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Best practices when sending FCM messages at scale

Whether you are growing a nascent app or already running a high-traffic service, you can benefit from this guide's insights and recommendations on how to scale smoothly with FCM. These concepts and practices can help you avoid negative impacts when you need to send large volumes of messages.

Message Request: A FCM message request; used interchangeably with "request", "message", or "query".

Requests-per-second (RPS): A metric to describe the rate of incoming requests to FCM; used interchangeably with Queries-per-second (QPS).

Quota Tokens, Token Buckets, and Refills: When sending messages against the FCM HTTP v1 API, each request consumes an allotted Quota Token in a given time window. This window, called a "Token Bucket", refills to full at the end of the time window. For example: the HTTP v1 API allots 600K Quota Tokens for each 1-minute Token Bucket, which refills to full at the end of each 1-minute window.

Server-side Throttling: When traffic volume exceeds the FCM service's capacity, requests beyond serving capacity are rejected to rate-limit ingress flow. 429 error responses with retry-after headers may be returned to indicate that you should wait a given time period before retrying the request.

Client-side Throttling: When clients observe request failures, high latency, or 429 errors, they should voluntarily rate-limit egress flow to avoid exacerbating congestion.

Exponential backoff: When retrying errors, add exponentially increasing time delays. For example: 1s, 2s, 4s, 8s, 16s, 32s, and so forth.

Jittering: Avoiding retrying requests at exact intervals. With jittering, you vary the retry delays through a random process to distribute them uniformly over time (for example: 0.9s, 2.3s, 4.1s, 8.5s, 17.9s, 34.7s).

Retry amplification: When failed requests are retried without exponential backoff/jittering, they often accumulate and add to ongoing traffic load, potentially "amplifying" and exacerbating traffic congestion problems.

FCM processes millions of requests per second (RPS). The biggest contributor to systemic congestion, latency problems, and outages is traffic spikes.

There are several different types of traffic spikes.

On-the-hour spikes: FCM receives more than double traffic during the first 30 seconds to 2 minutes of each hour. Similar, albeit lesser, spikes are also observed at the half-hour and quarter-hour marks (examples: 00:15, 00:30, 00:45)

Retry amplification: Retrying failed or timed-out requests without Exponential backoff can accumulate into repeating waves of traffic on top of existing traffic crests.

Abrupt traffic pattern changes: Directing new traffic to FCM or moving traffic to FCM across regions without smoothing factors such as gradual ramp-up can cause spikes.

Front-loading quota token usage: Exhausting all quota tokens at the start of quota windows instead of spreading out the requests evenly across the quota windows will create on-off oscillations that are difficult and expensive to load-balance.

Special events: Traffic spikes during holidays (New Year's Eve) or sports events (FIFA World Cup).

This section describes strategies to smooth out traffic spikes where possible—strategies to "flatten the curve."

There are some use cases where using FCM to deliver a notification is not necessary or appropriate.

For example, for calendar event notifications, you can schedule a local task in your app to display a notification at the appropriate times instead of sending it from your app server. Limit FCM messages to calendar syncs.

One scaling anti-pattern is to send FCM notifications as quickly as systems will allow, instead of applying server-side throttling. Consider the following:

• Do all of your customers need to receive the same notification all within a 1 minute window? Would a 5 minute delivery window, for instance, still meet your business needs?
• Can your customers be segmented based on priority to smooth over the spikes?
• Can your notifications be scheduled ahead of time?

Wherever possible: avoid strategies that result in immediately exhausting your FCM send quota, only to repeat the pattern as soon as your token bucket refills. This access pattern creates load-balancing problems for FCM and its dependent systems. Ramp up traffic as gradually as possible. At minimum, ramp from 0 to the max RPS across a 60 second time-window. Prefer longer windows for higher RPS.

Where possible: avoid sending messages within a 2 minute window of each of the :00, :15, :30, and :45 minute marks.

Implement server-side throttling to monitor and manage the flow of traffic to FCM.

While FCM strives to be highly available, at times some requests will time out or fail. While the reasons vary, the following best practices optimize retry behavior to deliver messages as soon as possible while minimizing impact to traffic congestion.

Set at least a 10 second timeout on send requests before retrying. Most of FCM's internal Remote Procedure Calls use a 10 second timeout.

• For 400, 401, 403, 404 errors: abort, and do not retry.
• For 429 errors: retry after waiting for the duration set in the retry-after header. If no retry-after header is set, default to 60 seconds.
• For 500 errors: retry with exponential backoff.

To avoid retry amplification, implement exponential back-off with jittering for retrying requests. The Firebase Admin SDK, for example, implements exponential backoff.

Here are some more recommended settings:

• Minimum Interval: Do not immediately retry a failed request with FCM. Wait at least 10 seconds before retrying a failed request.
• Maximum Interval: Set a maximum interval for dropping requests that are no longer timely, instead of retrying indefinitely.

If a request is continually retried with exponential backoff and is still failing 60 minutes later, it is either miscategorized as a retryable error, or FCM is experiencing an outage where retries may be inadvertently exacerbating the situation.

When making large-scale traffic changes, such as increasing traffic to FCM or shifting traffic across regions or networks, designing a rollout/rollback plan and implementing gradual changes will protect your users, your service, and FCM.

• A rollout plan aligns expectations for stakeholders. In certain situations (discussed below), you may want to share your rollout plan ahead of time with the FCM team to avoid surprises.
• A rollback plan allows you to account for contingencies and prepare mechanisms to quickly and safely recover from unanticipated failures.
• Making gradual changes has two aspects:
  • "Step-wise" ramp-ups: Steps should be 1% -> 5% -> 10% -> 25% -> 50% -> 75% -> 100% or finer. "Soak" (observe system behavior under load) each step for 1 day to 1 week. This allows you to spot potential problems before the next "step-up"
  • Gradual traffic ramp-ups: When taking each "step" to ramp up traffic, smooth out the traffic over the span of at least an hour. This allows FCM's load-balancing infrastructure to appropriately scale your new traffic while minimizing the potential for hotspots and congestion.

Here is a hypothetical scenario for migrating 500,000 RPS globally from the FCM Legacy HTTP API to the FCM HTTP v1 API:

Hypothetical rollback plan:

• If 95-percentile latency increases to greater than 500 ms or if the error ratio exceeds 1% for more than an hour at any step, use dynamic configuration to roll back to the previous step immediately.
• Continue rollbacks to earlier steps until latency and error ratio return to nominal levels.

Contact FCM through Firebase Support if any of the following apply:

• Default quotas no longer meet your use case
• You are changing your sending patterns within a 3 month window at a scale of 100,000 RPS globally or 30,000 RPS continentally.

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