Grokking the System Design Interview PDF Study Guide

The Grokking the System Design Interview PDF Study Guide is a comprehensive resource designed to help you prepare for the system design interview. It's a game-changer for anyone looking to break into the tech industry.

The guide is based on the popular online course "Grokking the System Design Interview" and covers everything from the basics of system design to advanced topics like scalability and fault tolerance. It's a must-have for anyone serious about acing their system design interview.

One of the standout features of the guide is its focus on practical problem-solving skills. It includes real-world examples and case studies to help you learn by doing, rather than just reading theory. This approach has been shown to be highly effective in helping students prepare for the system design interview.

For more insights, see: ACM Software System Award

System design interviews can be intimidating, but understanding the fundamentals can make a big difference. The unstructured nature of these interviews is a major reason why many software engineers struggle.

It's essential to ask questions about the problem's scope to avoid misunderstandings and clarify ambiguities early on. This is critical because design questions don't have a single correct answer. A good performance in a system design interview can result in a better offer, including a higher position and salary.

Clarifying what parts of the system to focus on is also crucial, especially when time is limited, as it often is in these interviews. In the case of designing a Twitter-like service, asking questions like whether users can post tweets and follow other people helps define the end goals of the system.

For another approach, see: Claude 3 System Prompt

System design interviews can be daunting, but understanding the right approach can make all the difference. The unstructured nature of these interviews is a primary reason why many software engineers struggle with them.

Clarifying the problem scope is crucial in system design interviews. It's essential to ask questions about the exact scope of the problem to avoid ambiguities. Candidates who spend enough time defining the end goals of the system have a better chance of success.

Related reading: Grokking the System Design

Asking the right questions can make a big difference. For example, when designing a Twitter-like service, you should consider questions like whether users can post tweets and follow other people. You should also think about whether to design the backend only or develop the front-end too.

In system design interviews, there's no one correct answer, which is why it's essential to clarify ambiguities early on. This approach can help you stand out from the competition, especially at top companies like Google, Facebook, and Amazon.

Designing a social media system requires careful consideration of scalability, performance, and consistency. You can't just use a single counter for likes, as it can lead to contention and scaling issues.

Relational databases aren't the best choice for handling likes, as they can cause scale issues with optimistic locking and retry logic. You can use Redis for atomic operations like increment and decrement.

To scale your service, you can use a Round Robin approach with multiple Redis nodes. This way, you can distribute the load and avoid overloading a single node.

The Count-Min Sketch approach can be used if you're okay with approximate values for the like counter. You can also use a Queue event model to let the count aggregator service sum the counts across all Redis nodes and save that to a DB.

A block diagram with 5-6 boxes can help you identify the core components of your system. For Twitter, you'll need multiple application servers to serve read/write requests with load balancers in front of them.

Capacity planning is a crucial step in system design, and it's essential to consider various factors to ensure your system can handle the expected load. To estimate the capacity of your servers, you need to consider how many users will be accessing the service, how much storage is required, and what network bandwidth is needed.

You'll also need to determine the acceptable latency, which can be calculated by considering sequential and parallel latency. For example, if you have a sequential latency of 100 ms and a parallel latency of 75 ms, you'll need to balance these two factors when designing your system.

To give you a better idea, here's a rough estimate of the resources you'll need based on the load estimation:

Based on this, you can estimate that you'll need at least 10 CPU cores to handle the expected load. Additionally, you'll need to consider vertical scaling, which involves increasing resources like memory and CPU, and horizontal scaling, which involves adding more servers.

Capacity planning is all about understanding the demands of your system and scaling accordingly. You need to consider how many servers you'll need, how many users will be accessing the service, and what kind of storage and network bandwidth are required.

To estimate the number of servers needed, you should consider the average total requests per second, which is 100 req/sec. This will give you an idea of the load your system can handle.

You should also think about the average cpu processing time per request, which is 100 ms/req. This will help you determine how many CPU cores you'll need to handle the load.

Here's a rough estimate of the number of CPU cores needed based on the average total requests per second and CPU processing time:

As you can see, with an average total requests per second of 100 req/sec and an average cpu processing time per second of 10^6 ms/sec, you'll need at least 10 CPU cores to handle the load.

Storage requirements are also crucial to consider. With an average size of request per day of 20 TB, you'll need to ensure your system has enough storage to handle this amount of data.

Network bandwidth is another important factor to consider. With an average size of request per second of 230 MB/sec, you'll need to ensure your system has enough network bandwidth to handle this amount of data.

Latency is also a critical factor to consider. With a sequential latency of 100 ms and a parallel latency of 75 ms, you'll need to ensure your system can handle these latency requirements.

Related reading: Grokking Algorithms Second Edition

In conclusion, capacity planning is all about understanding the demands of your system and scaling accordingly. By considering the average total requests per second, average cpu processing time per request, storage requirements, network bandwidth, and latency requirements, you can ensure your system is properly scaled to meet the demands of your users.

Cloud scaling is all about adjusting your system's resources to meet changing demands. You can do this in two main ways: vertical scaling and horizontal scaling.

Vertical scaling involves adding more resources to your existing servers, like increasing memory or CPU power. This can be a quick fix, but it has its limits.

With vertical scaling, you're essentially upgrading your current infrastructure. You might add more RAM or swap out an old CPU for a faster one. This can be a cost-effective solution, but it might not be enough to handle a huge surge in traffic.

Horizontal scaling, on the other hand, is about adding more servers to your system. This can be a more scalable solution, but it requires more planning and coordination.

