system-design-primer Documentation

1. Project Overview & Quickstart (donnemartin/system-design-primer) Copy Chapter

File: README.md Copy Chapter

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The System Design Primer

Motivation Copy Chapter

Learn how to design large-scale systems.

Prep for the system design interview.

Learn how to design large-scale systems Copy Section

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

System design is a broad topic. There are a vast number 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.

Learn from the open source community Copy Section

This is a continually updated, open source project.

Contributions are welcome!

Prep for the system design interview Copy Section

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

Anki flashcards Copy Chapter

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.

Coding Resource: Interactive Coding Challenges Copy Section

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

Contributing Copy Chapter

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.

Index of system design topics Copy Chapter

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

Study guide Copy Chapter

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 approach a system design interview question Copy Chapter

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.

Step 1: Outline use cases, constraints, and assumptions Copy Section

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?

Step 2: Create a high level design Copy Section

Outline a high level design with all important components.

• Sketch the main components and connections
• Justify your ideas

Step 3: Design core components Copy Section

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

Step 4: Scale the design Copy Section

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.

Back-of-the-envelope calculations Copy Section

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

Source(s) and further reading Copy Section

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

System design interview questions with solutions Copy Chapter

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

Solutions linked to content in the solutions/ folder.

Design Pastebin.com (or Bit.ly) Copy Section

View exercise and solution

Design the Twitter timeline and search (or Facebook feed and search) Copy Section

View exercise and solution

Design a web crawler Copy Section

View exercise and solution

Design Mint.com Copy Section

View exercise and solution

Design the data structures for a social network Copy Section

View exercise and solution

Design a key-value store for a search engine Copy Section

View exercise and solution

Design Amazon's sales ranking by category feature Copy Section

View exercise and solution

Design a system that scales to millions of users on AWS Copy Section

View exercise and solution

Object-oriented design interview questions with solutions Copy Chapter

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

System design topics: start here Copy Chapter

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.

Step 1: Review the scalability video lecture Copy Section

Scalability Lecture at Harvard

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

Step 2: Review the scalability article Copy Section

Scalability

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

Next steps Copy Section

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.

Performance vs scalability Copy Chapter

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.

Source(s) and further reading Copy Section

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

Latency vs throughput Copy Chapter

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.

Source(s) and further reading Copy Section

• Understanding latency vs throughput

Availability vs consistency Copy Chapter

CAP theorem Copy Section

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.

CP - consistency and partition tolerance

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.

AP - availability and partition tolerance

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.

Source(s) and further reading Copy Section

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

Consistency patterns Copy Chapter

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.

Weak consistency Copy Section

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.

Eventual consistency Copy Section

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 Copy Section

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.

Source(s) and further reading Copy Section

• Transactions across data centers

Availability patterns Copy Chapter

There are two complementary patterns to support high availability: fail-over and replication.

Fail-over Copy Section

Active-passive

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.

Active-active

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.

Disadvantage(s): failover Copy Section

• 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.

Replication Copy Section

Master-slave and master-master

This topic is further discussed in the Database section:

• Master-slave replication
• Master-master replication

Availability in numbers Copy Section

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.

99.9% availability - three 9s

99.99% availability - four 9s

Availability in parallel vs in sequence

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.

In sequence

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%.

In parallel

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%.

Domain name system Copy Chapter

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

Disadvantage(s): DNS Copy Section

• 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).

Source(s) and further reading Copy Section

• DNS architecture
• Wikipedia
• DNS articles

Content delivery network Copy Chapter

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 Copy Section

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 Copy Section

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.

Disadvantage(s): CDN Copy Section

• 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.

Source(s) and further reading Copy Section

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

Load balancer Copy Chapter

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 balancing Copy Section

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 balancing Copy Section

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.

Horizontal scaling Copy Section

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.

Disadvantage(s): horizontal scaling

• 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

Disadvantage(s): load balancer Copy Section

• 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.

Source(s) and further reading Copy Section

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

Reverse proxy (web server) Copy Chapter

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

Load balancer vs reverse proxy Copy Section

• 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.

Disadvantage(s): reverse proxy Copy Section

• 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.

Source(s) and further reading Copy Section

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

Application layer Copy Chapter

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.

Microservices Copy Section

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.

Service Discovery Copy Section

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.

Disadvantage(s): application layer Copy Section

• 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.

Source(s) and further reading Copy Section

• 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

Database Copy Chapter

Relational database management system (RDBMS) Copy Section

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.

Master-slave replication

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.

Disadvantage(s): master-slave replication

• 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.

Master-master replication

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.

Disadvantage(s): master-master replication

• 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.

Disadvantage(s): replication

• 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.

Source(s) and further reading: replication

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

Federation

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.

Disadvantage(s): federation

• 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.

Source(s) and further reading: federation

• Scaling up to your first 10 million users

Sharding

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.

Disadvantage(s): sharding

• 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.

Source(s) and further reading: sharding

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

Denormalization

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.

Disadvantage(s): denormalization

• 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.

Source(s) and further reading: denormalization

• Denormalization

SQL tuning

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.

Tighten up the schema

• 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.

Use good indices

• 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.

Avoid expensive joins

• Denormalize where performance demands it.

Partition tables

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

Tune the query cache

• In some cases, the query cache could lead to performance issues.

Source(s) and further reading: SQL tuning

• Tips for optimizing MySQL queries
• Is there a good reason i see VARCHAR(255) used so often?
• How do null values affect performance?
• Slow query log

NoSQL Copy Section

NoSQL is a collection of data items represented in a key-value store, document store, wide column store, or a graph database. Data is denormalized, and joins are generally done in the application code. Most NoSQL stores lack true ACID transactions and favor eventual consistency.

BASE is often used to describe the properties of NoSQL databases. In comparison with the CAP Theorem, BASE chooses availability over consistency.

• Basically available - the system guarantees availability.
• Soft state - the state of the system may change over time, even without input.
• Eventual consistency - the system will become consistent over a period of time, given that the system doesn't receive input during that period.

In addition to choosing between SQL or NoSQL, it is helpful to understand which type of NoSQL database best fits your use case(s). We'll review key-value stores, document stores, wide column stores, and graph databases in the next section.

Key-value store

Abstraction: hash table

A key-value store generally allows for O(1) reads and writes and is often backed by memory or SSD. Data stores can maintain keys in lexicographic order, allowing efficient retrieval of key ranges. Key-value stores can allow for storing of metadata with a value.

Key-value stores provide high performance and are often used for simple data models or for rapidly-changing data, such as an in-memory cache layer. Since they offer only a limited set of operations, complexity is shifted to the application layer if additional operations are needed.

A key-value store is the basis for more complex systems such as a document store, and in some cases, a graph database.

Source(s) and further reading: key-value store

• Key-value database
• Disadvantages of key-value stores
• Redis architecture
• Memcached architecture

Document store

Abstraction: key-value store with documents stored as values

A document store is centered around documents (XML, JSON, binary, etc), where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types.

Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.

Some document stores like MongoDB and CouchDB also provide a SQL-like language to perform complex queries. DynamoDB supports both key-values and documents.

Document stores provide high flexibility and are often used for working with occasionally changing data.

Source(s) and further reading: document store

• Document-oriented database
• MongoDB architecture
• CouchDB architecture
• Elasticsearch architecture

Wide column store

Abstraction: nested map ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>

A wide column store's basic unit of data is a column (name/value pair). A column can be grouped in column families (analogous to a SQL table). Super column families further group column families. You can access each column independently with a row key, and columns with the same row key form a row. Each value contains a timestamp for versioning and for conflict resolution.

Google introduced Bigtable as the first wide column store, which influenced the open-source HBase often-used in the Hadoop ecosystem, and Cassandra from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges.

Wide column stores offer high availability and high scalability. They are often used for very large data sets.

Source(s) and further reading: wide column store

• SQL & NoSQL, a brief history
• Bigtable architecture
• HBase architecture
• Cassandra architecture

Graph database

Abstraction: graph

In a graph database, each node is a record and each arc is a relationship between two nodes. Graph databases are optimized to represent complex relationships with many foreign keys or many-to-many relationships.

Graphs databases offer high performance for data models with complex relationships, such as a social network. They are relatively new and are not yet widely-used; it might be more difficult to find development tools and resources. Many graphs can only be accessed with REST APIs.

Source(s) and further reading: graph

• Graph database
• Neo4j
• FlockDB

Source(s) and further reading: NoSQL

• Explanation of base terminology
• NoSQL databases a survey and decision guidance
• Scalability
• Introduction to NoSQL
• NoSQL patterns

SQL or NoSQL Copy Section

Reasons for SQL:

• Structured data
• Strict schema
• Relational data
• Need for complex joins
• Transactions
• Clear patterns for scaling
• More established: developers, community, code, tools, etc
• Lookups by index are very fast

Reasons for NoSQL:

• Semi-structured data
• Dynamic or flexible schema
• Non-relational data
• No need for complex joins
• Store many TB (or PB) of data
• Very data intensive workload
• Very high throughput for IOPS

Sample data well-suited for NoSQL:

• Rapid ingest of clickstream and log data
• Leaderboard or scoring data
• Temporary data, such as a shopping cart
• Frequently accessed ('hot') tables
• Metadata/lookup tables

Source(s) and further reading: SQL or NoSQL

• Scaling up to your first 10 million users
• SQL vs NoSQL differences

Cache Copy Chapter

Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution.

Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic.

Client caching Copy Section

Caches can be located on the client side (OS or browser), server side, or in a distinct cache layer.

CDN caching Copy Section

CDNs are considered a type of cache.

Web server caching Copy Section

Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.

Database caching Copy Section

Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance.

Application caching Copy Section

In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so cache invalidation algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM.

Redis has the following additional features:

• Persistence option
• Built-in data structures such as sorted sets and lists

There are multiple levels you can cache that fall into two general categories: database queries and objects:

• Row level
• Query-level
• Fully-formed serializable objects
• Fully-rendered HTML

Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.

Caching at the database query level Copy Section

Whenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues:

• Hard to delete a cached result with complex queries
• If one piece of data changes such as a table cell, you need to delete all cached queries that might include the changed cell

Caching at the object level Copy Section

See your data as an object, similar to what you do with your application code. Have your application assemble the dataset from the database into a class instance or a data structure(s):

• Remove the object from cache if its underlying data has changed
• Allows for asynchronous processing: workers assemble objects by consuming the latest cached object

Suggestions of what to cache:

• User sessions
• Fully rendered web pages
• Activity streams
• User graph data

When to update the cache Copy Section

Since you can only store a limited amount of data in cache, you'll need to determine which cache update strategy works best for your use case.

Cache-aside

The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:

• Look for entry in cache, resulting in a cache miss
• Load entry from the database
• Add entry to cache
• Return entry

def get_user(self, user_id):
    user = cache.get("user.{0}", user_id)
    if user is None:
        user = db.query("SELECT * FROM users WHERE user_id = {0}", user_id)
        if user is not None:
            key = "user.{0}".format(user_id)
            cache.set(key, json.dumps(user))
    return user

Memcached is generally used in this manner.

Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.

Disadvantage(s): cache-aside

• Each cache miss results in three trips, which can cause a noticeable delay.
• Data can become stale if it is updated in the database. This issue is mitigated by setting a time-to-live (TTL) which forces an update of the cache entry, or by using write-through.
• When a node fails, it is replaced by a new, empty node, increasing latency.

Write-through

The application uses the cache as the main data store, reading and writing data to it, while the cache is responsible for reading and writing to the database:

• Application adds/updates entry in cache
• Cache synchronously writes entry to data store
• Return

Application code:

set_user(12345, {"foo":"bar"})

Cache code:

def set_user(user_id, values):
    user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
    cache.set(user_id, user)

Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale.

Disadvantage(s): write through

• When a new node is created due to failure or scaling, the new node will not cache entries until the entry is updated in the database. Cache-aside in conjunction with write through can mitigate this issue.
• Most data written might never be read, which can be minimized with a TTL.

Write-behind (write-back)

In write-behind, the application does the following:

• Add/update entry in cache
• Asynchronously write entry to the data store, improving write performance

Disadvantage(s): write-behind

• There could be data loss if the cache goes down prior to its contents hitting the data store.
• It is more complex to implement write-behind than it is to implement cache-aside or write-through.

Refresh-ahead

You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration.

Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.

Disadvantage(s): refresh-ahead

• Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.

Disadvantage(s): cache Copy Section

• Need to maintain consistency between caches and the source of truth such as the database through cache invalidation.
• Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
• Need to make application changes such as adding Redis or memcached.

Source(s) and further reading Copy Section

• From cache to in-memory data grid
• Scalable system design patterns
• Introduction to architecting systems for scale
• Scalability, availability, stability, patterns
• Scalability
• AWS ElastiCache strategies
• Wikipedia

Asynchronism Copy Chapter

Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data.

Message queues Copy Section

Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow:

• An application publishes a job to the queue, then notifies the user of job status
• A worker picks up the job from the queue, processes it, then signals the job is complete

The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.

Redis is useful as a simple message broker but messages can be lost.

RabbitMQ is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.

Amazon SQS is hosted but can have high latency and has the possibility of messages being delivered twice.

Task queues Copy Section

Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.

Celery has support for scheduling and primarily has python support.

Back pressure Copy Section

If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. Back pressure can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with exponential backoff.

Disadvantage(s): asynchronism Copy Section

• Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.

Source(s) and further reading Copy Section

• It's all a numbers game
• Applying back pressure when overloaded
• Little's law
• What is the difference between a message queue and a task queue?

Communication Copy Chapter

Hypertext transfer protocol (HTTP) Copy Section

HTTP is a method for encoding and transporting data between a client and a server. It is a request/response protocol: clients issue requests and servers issue responses with relevant content and completion status info about the request. HTTP is self-contained, allowing requests and responses to flow through many intermediate routers and servers that perform load balancing, caching, encryption, and compression.

A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs:

*Can be called many times without different outcomes.

HTTP is an application layer protocol relying on lower-level protocols such as TCP and UDP.

Source(s) and further reading: HTTP

• What is HTTP?
• Difference between HTTP and TCP
• Difference between PUT and PATCH

Transmission control protocol (TCP) Copy Section

TCP is a connection-oriented protocol over an IP network. Connection is established and terminated using a handshake. All packets sent are guaranteed to reach the destination in the original order and without corruption through:

• Sequence numbers and checksum fields for each packet
• Acknowledgement packets and automatic retransmission

If the sender does not receive a correct response, it will resend the packets. If there are multiple timeouts, the connection is dropped. TCP also implements flow control and congestion control. These guarantees cause delays and generally result in less efficient transmission than UDP.

To ensure high throughput, web servers can keep a large number of TCP connections open, resulting in high memory usage. It can be expensive to have a large number of open connections between web server threads and say, a memcached server. Connection pooling can help in addition to switching to UDP where applicable.

TCP is useful for applications that require high reliability but are less time critical. Some examples include web servers, database info, SMTP, FTP, and SSH.

Use TCP over UDP when:

• You need all of the data to arrive intact
• You want to automatically make a best estimate use of the network throughput

User datagram protocol (UDP) Copy Section

UDP is connectionless. Datagrams (analogous to packets) are guaranteed only at the datagram level. Datagrams might reach their destination out of order or not at all. UDP does not support congestion control. Without the guarantees that TCP support, UDP is generally more efficient.

UDP can broadcast, sending datagrams to all devices on the subnet. This is useful with DHCP because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address.

UDP is less reliable but works well in real time use cases such as VoIP, video chat, streaming, and realtime multiplayer games.

Use UDP over TCP when:

• You need the lowest latency
• Late data is worse than loss of data
• You want to implement your own error correction

Source(s) and further reading: TCP and UDP

• Networking for game programming
• Key differences between TCP and UDP protocols
• Difference between TCP and UDP
• Transmission control protocol
• User datagram protocol
• Scaling memcache at Facebook

Remote procedure call (RPC) Copy Section

In an RPC, a client causes a procedure to execute on a different address space, usually a remote server. The procedure is coded as if it were a local procedure call, abstracting away the details of how to communicate with the server from the client program. Remote calls are usually slower and less reliable than local calls so it is helpful to distinguish RPC calls from local calls. Popular RPC frameworks include Protobuf, Thrift, and Avro.

RPC is a request-response protocol:

• Client program - Calls the client stub procedure. The parameters are pushed onto the stack like a local procedure call.
• Client stub procedure - Marshals (packs) procedure id and arguments into a request message.
• Client communication module - OS sends the message from the client to the server.
• Server communication module - OS passes the incoming packets to the server stub procedure.
• Server stub procedure - Unmarshalls the results, calls the server procedure matching the procedure id and passes the given arguments.
• The server response repeats the steps above in reverse order.

Sample RPC calls:

GET /someoperation?data=anId

POST /anotheroperation
{
  "data":"anId";
  "anotherdata": "another value"
}

RPC is focused on exposing behaviors. RPCs are often used for performance reasons with internal communications, as you can hand-craft native calls to better fit your use cases.

Choose a native library (aka SDK) when:

• You know your target platform.
• You want to control how your "logic" is accessed.
• You want to control how error control happens off your library.
• Performance and end user experience is your primary concern.

HTTP APIs following REST tend to be used more often for public APIs.

Disadvantage(s): RPC

• RPC clients become tightly coupled to the service implementation.
• A new API must be defined for every new operation or use case.
• It can be difficult to debug RPC.
• You might not be able to leverage existing technologies out of the box. For example, it might require additional effort to ensure RPC calls are properly cached on caching servers such as Squid.

Representational state transfer (REST) Copy Section

REST is an architectural style enforcing a client/server model where the client acts on a set of resources managed by the server. The server provides a representation of resources and actions that can either manipulate or get a new representation of resources. All communication must be stateless and cacheable.

There are four qualities of a RESTful interface:

• Identify resources (URI in HTTP) - use the same URI regardless of any operation.
• Change with representations (Verbs in HTTP) - use verbs, headers, and body.
• Self-descriptive error message (status response in HTTP) - Use status codes, don't reinvent the wheel.
• HATEOAS (HTML interface for HTTP) - your web service should be fully accessible in a browser.

Sample REST calls:

GET /someresources/anId

PUT /someresources/anId
{"anotherdata": "another value"}

REST is focused on exposing data. It minimizes the coupling between client/server and is often used for public HTTP APIs. REST uses a more generic and uniform method of exposing resources through URIs, representation through headers, and actions through verbs such as GET, POST, PUT, DELETE, and PATCH. Being stateless, REST is great for horizontal scaling and partitioning.

Disadvantage(s): REST

• With REST being focused on exposing data, it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy. For example, returning all updated records from the past hour matching a particular set of events is not easily expressed as a path. With REST, it is likely to be implemented with a combination of URI path, query parameters, and possibly the request body.
• REST typically relies on a few verbs (GET, POST, PUT, DELETE, and PATCH) which sometimes doesn't fit your use case. For example, moving expired documents to the archive folder might not cleanly fit within these verbs.
• Fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views, e.g. fetching content of a blog entry and the comments on that entry. For mobile applications operating in variable network conditions, these multiple roundtrips are highly undesirable.
• Over time, more fields might be added to an API response and older clients will receive all new data fields, even those that they do not need, as a result, it bloats the payload size and leads to larger latencies.

RPC and REST calls comparison Copy Section

Source(s) and further reading: REST and RPC

• Do you really know why you prefer REST over RPC
• When are RPC-ish approaches more appropriate than REST?
• REST vs JSON-RPC
• Debunking the myths of RPC and REST
• What are the drawbacks of using REST
• Crack the system design interview
• Thrift
• Why REST for internal use and not RPC

Security Copy Chapter

This section could use some updates. Consider contributing!

Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics:

• Encrypt in transit and at rest.
• Sanitize all user inputs or any input parameters exposed to user to prevent XSS and SQL injection.
• Use parameterized queries to prevent SQL injection.
• Use the principle of least privilege.

Source(s) and further reading Copy Section

• API security checklist
• Security guide for developers
• OWASP top ten

Appendix Copy Chapter

You'll sometimes be asked to do 'back-of-the-envelope' estimates. For example, you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take. The Powers of two table and Latency numbers every programmer should know are handy references.

Powers of two table Copy Section

Power           Exact Value         Approx Value        Bytes
---------------------------------------------------------------
7                             128
8                             256
10                           1024   1 thousand           1 KB
16                         65,536                       64 KB
20                      1,048,576   1 million            1 MB
30                  1,073,741,824   1 billion            1 GB
32                  4,294,967,296                        4 GB
40              1,099,511,627,776   1 trillion           1 TB

Source(s) and further reading

• Powers of two

Latency numbers every programmer should know Copy Section

Latency Comparison Numbers
--------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy            10,000   ns       10 us
Send 1 KB bytes over 1 Gbps network     10,000   ns       10 us
Read 4 KB randomly from SSD*           150,000   ns      150 us          ~1GB/sec SSD
Read 1 MB sequentially from memory     250,000   ns      250 us
Round trip within same datacenter      500,000   ns      500 us
Read 1 MB sequentially from SSD*     1,000,000   ns    1,000 us    1 ms  ~1GB/sec SSD, 4X memory
HDD seek                            10,000,000   ns   10,000 us   10 ms  20x datacenter roundtrip
Read 1 MB sequentially from 1 Gbps  10,000,000   ns   10,000 us   10 ms  40x memory, 10X SSD
Read 1 MB sequentially from HDD     30,000,000   ns   30,000 us   30 ms 120x memory, 30X SSD
Send packet CA->Netherlands->CA    150,000,000   ns  150,000 us  150 ms

Notes
-----
1 ns = 10^-9 seconds
1 us = 10^-6 seconds = 1,000 ns
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns

Handy metrics based on numbers above:

• Read sequentially from HDD at 30 MB/s
• Read sequentially from 1 Gbps Ethernet at 100 MB/s
• Read sequentially from SSD at 1 GB/s
• Read sequentially from main memory at 4 GB/s
• 6-7 world-wide round trips per second
• 2,000 round trips per second within a data center

Latency numbers visualized

Source(s) and further reading

• Latency numbers every programmer should know - 1
• Latency numbers every programmer should know - 2
• Designs, lessons, and advice from building large distributed systems
• Software Engineering Advice from Building Large-Scale Distributed Systems

Additional system design interview questions Copy Section

Common system design interview questions, with links to resources on how to solve each.

