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Learn how to design large-scale systems.

Prep for the system design interview.

Learning how to design scalable systems will help you become a better engineer.

System design is a broad topic. There is a vast amount of resources scattered throughout the web on system design principles.

This repo is an organized collection of resources to help you learn how to build systems at scale.

This is a continually updated, open source project.

Contributions are welcome!

In addition to coding interviews, system design is a required component of the technical interview process at many tech companies.

Practice common system design interview questions and compare your results with sample solutions: discussions, code, and diagrams.

Additional topics for interview prep:

• Study guide
• How to approach a system design interview question
• System design interview questions, with solutions
• Object-oriented design interview questions, with solutions
• Additional system design interview questions

The provided Anki flashcard decks use spaced repetition to help you retain key system design concepts.

• System design deck
• System design exercises deck
• Object oriented design exercises deck

Great for use while on-the-go.

Looking for resources to help you prep for the Coding Interview?

Check out the sister repo Interactive Coding Challenges, which contains an additional Anki deck:

• Coding deck

Learn from the community.

Feel free to submit pull requests to help:

• Fix errors
• Improve sections
• Add new sections
• Translate

Content that needs some polishing is placed under development.

Review the Contributing Guidelines.

Summaries of various system design topics, including pros and cons. Everything is a trade-off.

Each section contains links to more in-depth resources.

• System design topics: start here
  • Step 1: Review the scalability video lecture
  • Step 2: Review the scalability article
  • Next steps
• Performance vs scalability
• Latency vs throughput
• Availability vs consistency
  • CAP theorem
    • CP - consistency and partition tolerance
    • AP - availability and partition tolerance
• Consistency patterns
  • Weak consistency
  • Eventual consistency
  • Strong consistency
• Availability patterns
  • Fail-over
  • Replication
  • Availability in numbers
• Domain name system
• Content delivery network
  • Push CDNs
  • Pull CDNs
• Load balancer
  • Active-passive
  • Active-active
  • Layer 4 load balancing
  • Layer 7 load balancing
  • Horizontal scaling
• Reverse proxy (web server)
  • Load balancer vs reverse proxy
• Application layer
  • Microservices
  • Service discovery
• Database
  • Relational database management system (RDBMS)
    • Master-slave replication
    • Master-master replication
    • Federation
    • Sharding
    • Denormalization
    • SQL tuning
  • NoSQL
    • Key-value store
    • Document store
    • Wide column store
    • Graph Database
  • SQL or NoSQL
• Cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
  • Caching at the database query level
  • Caching at the object level
  • When to update the cache
    • Cache-aside
    • Write-through
    • Write-behind (write-back)
    • Refresh-ahead
• Asynchronism
  • Message queues
  • Task queues
  • Back pressure
• Communication
  • Transmission control protocol (TCP)
  • User datagram protocol (UDP)
  • Remote procedure call (RPC)
  • Representational state transfer (REST)
• Security
• Appendix
  • Powers of two table
  • Latency numbers every programmer should know
  • Additional system design interview questions
  • Real world architectures
  • Company architectures
  • Company engineering blogs
• Under development
• Credits
• Contact info
• License

Suggested topics to review based on your interview timeline (short, medium, long).

Q: For interviews, do I need to know everything here?

A: No, you don't need to know everything here to prepare for the interview.

What you are asked in an interview depends on variables such as:

• How much experience you have
• What your technical background is
• What positions you are interviewing for
• Which companies you are interviewing with
• Luck

More experienced candidates are generally expected to know more about system design. Architects or team leads might be expected to know more than individual contributors. Top tech companies are likely to have one or more design interview rounds.

Start broad and go deeper in a few areas. 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.

• Short timeline - Aim for breadth with system design topics. Practice by solving some interview questions.
• Medium timeline - Aim for breadth and some depth with system design topics. Practice by solving many interview questions.
• Long timeline - Aim for breadth and more depth with system design topics. Practice by solving most interview questions.

How to tackle a system design interview question.

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.

• Who is going to use it?
• How are they going to use it?
• How many users are there?
• What does the system do?
• What are the inputs and outputs of the system?
• How much data do we expect to handle?
• How many requests per second do we expect?
• What is the expected read to write ratio?