Here's a quick rundown of the two approaches:

Horizontal scaling can be a bit more complex, but it offers more flexibility in the long run. By adding more servers, you can distribute the workload and ensure that your system can handle increased traffic.

Database scaling is crucial for handling large amounts of data. You can achieve this through replication, where all writes go to one database node, which gets replicated to all read node databases, resulting in eventual consistency.

Replication is a key component of read scaling. This approach ensures that your database can handle a high volume of read requests while maintaining data consistency.

To scale writes, sharding is often employed. Sharding involves dividing your database into smaller, independent pieces, each handling a portion of the data.

Replication and sharding can be used together to achieve optimal database scaling. By understanding how to apply these techniques, you can ensure your database is equipped to handle growing demands.

Here are some key database scaling techniques:

Rate Limit is a crucial aspect of Capacity Planning and Scaling. It helps prevent overwhelming your system with too many requests at once, which can lead to crashes and downtime.

Token Bucket algorithms can be used to implement Rate Limiting, with two main variations: Burst and Sustain. Burst adds tokens to the bucket at a fixed rate, which can lead to a burst of traffic, while Sustain adds tokens only if previous tokens are consumed, resulting in smooth traffic.

Leaky Bucket is another algorithm that can be used, where the bucket size is fixed, and if it's full, requests are rejected, and the bucket is dequeued at a fixed rate.

Fixed Window Rate Limiting can also be implemented, where a key-value pair is maintained for a time period, and if the counter exceeds the rate limit, requests are rejected. This can lead to burst traffic around the edges of the time period.

Sliding Log and Sliding Window Counter are two more algorithms that can be used to implement Rate Limiting, where all previous nodes are checked up to the time interval, and if the rate limit is exceeded, requests are rejected.

To help you visualize the differences between these algorithms, here's a comparison:

By implementing Rate Limiting, you can prevent overwhelming your system and ensure a smooth user experience.

Cache and High Availability is a crucial aspect of system design. Improving performance of an application through caching can reduce latency, load on the DB, and network cost, while also increasing read throughput.

Caching can be done at different levels: client side, server side, global/distributed caching, and proxy/gateway side caching. Each has its own advantages and challenges.

To ensure high availability, consider using active-active or active-passive deployment strategies. Active-active deployment involves two nodes running in parallel, while active-passive deployment has a primary and secondary service running in tandem. If the primary fails, the loadbalancer routes traffic to the secondary. Examples of tools that can help with this include Consul, etcd, and Zookeeper.

Caching can greatly improve the performance of an application, reducing latency, load on the database, and network cost, while also increasing read throughput.

Improving performance is one of the main advantages of caching, and it can be achieved through various methods, including caching points.

Caching points can be placed in different locations, such as client-side, server-side, global/distributed, or proxy/gateway side.

Here are the different types of caching points:

However, caching also comes with its own set of problems, such as cache invalidation, stale data, and high churn if the time-to-live (TTL) is set wrong.

In addition to caching points, there are different types of cache, such as spatial cache, temporal cache, and distributed cache.

For example, a spatial cache can be used to load nearby associated data from disk to cache, while a temporal cache can be used to store elements that are frequently used.

Consider reading: Data Labeling in Machine Learning with Python Pdf

High Availability Deployment is a crucial aspect of ensuring your system remains up and running, even in the face of failures or high loads. This can be achieved through Active-Active and Active-Passive configurations.

In an Active-Active setup, two nodes of the service run in parallel, with a load balancer routing traffic to both. This ensures that if one node fails, the other can take over and maintain service continuity.

For example, Consul, etcd, and Zookeeper are all examples of tools that support Active-Active configurations.

In an Active-Passive setup, the primary and secondary service run in parallel, with the primary serving all requests. If the primary fails, the load balancer will route traffic to the secondary and designate it as primary.

Here are some key points to consider when designing a High Availability Deployment:

By understanding these concepts and choosing the right tools, you can ensure that your system remains highly available and resilient to failures or high loads.

A circuit breaker is a mechanism that helps prevent overloading a service when it's down. It's like a safety switch that trips when too many requests fail.

If a service is down, we don't want to keep sending requests to it until it recovers. So, we set a threshold for the number of request failures. Once that threshold is reached, we start returning a default response.

Here's how a circuit breaker works: it has three states - Open, Closed, and Half-Open. We can think of it like a light switch.

In the Half-Open state, we let a few requests through to see if the service is recovering. If the response is good, we switch back to the Closed state and allow all traffic again.

Consistent Hashing is a technique that helps distribute traffic among nodes in a distributed system uniformly. It prevents a single point of failure and ensures that the same client request is sent to the server that has all the data already cached or locally stored.

Services often cache some data or store local data, so it makes sense to send the same client request to the server that has all the data already cached or locally stored. This approach can lead to better performance and reduced latency.

For more insights, see: Grokking Data Structures

Consistent Hashing also prevents DOS attacks to some extent by limiting the impact to only certain nodes. This is because the distribution of requests is based on the hash of both the request and the server, rather than just the request.

If you just hash the request and map it to a server, adding a new server will affect all the requests. But with Consistent Hashing, adding a new server affects only a few requests. This is because the distribution of servers in a hash ring may not be uniform, and virtual servers can be used to balance the distribution.

Virtual servers can help avoid a full outage if one node goes down by distributing the requests among multiple nodes. For example, if there are 60K user requests and 6 servers, each server can handle 10K requests.