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Implementation considerations for service workers


What to expect when you’re expecting to implement

So you’ve got a browser engine, are looking into this whole "Service Workers" thing and have some questions. Take heart, dear reader! Your friendly spec designers/authors are here to answer your (anticipated) questions. Want more answers? File a bug and tag it implementation-concern.

This doc looks first at key concepts then outlines the areas of helpful built-in ambiguity that provide opportunities to go fast. Each opportunity includes a list of strategies that implementations might explore to improve service worker performance.

Key concepts

Event-driven workers

The biggest thing to understand about service workers is that they are event driven. Unlike other forms of Web Worker, the lifetime of the script execution context of the worker is not related to documents which have handles to the worker. Instead, service workers begin life when events are sent to them, and they can end between events.

The most performance-sensitive of these events is the fetch event for top-level document navigation. To ensure that the decision regarding which (if any) service worker to boot up to handle a fetch event, the spec uses a static tuple of origin + URL pattern to map service workers to navigations. This list will change infrequently and is likely to be something you can cache in memory for fast lookup.

Once a service worker is a match for a particular navigation, a Service Worker instance is created (if one isn’t already running) using the previously cached scripts. It’s reasonable to assume that all of the scripts required to start the worker will be in the local cache at this point, so there’s little reason to worry about network speed or flakiness here (disk flakiness is another question entirely, see “Racing Allowed” below).

Next, an event is dispatched to the now-running worker. If the worker’s event handler doesn’t call e.respondWith(), no further script is implicated in servicing the fetch and normal resource loading can proceed as per usual. If the worker does take a crack at responding to the event, the worker’s life is typically extended to enable it to do whatever work it needs to do to respond (asynchronously, via the Promise provided to respondWith()) . Once the worker is done handling the event (or not), the UA is free to shut the worker down and only revive it again for the next event.

It bears repeating: if no event’s lifecycle is keeping the service worker alive, UAs can shut them down and reclaim whatever resources they might have been using. If this doesn’t warm your cold, cold implementer’s heart then you might truly be dead inside. The opportunities to trade space for speed (and vice versa) are massive. The freedom to start and end service workers at a time of your choosing is the stuff good optimization strategies are made off.


Installation of service workers happens as a result of a document calling:

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The service worker might be homunculus… dozens of imports, loads of code. This is the web and every terrible thing that authors can do will be done. This looks bad, right?

Turns out that service workers have a defined lifecycle that enables us to think about installation as a separate, asynchronous, potentially long-running phase that doesn’t affect the rest of index.html’s loading process.

Installation can be low-priority, and no documents will be controlled by the service worker until it succeeds. That means that it’s possible to depend on lots of script and cache many resources in an service worker without worrying about the “load time” of the service worker itself since all subsequent starts of the service worker will come from local disk cache.

Event Dispatch

Service workers lean on the DOM Events as the entry point for nearly all work. The contract that developers must learn is that they must call e.waitUntil() or e.respondWith() to lengthen the life of the overall operation. All of the APIs available to them are asynchronous, and the combination of these factors implies that service workers are meant to be async by default. The tools at hand lead developers down this path, and cooperative multi-tasking (à la Node.js) is the way that libraries must be structured, both for performance and to work naturally with the service worker execution model. Lastly, remember that developers can move work off-thread using sub-workers. They’ll also be killed along with their parent service workers, but long-running or CPU intensive work that might otherwise cause potential for becoming unresponsive can be delegated to contexts which will not block the service worker thread.

Good news, everybody!

Service workers are designed to enable many optimizations. This section covers some of these opportunities and outlines potential strategies for exploiting them.

Caveat Emptor: This document was drafted in early 2014. Architectures and constraints change. The set of areas to consider outlined here is influenced by a lack perfect foreknowledge. Memory and IO might become free. CPU might become expensive. Genetically engineered super-intelligent catdog hybrids may take over the world. YMMV.

Only matching navigations boot service workers

Lets say has a service worker registered and embeds the image Since the service worker matching algorithm is primarily based on navigations and not sub-resource requests, a visit to does not boot the service worker for for this fetch. Instead, the onfetch event is sent to the service worker (if there is one).