Real world architectures Copy Section

Articles on how real world systems are designed.

Don't focus on nitty gritty details for the following articles, instead:

• Identify shared principles, common technologies, and patterns within these articles
• Study what problems are solved by each component, where it works, where it doesn't
• Review the lessons learned

Company architectures Copy Section

Company engineering blogs Copy Section

Architectures for companies you are interviewing with.

Questions you encounter might be from the same domain.

• Airbnb Engineering
• Atlassian Developers
• AWS Blog
• Bitly Engineering Blog
• Box Blogs
• Cloudera Developer Blog
• Dropbox Tech Blog
• Engineering at Quora
• Ebay Tech Blog
• Evernote Tech Blog
• Etsy Code as Craft
• Facebook Engineering
• Flickr Code
• Foursquare Engineering Blog
• GitHub Engineering Blog
• Google Research Blog
• Groupon Engineering Blog
• Heroku Engineering Blog
• Hubspot Engineering Blog
• High Scalability
• Instagram Engineering
• Intel Software Blog
• Jane Street Tech Blog
• LinkedIn Engineering
• Microsoft Engineering
• Microsoft Python Engineering
• Netflix Tech Blog
• Paypal Developer Blog
• Pinterest Engineering Blog
• Reddit Blog
• Salesforce Engineering Blog
• Slack Engineering Blog
• Spotify Labs
• Stripe Engineering Blog
• Twilio Engineering Blog
• Twitter Engineering
• Uber Engineering Blog
• Yahoo Engineering Blog
• Yelp Engineering Blog
• Zynga Engineering Blog

Source(s) and further reading

Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo:

• kilimchoi/engineering-blogs

Under development Copy Chapter

Interested in adding a section or helping complete one in-progress? Contribute!

• Distributed computing with MapReduce
• Consistent hashing
• Scatter gather
• Contribute

Credits Copy Chapter

Credits and sources are provided throughout this repo.

Special thanks to:

• Hired in tech
• Cracking the coding interview
• High scalability
• checkcheckzz/system-design-interview
• shashank88/system_design
• mmcgrana/services-engineering
• System design cheat sheet
• A distributed systems reading list
• Cracking the system design interview

Contact info Copy Chapter

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page.

License Copy Chapter

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

Copyright 2017 Donne Martin

Creative Commons Attribution 4.0 International License (CC BY 4.0)

http://creativecommons.org/licenses/by/4.0/

File: solutions/system_design/web_crawler/README.md Copy Chapter

Design a web crawler

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

We'll scope the problem to handle only the following use cases

• Service crawls a list of urls:
  • Generates reverse index of words to pages containing the search terms
  • Generates titles and snippets for pages
    • Title and snippets are static, they do not change based on search query
• User inputs a search term and sees a list of relevant pages with titles and snippets the crawler generated
  • Only sketch high level components and interactions for this use case, no need to go into depth
• Service has high availability

Out of scope

• Search analytics
• Personalized search results
• Page rank

Constraints and assumptions Copy Section

State assumptions

• Traffic is not evenly distributed
  • Some searches are very popular, while others are only executed once
• Support only anonymous users
• Generating search results should be fast
• The web crawler should not get stuck in an infinite loop
  • We get stuck in an infinite loop if the graph contains a cycle
• 1 billion links to crawl
  • Pages need to be crawled regularly to ensure freshness
  • Average refresh rate of about once per week, more frequent for popular sites
    • 4 billion links crawled each month
  • Average stored size per web page: 500 KB
    • For simplicity, count changes the same as new pages
• 100 billion searches per month

Exercise the use of more traditional systems - don't use existing systems such as solr or nutch.

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• 2 PB of stored page content per month
  • 500 KB per page * 4 billion links crawled per month
  • 72 PB of stored page content in 3 years
• 1,600 write requests per second
• 40,000 search requests per second

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: Service crawls a list of urls Copy Section

We'll assume we have an initial list of links_to_crawl ranked initially based on overall site popularity. If this is not a reasonable assumption, we can seed the crawler with popular sites that link to outside content such as Yahoo, DMOZ, etc.

We'll use a table crawled_links to store processed links and their page signatures.

We could store links_to_crawl and crawled_links in a key-value NoSQL Database. For the ranked links in links_to_crawl, we could use Redis with sorted sets to maintain a ranking of page links. We should discuss the use cases and tradeoffs between choosing SQL or NoSQL.

• The Crawler Service processes each page link by doing the following in a loop:
  • Takes the top ranked page link to crawl
    • Checks crawled_links in the NoSQL Database for an entry with a similar page signature
      • If we have a similar page, reduces the priority of the page link
        • This prevents us from getting into a cycle
        • Continue
      • Else, crawls the link
        • Adds a job to the Reverse Index Service queue to generate a reverse index
        • Adds a job to the Document Service queue to generate a static title and snippet
        • Generates the page signature
        • Removes the link from links_to_crawl in the NoSQL Database
        • Inserts the page link and signature to crawled_links in the NoSQL Database

Clarify with your interviewer how much code you are expected to write.

PagesDataStore is an abstraction within the Crawler Service that uses the NoSQL Database:

class PagesDataStore(object):

    def __init__(self, db);
        self.db = db
        ...

    def add_link_to_crawl(self, url):
        """Add the given link to `links_to_crawl`."""
        ...

    def remove_link_to_crawl(self, url):
        """Remove the given link from `links_to_crawl`."""
        ...

    def reduce_priority_link_to_crawl(self, url)
        """Reduce the priority of a link in `links_to_crawl` to avoid cycles."""
        ...

    def extract_max_priority_page(self):
        """Return the highest priority link in `links_to_crawl`."""
        ...

    def insert_crawled_link(self, url, signature):
        """Add the given link to `crawled_links`."""
        ...

    def crawled_similar(self, signature):
        """Determine if we've already crawled a page matching the given signature"""
        ...

Page is an abstraction within the Crawler Service that encapsulates a page, its contents, child urls, and signature:

class Page(object):

    def __init__(self, url, contents, child_urls, signature):
        self.url = url
        self.contents = contents
        self.child_urls = child_urls
        self.signature = signature

Crawler is the main class within Crawler Service, composed of Page and PagesDataStore.

class Crawler(object):

    def __init__(self, data_store, reverse_index_queue, doc_index_queue):
        self.data_store = data_store
        self.reverse_index_queue = reverse_index_queue
        self.doc_index_queue = doc_index_queue

    def create_signature(self, page):
        """Create signature based on url and contents."""
        ...

    def crawl_page(self, page):
        for url in page.child_urls:
            self.data_store.add_link_to_crawl(url)
        page.signature = self.create_signature(page)
        self.data_store.remove_link_to_crawl(page.url)
        self.data_store.insert_crawled_link(page.url, page.signature)

    def crawl(self):
        while True:
            page = self.data_store.extract_max_priority_page()
            if page is None:
                break
            if self.data_store.crawled_similar(page.signature):
                self.data_store.reduce_priority_link_to_crawl(page.url)
            else:
                self.crawl_page(page)

Handling duplicates Copy Section

We need to be careful the web crawler doesn't get stuck in an infinite loop, which happens when the graph contains a cycle.

Clarify with your interviewer how much code you are expected to write.

We'll want to remove duplicate urls:

• For smaller lists we could use something like sort | unique
• With 1 billion links to crawl, we could use MapReduce to output only entries that have a frequency of 1

class RemoveDuplicateUrls(MRJob):

    def mapper(self, _, line):
        yield line, 1

    def reducer(self, key, values):
        total = sum(values)
        if total == 1:
            yield key, total

Detecting duplicate content is more complex. We could generate a signature based on the contents of the page and compare those two signatures for similarity. Some potential algorithms are Jaccard index and cosine similarity.

Determining when to update the crawl results Copy Section

Pages need to be crawled regularly to ensure freshness. Crawl results could have a timestamp field that indicates the last time a page was crawled. After a default time period, say one week, all pages should be refreshed. Frequently updated or more popular sites could be refreshed in shorter intervals.

Although we won't dive into details on analytics, we could do some data mining to determine the mean time before a particular page is updated, and use that statistic to determine how often to re-crawl the page.

We might also choose to support a Robots.txt file that gives webmasters control of crawl frequency.

Use case: User inputs a search term and sees a list of relevant pages with titles and snippets Copy Section

• The Client sends a request to the Web Server, running as a reverse proxy
• The Web Server forwards the request to the Query API server
• The Query API server does the following:
  • Parses the query
    • Removes markup
    • Breaks up the text into terms
    • Fixes typos
    • Normalizes capitalization
    • Converts the query to use boolean operations
  • Uses the Reverse Index Service to find documents matching the query
    • The Reverse Index Service ranks the matching results and returns the top ones
  • Uses the Document Service to return titles and snippets

We'll use a public REST API:

$ curl https://search.com/api/v1/search?query=hello+world

Response:

{
    "title": "foo's title",
    "snippet": "foo's snippet",
    "link": "https://foo.com",
},
{
    "title": "bar's title",
    "snippet": "bar's snippet",
    "link": "https://bar.com",
},
{
    "title": "baz's title",
    "snippet": "baz's snippet",
    "link": "https://baz.com",
},

For internal communications, we could use Remote Procedure Calls.

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Important: Do not simply jump right into the final design from the initial design!

State you would 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See Design a system that scales to millions of users on AWS as a sample on how to iteratively scale the initial design.

