Cloudflare Learning Center: What is a CDN?

Ideas you will meet

CDN

Serve content from edge locations close to users.

DNS

How names resolve, TTLs, and DNS-based traffic routing.

Caching

Cache-aside, write-through, eviction and invalidation strategies.

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 · 11 minNetworking, APIs and CDNsWhat happens between a click and your server: DNS, HTTP, API styles, and how a CDN moves content closer to users.Read hereClose

  The journey of a request

  Before your code runs, a lot happens. When a browser loads https://shop.example.com:

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  Figure SDA-FND-09-1 · Request journey: DNS, CDN, origin · © forcsguy.com

  1. DNS turns the name into an IP address. Answers are cached at several levels for a time given by the record's TTL, so changes spread gradually.
  2. A connection is opened (TCP) and secured (TLS). Each step costs round trips, so long distances hurt.
  3. HTTP carries the request and response.

  Latency is mostly physics: a round trip across an ocean costs over 100 ms no matter how fast your code is. The only fixes are fewer round trips and moving data closer to the user.

  Image: Syced · CC0 · Source · Wikimedia Commons: Fujikura optic fiber at Science Museum

  Name lookup is a short chain of questions, each answered by a more specific server:

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  Figure SDA-FND-09-2 · How a name becomes an address · © forcsguy.com

  Image: Howe's Adventures & Achievements of Americans · Public domain · Source · Wikimedia Commons: Transatlantic submarine cable map

  CDNs

  A content delivery network is a worldwide set of servers (edges) that keep copies of your content close to users.

  • Great for static files: images, video, scripts, stylesheets, and often whole pages that rarely change.
  • Cuts latency, absorbs traffic spikes and shields your origin from load.
  • Controlled by cache headers (Cache-Control: max-age=...) and purge APIs when content changes.
  • Use a versioned file name (app.3f9a.js) so you can cache for a year and still ship updates instantly.

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  Figure SDA-FND-09-3 · Content delivery in tiers · © forcsguy.com

  Before: everyone goes to the origin

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  After: a CDN serves copies near users

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  What changed

  • Users get files from a nearby location
  • The origin handles far fewer requests
  • Changed files need invalidation or versioned names

  Choosing an API style

  Style	Strength	Watch out for
  REST over HTTP/JSON	Simple, cacheable, universal	Over-fetching, many round trips for complex screens
  GraphQL	Client asks for exactly the fields it needs	Query cost control, caching is harder
  gRPC	Fast binary protocol, strong contracts	Needs tooling, awkward in browsers
  SOAP	XML messages with a formal contract (WSDL) and built-in standards for security and transactions, still common in banks and older enterprise systems	Verbose, heavier tooling, rarely chosen for new public APIs
  WebSockets / SSE	Server can push live updates	Long-lived connections need extra capacity and care

  A good default is REST for public APIs, gRPC between internal services if you need speed, and WebSockets only where live push is truly needed.

  Designing good APIs

  • Version them so old clients keep working.
  • Paginate list endpoints (cursor-based pagination stays stable as data changes).
  • Make writes idempotent with idempotency keys, so a retry does not double-charge.
  • Return clear errors with correct status codes.
  • Authenticate and rate limit at the edge, often in an API gateway that also handles routing, logging and TLS.

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  Figure SDA-FND-09-4 · API gateway in front of services · © forcsguy.com

  More on this site

  Related reading

  • High Performance Browser NetworkingIlya Grigorik · O'ReillyBooks
  • HTTP/3: the past, the present, and the futureCloudflareBlogs
  • MDN: HTTP cachingMDN contributors · MozillaDocumentation
  • Cloudflare BlogCloudflare engineers · CloudflareBlogs

  Show 6 moreShow fewer

  • The Road to QUICCloudflareBlogs
  • Flannel: An Application-Level Edge Cache to Make Slack ScaleSlack Engineering · SlackCase Studies
  • Cloudflare Learning Center: What is a CDN?CloudflareDocumentation
  • Cloudflare EngineeringCloudflare engineers · CloudflareBlogs
  • Justin.tv's Live Video Broadcasting Architecture - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • Service Workers at SlackSlack engineers · SlackCase Studies

  Open this lesson with its quiz and exercise

• System Design Foundations · 11 minCachingHow keeping popular data in fast memory makes systems quicker, and the invalidation problems that come with it.Read hereClose

  Why a cache helps

  Reading from memory is thousands of times faster than a database round trip (lesson 3). A cache keeps copies of frequently needed data in fast storage so most requests never reach the slower source. The results: lower latency, less load on the database, and lower cost.

