Scaling Push Messaging for Millions of Devices at Netflix

Ideas you will meet

Event Driven Architecture

Loosely coupled services reacting to events.

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.

• Classic System Designs · 10 minDesign a notification systemSend push, SMS and email notifications reliably at scale: queues per channel, user preferences, retries and avoiding spam.Read hereClose

  The problem

  Applications tell users things: "your order shipped", "someone replied", "your code is 482913". A notification system delivers such messages over several channels:

  • Mobile push through Apple (APNs) and Google (FCM) services.
  • SMS through a telecom provider.
  • Email through an email provider.
  • Sometimes in-app messages or webhooks.

  Requirements: soft real time (seconds, not hours), high volume with spikes, no lost notifications, no duplicate floods, user control over what they receive.

  High-level design

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  Figure SDA-CSD-06-1 · Notification pipeline with a queue per channel · © forcsguy.com

  1. Other services call the notification service (an API or an event) with who and what.
  2. It validates, applies preferences, renders the message from a template and picks the channel.
  3. It places a job on a per-channel queue.
  4. Workers call the third-party provider and record the result.

  Putting a queue between each stage absorbs spikes and lets you scale the channels independently.

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  Figure SDA-CSD-06-2 · Should this event become a notification? · © forcsguy.com

  Image: Océanos y dados · CC0 · Source · Wikimedia Commons: Smartphone Use

  Channels and their providers

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  Figure SDA-CSD-06-3 · One provider per channel · © forcsguy.com

  Collecting contact details

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  Figure SDA-CSD-06-4 · Contact information flow and tables · © forcsguy.com

  Before: one shared queue

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  After: separate lanes

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  What changed

  • A marketing blast no longer delays login codes
  • Each channel scales and fails on its own
  • Priorities can be set per lane

  Supporting data

  Data	Purpose
  Contact info and device tokens	where to send
  User preferences	channels allowed, topics, quiet hours, language
  Templates	consistent text, translations
  Notification log	state per notification (queued, sent, failed), for debugging and dedupe

  Reliability

  • Persist first: write the notification to the log before queueing, so a crash does not lose it.
  • At-least-once delivery from the queue, plus a dedupe check (notification_id already sent?) so retries do not double send.
  • Retries with exponential backoff and jitter; after repeated failures move to a dead-letter queue and alert.
  • Provider failures: use a second provider as a fallback for critical channels such as SMS verification codes.
  • Invalid tokens: providers tell you when a device token is dead; remove it.

  Protecting users and the system

  • Preferences and opt-out are mandatory for marketing messages (and legally required in many places).
  • Rate limit per user and per topic, and collapse bursts into a digest ("12 new comments").
  • Priority: security codes and payment alerts jump the queue ahead of marketing.
  • Scheduling: delay non-urgent notifications until a sensible local time.

  Why the design looks this way

  A first version with one notification server has three weaknesses: it is a single point of failure, everything scales together (database, cache and sending), and slow third-party calls drag down the whole thing at peak. The fixes are the ones in the design: move state out of the server, run several servers, and put a queue per channel between the API and the workers so an outage at one provider (say the SMS vendor) only backs up its own queue.

  Other points worth knowing: providers differ by region (a push service available in one market may not be in another), so keep each provider behind a plug-in interface. Only internal services or verified clients may call the send API, using an app key and secret, or the system becomes a spam relay. Notifications may be delayed or reordered but must never be silently lost, so persist each one before sending and retry from the stored record. And the single most useful health signal is the number of queued notifications: if it keeps growing, add workers.

  Life of a notification

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  Figure SDA-CSD-06-5 · Notification states for tracking · © forcsguy.com

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  Figure SDA-CSD-06-6 · The complete notification path · © forcsguy.com

  Observability

  Track sent, delivered, opened and failed counts per channel and per provider, plus queue depth and age. A growing queue age is the earliest sign something is wrong. Keep a trail so support can answer "did this user get the message?".

  Worked numbers: a steady stream and a blast

  Take 10 million users who each get about 5 notifications a day across push, email and in-app: 50 million a day, about 580 per second on average. That is easy. The hard case is a campaign blast: a push to all 10 million users within 10 minutes means 16,700 sends per second, and the push providers rate-limit each connection, so you need many parallel workers and connections, and you must prevent the blast from delaying a login code.

  Log volume: at about 200 bytes per delivery record, 50 million a day is 10 GB a day. Keeping 30 days of detail is about 300 GB, which is manageable; keep aggregates much longer.

  Provider failure: a push provider that fails for 10 minutes during a blast would otherwise cause 10 million retries at once. The queue absorbs it, workers back off, and delivery continues when the provider recovers.

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  Figure SDA-CSD-06-7 · Separate lanes for urgent and bulk messages · © forcsguy.com

  One notification, start to finish

  1. Event: the order service publishes "order 4711 shipped" with the user id.
  2. Decide: the notification service loads the user's preferences (allowed channels, quiet hours, language), checks the per-user limit (not more than a few promotional messages a day) and the dedupe key (order-4711-shipped), and renders the template.
  3. Enqueue: a message is placed on the right queue with an idempotency key.
  4. Send: a worker calls the provider. On success it records "sent". On a temporary error it retries with backoff. On a permanent error (for example, an invalid device token) it removes the token and records the failure.
  5. Track: delivery and open receipts update the record; the data feeds dashboards and "did not receive" support tools.

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  Figure SDA-CSD-06-8 · Deduplicate before sending · © forcsguy.com

  Mistakes people make

  • Sharing one queue between login codes and marketing. A blast delays the code people are waiting for. Use separate lanes.
  • No per-user rate limit. A bug that emits an event in a loop can send a user a hundred messages.
  • Ignoring preferences and legal rules. Unsubscribe, quiet hours and consent are requirements, not nice-to-haves.
  • Keeping dead device tokens. Providers tell you when a token is invalid. Remove it, or your sender reputation and efficiency suffer.
  • Retry storms. Back off with jitter and cap retries, or you will hammer a provider that is already failing.
  • Time zones. "9 am local time" needs the user's zone, and the send time must be scheduled per zone.

  Try it yourself

  Use the System Builder to see a burst absorbed by a queue, and read the rule engine lesson for how triggers, cooldowns and idempotent actions fit the "decide" step.

  More on this site

  Related reading

  • Avoiding insurmountable queue backlogsAmazon Builders' Library · Amazon Web ServicesBlogs
  • Timeouts, retries, and backoff with jitterAmazon Builders' Library · Amazon Web ServicesBlogs
  • Event SourcingMartin FowlerBlogs
  • Uber's Real-Time Push PlatformUber Engineering · UberCase Studies

  Show 6 moreShow fewer

  • NATS ServerNATS · CNCF / NATSGitHub
  • CQRSMartin FowlerBlogs
  • CQRS patternMicrosoft · Microsoft AzureDocumentation
  • Scaling Push Messaging for Millions of Devices at NetflixNetflix engineers · NetflixCase Studies
  • Reference Architectures for Big Data SystemsCmu engineers · CmuCase Studies
  • Messaging System Model at DropboxDropbox engineers · DropboxCase Studies

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