Outline a high level design with all important components.

• Sketch the main components and connections
• Justify your ideas

Dive into details for each core component. For example, if you were asked to design a url shortening service, discuss:

• Generating and storing a hash of the full url
  • MD5 and Base62
  • Hash collisions
  • SQL or NoSQL
  • Database schema
• Translating a hashed url to the full url
  • Database lookup
• API and object-oriented design

Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues?

• Load balancer
• Horizontal scaling
• Caching
• Database sharding

Discuss potential solutions and trade-offs. Everything is a trade-off. 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:

• Use back of the envelope calculations
• Powers of two table
• Latency numbers every programmer should know

Check out the following links to get a better idea of what to expect:

• How to ace a systems design interview
• The system design interview
• Intro to Architecture and Systems Design Interviews
• System design template

Common system design interview questions with sample discussions, code, and diagrams.

Solutions linked to content in the solutions/ folder.

View exercise and solution

View exercise and solution

View exercise and solution

View exercise and solution

View exercise and solution

View exercise and solution

View exercise and solution

View exercise and solution

Common object-oriented design interview questions with sample discussions, code, and diagrams.

Solutions linked to content in the solutions/ folder.

Note: This section is under development

New to system design?

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

• Topics covered:
  • Vertical scaling
  • Horizontal scaling
  • Caching
  • Load balancing
  • Database replication
  • Database partitioning

Scalability

• Topics covered:
  • Clones
  • Databases
  • Caches
  • Asynchronism

Next, we'll look at high-level trade-offs:

• Performance vs scalability
• Latency vs throughput
• Availability vs consistency

Keep in mind that everything is a trade-off.

Then we'll dive into more specific topics such as DNS, CDNs, and load balancers.

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.¹

Another way to look at performance vs scalability:

• If you have a performance problem, your system is slow for a single user.
• If you have a scalability problem, your system is fast for a single user but slow under heavy load.

• A word on scalability
• Scalability, availability, stability, patterns

Latency is the time to perform some action or to produce some result.

Throughput is the number of such actions or results per unit of time.

Generally, you should aim for maximal throughput with acceptable latency.

• Understanding latency vs throughput

Source: CAP theorem revisited

In a distributed computer system, you can only support two of the following guarantees:

• Consistency - Every read receives the most recent write or an error
• Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
• Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures

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 to allow for eventual consistency or when the system needs to continue working despite external errors.

• CAP theorem revisited)
• A plain english introduction to CAP theorem
• CAP FAQ
• The CAP theorem

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.

After a write, reads may or may not see it. A best effort approach is taken.

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.

After a write, reads will see it. Data is replicated synchronously.

This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions.

• Transactions across data centers

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.

Active-passive failover can also be referred to as master-slave failover.

In active-active, both servers are managing traffic, spreading the load between them.

If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers.

Active-active failover can also be referred to as master-master failover.

• Fail-over adds more hardware and additional complexity.
• There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive.

This topic is further discussed in the Database section:

• Master-slave replication
• Master-master replication

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 99.99% availability is described as having four 9s.

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.

Overall availability decreases when two components with availability < 100% are in sequence:

Availability (Total) = Availability (Foo) * Availability (Bar)

If both Foo and Bar each had 99.9% availability, their total availability in sequence would be 99.8%.

Overall availability increases when two components with availability < 100% are in parallel:

Availability (Total) = 1 - (1 - Availability (Foo)) * (1 - Availability (Bar))

If both Foo and Bar each had 99.9% availability, their total availability in parallel would be 99.9999%.

Source: DNS security presentation

A Domain Name System (DNS) translates a domain name such as www.example.com to an IP address.

DNS is hierarchical, with a few authoritative servers at the top level. Your router or ISP provides information about which DNS server(s) to contact when doing a lookup. Lower level DNS servers cache mappings, which could become stale due to DNS propagation delays. DNS results can also be cached by your browser or OS for a certain period of time, determined by the time to live (TTL).

• NS record (name server) - Specifies the DNS servers for your domain/subdomain.
• MX record (mail exchange) - Specifies the mail servers for accepting messages.
• A record (address) - Points a name to an IP address.
• CNAME (canonical) - Points a name to another name or CNAME (example.com to www.example.com) or to an A record.