It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a Load Balancer with multiple Web Servers? CDN? Master-Slave Replicas? What are the alternatives and Trade-Offs for each?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

• DNS
• Load balancer
• Horizontal scaling
• Web server (reverse proxy)
• API server (application layer)
• Cache
• NoSQL
• Consistency patterns
• Availability patterns

Some searches are very popular, while others are only executed once. Popular queries can be served from a Memory Cache such as Redis or Memcached to reduce response times and to avoid overloading the Reverse Index Service and Document Service. The Memory Cache is also useful for handling the unevenly distributed traffic and traffic spikes. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹

Below are a few other optimizations to the Crawling Service:

• To handle the data size and request load, the Reverse Index Service and Document Service will likely need to make heavy use sharding and federation.
• DNS lookup can be a bottleneck, the Crawler Service can keep its own DNS lookup that is refreshed periodically
• The Crawler Service can improve performance and reduce memory usage by keeping many open connections at a time, referred to as connection pooling
  • Switching to UDP could also boost performance
• Web crawling is bandwidth intensive, ensure there is enough bandwidth to sustain high throughput

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

SQL scaling patterns Copy Section

• Read replicas
• Federation
• Sharding
• Denormalization
• SQL Tuning

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

File: solutions/system_design/twitter/README.md Copy Chapter

Design the Twitter timeline and search

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Design the Facebook feed and Design Facebook search are similar questions.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

We'll scope the problem to handle only the following use cases

• User posts a tweet
  • Service pushes tweets to followers, sending push notifications and emails
• User views the user timeline (activity from the user)
• User views the home timeline (activity from people the user is following)
• User searches keywords
• Service has high availability

Out of scope

• Service pushes tweets to the Twitter Firehose and other streams
• Service strips out tweets based on users' visibility settings
  • Hide @reply if the user is not also following the person being replied to
  • Respect 'hide retweets' setting
• Analytics

Constraints and assumptions Copy Section

State assumptions

General

• Traffic is not evenly distributed
• Posting a tweet should be fast
  • Fanning out a tweet to all of your followers should be fast, unless you have millions of followers
• 100 million active users
• 500 million tweets per day or 15 billion tweets per month
  • Each tweet averages a fanout of 10 deliveries
  • 5 billion total tweets delivered on fanout per day
  • 150 billion tweets delivered on fanout per month
• 250 billion read requests per month
• 10 billion searches per month

Timeline

• Viewing the timeline should be fast
• Twitter is more read heavy than write heavy
  • Optimize for fast reads of tweets
• Ingesting tweets is write heavy

Search

• Searching should be fast
• Search is read-heavy

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• Size per tweet:
  • tweet_id - 8 bytes
  • user_id - 32 bytes
  • text - 140 bytes
  • media - 10 KB average
  • Total: ~10 KB
• 150 TB of new tweet content per month
  • 10 KB per tweet * 500 million tweets per day * 30 days per month
  • 5.4 PB of new tweet content in 3 years
• 100 thousand read requests per second
  • 250 billion read requests per month * (400 requests per second / 1 billion requests per month)
• 6,000 tweets per second
  • 15 billion tweets per month * (400 requests per second / 1 billion requests per month)
• 60 thousand tweets delivered on fanout per second
  • 150 billion tweets delivered on fanout per month * (400 requests per second / 1 billion requests per month)
• 4,000 search requests per second
  • 10 billion searches per month * (400 requests per second / 1 billion requests per month)

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: User posts a tweet Copy Section

We could store the user's own tweets to populate the user timeline (activity from the user) in a relational database. We should discuss the use cases and tradeoffs between choosing SQL or NoSQL.

Delivering tweets and building the home timeline (activity from people the user is following) is trickier. Fanning out tweets to all followers (60 thousand tweets delivered on fanout per second) will overload a traditional relational database. We'll probably want to choose a data store with fast writes such as a NoSQL database or Memory Cache. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹

We could store media such as photos or videos on an Object Store.

• The Client posts a tweet to the Web Server, running as a reverse proxy
• The Web Server forwards the request to the Write API server
• The Write API stores the tweet in the user's timeline on a SQL database
• The Write API contacts the Fan Out Service, which does the following:
  • Queries the User Graph Service to find the user's followers stored in the Memory Cache
  • Stores the tweet in the home timeline of the user's followers in a Memory Cache
    • O(n) operation: 1,000 followers = 1,000 lookups and inserts
  • Stores the tweet in the Search Index Service to enable fast searching
  • Stores media in the Object Store
  • Uses the Notification Service to send out push notifications to followers:
    • Uses a Queue (not pictured) to asynchronously send out notifications

Clarify with your interviewer how much code you are expected to write.

If our Memory Cache is Redis, we could use a native Redis list with the following structure:

tweet n+2                   tweet n+1                   tweet n
| 8 bytes   8 bytes  1 byte | 8 bytes   8 bytes  1 byte | 8 bytes   8 bytes  1 byte |
| tweet_id  user_id  meta   | tweet_id  user_id  meta   | tweet_id  user_id  meta   |

The new tweet would be placed in the Memory Cache, which populates the user's home timeline (activity from people the user is following).

We'll use a public REST API:

$ curl -X POST --data '{ "user_id": "123", "auth_token": "ABC123", \
    "status": "hello world!", "media_ids": "ABC987" }' \
    https://twitter.com/api/v1/tweet

Response:

{
    "created_at": "Wed Sep 05 00:37:15 +0000 2012",
    "status": "hello world!",
    "tweet_id": "987",
    "user_id": "123",
    ...
}

For internal communications, we could use Remote Procedure Calls.

Use case: User views the home timeline Copy Section

• The Client posts a home timeline request to the Web Server
• The Web Server forwards the request to the Read API server
• The Read API server contacts the Timeline Service, which does the following:
  • Gets the timeline data stored in the Memory Cache, containing tweet ids and user ids - O(1)
  • Queries the Tweet Info Service with a multiget to obtain additional info about the tweet ids - O(n)
  • Queries the User Info Service with a multiget to obtain additional info about the user ids - O(n)

REST API:

$ curl https://twitter.com/api/v1/home_timeline?user_id=123

Response:

{
    "user_id": "456",
    "tweet_id": "123",
    "status": "foo"
},
{
    "user_id": "789",
    "tweet_id": "456",
    "status": "bar"
},
{
    "user_id": "789",
    "tweet_id": "579",
    "status": "baz"
},

Use case: User views the user timeline Copy Section

• The Client posts a user timeline request to the Web Server
• The Web Server forwards the request to the Read API server
• The Read API retrieves the user timeline from the SQL Database

The REST API would be similar to the home timeline, except all tweets would come from the user as opposed to the people the user is following.

Use case: User searches keywords Copy Section

• The Client sends a search request to the Web Server
• The Web Server forwards the request to the Search API server
• The Search API contacts the Search Service, which does the following:
  • Parses/tokenizes the input query, determining what needs to be searched
    • Removes markup
    • Breaks up the text into terms
    • Fixes typos
    • Normalizes capitalization
    • Converts the query to use boolean operations
  • Queries the Search Cluster (ie Lucene) for the results:
    • Scatter gathers each server in the cluster to determine if there are any results for the query
    • Merges, ranks, sorts, and returns the results

REST API:

$ curl https://twitter.com/api/v1/search?query=hello+world

The response would be similar to that of the home timeline, except for tweets matching the given query.

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Important: Do not simply jump right into the final design from the initial design!

State you would 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See Design a system that scales to millions of users on AWS as a sample on how to iteratively scale the initial design.

It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a Load Balancer with multiple Web Servers? CDN? Master-Slave Replicas? What are the alternatives and Trade-Offs for each?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

• DNS
• CDN
• Load balancer
• Horizontal scaling
• Web server (reverse proxy)
• API server (application layer)
• Cache
• Relational database management system (RDBMS)
• SQL write master-slave failover
• Master-slave replication
• Consistency patterns
• Availability patterns

The Fanout Service is a potential bottleneck. Twitter users with millions of followers could take several minutes to have their tweets go through the fanout process. This could lead to race conditions with @replies to the tweet, which we could mitigate by re-ordering the tweets at serve time.

We could also avoid fanning out tweets from highly-followed users. Instead, we could search to find tweets for highly-followed users, merge the search results with the user's home timeline results, then re-order the tweets at serve time.

Additional optimizations include:

• Keep only several hundred tweets for each home timeline in the Memory Cache
• Keep only active users' home timeline info in the Memory Cache
  • If a user was not previously active in the past 30 days, we could rebuild the timeline from the SQL Database
    • Query the User Graph Service to determine who the user is following
    • Get the tweets from the SQL Database and add them to the Memory Cache
• Store only a month of tweets in the Tweet Info Service
• Store only active users in the User Info Service
• The Search Cluster would likely need to keep the tweets in memory to keep latency low

We'll also want to address the bottleneck with the SQL Database.

Although the Memory Cache should reduce the load on the database, it is unlikely the SQL Read Replicas alone would be enough to handle the cache misses. We'll probably need to employ additional SQL scaling patterns.

The high volume of writes would overwhelm a single SQL Write Master-Slave, also pointing to a need for additional scaling techniques.

• Federation
• Sharding
• Denormalization
• SQL Tuning

We should also consider moving some data to a NoSQL Database.

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

File: solutions/system_design/social_graph/README.md Copy Chapter

Design the data structures for a social network

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

We'll scope the problem to handle only the following use cases

• User searches for someone and sees the shortest path to the searched person
• Service has high availability

Constraints and assumptions Copy Section

State assumptions

• Traffic is not evenly distributed
  • Some searches are more popular than others, while others are only executed once
• Graph data won't fit on a single machine
• Graph edges are unweighted
• 100 million users
• 50 friends per user average
• 1 billion friend searches per month

Exercise the use of more traditional systems - don't use graph-specific solutions such as GraphQL or a graph database like Neo4j

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• 5 billion friend relationships
  • 100 million users * 50 friends per user average
• 400 search requests per second

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: User searches for someone and sees the shortest path to the searched person Copy Section

Clarify with your interviewer how much code you are expected to write.

Without the constraint of millions of users (vertices) and billions of friend relationships (edges), we could solve this unweighted shortest path task with a general BFS approach:

class Graph(Graph):

    def shortest_path(self, source, dest):
        if source is None or dest is None:
            return None
        if source is dest:
            return [source.key]
        prev_node_keys = self._shortest_path(source, dest)
        if prev_node_keys is None:
            return None
        else:
            path_ids = [dest.key]
            prev_node_key = prev_node_keys[dest.key]
            while prev_node_key is not None:
                path_ids.append(prev_node_key)
                prev_node_key = prev_node_keys[prev_node_key]
            return path_ids[::-1]

    def _shortest_path(self, source, dest):
        queue = deque()
        queue.append(source)
        prev_node_keys = {source.key: None}
        source.visit_state = State.visited
        while queue:
            node = queue.popleft()
            if node is dest:
                return prev_node_keys
            prev_node = node
            for adj_node in node.adj_nodes.values():
                if adj_node.visit_state == State.unvisited:
                    queue.append(adj_node)
                    prev_node_keys[adj_node.key] = prev_node.key
                    adj_node.visit_state = State.visited
        return None

We won't be able to fit all users on the same machine, we'll need to shard users across Person Servers and access them with a Lookup Service.

• The Client sends a request to the Web Server, running as a reverse proxy
• The Web Server forwards the request to the Search API server
• The Search API server forwards the request to the User Graph Service
• The User Graph Service does the following:
  • Uses the Lookup Service to find the Person Server where the current user's info is stored
  • Finds the appropriate Person Server to retrieve the current user's list of friend_ids
  • Runs a BFS search using the current user as the source and the current user's friend_ids as the ids for each adjacent_node
  • To get the adjacent_node from a given id:
    • The User Graph Service will again need to communicate with the Lookup Service to determine which Person Server stores theadjacent_node matching the given id (potential for optimization)

Clarify with your interviewer how much code you should be writing.

Note: Error handling is excluded below for simplicity. Ask if you should code proper error handing.