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  Figure SDA-FND-06-1 · Cache-aside read path · © forcsguy.com

  Before: every read hits the database

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  After: a cache answers most reads

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  What changed

  • Most reads never reach the database
  • Responses are faster because memory is quicker than disk
  • The cache can be stale, so you decide how long is acceptable

  Where caches live

  • In the browser: controlled with HTTP headers such as Cache-Control.
  • At the edge (CDN): copies of pages and files near the user (lesson 9).
  • In the application tier: a shared cache like Redis or Memcached.
  • Inside the database: buffer pools and query caches.

  You can use several at once, each answering whatever it can.

  Common strategies

  Strategy	How it works	Good for
  Cache-aside	App checks cache, on miss reads the database and fills the cache	the default for most read-heavy data
  Read-through	The cache itself loads from the database on a miss	simpler app code
  Write-through	Writes go to the cache and the database together	data you read right after writing
  Write-behind	Writes go to the cache first and reach the database later	very high write rates, if you can risk losing a little

  Write-behind is fast but dangerous: if the cache fails before it flushes, writes are lost.

  Writes can use the cache in three different ways. The sequence shows when the database learns about the change:

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  Figure SDA-FND-06-2 · Write-through: update the cache and the database together · © forcsguy.com

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  Figure SDA-FND-06-3 · Write-back: acknowledge first, save later · © forcsguy.com

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  Figure SDA-FND-06-4 · Write-around: skip the cache on writes · © forcsguy.com

  Watch one read with and without a warm cache. Press Play both together, and use the Simulate tab to slow the network and see the gap grow.

  Before: no cache

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  After: cache hit

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  What changed

  • The database is not asked at all
  • Two network hops disappear from the request
  • The saving grows as the network gets slower

  The hard part: invalidation

  There are only two hard things in computer science: cache invalidation and naming things.

  A cache holds a copy, and copies go stale. Your options:

  1. Time to live (TTL): entries expire after a set time. Simple, but data can be stale until then.
  2. Explicit invalidation: delete or update the cache entry when the source changes. Fresher, but you must never forget a code path.
  3. Versioned keys: put a version in the key (product:42:v7) so a change simply starts using a new key.

  Most real systems combine a TTL (a safety net) with explicit invalidation on writes.

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  Figure SDA-FND-06-5 · Defending the database from lookups of keys that do not exist · © forcsguy.com

  Eviction: what to drop when full

  Memory is limited, so a cache removes items when it fills. Common policies: LRU (least recently used), LFU (least frequently used) and FIFO. LRU is a good default.

  Failure modes to know

  • Cache stampede: a hot key expires and many requests rebuild it at once. Fix with a lock, request coalescing, or adding random jitter to expiry times.
  • Cache penetration: requests for keys that do not exist always miss and reach the database. Fix by caching "not found" briefly, or with a Bloom filter.
  • Cold start: after a restart the cache is empty and the database takes the full load. Warm the cache or ramp traffic up.
  • Inconsistency: two writers update the cache and database in different orders. Prefer deleting the cache entry on write over updating it.

  A worked example: what a cache is really worth

  Suppose a product page reads from a database in 20 ms and from a cache in 1 ms, and the site serves 10,000 reads per second.

  Cache hit rate	Average latency	Reads reaching the database
  0% (no cache)	20 ms	10,000 per second
  50%	10.5 ms	5,000 per second
  90%	2.9 ms	1,000 per second
  99%	1.2 ms	100 per second

  The average latency is hit x 1 + (1 - hit) x 20. Notice two things. First, going from 90% to 99% barely changes the average (2.9 to 1.2 ms) but cuts database load ten times. That is usually the real prize: a cache is a way to protect the database. Second, the first 50% is cheap and the last 9% is expensive: it needs a much bigger cache, because the remaining misses are the long tail of rarely requested items.

  How big should it be? Traffic is usually skewed: a small share of items gets most requests (often described as "80% of requests go to 20% of items", and for popular content it is more extreme). Estimate your working set: the items requested within your cache lifetime. If 2 million product pages exist but 100,000 get 90% of the traffic, a cache that holds 150,000 of them at about 5 KB each needs only 750 MB. Measure the hit rate while changing the size, and stop growing when the curve flattens.