Services such as CloudFlare and Route 53 provide managed DNS services. Some DNS services can route traffic through various methods:

• Weighted round robin
  • Prevent traffic from going to servers under maintenance
  • Balance between varying cluster sizes
  • A/B testing
• Latency-based
• Geolocation-based

• Accessing a DNS server introduces a slight delay, although mitigated by caching described above.
• DNS server management could be complex and is generally managed by governments, ISPs, and large companies.
• DNS services have recently come under DDoS attack, preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es).

• DNS architecture
• Wikipedia
• DNS articles

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. Generally, static files such as HTML/CSS/JS, photos, and videos are served from CDN, although some CDNs such as Amazon's CloudFront support dynamic content. The site's DNS resolution will tell clients which server to contact.

Serving content from CDNs can significantly improve performance in two ways:

• Users receive content from data centers close to them
• Your servers do not have to serve requests that the CDN fulfills

Push CDNs receive new content whenever changes occur on your server. You take full responsibility for providing content, uploading directly to the CDN and rewriting URLs to point to the CDN. 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. You leave the content on your server and rewrite URLs to point to the CDN. 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.

• CDN costs could be significant depending on traffic, although this should be weighed with additional costs you would incur not using a CDN.
• Content might be stale if it is updated before the TTL expires it.
• CDNs require changing URLs for static content to point to the CDN.

• Globally distributed content delivery
• The differences between push and pull CDNs
• Wikipedia

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:

• Preventing requests from going to unhealthy servers
• Preventing overloading resources
• Helping to eliminate a single point of failure

Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.

Additional benefits include:

• SSL termination - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
  • Removes the need to install X.509 certificates on each server
• Session persistence - Issue cookies and route a specific client's requests to same instance if the web apps do not keep track of sessions

To protect against failures, it's common to set up multiple load balancers, either in active-passive or active-active mode.

Load balancers can route traffic based on various metrics, including:

• Random
• Least loaded
• Session/cookies
• Round robin or weighted round robin
• Layer 4
• Layer 7

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.

• Scaling horizontally introduces complexity and involves cloning servers
  • Servers should be stateless: they should not contain any user-related data like sessions or profile pictures
  • Sessions can be stored in a centralized data store such as a database (SQL, NoSQL) or a persistent cache (Redis, Memcached)
• Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out

• The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
• Introducing a load balancer to help eliminate a single point of failure results in increased complexity.
• A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.

• NGINX architecture
• HAProxy architecture guide
• Scalability
• Wikipedia
• Layer 4 load balancing
• Layer 7 load balancing
• ELB listener config

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.

Additional benefits include:

• Increased security - Hide information about backend servers, blacklist IPs, limit number of connections per client
• Increased scalability and flexibility - Clients only see the reverse proxy's IP, allowing you to scale servers or change their configuration
• SSL termination - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
  • Removes the need to install X.509 certificates on each server
• Compression - Compress server responses
• Caching - Return the response for cached requests
• Static content - Serve static content directly
  • HTML/CSS/JS
  • Photos
  • Videos
  • Etc

• Deploying a load balancer is useful when you have multiple servers. Often, load balancers route traffic to a set of servers serving the same function.
• Reverse proxies can be useful even with just one web server or application server, opening up the benefits described in the previous section.
• Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing.

• Introducing a reverse proxy results in increased complexity.
• A single reverse proxy is a single point of failure, configuring multiple reverse proxies (ie a failover) further increases complexity.

• Reverse proxy vs load balancer
• NGINX architecture
• HAProxy architecture guide
• Wikipedia

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. ¹

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.

• Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system).
• Microservices can add complexity in terms of deployments and operations.

• Intro to architecting systems for scale
• Crack the system design interview
• Service oriented architecture
• Introduction to Zookeeper
• Here's what you need to know about building microservices

Source: Scaling up to your first 10 million users

A relational database like SQL is a collection of data items organized in tables.

ACID is a set of properties of relational database transactions.

