System design interview download pdf

It helps to know a little about various key system design topics. Adjust the following guide based on your timeline, experience, what positions you are interviewing for, and which companies you are interviewing with. The system design interview is an open-ended conversation. You are expected to lead it. You can use the following steps to guide the discussion.

To help solidify this process, work through the System design interview questions with solutions section using the following steps. Gather requirements and scope the problem.

Ask questions to clarify use cases and constraints. Discuss assumptions. Dive into details for each core component. For example, if you were asked to design a url shortening service , discuss:.

Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues? Discuss potential solutions and trade-offs. Address bottlenecks using principles of scalable system design. You might be asked to do some estimates by hand. Refer to the Appendix for the following resources:. Question Design Pastebin. View exercise and solution. Question Design a hash map Solution Design a least recently used cache Solution Design a call center Solution Design a deck of cards Solution Design a parking lot Solution Design a chat server Solution Design a circular array Contribute Add an object-oriented design question Contribute.

First, you'll need a basic understanding of common principles, learning about what they are, how they are used, and their pros and cons. Scalability Lecture at Harvard. A service is scalable if it results in increased performance in a manner proportional to resources added.

Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow. Generally, you should aim for maximal throughput with acceptable latency. Source: CAP theorem revisited. Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability.

Waiting for a response from the partitioned node might result in a timeout error. CP is a good choice if your business needs require atomic reads and writes. Responses return the most readily available version of the data available on any node, which might not be the latest. Writes might take some time to propagate when the partition is resolved. AP is a good choice if the business needs allow for eventual consistency or when the system needs to continue working despite external errors.

With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the CAP theorem - Every read receives the most recent write or an error.

This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss. After a write, reads will eventually see it typically within milliseconds. Data is replicated asynchronously. This approach is seen in systems such as DNS and email.

Eventual consistency works well in highly available systems. Strong consistency works well in systems that need transactions. There are two complementary patterns to support high availability: fail-over and replication. With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service. The length of downtime is determined by whether the passive server is already running in 'hot' standby or whether it needs to start up from 'cold' standby.

Only the active server handles traffic. If the servers are internal-facing, application logic would need to know about both servers. Availability is often quantified by uptime or downtime as a percentage of time the service is available.

Availability is generally measured in number of 9s--a service with Duration Acceptable downtime Downtime per year 8h 45min 57s Downtime per month 43m Duration Acceptable downtime Downtime per year 52min If a service consists of multiple components prone to failure, the service's overall availability depends on whether the components are in sequence or in parallel.

If both Foo and Bar each had Source: DNS security presentation. DNS is hierarchical, with a few authoritative servers at the top level. Some DNS services can route traffic through various methods:. Source: Why use a CDN.

A content delivery network CDN is a globally distributed network of proxy servers, serving content from locations closer to the user.

The site's DNS resolution will tell clients which server to contact. Push CDNs receive new content whenever changes occur on your server. You can configure when content expires and when it is updated. Content is uploaded only when it is new or changed, minimizing traffic, but maximizing storage. Sites with a small amount of traffic or sites with content that isn't often updated work well with push CDNs. Content is placed on the CDNs once, instead of being re-pulled at regular intervals.

Pull CDNs grab new content from your server when the first user requests the content. This results in a slower request until the content is cached on the CDN. A time-to-live TTL determines how long content is cached. Pull CDNs minimize storage space on the CDN, but can create redundant traffic if files expire and are pulled before they have actually changed.

Sites with heavy traffic work well with pull CDNs, as traffic is spread out more evenly with only recently-requested content remaining on the CDN. Source: Scalable system design patterns. Load balancers distribute incoming client requests to computing resources such as application servers and databases. In each case, the load balancer returns the response from the computing resource to the appropriate client.

Load balancers are effective at:. To protect against failures, it's common to set up multiple load balancers, either in active-passive or active-active mode. Layer 4 load balancers look at info at the transport layer to decide how to distribute requests. Generally, this involves the source, destination IP addresses, and ports in the header, but not the contents of the packet.

Layer 4 load balancers forward network packets to and from the upstream server, performing Network Address Translation NAT. Layer 7 load balancers look at the application layer to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminate network traffic, reads the message, makes a load-balancing decision, then opens a connection to the selected server. For example, a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security-hardened servers.

At the cost of flexibility, layer 4 load balancing requires less time and computing resources than Layer 7, although the performance impact can be minimal on modern commodity hardware. Load balancers can also help with horizontal scaling, improving performance and availability. Scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware, called Vertical Scaling.

It is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems. Source: Wikipedia. A reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public.

Requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server's response to the client. Source: Intro to architecting systems for scale. Separating out the web layer from the application layer also known as platform layer allows you to scale and configure both layers independently.

Adding a new API results in adding application servers without necessarily adding additional web servers. The single responsibility principle advocates for small and autonomous services that work together.

Small teams with small services can plan more aggressively for rapid growth. Workers in the application layer also help enable asynchronism.

Related to this discussion are microservices , which can be described as a suite of independently deployable, small, modular services.

Each service runs a unique process and communicates through a well-defined, lightweight mechanism to serve a business goal. Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc. Systems such as Consul , Etcd , and Zookeeper can help services find each other by keeping track of registered names, addresses, and ports. Health checks help verify service integrity and are often done using an HTTP endpoint.

Both Consul and Etcd have a built in key-value store that can be useful for storing config values and other shared data. Source: Scaling up to your first 10 million users. ACID is a set of properties of relational database transactions. There are many techniques to scale a relational database: master-slave replication , master-master replication , federation , sharding , denormalization , and SQL tuning. The master serves reads and writes, replicating writes to one or more slaves, which serve only reads.

Slaves can also replicate to additional slaves in a tree-like fashion. If the master goes offline, the system can continue to operate in read-only mode until a slave is promoted to a master or a new master is provisioned. Source: Scalability, availability, stability, patterns. Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes.

Federation or functional partitioning splits up databases by function. For example, instead of a single, monolithic database, you could have three databases: forums , users , and products , resulting in less read and write traffic to each database and therefore less replication lag. Smaller databases result in more data that can fit in memory, which in turn results in more cache hits due to improved cache locality.

With no single central master serializing writes you can write in parallel, increasing throughput. Sharding distributes data across different databases such that each database can only manage a subset of the data. Taking a users database as an example, as the number of users increases, more shards are added to the cluster.

Similar to the advantages of federation , sharding results in less read and write traffic, less replication, and more cache hits. Index size is also reduced, which generally improves performance with faster queries.

If one shard goes down, the other shards are still operational, although you'll want to add some form of replication to avoid data loss.

They 're used to gather information about the pages you visit and how clicks. Finally, a book for getting better at architecting systems. Grokking the object oriented design interview pdf github.

Most read [educative. Anyone with grokking the coding interview ' by gayle mcdowell this is one of the intimidating! Typically, system requirements, constraints and bottlenecks should be well understood to shape the direction of both the interviewer and interviewee. The objective of this book is to provide a reliable strategy to approach the system design questions.

The right strategy and knowledge are vital to the success of an interview. This book provides solid knowledge in building a scalable system. The more knowledge gained from reading this book, the better you are equipped in solving the system design questions. This book also provides a step by step framework on how to tackle a system design question.

It provides many examples to illustrate the systematic approach with detailed steps that you can follow. Step 1 - Understand the problem and establish design scope. Step 2 - Propose high-level design and get buy-in. Step 3 - Design deep dive. Two issues in the basic approach. Single server key-value store. Distributed key-value store. Inconsistency resolution: versioning. System architecture diagram. Company engineering blogs. This text-based course makes your system design interview preparation easier.