Grokking the System Design Interview to Boost Your Career

The system design interview can be a daunting experience, especially for those who are new to the tech industry.

The average candidate spends around 30-60 minutes on each question, which can be a significant time commitment.

To prepare, it's essential to understand the types of questions you'll be asked, such as scaling a database or designing a caching layer.

The system design interview is not just about technical skills, but also about your ability to think critically and communicate effectively.

Readers also liked: ACM Software System Award

System design interview preparation is crucial to crack the interview. You must understand the problem being asked, clarify on the onset rather than assuming anything.

To prepare, start by understanding the use-cases of the system. This will help you figure out the data structures, components, and abstract design of the overall model. You MUST know what is the system going to be used for, what is the scale it is going to be used for, and constraints like requests per second, requests types, data written per second, and data read per second.

Take a look at this: Claude 3 System Prompt

It's essential to practice mock interviews, pick any topic, and try to come up with a design. Then, go and see how and why it is designed in that manner. There is absolutely no alternative to practice. Whiteboarding a system design question is similar to actually writing code and testing it.

Here are the steps to approach system design questions in interviews:

Remember, just reading will only take you so far. You need to practice and understand the thought process of designing a large scale system.

Capacity planning is all about estimating the resources your system will need to handle a certain amount of traffic. To do this, you need to consider several factors, including the number of servers, storage, network bandwidth, and latency.

To estimate the number of servers needed, you can use the load estimation table from the article, which shows that 1 million average users per day will result in 100 requests per second. This can help you determine the number of servers required to handle the traffic.

For storage estimation, you can use the average size of request per day, which is 20 TB. This can help you determine the amount of storage needed for your system.

When it comes to network bandwidth, you can use the average size of request per second, which is 230 MB/Sec. This can help you determine the network bandwidth needed for your system.

Here's a summary of the key factors to consider when planning for capacity:

Keep in mind that these are just estimates, and the actual values may vary depending on the specifics of your system.

Capacity planning is a crucial step in ensuring your system can handle the expected load. It involves estimating the resources required to meet the demands of your users.

To determine the number of servers needed, you'll want to consider the average number of users per day, which is 1 million. This is based on the load estimation that shows 10^6 average users per day.

The type of processor required also plays a significant role in capacity planning. You may need GPU specific processors or CPU specific processors, depending on your system's requirements.

Network bandwidth is another essential factor to consider. The average size of a request per second is 230 MB/sec, which can help you estimate the required bandwidth.

Latency is also a critical aspect of capacity planning. You may need to consider both sequential latency, which is 100 ms, and parallel latency, which is 75 ms.

To calculate the number of CPU cores needed, you can use the following formula: (Average total requests per sec * Average cpu processing time per request) / Average 1 cpu core processing per sec. Based on the resource estimation, this would give you approximately 10 CPU cores.

Here's a summary of the key factors to consider in capacity planning:

By considering these factors, you can create a solid capacity plan that meets the demands of your users and ensures your system runs smoothly.

Cloud scaling is all about adjusting your resources to meet changing demands. You have two main options: vertical scaling and horizontal scaling.

Vertical scaling involves adding more memory, CPU, or other resources to your existing servers. This can be a quick fix, but it might not be the most cost-effective solution in the long run.

With horizontal scaling, you add more servers to handle the increased load. This approach is often more efficient and can be more cost-effective, especially for large-scale applications.

To give you a better idea, here's a comparison between vertical and horizontal scaling:

In practice, the choice between vertical and horizontal scaling depends on your specific needs and resources.

Database and Storage is a critical component of system design.

Relational databases store data in tables with rows and columns, making it easy to query and manipulate data. This is why PostgreSQL and MySQL are popular choices.

Columnar databases, on the other hand, store data by columns, which makes them ideal for handling write-heavy workloads. Apache Cassandra and HBase are great examples of this type of database.

You might enjoy: Grokking Data Structures

Here are some common types of databases:

Database and storage choices can greatly impact the scalability and performance of your system.

Relational DBs are built on the idea of structured data, where every piece of information has a specific place in a table. This is in contrast to NoSQL databases, which can handle unstructured data.

Relational DBs require a predefined schema, which can be limiting if your data doesn't fit the mold. For example, if you're trying to store a large amount of unstructured data, a relational DB might not be the best choice.

The ACID properties of relational DBs ensure that transactions are processed reliably and consistently. This is a key difference from NoSQL databases, which often follow the BASE principle of eventual consistency.

Relational DBs are table-based, with each table consisting of rows and columns. This structure can make it easier to perform complex queries and joins. However, it can also make it more difficult to scale the database.

Here's a comparison of relational DBs and NoSQL databases:

Overall, relational DBs are a good choice when you need to store and manage structured data. However, they may not be the best fit for applications that require flexible schema or high scalability.