Lookup Service implementation:

class LookupService(object):

    def __init__(self):
        self.lookup = self._init_lookup()  # key: person_id, value: person_server

    def _init_lookup(self):
        ...

    def lookup_person_server(self, person_id):
        return self.lookup[person_id]

Person Server implementation:

class PersonServer(object):

    def __init__(self):
        self.people = {}  # key: person_id, value: person

    def add_person(self, person):
        ...

    def people(self, ids):
        results = []
        for id in ids:
            if id in self.people:
                results.append(self.people[id])
        return results

Person implementation:

class Person(object):

    def __init__(self, id, name, friend_ids):
        self.id = id
        self.name = name
        self.friend_ids = friend_ids

User Graph Service implementation:

/* Detailed source-code truncated for AI context efficiency. */

We'll use a public REST API:

$ curl https://social.com/api/v1/friend_search?person_id=1234

Response:

{
    "person_id": "100",
    "name": "foo",
    "link": "https://social.com/foo",
},
{
    "person_id": "53",
    "name": "bar",
    "link": "https://social.com/bar",
},
{
    "person_id": "1234",
    "name": "baz",
    "link": "https://social.com/baz",
},

For internal communications, we could use Remote Procedure Calls.

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Important: Do not simply jump right into the final design from the initial design!

State you would 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See Design a system that scales to millions of users on AWS as a sample on how to iteratively scale the initial design.

It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a Load Balancer with multiple Web Servers? CDN? Master-Slave Replicas? What are the alternatives and Trade-Offs for each?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

• DNS
• Load balancer
• Horizontal scaling
• Web server (reverse proxy)
• API server (application layer)
• Cache
• Consistency patterns
• Availability patterns

To address the constraint of 400 average read requests per second (higher at peak), person data can be served from a Memory Cache such as Redis or Memcached to reduce response times and to reduce traffic to downstream services. This could be especially useful for people who do multiple searches in succession and for people who are well-connected. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹

Below are further optimizations:

• Store complete or partial BFS traversals to speed up subsequent lookups in the Memory Cache
• Batch compute offline then store complete or partial BFS traversals to speed up subsequent lookups in a NoSQL Database
• Reduce machine jumps by batching together friend lookups hosted on the same Person Server
  • Shard Person Servers by location to further improve this, as friends generally live closer to each other
• Do two BFS searches at the same time, one starting from the source, and one from the destination, then merge the two paths
• Start the BFS search from people with large numbers of friends, as they are more likely to reduce the number of degrees of separation between the current user and the search target
• Set a limit based on time or number of hops before asking the user if they want to continue searching, as searching could take a considerable amount of time in some cases
• Use a Graph Database such as Neo4j or a graph-specific query language such as GraphQL (if there were no constraint preventing the use of Graph Databases)

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

SQL scaling patterns Copy Section

• Read replicas
• Federation
• Sharding
• Denormalization
• SQL Tuning

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

File: solutions/system_design/scaling_aws/README.md Copy Chapter

Design a system that scales to millions of users on AWS

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

Solving this problem takes an iterative approach of: 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat, which is good pattern for evolving basic designs to scalable designs.

Unless you have a background in AWS or are applying for a position that requires AWS knowledge, AWS-specific details are not a requirement. However, much of the principles discussed in this exercise can apply more generally outside of the AWS ecosystem.

We'll scope the problem to handle only the following use cases

• User makes a read or write request
  • Service does processing, stores user data, then returns the results
• Service needs to evolve from serving a small amount of users to millions of users
  • Discuss general scaling patterns as we evolve an architecture to handle a large number of users and requests
• Service has high availability

Constraints and assumptions Copy Section

State assumptions

• Traffic is not evenly distributed
• Need for relational data
• Scale from 1 user to tens of millions of users
  • Denote increase of users as:
    • Users+
    • Users++
    • Users+++
    • ...
  • 10 million users
  • 1 billion writes per month
  • 100 billion reads per month
  • 100:1 read to write ratio
  • 1 KB content per write

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• 1 TB of new content per month
  • 1 KB per write * 1 billion writes per month
  • 36 TB of new content in 3 years
  • Assume most writes are from new content instead of updates to existing ones
• 400 writes per second on average
• 40,000 reads per second on average

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: User makes a read or write request Copy Section

Goals

• With only 1-2 users, you only need a basic setup
  • Single box for simplicity
  • Vertical scaling when needed
  • Monitor to determine bottlenecks

Start with a single box

• Web server on EC2
  • Storage for user data
  • MySQL Database

Use Vertical Scaling:

• Simply choose a bigger box
• Keep an eye on metrics to determine how to scale up
  • Use basic monitoring to determine bottlenecks: CPU, memory, IO, network, etc
  • CloudWatch, top, nagios, statsd, graphite, etc
• Scaling vertically can get very expensive
• No redundancy/failover

Trade-offs, alternatives, and additional details:

• The alternative to Vertical Scaling is Horizontal scaling

Start with SQL, consider NoSQL

The constraints assume there is a need for relational data. We can start off using a MySQL Database on the single box.

Trade-offs, alternatives, and additional details:

• See the Relational database management system (RDBMS) section
• Discuss reasons to use SQL or NoSQL

Assign a public static IP

• Elastic IPs provide a public endpoint whose IP doesn't change on reboot
• Helps with failover, just point the domain to a new IP

Use a DNS

Add a DNS such as Route 53 to map the domain to the instance's public IP.

Trade-offs, alternatives, and additional details:

• See the Domain name system section

Secure the web server

• Open up only necessary ports
  • Allow the web server to respond to incoming requests from:
    • 80 for HTTP
    • 443 for HTTPS
    • 22 for SSH to only whitelisted IPs
  • Prevent the web server from initiating outbound connections

Trade-offs, alternatives, and additional details:

• See the Security section

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Users+ Copy Section

Assumptions

Our user count is starting to pick up and the load is increasing on our single box. Our Benchmarks/Load Tests and Profiling are pointing to the MySQL Database taking up more and more memory and CPU resources, while the user content is filling up disk space.

We've been able to address these issues with Vertical Scaling so far. Unfortunately, this has become quite expensive and it doesn't allow for independent scaling of the MySQL Database and Web Server.

Goals

• Lighten load on the single box and allow for independent scaling
  • Store static content separately in an Object Store
  • Move the MySQL Database to a separate box
• Disadvantages
  • These changes would increase complexity and would require changes to the Web Server to point to the Object Store and the MySQL Database
  • Additional security measures must be taken to secure the new components
  • AWS costs could also increase, but should be weighed with the costs of managing similar systems on your own

Store static content separately

• Consider using a managed Object Store like S3 to store static content
  • Highly scalable and reliable
  • Server side encryption
• Move static content to S3
  • User files
  • JS
  • CSS
  • Images
  • Videos

Move the MySQL database to a separate box

• Consider using a service like RDS to manage the MySQL Database
  • Simple to administer, scale
  • Multiple availability zones
  • Encryption at rest

Secure the system

• Encrypt data in transit and at rest
• Use a Virtual Private Cloud
  • Create a public subnet for the single Web Server so it can send and receive traffic from the internet
  • Create a private subnet for everything else, preventing outside access
  • Only open ports from whitelisted IPs for each component
• These same patterns should be implemented for new components in the remainder of the exercise

Trade-offs, alternatives, and additional details:

• See the Security section

Users++ Copy Section

Assumptions

Our Benchmarks/Load Tests and Profiling show that our single Web Server bottlenecks during peak hours, resulting in slow responses and in some cases, downtime. As the service matures, we'd also like to move towards higher availability and redundancy.

Goals

• The following goals attempt to address the scaling issues with the Web Server
  • Based on the Benchmarks/Load Tests and Profiling, you might only need to implement one or two of these techniques
• Use Horizontal Scaling to handle increasing loads and to address single points of failure
  • Add a Load Balancer such as Amazon's ELB or HAProxy
    • ELB is highly available
    • If you are configuring your own Load Balancer, setting up multiple servers in active-active or active-passive in multiple availability zones will improve availability
    • Terminate SSL on the Load Balancer to reduce computational load on backend servers and to simplify certificate administration
  • Use multiple Web Servers spread out over multiple availability zones
  • Use multiple MySQL instances in Master-Slave Failover mode across multiple availability zones to improve redundancy
• Separate out the Web Servers from the Application Servers
  • Scale and configure both layers independently
  • Web Servers can run as a Reverse Proxy
  • For example, you can add Application Servers handling Read APIs while others handle Write APIs
• Move static (and some dynamic) content to a Content Delivery Network (CDN) such as CloudFront to reduce load and latency

Trade-offs, alternatives, and additional details:

• See the linked content above for details

Users+++ Copy Section

Note: Internal Load Balancers not shown to reduce clutter

Assumptions

Our Benchmarks/Load Tests and Profiling show that we are read-heavy (100:1 with writes) and our database is suffering from poor performance from the high read requests.

Goals

• The following goals attempt to address the scaling issues with the MySQL Database
  • Based on the Benchmarks/Load Tests and Profiling, you might only need to implement one or two of these techniques
• Move the following data to a Memory Cache such as Elasticache to reduce load and latency:
  • Frequently accessed content from MySQL
    • First, try to configure the MySQL Database cache to see if that is sufficient to relieve the bottleneck before implementing a Memory Cache
  • Session data from the Web Servers
    • The Web Servers become stateless, allowing for Autoscaling
  • Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹
• Add MySQL Read Replicas to reduce load on the write master
• Add more Web Servers and Application Servers to improve responsiveness

Trade-offs, alternatives, and additional details:

• See the linked content above for details

Add MySQL read replicas

• In addition to adding and scaling a Memory Cache, MySQL Read Replicas can also help relieve load on the MySQL Write Master
• Add logic to Web Server to separate out writes and reads
• Add Load Balancers in front of MySQL Read Replicas (not pictured to reduce clutter)
• Most services are read-heavy vs write-heavy

Trade-offs, alternatives, and additional details:

• See the Relational database management system (RDBMS) section

Users++++ Copy Section

Assumptions

Our Benchmarks/Load Tests and Profiling show that our traffic spikes during regular business hours in the U.S. and drop significantly when users leave the office. We think we can cut costs by automatically spinning up and down servers based on actual load. We're a small shop so we'd like to automate as much of the DevOps as possible for Autoscaling and for the general operations.

Goals

• Add Autoscaling to provision capacity as needed
  • Keep up with traffic spikes
  • Reduce costs by powering down unused instances
• Automate DevOps
  • Chef, Puppet, Ansible, etc
• Continue monitoring metrics to address bottlenecks
  • Host level - Review a single EC2 instance
  • Aggregate level - Review load balancer stats
  • Log analysis - CloudWatch, CloudTrail, Loggly, Splunk, Sumo
  • External site performance - Pingdom or New Relic
  • Handle notifications and incidents - PagerDuty
  • Error Reporting - Sentry

Add autoscaling

• Consider a managed service such as AWS Autoscaling
  • Create one group for each Web Server and one for each Application Server type, place each group in multiple availability zones
  • Set a min and max number of instances
  • Trigger to scale up and down through CloudWatch
    • Simple time of day metric for predictable loads or
    • Metrics over a time period:
      • CPU load
      • Latency
      • Network traffic
      • Custom metric
  • Disadvantages
    • Autoscaling can introduce complexity
    • It could take some time before a system appropriately scales up to meet increased demand, or to scale down when demand drops

Users+++++ Copy Section

Note: Autoscaling groups not shown to reduce clutter

Assumptions

As the service continues to grow towards the figures outlined in the constraints, we iteratively run Benchmarks/Load Tests and Profiling to uncover and address new bottlenecks.

