row). We might avoid updating clickCount synchronously and instead log clicks to an in-memory counter or queue for batch processing. This touches on eventual consistency but is an optimization.

11. High-Level Design:

    • Use a Load Balancer to front the service (users will access via a well-known domain which points to LB).

    • Multiple Application Servers running the URL shortening application (logic for handling those APIs). These could be stateless servers so that any can handle any request. They will connect to the database and cache. If traffic grows, we can add more app servers easily.

    • Database: A primary SQL database to store the URL mappings. Possibly have a read replica for handling heavy read load (redirects) if needed. The app servers on a redirect could read from a replica (if eventual consistency on very recent links is acceptable – probably fine, or just always read from primary since it's key lookup which is fast). If using NoSQL, then it's a distributed store (but let's say SQL with replication for now).

    • Cache: A Redis cluster (or memcached) caching the most accessed shortCode->URL mappings. The application check cache first on each redirect request. Cache miss leads to DB query and then we populate cache. For creates, we could also pre-populate cache on creation, though not strictly necessary unless we expect that link to be hit immediately.

    • Key Generator: A component or service that generates unique short codes. If using DB auto-increment, the DB itself can be the generator (the ID). Alternatively, we might have a separate service if we want more complex generation logic. For high availability, one trick is to generate multiple IDs in advance (like reserve a block of IDs) so that the service can hand out IDs even if DB is momentarily slow. This could be an internal detail hidden behind the app servers – e.g., the app server when creating a link asks the DB for the next id by inserting a new row and getting the primary key, or calls an external generator.

    • Analytics/Logging: (Optional) A simple approach: the app servers log each redirect to a log file or send to a logging service (like Kafka or even just an append-only file or in-memory buffer). This can be processed asynchronously to produce usage stats, rather than updating the DB each time synchronously. If we skip this, the simplest is just increment a counter in the DB on each redirect (but as said, that could cause write load or contention at scale).

12. In a diagram, we'd have: Client -> LB -> App Servers -> (Cache and DB). The DB might replicate to a secondary. The app servers connect to cache first then DB. The key generator could be shown either as part of DB or a separate small service (with its own storage maybe).

13. Detailed Considerations:

    • Generating Short Codes: We'll use an auto-increment integer as the unique ID. The first URL gets ID 1, second gets 2, and so on. We convert that ID to a base-62 string. ID 1 might be "a", ID 61 = "9", ID 62 = "A", etc. This ensures no collisions and very short codes. The DB itself can handle uniqueness. However, the auto-increment could be a bottleneck if we had a distributed setup with multiple writer nodes. To scale writes beyond one DB node, we might use a different approach (like each server node gets a range of IDs to use or use a distributed ID generator). For our scale (100 writes/sec), one DB is fine. We just need to ensure the DB's sequence won't overflow. Using 64-bit int for ID is huge (~9e18) which is more than enough for all practical purposes.

    • Cache Policy: We expect a Zipfian distribution (some links are massively popular, e.g., a single link might get millions of hits if it goes viral). So caching is extremely beneficial. We'll use an LRU eviction strategy; less recently used entries get evicted if cache is full. We might size the cache to hold, say, 10% of the total links (~10 million entries in the far future) which is a lot – maybe unrealistic for memory (if each entry is, say, 100 bytes, 10M would be ~1GB of memory - actually that's doable on a cluster of Redis nodes). We can adjust this depending on budget. The cache being in-memory means sub-millisecond lookups, taking huge load off the DB for hot entries. If a link is not found in cache, the slight extra DB hit is fine. The consistency model is simple: if a new link is created, it’s not in cache yet (no problem, first user will just get a DB miss and then it's cached). If a link is deleted/expired (if we had that feature), we must delete it from cache too.

    • Database scaling: The single-master with replicas strategy can handle a lot: the writes (100/sec) all to master – trivial; the reads (couple thousand/sec) could be spread across, say, 5 replicas – also trivial per machine. If one DB can't handle all writes in the future (like tens of thousands per second), we would shard the data. How to shard? Perhaps by the shortCode range or hash. But sharding by shortCode might be tricky since shortCode is essentially random-ish (distribution is fine though). Another approach is to partition by creation time (like a new table each year), but that complicates lookups. If truly needed, we could use a NoSQL store like Cassandra which automatically shards by key. For now, we note that sharding is a future scaling strategy.

    • Redundancy: We’ll deploy DB master and replica in different availability zones so that if one zone goes down, we have the data safe. The load balancer and app servers similarly across zones. The cache could be a cluster with partitioning + replication too (Redis cluster allows replication).

    • Failure modes: If the cache cluster fails, the system still works (reads go to DB, which might become a new bottleneck if suddenly 100x load goes to it, but at least functionality is there). If the DB master fails, we promote a replica to master. During that failover (few seconds), writes might fail – but that’s an acceptable brief downtime in extreme case. We can mention more advanced multi-master or use of distributed databases if needing zero downtime.

    • Security: Ensure the API is secured (if it's internal, maybe not a big issue; if public, add an API key or auth if needed to prevent abuse). Rate limiting on the POST /shorten to avoid spam. Also validate that input URLs are proper and maybe not malicious. Possibly prevent certain URLs (like phishing) – beyond scope, but worth a mention if time permits.

14. Bottlenecks & Trade-offs:

    • The design's potential bottleneck is the database when the number of links grows huge. The mitigation is scaling out via sharding or switching to a scalable datastore.

    • Another bottleneck: the key generation approach – using a single sequence in the DB is simple, but if we had multiple data center active-active writes, an auto-increment doesn't work across them. A trade-off could be to generate random 7-char strings and check for collisions (which at scale, probability isn't zero, but with 3.5e12 space, the birthday paradox says collisions unlikely until huge numbers). That approach makes each create potentially do a check in DB for collision (most likely none). It's worth noting but many accept the sequential ID method for simplicity.

    • Consistency: We prioritized availability and partition tolerance over strong consistency in one area: using cache and possibly read replicas means a new link might not propagate to all nodes instantly. E.g., right after creation, if a user hits a replica that hasn't gotten that data yet, they'd get a 404. We can avoid this by directing recent-lookups to master or by slightly delaying availability of the link until replication (maybe negligible delay). Or simply note that within a second it will be available globally – acceptable for this service. This is a trade-off for read scalability.

    • Latency: Should be very good here. The redirect path is: check cache (in-memory, fast), or DB (which even if on SSD, a primary key lookup is perhaps a couple milliseconds). Network adds a few ms. We can easily keep the additional latency under, say, 50ms. So total with internet latency might be ~100ms, which is user-noticeable but okay for a redirect. To improve it further globally, one might deploy edge servers in multiple regions (or use a CDN that can do edge logic, though CDNs typically cache static content, not this dynamic mapping).

    • Evolution: If we add features like user accounts or link analytics dashboard, we might introduce new services or databases (for storing user profiles, for storing detailed click logs). But those can be added without altering the core design much – a good sign of a flexible architecture.

This case study demonstrates applying the framework in practice.

In an interview, you would adapt this depth based on time – often, not all of these details would be discussed, but it's important to be prepared to discuss them if asked.

The goal is to show the interviewer you can go from a blank slate to a workable system design while considering scalability, reliability, and maintainability.