Indexing is a crucial aspect of database management that helps improve the speed and efficiency of querying data. Database indexes reorder the way records in a table are physically stored, making it faster to retrieve data.

A clustered index reorders the records in the table, whereas a non-clustered index does not match the physical stored order of the rows. Clustered indexes are faster and require less memory, but tables can only have one clustered index. Non-clustered indexes are slower and require more memory, but tables can have multiple non-clustered indexes.

Here's a comparison of clustered and non-clustered indexes:

Indexes can slow down write operations, as they must be updated when data is inserted, updated, or deleted. However, they can significantly reduce the time it takes to read data, making them a crucial component of database management.

The JVM Garbage Collectors are a crucial part of any Java application, responsible for managing memory and preventing memory leaks.

They work by periodically scanning through the heap to identify and delete any objects that are no longer in use. This process is known as garbage collection.

The JVM provides several types of garbage collectors, each with its own strengths and weaknesses. The most commonly used garbage collectors are the Serial GC, Parallel GC, and G1 GC.

The Serial GC is a simple and efficient collector that is suitable for small applications with limited heap size. It works by pausing the entire application during garbage collection.

The Parallel GC is a more advanced collector that can collect garbage in parallel with the application, reducing pause times. It is suitable for larger applications with a medium-sized heap.

The G1 GC is a low-pause-time collector that is suitable for large applications with a large heap. It works by dividing the heap into smaller regions and collecting garbage in each region concurrently.

Overall, the JVM garbage collectors play a critical role in ensuring the performance and reliability of Java applications.

Cache and Eviction is a crucial aspect of system design, and understanding how it works is essential for acing a system design interview. Caching improves performance by reducing latency, load on the DB, and network cost, but it comes with its own set of problems like cache invalidation and stale data.

A distributed cache has its own set of problems like consistency and node affinity. Different places to cache include client-side, server-side, global/distributed, and proxy/gateway side caching. The type of cache used depends on the application's requirements and the data being cached.

Caching strategies include Read-Cache-aside, Read-Through, Write-Around, Write-Behind/Write-Back, and Write-through. Cache eviction policies include FIFO, LIFO, LRU, MRU, LFU, and RR. The choice of eviction policy depends on the application's requirements and the type of data being cached.

If this caught your attention, see: Grokking the System Design Interview Pdf

Cache eviction policies are a crucial aspect of caching, as they determine which items to remove from the cache when it's full. This helps maintain a balance between cache size and the amount of data being stored.

FIFO (First In First Out) is a simple eviction policy where the first item added to the cache is the first one to be removed. This is like a queue, where items are added and removed from the front and back respectively.

LIFO (Last In First Out) is another policy where the last item added to the cache is the first one to be removed. This is similar to a stack, where items are added and removed from the top and bottom respectively.

LRU (Least Recently Used) is a more effective policy where the item that hasn't been used for the longest time is removed first. This is based on the timestamp of when each item was last accessed, and items that are frequently accessed remain in the cache.

MRU (Most Recently Used) is the opposite of LRU, where the item that was most recently used is removed first. This policy is less common and may not be as effective as LRU.

LFU (Least Frequently Used) is another policy where the item that has been used the least number of times is removed first. This is based on the count of how many times each item has been accessed, and items that are frequently accessed remain in the cache.

RR (Random Replacement) is a policy where items are removed randomly from the cache. This is not a recommended policy as it can lead to inconsistent results.

Here are some common cache eviction policies:

A Bloom Filter is a space-efficient algorithm that determines if a given element is present in a set or not. It's a probabilistic algorithm that can give false positives, but never false negatives.

To use a Bloom Filter, you need to understand that more hash functions and a wider bit array result in fewer collisions. This means you can store more data in a smaller space.

One of the best things about Bloom Filters is that they use less memory, making them perfect for sending results over a wire. This is especially useful in applications where bandwidth is limited.

Here are some common use-cases for Bloom Filters:

Redis is an in-memory data store that's incredibly fast, capable of delivering up to 1 million requests per second on an average Linux system. This is because reading and writing to RAM is always faster than disk.

One of the key advantages of Redis is its single-threaded architecture, which eliminates the need for locks, thread synchronization, and context switching. This makes it highly efficient and scalable.

Redis supports Non-blocking IO, which allows a single thread to wait on many socket connections for read/write operations. This is why it can handle such a high volume of requests without slowing down.

Redis can store a variety of data structures, including strings, bitmaps, hashes, lists, sets, and more. Here are some of the data structures it supports:

For persistence, Redis offers several options, including RDB (Redis Database), AOF (Append Only File), and the ability to combine both. RDB takes point-in-time snapshots of your dataset at specified intervals, while AOF logs every write operation received by the server.