Dev Handbook
Handbook/System design

System design appendix

Powers of two, latency figures, architecture references, and engineering resources.

8 min read·Dev Handbook
In this article

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

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

Latency numbers every programmer should know

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

Fig 1Latency spans many orders of magnitude
STUDY MODEL
Inspect one part at a timeCPU and caches
  1. CPU and caches

    Illustrative values: CPU cycle 1 ns, L1 cache 0.5 ns, branch mispredict 5 ns, and L2 cache 7 ns.

  2. Memory

    Mutex lock 25 ns and main memory 100 ns are illustrative values from the original reference.

  3. Storage

    The original reference gives an SSD read as approximately 150 μs.

  4. Network

    A network round trip is illustrated as 150 ms. Hardware, locality, load, and network distance change these values. Source: Jeff Dean’s latency numbers.

These are historical approximate teaching values, not measurements of your hardware.

How to read this model

A logarithmic comparison dial with an operation selector. The object is a teaching analogy. Inspecting a part highlights its role; it does not execute or simulate the software. Cabinet trays are records, workpieces are data, and latches are checks. Unless an explanation states a quantity, compartments and sample workpieces do not denote data counts. Part dimensions are not a quantitative scale.

Source(s) and further reading

Additional system design questions

Common system design questions, with links to resources for each design.

Question Reference(s)
Design a file sync service like Dropbox youtube.com
Design a search engine like Google queue.acm.orgstackexchange.comardendertat.comstanford.edu
Design a scalable web crawler like Google quora.com
Design Google docs code.google.comneil.fraser.name
Design a key-value store like Redis codecapsule.comallthingsdistributed.com
Design a cache system like Memcached slideshare.net
Design a recommendation system like Amazon's hulu.comijcai13.org
Design a tinyurl system like Bitly n00tc0d3r.blogspot.com
Design a chat app like WhatsApp highscalability.com
Design a picture sharing system like Instagram highscalability.comhighscalability.com
Design the Facebook news feed function quora.comquora.comslideshare.net
Design the Facebook timeline function facebook.comhighscalability.com
Design the Facebook chat function erlang-factory.comfacebook.com
Design a graph search function like Facebook's facebook.comfacebook.comfacebook.com
Design a content delivery network like CloudFlare figshare.com
Design a trending topic system like Twitter's michael-noll.comsnikolov.wordpress.com
Design a random ID generation system blog.twitter.comgithub.com
Return the top k requests during a time interval cs.ucsb.eduwpi.edu
Design a system that serves data from multiple data centers highscalability.com
Design an online multiplayer card game indieflashblog.combuildnewgames.com
Design a garbage collection system stuffwithstuff.comwashington.edu
Design an API rate limiter https://stripe.com/blog/
Design a Stock Exchange (like NASDAQ or Binance) Jane StreetGolang ImplementationGo Implementation
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Real world architectures

Articles on how real world systems are designed.

Fig 2Writes prepare data for several read paths
STUDY MODEL
Inspect one part at a timeWrite path
  1. Write path

    The post composer enters the Write API, ingester, and fan-out service.

  2. Indexes and timelines

    Search index and Earlybird shards support search; timeline cache and Redis lists support timeline reads.

  3. Read composition

    Blender and timeline services compose results for web and mobile clients.

  4. Other delivery

    Push compute supports HTTP and mobile push, while Hadoop batch jobs support offline processing.

The architecture has separate fan-out, search, timeline, push, and batch paths.

How to read this model

A publishing sorter beside a timeline archive and delivery console. The object is a teaching analogy. Inspecting a part highlights its role; it does not execute or simulate the software. Cabinet trays are records, workpieces are data, and latches are checks. Unless an explanation states a quantity, compartments and sample workpieces do not denote data counts. Part dimensions are not a quantitative scale.

Source: Twitter timelines at scale

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
Type System Reference(s)
Data processing MapReduce - Distributed data processing from Google research.google.com
Data processing Spark - Distributed data processing from Databricks slideshare.net
Data processing Storm - Distributed data processing from Twitter slideshare.net
Data store Bigtable - Distributed column-oriented database from Google harvard.edu
Data store HBase - Open source implementation of Bigtable slideshare.net
Data store Cassandra - Distributed column-oriented database from Facebook slideshare.net
Data store DynamoDB - Document-oriented database from Amazon harvard.edu
Data store MongoDB - Document-oriented database slideshare.net
Data store Spanner - Globally-distributed database from Google research.google.com
Data store Memcached - Distributed memory caching system slideshare.net
Data store Redis - Distributed memory caching system with persistence and value types slideshare.net
File system Google File System (GFS) - Distributed file system research.google.com
File system Hadoop File System (HDFS) - Open source implementation of GFS apache.org
Misc Chubby - Lock service for loosely-coupled distributed systems from Google research.google.com
Misc Dapper - Distributed systems tracing infrastructure research.google.com
Misc Kafka - Pub/sub message queue from LinkedIn slideshare.net
Misc Zookeeper - Centralized infrastructure and services enabling synchronization slideshare.net
Add an architecture Contribute

Company architectures

Company Reference(s)
Amazon Amazon architecture
Cinchcast Producing 1,500 hours of audio every day
DataSift Realtime datamining At 120,000 tweets per second
Dropbox How we've scaled Dropbox
ESPN Operating At 100,000 duh nuh nuhs per second
Google Google architecture
Instagram 14 million users, terabytes of photosWhat powers Instagram
Justin.tv Justin.Tv's live video broadcasting architecture
Facebook Scaling memcached at FacebookTAO: Facebook's distributed data store for the social graphFacebook's photo storageHow Facebook Live Streams To 800,000 Simultaneous Viewers
Flickr Flickr architecture
Mailbox From 0 to one million users in 6 weeks
Netflix A 360 Degree View Of The Entire Netflix StackNetflix: What Happens When You Press Play?
Pinterest From 0 To 10s of billions of page views a month18 million visitors, 10x growth, 12 employees
Playfish 50 million monthly users and growing
PlentyOfFish PlentyOfFish architecture
Salesforce How they handle 1.3 billion transactions a day
Stack Overflow Stack Overflow architecture
TripAdvisor 40M visitors, 200M dynamic page views, 30TB data
Tumblr 15 billion page views a month
Twitter Making Twitter 10000 percent fasterStoring 250 million tweets a day using MySQL150M active users, 300K QPS, a 22 MB/S firehoseTimelines at scaleBig and small data at TwitterOperations at Twitter: scaling beyond 100 million usersHow Twitter Handles 3,000 Images Per Second
Uber How Uber scales their real-time market platformLessons Learned From Scaling Uber To 2000 Engineers, 1000 Services, And 8000 Git Repositories
WhatsApp The WhatsApp architecture Facebook bought for $19 billion
YouTube YouTube scalabilityYouTube architecture

Company engineering blogs

Architectures for companies you may work with.

Questions in this domain often reuse the same patterns.

Source(s) and further reading

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

Terms: GLOSSARY · Roadmap: ROADMAP

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