Instagram Architecture: 14 Million users, Terabytes of Photos, 100s of Instances, Dozens of Technologies - High Scalability -

Ideas you will meet

Distributed Systems

Consensus, time, failure and the fundamentals of scale-out.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

• System Design Foundations · 9 minWhat system design is, and how to approach itA repeatable way to turn a vague request like 'design Twitter' into a concrete, defensible architecture.Read hereClose

  Design is choosing under constraints

  A system is the set of servers, databases, caches, queues and networks that together deliver a product. System design is the work of deciding which pieces you need, how they connect, and what you give up with each choice.

  It matters because code that works for ten users often fails at ten million. Not because the code is wrong, but because new problems appear with scale: one machine is no longer enough, networks drop messages, disks fail, and two users change the same data at once.

  A five-step approach

  You can use the same sequence for a real project or an interview.

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  Figure SDA-FND-01-1 · Five-step design approach · © forcsguy.com

  1. Clarify requirements. Separate what the system must do (functional) from how well it must do it (non-functional). Ask until the problem is small enough to solve.

  • Functional: "users can post a photo and follow other users"
  • Non-functional: "feeds load in under 300 ms", "uploads are never lost", "the site stays up if one data center fails"

  2. Estimate scale. A rough guess of requests per second and data size tells you whether one database is enough or you need to split data across many. Lesson 3 covers this.

  3. Sketch the high-level design. Boxes and arrows: clients, a load balancer, application servers, a database, maybe a cache and a queue. Keep it simple first.

  4. Go deep on the hard parts. Pick the two or three places where the problem is genuinely difficult, such as how a feed is built, and design those carefully.

  5. Check bottlenecks and failures. Ask what breaks first as traffic grows, and what happens when each component fails. Fix single points of failure.

  Requirements: the part people skip

  Compare these two requests:

  Vague	Useful
  "Build a chat app"	"1-to-1 and group chat for 10 million daily users, messages delivered in under a second, history kept forever, no message ever lost"

  The second one tells you that you need persistent storage, a way to push messages to online users and a durability guarantee. The first tells you nothing.

  Trade-offs are the whole game

  Every decision has a cost:

  • A cache makes reads fast but can serve stale data.
  • Replicating data across data centers survives failures but makes writes slower or harder to keep consistent.
  • Splitting an application into microservices lets teams work independently but adds network calls that can fail.

  State the cost whenever you state a choice: "I would use a cache here, accepting that a user may see data up to a minute old, because reads outnumber writes 100 to 1."

  Common mistakes

  • Naming technologies before understanding the problem ("we will use Kafka")
  • Designing for a billion users when you have a thousand. Plan for roughly 10 times your expected scale, not 1000 times.
  • Ignoring failure. Networks and machines fail constantly at scale, so failure handling is part of the design, not an add-on.
  • Never writing down assumptions. When estimates are wrong, the design built on them is wrong too.

  In the next lesson we start with the simplest question: how does one server become many?

  Real-world reading

  Related reading

  • The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 SecondsHigh Scalability engineers · High ScalabilityCase Studies
  • LISA '10 - Operations at Twitter: Scaling Beyond 100 Million UsersYouTubeYouTube
  • Scaling Load Balancing Infra to Support 1.3 Billion Users at Facebook - Patrick Shuff, Production Engineer at FacebookFacebookYouTube
  • Scaling Mailbox - From 0 to One Million Users in 6 Weeks and 100 Million Messages Per Day - High Scalability -High Scalability engineers · High ScalabilityCase Studies

  Show 6 moreShow fewer

  • Designing Data-Intensive ApplicationsMartin Kleppmann · O'ReillyBooks
  • Azure Architecture CenterMicrosoft · Microsoft AzureDocumentation
  • Instagram Architecture: 14 Million users, Terabytes of Photos, 100s of Instances, Dozens of Technologies - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • How Discord Scaled Elixir to 5,000,000 Concurrent UsersDiscordCase Studies
  • Designing a Microservices Architecture for FailureRisingstack engineers · RisingstackCase Studies
  • Spotify EngineeringSpotify engineers · SpotifyBlogs