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  Figure SDA-FND-06-6 · Hit rate rises quickly, then flattens · © forcsguy.com

  Stampedes: when a popular key expires

  Take a homepage fragment read 5,000 times a second and cached for 60 seconds. At the moment it expires, thousands of requests miss at once, and every one of them rebuilds it from the database. The database, which was lightly loaded because of the cache, suddenly takes a spike. If the rebuild is slow, requests pile up, the spike lasts longer, and the system can fall over exactly when it is busiest.

  Ways to defend:

  • Request coalescing (single flight). Let one request rebuild the value while the others wait for its result.
  • A short lock on rebuild. The first request takes a lock and rebuilds; the rest either wait briefly or serve the old value.
  • Stale-while-revalidate. Keep serving the expired value for a few more seconds while one background request refreshes it. Users see slightly old data and never see a spike.
  • Jittered expiry. Add a random amount to each TTL (for example 60 seconds plus up to 10) so thousands of keys written together do not all expire together.
  • Refresh ahead. For a handful of known-hot keys, refresh them on a schedule before they expire.

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  Figure SDA-FND-06-7 · One request rebuilds, the others wait or use the old value · © forcsguy.com

  Mistakes people make

  • Caching without a plan for invalidation. "Cache it for an hour" is a promise that users may see hour-old data. Decide what staleness each kind of data tolerates (a like count: minutes; a price or permission: seconds or never).
  • Caching per-user data under a shared key. If a personalised page is cached under a key that does not include the user, the next visitor sees someone else's data. Include everything that changes the response in the key, or do not cache it.
  • Treating the cache as the source of truth. Caches lose data on restart or eviction. The database must be able to rebuild everything.
  • Ignoring the hot key. One enormously popular key can overload a single cache node. Replicate that key across nodes or add a small in-process cache in front.
  • Not measuring. Track hit rate, evictions, memory use and the latency of misses. A cache you do not measure is a guess.

  Try it yourself

  In the System Builder, open "The database becomes the bottleneck". Run it without a cache and watch the database turn red, then add a cache and try different sizes. In the sandbox, raise the "popular-item skew" slider and notice how much a small cache helps when a few items dominate. You can also open the cached design on the free canvas and try wiring the cache in front of different parts.

  More on this site

  Related reading

  • Flannel: An Application-Level Edge Cache to Make Slack ScaleSlack Engineering · SlackCase Studies
  • Redis UniversityRedis · Redis Ltd.Courses
  • Redis BlogRedis engineers · Redis Ltd.Blogs
  • RedisRedis · Redis Ltd.GitHub

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  • Redis DocumentationRedis · Redis Ltd.Documentation
  • TAO: Facebook's Distributed Data Store for the Social Graph (paper)Bronson et al. · Meta (Facebook)Papers
  • Build Your Own XCodeCrafters · GitHubGitHub
  • Memory Optimization in Redis at WattpadWattpad engineers · WattpadCase Studies
  • Redis Fleet at HerokuHeroku engineers · HerokuCase Studies
  • Redis at TrivagoTrivago engineers · TrivagoCase Studies

  Open this lesson with its quiz and exercise

• Classic System Designs · 13 minDesign a video streaming serviceUpload, process and stream video to millions: transcoding pipelines, adaptive bitrate and using a CDN without going bankrupt.Read hereClose

  The problem

  A video streaming service lets users upload videos and lets many others watch them smoothly on any device and connection.

  Requirements

  • Upload large files reliably.
  • Playback starts quickly and does not buffer.
  • Works on phones, TVs and browsers, on fast and slow networks.
  • Huge scale, with traffic dominated by viewing, not uploading.
  • Keep cost under control.

  Estimates that shape the design

  • Video files are big: a few GB uploaded, then several versions stored.
  • Views outnumber uploads by orders of magnitude, and bandwidth is the main bill.

  High-level design

  Two separate flows: upload and process, and stream.

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  Figure SDA-CSD-10-1 · Upload-and-process and streaming flows · © forcsguy.com

  Upload

  • The client asks the API for an upload URL and sends the file directly to object storage, in chunks that can resume after a network drop. Your application servers never carry the video bytes.
  • When the upload finishes, storage emits an event that starts processing.