• Atomicity - Each transaction is all or nothing
• Consistency - Any transaction will bring the database from one valid state to another
• Isolation - Executing transactions concurrently has the same results as if the transactions were executed serially
• Durability - Once a transaction has been committed, it will remain so

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

• Additional logic is needed to promote a slave to a master.
• See Disadvantage(s): replication for points related to both master-slave and master-master.

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.

Source: Scalability, availability, stability, patterns

• You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
• Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
• Conflict resolution comes more into play as more write nodes are added and as latency increases.
• See Disadvantage(s): replication for points related to both master-slave and master-master.

• There is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes.
• Writes are replayed to the read replicas. If there are a lot of writes, the read replicas can get bogged down with replaying writes and can't do as many reads.
• The more read slaves, the more you have to replicate, which leads to greater replication lag.
• On some systems, writing to the master can spawn multiple threads to write in parallel, whereas read replicas only support writing sequentially with a single thread.
• Replication adds more hardware and additional complexity.

• Scalability, availability, stability, patterns
• Multi-master replication

Source: Scaling up to your first 10 million users

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.

• Federation is not effective if your schema requires huge functions or tables.
• You'll need to update your application logic to determine which database to read and write.
• Joining data from two databases is more complex with a server link.
• Federation adds more hardware and additional complexity.

• Scaling up to your first 10 million users

Source: Scalability, availability, stability, patterns

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. Like federation, there is no single central master serializing writes, allowing you to write in parallel with increased throughput.

Common ways to shard a table of users is either through the user's last name initial or the user's geographic location.

• You'll need to update your application logic to work with shards, which could result in complex SQL queries.
• Data distribution can become lopsided in a shard. For example, a set of power users on a shard could result in increased load to that shard compared to others.
  • Rebalancing adds additional complexity. A sharding function based on consistent hashing can reduce the amount of transferred data.
• Joining data from multiple shards is more complex.
• Sharding adds more hardware and additional complexity.

• The coming of the shard
• Shard database architecture
• Consistent hashing

Denormalization attempts to improve read performance at the expense of some write performance. Redundant copies of the data are written in multiple tables to avoid expensive joins. Some RDBMS such as PostgreSQL and Oracle support materialized views which handle the work of storing redundant information and keeping redundant copies consistent.

Once data becomes distributed with techniques such as federation and sharding, managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins.

In most systems, reads can heavily outnumber writes 100:1 or even 1000:1. A read resulting in a complex database join can be very expensive, spending a significant amount of time on disk operations.

• Data is duplicated.
• Constraints can help redundant copies of information stay in sync, which increases complexity of the database design.
• A denormalized database under heavy write load might perform worse than its normalized counterpart.

• Denormalization

SQL tuning is a broad topic and many books have been written as reference.

It's important to benchmark and profile to simulate and uncover bottlenecks.

• Benchmark - Simulate high-load situations with tools such as ab.
• Profile - Enable tools such as the slow query log to help track performance issues.

Benchmarking and profiling might point you to the following optimizations.

• MySQL dumps to disk in contiguous blocks for fast access.
• Use CHAR instead of VARCHAR for fixed-length fields.
  • CHAR effectively allows for fast, random access, whereas with VARCHAR, you must find the end of a string before moving onto the next one.
• Use TEXT for large blocks of text such as blog posts. TEXT also allows for boolean searches. Using a TEXT field results in storing a pointer on disk that is used to locate the text block.
• Use INT for larger numbers up to 2^32 or 4 billion.
• Use DECIMAL for currency to avoid floating point representation errors.
• Avoid storing large BLOBS, store the location of where to get the object instead.
• VARCHAR(255) is the largest number of characters that can be counted in an 8 bit number, often maximizing the use of a byte in some RDBMS.
• Set the NOT NULL constraint where applicable to improve search performance.

• Columns that you are querying (SELECT, GROUP BY, ORDER BY, JOIN) could be faster with indices.
• Indices are usually represented as self-balancing B-tree that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.
• Placing an index can keep the data in memory, requiring more space.
• Writes could also be slower since the index also needs to be updated.
• When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices.

• Denormalize where performance demands it.

• Break up a table by putting hot spots in a separate table to help keep it in memory.

• In some cases, the