  Open this lesson with its quiz and exercise

• System Design Foundations · 9 minBack-of-the-envelope estimationQuick arithmetic that tells you whether you need one server or a thousand, and which resource will run out first.Read hereClose

  Why estimate at all

  You do not need exact numbers. You need to know the order of magnitude: is it 100 requests per second or 100,000? Is the data 10 GB or 10 PB? Those answers change the architecture completely, and you can get them with simple arithmetic.

  Numbers to remember

  Powers of two for sizes:

  Unit	Approx. size
  1 KB	a thousand bytes
  1 MB	a million bytes
  1 GB	a billion bytes
  1 TB	a trillion bytes
  1 PB	a thousand TB

  Time: one day is about 86,400 seconds, which we round to 100,000 (10^5) to make mental maths easy.

  Rough latencies (the exact values change with hardware, the ratios matter):

  Operation	About
  Read from memory	100 ns
  Read 1 MB sequentially from memory	tens of microseconds
  SSD random read	100 microseconds
  Round trip inside one data center	0.5 ms
  Disk seek	10 ms
  Round trip across continents	100 to 150 ms

  Two lessons fall out of this table: memory beats disk by orders of magnitude, and crossing a network or an ocean is expensive, so avoid unnecessary round trips.

  A method

  1. Start from users: daily active users (DAU).
  2. Actions per user per day, giving total requests per day.
  3. Divide by 100,000 for average requests per second (QPS).
  4. Multiply by 2 to 5 for peak QPS.
  5. Size per item times items per day gives storage per day; multiply by retention and replication.

  The method as a picture

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  Figure SDA-FND-03-1 · From users to requests and storage · © forcsguy.com

  Worked example: a short-link service

  • 100 million new links per month.
  • Reads outnumber writes 10 to 1.

  Writes per second: 100,000,000 / (30 x 86,400) is about 40 per second. Reads: about 400 per second. Peak maybe 2,000 reads per second.

  Storage: each record is about 500 bytes (the short key, the long URL, a date). 100 million per month x 12 months x 5 years = 6 billion records x 500 bytes = 3 TB. Easily one modern database over a few years, with replicas for safety.

  What did we learn? The write rate is tiny, reads are modest, and the whole data set is small. This is not a problem that needs exotic technology, so the design should be simple. That conclusion is the point of estimating.

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  Figure SDA-FND-03-2 · Turning users into load and storage · © forcsguy.com

  Rules of thumb

  • Round aggressively. 86,400 becomes 100,000. 2.7 becomes 3.
  • Write units next to every number. Most mistakes are unit mistakes (bits versus bytes, MB versus GB).
  • State your assumptions. If someone disagrees with 10 requests per user, you can change one number and recompute.
  • Sanity check against reality. If your estimate says a small app needs a million servers, an assumption is off.

  Real-world reading

  Related reading

  • Cockroach Labs BlogCockroach Labs engineers · Cockroach LabsBlogs
  • The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 SecondsHigh Scalability engineers · High ScalabilityCase Studies
  • AWS Database BlogAWS database teams · Amazon Web ServicesBlogs
  • LISA '10 - Operations at Twitter: Scaling Beyond 100 Million UsersYouTubeYouTube

  Show 6 moreShow fewer

  • Instagram Architecture: 14 Million users, Terabytes of Photos, 100s of Instances, Dozens of Technologies - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • Playfish's Social Gaming Architecture - 50 Million Monthly Users and Growing - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • CockroachDBCockroach Labs · GitHubGitHub
  • AWS Compute BlogAmazon Web Services engineers · Amazon Web ServicesBlogs
  • Yelp Engineering BlogYelp engineers · YelpBlogs
  • Telstra BlogTelstra engineers · TelstraBlogs

  Open this lesson with its quiz and exercise