Goals

We'll continue to address scaling issues due to the problem's constraints:

• If our MySQL Database starts to grow too large, we might consider only storing a limited time period of data in the database, while storing the rest in a data warehouse such as Redshift
  • A data warehouse such as Redshift can comfortably handle the constraint of 1 TB of new content per month
• With 40,000 average read requests per second, read traffic for popular content can be addressed by scaling the Memory Cache, which is also useful for handling the unevenly distributed traffic and traffic spikes
  • The SQL Read Replicas might have trouble handling the cache misses, we'll probably need to employ additional SQL scaling patterns
• 400 average writes per second (with presumably significantly higher peaks) might be tough for a single SQL Write Master-Slave, also pointing to a need for additional scaling techniques

SQL scaling patterns include:

• Federation
• Sharding
• Denormalization
• SQL Tuning

To further address the high read and write requests, we should also consider moving appropriate data to a NoSQL Database such as DynamoDB.

We can further separate out our Application Servers to allow for independent scaling. Batch processes or computations that do not need to be done in real-time can be done Asynchronously with Queues and Workers:

• For example, in a photo service, the photo upload and the thumbnail creation can be separated:
  • Client uploads photo
  • Application Server puts a job in a Queue such as SQS
  • The Worker Service on EC2 or Lambda pulls work off the Queue then:
    • Creates a thumbnail
    • Updates a Database
    • Stores the thumbnail in the Object Store

Trade-offs, alternatives, and additional details:

• See the linked content above for details

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

SQL scaling patterns Copy Section

• Read replicas
• Federation
• Sharding
• Denormalization
• SQL Tuning

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

File: solutions/system_design/sales_rank/README.md Copy Chapter

Design Amazon's sales rank by category feature

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

We'll scope the problem to handle only the following use case

• Service calculates the past week's most popular products by category
• User views the past week's most popular products by category
• Service has high availability

Out of scope

• The general e-commerce site
  • Design components only for calculating sales rank

Constraints and assumptions Copy Section

State assumptions

• Traffic is not evenly distributed
• Items can be in multiple categories
• Items cannot change categories
• There are no subcategories ie foo/bar/baz
• Results must be updated hourly
  • More popular products might need to be updated more frequently
• 10 million products
• 1000 categories
• 1 billion transactions per month
• 100 billion read requests per month
• 100:1 read to write ratio

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• Size per transaction:
  • created_at - 5 bytes
  • product_id - 8 bytes
  • category_id - 4 bytes
  • seller_id - 8 bytes
  • buyer_id - 8 bytes
  • quantity - 4 bytes
  • total_price - 5 bytes
  • Total: ~40 bytes
• 40 GB of new transaction content per month
  • 40 bytes per transaction * 1 billion transactions per month
  • 1.44 TB of new transaction content in 3 years
  • Assume most are new transactions instead of updates to existing ones
• 400 transactions per second on average
• 40,000 read requests per second on average

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: Service calculates the past week's most popular products by category Copy Section

We could store the raw Sales API server log files on a managed Object Store such as Amazon S3, rather than managing our own distributed file system.

Clarify with your interviewer how much code you are expected to write.

We'll assume this is a sample log entry, tab delimited:

timestamp   product_id  category_id    qty     total_price   seller_id    buyer_id
t1          product1    category1      2       20.00         1            1
t2          product1    category2      2       20.00         2            2
t2          product1    category2      1       10.00         2            3
t3          product2    category1      3        7.00         3            4
t4          product3    category2      7        2.00         4            5
t5          product4    category1      1        5.00         5            6
...

The Sales Rank Service could use MapReduce, using the Sales API server log files as input and writing the results to an aggregate table sales_rank in a SQL Database. We should discuss the use cases and tradeoffs between choosing SQL or NoSQL.

We'll use a multi-step MapReduce:

• Step 1 - Transform the data to (category, product_id), sum(quantity)
• Step 2 - Perform a distributed sort

/* Detailed source-code truncated for AI context efficiency. */

The result would be the following sorted list, which we could insert into the sales_rank table:

(category1, 1), product4
(category1, 2), product1
(category1, 3), product2
(category2, 3), product1
(category2, 7), product3

The sales_rank table could have the following structure:

id int NOT NULL AUTO_INCREMENT
category_id int NOT NULL
total_sold int NOT NULL
product_id int NOT NULL
PRIMARY KEY(id)
FOREIGN KEY(category_id) REFERENCES Categories(id)
FOREIGN KEY(product_id) REFERENCES Products(id)

We'll create an index on id , category_id, and product_id to speed up lookups (log-time instead of scanning the entire table) and to keep the data in memory. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹

Use case: User views the past week's most popular products by category Copy Section

• The Client sends a request to the Web Server, running as a reverse proxy
• The Web Server forwards the request to the Read API server
• The Read API server reads from the SQL Database sales_rank table

We'll use a public REST API:

$ curl https://amazon.com/api/v1/popular?category_id=1234

Response:

{
    "id": "100",
    "category_id": "1234",
    "total_sold": "100000",
    "product_id": "50",
},
{
    "id": "53",
    "category_id": "1234",
    "total_sold": "90000",
    "product_id": "200",
},
{
    "id": "75",
    "category_id": "1234",
    "total_sold": "80000",
    "product_id": "3",
},

For internal communications, we could use Remote Procedure Calls.

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Important: Do not simply jump right into the final design from the initial design!

State you would 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See Design a system that scales to millions of users on AWS as a sample on how to iteratively scale the initial design.

It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a Load Balancer with multiple Web Servers? CDN? Master-Slave Replicas? What are the alternatives and Trade-Offs for each?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

• DNS
• CDN
• Load balancer
• Horizontal scaling
• Web server (reverse proxy)
• API server (application layer)
• Cache
• Relational database management system (RDBMS)
• SQL write master-slave failover
• Master-slave replication
• Consistency patterns
• Availability patterns

The Analytics Database could use a data warehousing solution such as Amazon Redshift or Google BigQuery.

We might only want to store a limited time period of data in the database, while storing the rest in a data warehouse or in an Object Store. An Object Store such as Amazon S3 can comfortably handle the constraint of 40 GB of new content per month.

To address the 40,000 average read requests per second (higher at peak), traffic for popular content (and their sales rank) should be handled by the Memory Cache instead of the database. The Memory Cache is also useful for handling the unevenly distributed traffic and traffic spikes. With the large volume of reads, the SQL Read Replicas might not be able to handle the cache misses. We'll probably need to employ additional SQL scaling patterns.

400 average writes per second (higher at peak) might be tough for a single SQL Write Master-Slave, also pointing to a need for additional scaling techniques.

SQL scaling patterns include:

• Federation
• Sharding
• Denormalization
• SQL Tuning

We should also consider moving some data to a NoSQL Database.

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

File: solutions/system_design/query_cache/README.md Copy Chapter

Design a key-value cache to save the results of the most recent web server queries

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

We'll scope the problem to handle only the following use cases

• User sends a search request resulting in a cache hit
• User sends a search request resulting in a cache miss
• Service has high availability

Constraints and assumptions Copy Section

State assumptions

• Traffic is not evenly distributed
  • Popular queries should almost always be in the cache
  • Need to determine how to expire/refresh
• Serving from cache requires fast lookups
• Low latency between machines
• Limited memory in cache
  • Need to determine what to keep/remove
  • Need to cache millions of queries
• 10 million users
• 10 billion queries per month

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• Cache stores ordered list of key: query, value: results
  • query - 50 bytes
  • title - 20 bytes
  • snippet - 200 bytes
  • Total: 270 bytes
• 2.7 TB of cache data per month if all 10 billion queries are unique and all are stored
  • 270 bytes per search * 10 billion searches per month
  • Assumptions state limited memory, need to determine how to expire contents
• 4,000 requests per second

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: User sends a request resulting in a cache hit Copy Section

Popular queries can be served from a Memory Cache such as Redis or Memcached to reduce read latency and to avoid overloading the Reverse Index Service and Document Service. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹

Since the cache has limited capacity, we'll use a least recently used (LRU) approach to expire older entries.

• The Client sends a request to the Web Server, running as a reverse proxy
• The Web Server forwards the request to the Query API server
• The Query API server does the following:
  • Parses the query
    • Removes markup
    • Breaks up the text into terms
    • Fixes typos
    • Normalizes capitalization
    • Converts the query to use boolean operations
  • Checks the Memory Cache for the content matching the query
    • If there's a hit in the Memory Cache, the Memory Cache does the following:
      • Updates the cached entry's position to the front of the LRU list
      • Returns the cached contents
    • Else, the Query API does the following:
      • Uses the Reverse Index Service to find documents matching the query
        • The Reverse Index Service ranks the matching results and returns the top ones
      • Uses the Document Service to return titles and snippets
      • Updates the Memory Cache with the contents, placing the entry at the front of the LRU list

Cache implementation

The cache can use a doubly-linked list: new items will be added to the head while items to expire will be removed from the tail. We'll use a hash table for fast lookups to each linked list node.

Clarify with your interviewer how much code you are expected to write.

Query API Server implementation:

class QueryApi(object):

    def __init__(self, memory_cache, reverse_index_service):
        self.memory_cache = memory_cache
        self.reverse_index_service = reverse_index_service

    def parse_query(self, query):
        """Remove markup, break text into terms, deal with typos,
        normalize capitalization, convert to use boolean operations.
        """
        ...

    def process_query(self, query):
        query = self.parse_query(query)
        results = self.memory_cache.get(query)
        if results is None:
            results = self.reverse_index_service.process_search(query)
            self.memory_cache.set(query, results)
        return results

Node implementation:

class Node(object):

    def __init__(self, query, results):
        self.query = query
        self.results = results

LinkedList implementation:

class LinkedList(object):

    def __init__(self):
        self.head = None
        self.tail = None

    def move_to_front(self, node):
        ...

    def append_to_front(self, node):
        ...

    def remove_from_tail(self):
        ...

Cache implementation:

class Cache(object):

    def __init__(self, MAX_SIZE):
        self.MAX_SIZE = MAX_SIZE
        self.size = 0
        self.lookup = {}  # key: query, value: node
        self.linked_list = LinkedList()

    def get(self, query)
        """Get the stored query result from the cache.

        Accessing a node updates its position to the front of the LRU list.
        """
        node = self.lookup[query]
        if node is None:
            return None
        self.linked_list.move_to_front(node)
        return node.results

    def set(self, results, query):
        """Set the result for the given query key in the cache.