  Processing pipeline

  Raw video cannot be streamed to everyone. The pipeline:

  1. Splits the video into short segments, so they can be processed in parallel.
  2. Transcodes each into several resolutions and bitrates (for example 240p to 4K) and several formats and codecs.
  3. Creates thumbnails, subtitles, and sometimes watermarks.
  4. Runs checks: content moderation, copyright matching, virus scanning.
  5. Writes a manifest listing the available versions.
  6. Updates the metadata (title, status) so the video becomes visible.

  Model it as a graph of tasks run by a workflow engine over a queue and a pool of workers, so steps parallelise, retry independently and scale with load.

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  Figure SDA-CSD-10-2 · Transcoding task graph · © forcsguy.com

  Inside the transcoding pipeline

  Transcoding is heavy and varies by creator (some want watermarks, some supply thumbnails, some upload very high resolution). So define the work as a task graph (a DAG): the original is split into video, audio and metadata; the video stage fans out into encodings at several resolutions and bitrates, a thumbnail and optionally a watermark; the audio stage encodes separately; a final stage merges. Independent branches run in parallel.

  The parts that make it run:

  • Pre-processor splits the video into short independently playable chunks (aligned to groups of pictures), builds the graph from configuration, and keeps the chunks in temporary storage so a failed step can retry without starting over.
  • Graph scheduler cuts the graph into stages and puts the tasks on a queue.
  • Resource manager keeps a priority queue of tasks, a priority queue of worker availability and a list of what is running, and pairs the most important task with the best-suited worker.
  • Workers run specific tasks; temporary storage holds metadata in memory and media in blob storage and is cleaned when the job ends.

  Two vocabulary points: a container (.mp4, .mov) is the wrapper holding video, audio and metadata; a codec (H.264, VP9, HEVC) compresses the content. Raw footage can be hundreds of GB per hour, which is the first reason to transcode; the others are device compatibility and adapting quality to bandwidth.

  Cloud services instead of building everything

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  Figure SDA-CSD-10-3 · Two paths: video and everything else · © forcsguy.com

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  Figure SDA-CSD-10-4 · Upload flow with completion events · © forcsguy.com

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  Figure SDA-CSD-10-5 · Metadata is updated in parallel with the upload · © forcsguy.com

  Streaming

  • The player downloads the manifest, then fetches small segments over plain HTTP (the HLS and MPEG-DASH formats work this way).
  • Adaptive bitrate: the player measures its throughput and switches between quality levels segment by segment, so playback adapts to the connection instead of stopping.
  • Segments are static files, ideal for CDN caching.

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  Figure SDA-CSD-10-6 · One video, many encodings · © forcsguy.com

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  Figure SDA-CSD-10-7 · Popular videos from the CDN, the rest from storage servers · © forcsguy.com

  The CDN and cost

  CDN traffic is expensive. Practical tactics:

  • Serve popular videos from edge caches; keep the long tail only at the origin or in a regional cache.
  • Pre-position new releases that you expect to be popular.
  • Choose bitrates carefully, and use efficient codecs for popular content (extra encoding cost is repaid by lower bandwidth).
  • Large services run their own cache servers inside ISPs to cut transit cost.

  Before: serve from the origin

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  After: serve from a CDN

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  What changed

  • Viewers stream from a nearby edge, so less buffering
  • The origin handles only cache misses
  • Bandwidth cost falls because edges absorb the traffic

  Metadata and other parts

  • A database for video metadata, ownership, comments and counts, with caches for the hot reads.
  • Search and recommendations as separate services fed by events.
  • View counts are written to a queue and aggregated, not updated row by row on every view.
  • Security: signed URLs for private videos, and DRM for premium content.

  Speed, safety and cost, in detail

  • Parallel and resumable uploads: the client cuts the file into chunks and uploads them independently, so a drop resumes at the last chunk. Put upload points near users (a CDN works as an upload edge).
  • Loose coupling: a queue between download, encode and publish steps lets each stage work as soon as items arrive instead of waiting for the previous stage to finish a whole file.
  • Pre-signed upload URLs: the API issues a short-lived address that authorises one upload to one location, so clients never hold storage credentials.
  • Protecting content: DRM, encrypted streams with an authorisation check at playback, and visible watermarks.
  • Cost: a rough sum shows CDN delivery alone could cost on the order of a hundred thousand dollars a day at this scale, and views follow a long tail. So serve only the popular videos from the CDN, serve the rest from your own storage servers, keep fewer encoded versions of rarely watched videos (encode short ones on demand), keep regional favourites in their region, and at very large scale partner with internet providers or run your own delivery network. Always check your own viewing data first.