        When updating an entry, updates its position to the front of the LRU list.
        If the entry is new and the cache is at capacity, removes the oldest entry
        before the new entry is added.
        """
        node = self.lookup[query]
        if node is not None:
            # Key exists in cache, update the value
            node.results = results
            self.linked_list.move_to_front(node)
        else:
            # Key does not exist in cache
            if self.size == self.MAX_SIZE:
                # Remove the oldest entry from the linked list and lookup
                self.lookup.pop(self.linked_list.tail.query, None)
                self.linked_list.remove_from_tail()
            else:
                self.size += 1
            # Add the new key and value
            new_node = Node(query, results)
            self.linked_list.append_to_front(new_node)
            self.lookup[query] = new_node

When to update the cache

The cache should be updated when:

• The page contents change
• The page is removed or a new page is added
• The page rank changes

The most straightforward way to handle these cases is to simply set a max time that a cached entry can stay in the cache before it is updated, usually referred to as time to live (TTL).

Refer to When to update the cache for tradeoffs and alternatives. The approach above describes cache-aside.

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Important: Do not simply jump right into the final design from the initial design!

State you would 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See Design a system that scales to millions of users on AWS as a sample on how to iteratively scale the initial design.

It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a Load Balancer with multiple Web Servers? CDN? Master-Slave Replicas? What are the alternatives and Trade-Offs for each?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

• DNS
• Load balancer
• Horizontal scaling
• Web server (reverse proxy)
• API server (application layer)
• Cache
• Consistency patterns
• Availability patterns

Expanding the Memory Cache to many machines Copy Section

To handle the heavy request load and the large amount of memory needed, we'll scale horizontally. We have three main options on how to store the data on our Memory Cache cluster:

• Each machine in the cache cluster has its own cache - Simple, although it will likely result in a low cache hit rate.
• Each machine in the cache cluster has a copy of the cache - Simple, although it is an inefficient use of memory.
• The cache is sharded across all machines in the cache cluster - More complex, although it is likely the best option. We could use hashing to determine which machine could have the cached results of a query using machine = hash(query). We'll likely want to use consistent hashing.

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

SQL scaling patterns Copy Section

• Read replicas
• Federation
• Sharding
• Denormalization
• SQL Tuning

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

File: solutions/system_design/pastebin/README.md Copy Chapter

Design Pastebin.com (or Bit.ly)

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Design Bit.ly - is a similar question, except pastebin requires storing the paste contents instead of the original unshortened url.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

We'll scope the problem to handle only the following use cases

• User enters a block of text and gets a randomly generated link
  • Expiration
    • Default setting does not expire
    • Can optionally set a timed expiration
• User enters a paste's url and views the contents
• User is anonymous
• Service tracks analytics of pages
  • Monthly visit stats
• Service deletes expired pastes
• Service has high availability

Out of scope

• User registers for an account
  • User verifies email
• User logs into a registered account
  • User edits the document
• User can set visibility
• User can set the shortlink

Constraints and assumptions Copy Section

State assumptions

• Traffic is not evenly distributed
• Following a short link should be fast
• Pastes are text only
• Page view analytics do not need to be realtime
• 10 million users
• 10 million paste writes per month
• 100 million paste reads per month
• 10:1 read to write ratio

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• Size per paste
  • 1 KB content per paste
  • shortlink - 7 bytes
  • expiration_length_in_minutes - 4 bytes
  • created_at - 5 bytes
  • paste_path - 255 bytes
  • total = ~1.27 KB
• 12.7 GB of new paste content per month
  • 1.27 KB per paste * 10 million pastes per month
  • ~450 GB of new paste content in 3 years
  • 360 million shortlinks in 3 years
  • Assume most are new pastes instead of updates to existing ones
• 4 paste writes per second on average
• 40 read requests per second on average

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: User enters a block of text and gets a randomly generated link Copy Section

We could use a relational database as a large hash table, mapping the generated url to a file server and path containing the paste file.

Instead of managing a file server, we could use a managed Object Store such as Amazon S3 or a NoSQL document store.

An alternative to a relational database acting as a large hash table, we could use a NoSQL key-value store. We should discuss the tradeoffs between choosing SQL or NoSQL. The following discussion uses the relational database approach.

• The Client sends a create paste request to the Web Server, running as a reverse proxy
• The Web Server forwards the request to the Write API server
• The Write API server does the following:
  • Generates a unique url
    • Checks if the url is unique by looking at the SQL Database for a duplicate
    • If the url is not unique, it generates another url
    • If we supported a custom url, we could use the user-supplied (also check for a duplicate)
  • Saves to the SQL Database pastes table
  • Saves the paste data to the Object Store
  • Returns the url

Clarify with your interviewer how much code you are expected to write.

The pastes table could have the following structure:

shortlink char(7) NOT NULL
expiration_length_in_minutes int NOT NULL
created_at datetime NOT NULL
paste_path varchar(255) NOT NULL
PRIMARY KEY(shortlink)

Setting the primary key to be based on the shortlink column creates an index that the database uses to enforce uniqueness. We'll create an additional index on created_at to speed up lookups (log-time instead of scanning the entire table) and to keep the data in memory. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹

To generate the unique url, we could:

• Take the MD5 hash of the user's ip_address + timestamp
  • MD5 is a widely used hashing function that produces a 128-bit hash value
  • MD5 is uniformly distributed
  • Alternatively, we could also take the MD5 hash of randomly-generated data
• Base 62 encode the MD5 hash
  • Base 62 encodes to [a-zA-Z0-9] which works well for urls, eliminating the need for escaping special characters
  • There is only one hash result for the original input and Base 62 is deterministic (no randomness involved)
  • Base 64 is another popular encoding but provides issues for urls because of the additional + and / characters
  • The following Base 62 pseudocode runs in O(k) time where k is the number of digits = 7:

def base_encode(num, base=62):
    digits = []
    while num > 0
      remainder = modulo(num, base)
      digits.push(remainder)
      num = divide(num, base)
    digits = digits.reverse

• Take the first 7 characters of the output, which results in 62^7 possible values and should be sufficient to handle our constraint of 360 million shortlinks in 3 years:

url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH]

We'll use a public REST API:

$ curl -X POST --data '{ "expiration_length_in_minutes": "60", \
    "paste_contents": "Hello World!" }' https://pastebin.com/api/v1/paste

Response:

{
    "shortlink": "foobar"
}

For internal communications, we could use Remote Procedure Calls.

Use case: User enters a paste's url and views the contents Copy Section

• The Client sends a get paste request to the Web Server
• The Web Server forwards the request to the Read API server
• The Read API server does the following:
  • Checks the SQL Database for the generated url
    • If the url is in the SQL Database, fetch the paste contents from the Object Store
    • Else, return an error message for the user

REST API:

$ curl https://pastebin.com/api/v1/paste?shortlink=foobar

Response:

{
    "paste_contents": "Hello World"
    "created_at": "YYYY-MM-DD HH:MM:SS"
    "expiration_length_in_minutes": "60"
}

Use case: Service tracks analytics of pages Copy Section

Since realtime analytics are not a requirement, we could simply MapReduce the Web Server logs to generate hit counts.

Clarify with your interviewer how much code you are expected to write.

class HitCounts(MRJob):

    def extract_url(self, line):
        """Extract the generated url from the log line."""
        ...

    def extract_year_month(self, line):
        """Return the year and month portions of the timestamp."""
        ...

    def mapper(self, _, line):
        """Parse each log line, extract and transform relevant lines.

        Emit key value pairs of the form:

        (2016-01, url0), 1
        (2016-01, url0), 1
        (2016-01, url1), 1
        """
        url = self.extract_url(line)
        period = self.extract_year_month(line)
        yield (period, url), 1

    def reducer(self, key, values):
        """Sum values for each key.

        (2016-01, url0), 2
        (2016-01, url1), 1
        """
        yield key, sum(values)

Use case: Service deletes expired pastes Copy Section

To delete expired pastes, we could just scan the SQL Database for all entries whose expiration timestamp are older than the current timestamp. All expired entries would then be deleted (or marked as expired) from the table.

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Important: Do not simply jump right into the final design from the initial design!

State you would do this iteratively: 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See Design a system that scales to millions of users on AWS as a sample on how to iteratively scale the initial design.

It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a Load Balancer with multiple Web Servers? CDN? Master-Slave Replicas? What are the alternatives and Trade-Offs for each?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

• DNS
• CDN
• Load balancer
• Horizontal scaling
• Web server (reverse proxy)
• API server (application layer)
• Cache
• Relational database management system (RDBMS)
• SQL write master-slave failover
• Master-slave replication
• Consistency patterns
• Availability patterns

The Analytics Database could use a data warehousing solution such as Amazon Redshift or Google BigQuery.

An Object Store such as Amazon S3 can comfortably handle the constraint of 12.7 GB of new content per month.

To address the 40 average read requests per second (higher at peak), traffic for popular content should be handled by the Memory Cache instead of the database. The Memory Cache is also useful for handling the unevenly distributed traffic and traffic spikes. The SQL Read Replicas should be able to handle the cache misses, as long as the replicas are not bogged down with replicating writes.

4 average paste writes per second (with higher at peak) should be do-able for a single SQL Write Master-Slave. Otherwise, we'll need to employ additional SQL scaling patterns:

• Federation
• Sharding
• Denormalization
• SQL Tuning

We should also consider moving some data to a NoSQL Database.

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

File: solutions/system_design/mint/README.md Copy Chapter

Design Mint.com

Note: This document links directly to relevant areas found in the system design topics to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.

Step 1: Outline use cases and constraints Copy Chapter

Gather requirements and scope the problem.
Ask questions to clarify use cases and constraints.
Discuss assumptions.

Without an interviewer to address clarifying questions, we'll define some use cases and constraints.

Use cases Copy Section

We'll scope the problem to handle only the following use cases

• User connects to a financial account
• Service extracts transactions from the account
  • Updates daily
  • Categorizes transactions
    • Allows manual category override by the user
    • No automatic re-categorization
  • Analyzes monthly spending, by category
• Service recommends a budget
  • Allows users to manually set a budget
  • Sends notifications when approaching or exceeding budget
• Service has high availability

Out of scope

• Service performs additional logging and analytics

Constraints and assumptions Copy Section

State assumptions

• Traffic is not evenly distributed
• Automatic daily update of accounts applies only to users active in the past 30 days
• Adding or removing financial accounts is relatively rare
• Budget notifications don't need to be instant
• 10 million users
  • 10 budget categories per user = 100 million budget items
  • Example categories:
    • Housing = $1,000
    • Food = $200
    • Gas = $100
  • Sellers are used to determine transaction category
    • 50,000 sellers
• 30 million financial accounts
• 5 billion transactions per month
• 500 million read requests per month
• 10:1 write to read ratio
  • Write-heavy, users make transactions daily, but few visit the site daily

Calculate usage

Clarify with your interviewer if you should run back-of-the-envelope usage calculations.