  Errors. Separate recoverable errors (a chunk fails to encode: retry a few times) from non-recoverable ones (malformed file: stop the job and return a clear error). A playbook per component: retry uploads and encodes; if an old client cannot chunk, chunk on the server; regenerate a graph or reschedule a task if the pre-processor or scheduler fails; use replicas for queues, caches and databases; promote a replica if a primary database fails.

  Live streaming differs in needing lower latency (a different protocol), less parallelism (chunks are small and processed as they arrive) and error handling that cannot afford long retries. Takedowns need both upload-time checks and user flagging.

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  Figure SDA-CSD-10-8 · Chunked, resumable and regional uploads · © forcsguy.com

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  Figure SDA-CSD-10-9 · Pre-signed upload URL · © forcsguy.com

  Failure handling

  Every processing task is idempotent and tracked, so a failed or retried segment does not corrupt the output. Failures go to a dead-letter queue and set a visible error status. Use multiple regions for storage and CDN origins so viewing survives a regional outage.

  Worked numbers: why a CDN is not optional

  Suppose 5 million viewers are watching at the same time, each at an average of 3 Mbit/s.

  • Egress: 5,000,000 x 3 Mbit/s = 15 Tbit/s. No single data centre network, and no sensible origin, can send that. Video is served from edge caches close to viewers; the origin only sends each popular segment to each edge once.
  • Why caching works so well: video segments are the same files for everyone watching, and demand is highly skewed. A popular show may be 90%+ cache hits at the edge, so the origin sees a small fraction of the traffic.
  • Upload side: if 1 million videos are uploaded a day at an average of 300 MB, that is 300 TB a day of originals, before the several encoded versions of each (often 5 to 8 renditions, which together can take as much space as the original or more). The long tail of rarely watched videos is the main storage cost, which is why some systems encode extra renditions only when a video becomes popular.

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  Figure SDA-CSD-10-10 · Fast transcoding by splitting a video into segments · © forcsguy.com

  How the player picks the quality

  Streaming uses adaptive bitrate (ABR): the video exists in several renditions (for example 240p to 1080p), each cut into the same short segments, and the player decides per segment which one to request.

  1. The player downloads the playlist (manifest) listing the renditions and segments.
  2. It starts with a conservative rendition so playback begins quickly.
  3. For each segment it measures how fast the download went and how full its buffer is.
  4. If the buffer is healthy and the network fast, it steps up a rendition; if the buffer is draining, it steps down before the video stalls.

  Because every rendition has identical segment boundaries, switching is seamless. Viewers see a slightly softer picture instead of a spinning wheel.

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  Figure SDA-CSD-10-11 · The player adapts to the network segment by segment · © forcsguy.com

  Mistakes people make

  • Encoding every rendition for every upload. Most videos are barely watched. Encode a base set first and add more when a video proves popular.
  • Serving from the origin. Without a CDN the origin saturates and far-away viewers buffer.
  • Cold caches on a launch. A new popular release hits empty edges. Pre-warm the CDN for expected hits.
  • Uploads that cannot resume. Large files over mobile networks fail often; use chunked, resumable uploads.
  • Forgetting access control. Public segment URLs are easily shared. Use signed, expiring URLs, and DRM where licences require it.
  • Ignoring start-up time. Viewers leave if playback takes more than a couple of seconds. Short first segments, a good first rendition and a nearby edge matter more than peak quality.

  Try it yourself

  Open the System Builder's viral link challenge to see how much traffic a CDN takes off your servers, and the WebRTC lesson for when low-latency live video needs a different approach than segment-based streaming.

  More on this site

  Related reading

  • HTTP/3: the past, the present, and the futureCloudflareBlogs
  • Cloudflare BlogCloudflare engineers · CloudflareBlogs
  • The Road to QUICCloudflareBlogs
  • Cloudflare EngineeringCloudflare engineers · CloudflareBlogs

  Show 6 moreShow fewer

  • High Performance Browser NetworkingIlya Grigorik · O'ReillyBooks
  • MDN: HTTP cachingMDN contributors · MozillaDocumentation
  • Meta Engineering BlogMeta Engineering · MetaBlogs
  • Flannel: An Application-Level Edge Cache to Make Slack ScaleSlack Engineering · SlackCase Studies
  • Netflix ResearchNetflix engineers · NetflixBlogs
  • Nvidia Engineering BlogNvidia engineers · NvidiaBlogs

  Open this lesson with its quiz and exercise