• Size per transaction:
  • user_id - 8 bytes
  • created_at - 5 bytes
  • seller - 32 bytes
  • amount - 5 bytes
  • Total: ~50 bytes
• 250 GB of new transaction content per month
  • 50 bytes per transaction * 5 billion transactions per month
  • 9 TB of new transaction content in 3 years
  • Assume most are new transactions instead of updates to existing ones
• 2,000 transactions per second on average
• 200 read requests per second on average

Handy conversion guide:

• 2.5 million seconds per month
• 1 request per second = 2.5 million requests per month
• 40 requests per second = 100 million requests per month
• 400 requests per second = 1 billion requests per month

Step 2: Create a high level design Copy Chapter

Outline a high level design with all important components.

Step 3: Design core components Copy Chapter

Dive into details for each core component.

Use case: User connects to a financial account Copy Section

We could store info on the 10 million users in a relational database. We should discuss the use cases and tradeoffs between choosing SQL or NoSQL.

• The Client sends a request to the Web Server, running as a reverse proxy
• The Web Server forwards the request to the Accounts API server
• The Accounts API server updates the SQL Database accounts table with the newly entered account info

Clarify with your interviewer how much code you are expected to write.

The accounts table could have the following structure:

id int NOT NULL AUTO_INCREMENT
created_at datetime NOT NULL
last_update datetime NOT NULL
account_url varchar(255) NOT NULL
account_login varchar(32) NOT NULL
account_password_hash char(64) NOT NULL
user_id int NOT NULL
PRIMARY KEY(id)
FOREIGN KEY(user_id) REFERENCES users(id)

We'll create an index on id, user_id , and created_at to speed up lookups (log-time instead of scanning the entire table) and to keep the data in memory. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.¹

We'll use a public REST API:

$ curl -X POST --data '{ "user_id": "foo", "account_url": "bar", \
    "account_login": "baz", "account_password": "qux" }' \
    https://mint.com/api/v1/account

For internal communications, we could use Remote Procedure Calls.

Next, the service extracts transactions from the account.

Use case: Service extracts transactions from the account Copy Section

We'll want to extract information from an account in these cases:

• The user first links the account
• The user manually refreshes the account
• Automatically each day for users who have been active in the past 30 days

Data flow:

• The Client sends a request to the Web Server
• The Web Server forwards the request to the Accounts API server
• The Accounts API server places a job on a Queue such as Amazon SQS or RabbitMQ
  • Extracting transactions could take awhile, we'd probably want to do this asynchronously with a queue, although this introduces additional complexity
• The Transaction Extraction Service does the following:
  • Pulls from the Queue and extracts transactions for the given account from the financial institution, storing the results as raw log files in the Object Store
  • Uses the Category Service to categorize each transaction
  • Uses the Budget Service to calculate aggregate monthly spending by category
    • The Budget Service uses the Notification Service to let users know if they are nearing or have exceeded their budget
  • Updates the SQL Database transactions table with categorized transactions
  • Updates the SQL Database monthly_spending table with aggregate monthly spending by category
  • Notifies the user the transactions have completed through the Notification Service:
    • Uses a Queue (not pictured) to asynchronously send out notifications

The transactions table could have the following structure:

id int NOT NULL AUTO_INCREMENT
created_at datetime NOT NULL
seller varchar(32) NOT NULL
amount decimal NOT NULL
user_id int NOT NULL
PRIMARY KEY(id)
FOREIGN KEY(user_id) REFERENCES users(id)

We'll create an index on id, user_id , and created_at.

The monthly_spending table could have the following structure:

id int NOT NULL AUTO_INCREMENT
month_year date NOT NULL
category varchar(32)
amount decimal NOT NULL
user_id int NOT NULL
PRIMARY KEY(id)
FOREIGN KEY(user_id) REFERENCES users(id)

We'll create an index on id and user_id .

Category service

For the Category Service, we can seed a seller-to-category dictionary with the most popular sellers. If we estimate 50,000 sellers and estimate each entry to take less than 255 bytes, the dictionary would only take about 12 MB of memory.

Clarify with your interviewer how much code you are expected to write.

class DefaultCategories(Enum):

    HOUSING = 0
    FOOD = 1
    GAS = 2
    SHOPPING = 3
    ...

seller_category_map = {}
seller_category_map['Exxon'] = DefaultCategories.GAS
seller_category_map['Target'] = DefaultCategories.SHOPPING
...

For sellers not initially seeded in the map, we could use a crowdsourcing effort by evaluating the manual category overrides our users provide. We could use a heap to quickly lookup the top manual override per seller in O(1) time.

class Categorizer(object):

    def __init__(self, seller_category_map, seller_category_crowd_overrides_map):
        self.seller_category_map = seller_category_map
        self.seller_category_crowd_overrides_map = \
            seller_category_crowd_overrides_map

    def categorize(self, transaction):
        if transaction.seller in self.seller_category_map:
            return self.seller_category_map[transaction.seller]
        elif transaction.seller in self.seller_category_crowd_overrides_map:
            self.seller_category_map[transaction.seller] = \
                self.seller_category_crowd_overrides_map[transaction.seller].peek_min()
            return self.seller_category_map[transaction.seller]
        return None

Transaction implementation:

class Transaction(object):

    def __init__(self, created_at, seller, amount):
        self.created_at = created_at
        self.seller = seller
        self.amount = amount

Use case: Service recommends a budget Copy Section

To start, we could use a generic budget template that allocates category amounts based on income tiers. Using this approach, we would not have to store the 100 million budget items identified in the constraints, only those that the user overrides. If a user overrides a budget category, which we could store the override in the TABLE budget_overrides.

class Budget(object):

    def __init__(self, income):
        self.income = income
        self.categories_to_budget_map = self.create_budget_template()

    def create_budget_template(self):
        return {
            DefaultCategories.HOUSING: self.income * .4,
            DefaultCategories.FOOD: self.income * .2,
            DefaultCategories.GAS: self.income * .1,
            DefaultCategories.SHOPPING: self.income * .2,
            ...
        }

    def override_category_budget(self, category, amount):
        self.categories_to_budget_map[category] = amount

For the Budget Service, we can potentially run SQL queries on the transactions table to generate the monthly_spending aggregate table. The monthly_spending table would likely have much fewer rows than the total 5 billion transactions, since users typically have many transactions per month.

As an alternative, we can run MapReduce jobs on the raw transaction files to:

• Categorize each transaction
• Generate aggregate monthly spending by category

Running analyses on the transaction files could significantly reduce the load on the database.

We could call the Budget Service to re-run the analysis if the user updates a category.

Clarify with your interviewer how much code you are expected to write.

Sample log file format, tab delimited:

user_id   timestamp   seller  amount

MapReduce implementation:

class SpendingByCategory(MRJob):

    def __init__(self, categorizer):
        self.categorizer = categorizer
        self.current_year_month = calc_current_year_month()
        ...

    def calc_current_year_month(self):
        """Return the current year and month."""
        ...

    def extract_year_month(self, timestamp):
        """Return the year and month portions of the timestamp."""
        ...

    def handle_budget_notifications(self, key, total):
        """Call notification API if nearing or exceeded budget."""
        ...

    def mapper(self, _, line):
        """Parse each log line, extract and transform relevant lines.

        Argument line will be of the form:

        user_id   timestamp   seller  amount

        Using the categorizer to convert seller to category,
        emit key value pairs of the form:

        (user_id, 2016-01, shopping), 25
        (user_id, 2016-01, shopping), 100
        (user_id, 2016-01, gas), 50
        """
        user_id, timestamp, seller, amount = line.split('\t')
        category = self.categorizer.categorize(seller)
        period = self.extract_year_month(timestamp)
        if period == self.current_year_month:
            yield (user_id, period, category), amount

    def reducer(self, key, value):
        """Sum values for each key.

        (user_id, 2016-01, shopping), 125
        (user_id, 2016-01, gas), 50
        """
        total = sum(values)
        yield key, sum(values)

Step 4: Scale the design Copy Chapter

Identify and address bottlenecks, given the constraints.

Important: Do not simply jump right into the final design from the initial design!

State you would 1) Benchmark/Load Test, 2) Profile for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See Design a system that scales to millions of users on AWS as a sample on how to iteratively scale the initial design.

It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a Load Balancer with multiple Web Servers? CDN? Master-Slave Replicas? What are the alternatives and Trade-Offs for each?

We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.

To avoid repeating discussions, refer to the following system design topics for main talking points, tradeoffs, and alternatives:

• DNS
• CDN
• Load balancer
• Horizontal scaling
• Web server (reverse proxy)
• API server (application layer)
• Cache
• Relational database management system (RDBMS)
• SQL write master-slave failover
• Master-slave replication
• Asynchronism
• Consistency patterns
• Availability patterns

We'll add an additional use case: User accesses summaries and transactions.

User sessions, aggregate stats by category, and recent transactions could be placed in a Memory Cache such as Redis or Memcached.

• The Client sends a read request to the Web Server
• The Web Server forwards the request to the Read API server
  • Static content can be served from the Object Store such as S3, which is cached on the CDN
• The Read API server does the following:
  • Checks the Memory Cache for the content
    • If the url is in the Memory Cache, returns the cached contents
    • Else
      • If the url is in the SQL Database, fetches the contents
        • Updates the Memory Cache with the contents

Refer to When to update the cache for tradeoffs and alternatives. The approach above describes cache-aside.

Instead of keeping the monthly_spending aggregate table in the SQL Database, we could create a separate Analytics Database using a data warehousing solution such as Amazon Redshift or Google BigQuery.

We might only want to store a month of transactions data in the database, while storing the rest in a data warehouse or in an Object Store. An Object Store such as Amazon S3 can comfortably handle the constraint of 250 GB of new content per month.

To address the 200 average read requests per second (higher at peak), traffic for popular content should be handled by the Memory Cache instead of the database. The Memory Cache is also useful for handling the unevenly distributed traffic and traffic spikes. The SQL Read Replicas should be able to handle the cache misses, as long as the replicas are not bogged down with replicating writes.

2,000 average transaction writes per second (higher at peak) might be tough for a single SQL Write Master-Slave. We might need to employ additional SQL scaling patterns:

• Federation
• Sharding
• Denormalization
• SQL Tuning

We should also consider moving some data to a NoSQL Database.

Additional talking points Copy Chapter

Additional topics to dive into, depending on the problem scope and time remaining.

NoSQL

• Key-value store
• Document store
• Wide column store
• Graph database
• SQL vs NoSQL

Caching Copy Section

• Where to cache
  • Client caching
  • CDN caching
  • Web server caching
  • Database caching
  • Application caching
• What to cache
  • 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 and microservices Copy Section

• Message queues
• Task queues
• Back pressure
• Microservices

Communications Copy Section

• Discuss tradeoffs:
  • External communication with clients - HTTP APIs following REST
  • Internal communications - RPC
• Service discovery

Security Copy Section

Refer to the security section.

Latency numbers Copy Section

See Latency numbers every programmer should know.

Ongoing Copy Section

• Continue benchmarking and monitoring your system to address bottlenecks as they come up
• Scaling is an iterative process

2. Official Technical Reference & Guides (donnemartin/donnemartin.github.io) Copy Chapter

donnemartin.github.io

Personal website powered by Bootstrap, Pelican, and GitHub Pages: http://donnemartin.com/

License Copy Section

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

Copyright 2014 Donne Martin

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.