The need to match navigations may still strike you as a performance burden, but there’s reason for hope. First, you’re already matching a likely-larger list to provide :visited metadata to renderers. Next, the globbing format is designed to be simple to speed matches. Lastly, since a UA may skip a service worker entirely, it’s feasible for a UA to only match against some set of registered service workers (say, the most recently installed or used).

UAs may even decide to try harder (match against a larger list of service workers) when offline to help provide a better offline experience.


  • Keep the match list in memory
  • Memory-bound the size of the match list
  • Only run matching test against top-level navigations. Sub-resource requests are always sent to the service worker that controls the page (if any) and so don’t need to be matched. Only top-level navigations every need to be sent through the matching algorithm.

Events implicitly filter

Only the event handlers registered at the top-level evaluation of the first evaluation (at install time) of a version of a service worker script are guaranteed to be dispatched against in the future. That is to say, if a service worker script doesn’t register an onfetch handler you can avoid ever sending it fetch events. This is a powerful mechanism for trimming the number of times you need to do potentially expensive work by invoking service workers.


  • Prune the navigation match list based on onfetch handlers
  • Warn service worker authors that use APIs which would need corresponding handlers which are not registered. E.g., a call to navigator.requestSync() against a service worker that doesn’t include an onsync handler isn’t likely to be what the author meant to do.

Startup matters

The performance of evaluation of the service worker script is a key bottleneck to address in ensuring that perceived latency of service worker–controlled documents remains low. It isn’t possible to send the fetch event to the worker until it has been evaluated, potentially including code (via importScripts()) from other files.


  • Fetch service workers from disk early. Links in documents to service worker–controlled space constitute one of perhaps many available signals about the likelihood that the next navigation will be to space controlled by a service worker. As discussed in the matching section, querying to understand if a navigation will match a service worker should be made cheap and, therefore, it’s possible to imagine that optimizing implementations may try to pre-fetch service worker scripts from disk based on speculative matches; e.g. the user typing in the address bar is perhaps a strong signal that a frequent visitor of will want a registered service worker for that origin to be available quickly. As with all speculative optimizations, the real work here is ensuring good hit rates.
  • Interpret. JIT-only VMs (like V8) are at a startup-time disadvantage due to the time taken by JIT codegen. Service worker scripts aren’t likely to be computation-intensive (and if they are, your engine can detect as much and flag future runs for JITing). Simpler compilation strategies that get the handlers available faster are a good area to investigate inside the service worker execution context.
  • Store service worker scripts as a single unit. Storage locality still matters, even in 2014. Commodity MLC flash latency is atrocious, and spinning disk isn’t getting (much) faster. Since service worker scripts nearly always require their previous importScripts() dependencies (which are cached as a group), it pays to store them in a format that reduces the number of seeks/reads necessary to get the service worker started. Also, remember that install-time is async, so there is time/energy available to optimize the storage layout up-front.
  • Cache parse trees or snapshot. Service worker scripts (and their dependencies) shouldn’t change in a single version of the application. Combined with a reduced API surface area, it’s possible to apply more exotic strategies for efficiently “rehydrating” the startup execution context of the service worker. The spec makes no guarantees about how frequently a service worker context is killed and re-evaluated in relationship to the number of overall events dispatched, so a UA is free to make non-side-effecting global startup appear to be a continuation of a previous execution.
  • Warn on nondeterministic imports. Service workers are likely to be invoked when offline. This means that any scripts included via importScripts() that aren’t from stable URLs are likely to break when offline. UAs can help keep developers on the optimization-friendly path by warning at the console whenever a call to importScripts() occurs at a location other than the top-level or is based on a non-static string value.
  • Warn on non-trivial global work. Since service workers “pay” for any globally executed code every time they start, it’s a good idea for implementations to STRONGLY discourage doing work outside the defined lifecycle events (oninstall and onactivate).
  • Learn storage/DB patterns. Service workers are going to need to lean on IDB for state keeping across invocations and Cache objects for dealing with HTTP responses. In both cases it’s possible to observe the set of resources most frequently accessed by a particular version of a service worker and work to ensure that they’re cheaply available by the time the service worker is sent a particular event. In particular, speculatively fetching indexes may pay off in some scenarios.
  • Delay shutdown. The cheapest service worker to start is the one you haven’t killed. Optimizing implementations may consider providing a “grace period” for the shutdown of service worker threads to reduce startup time of subsequent events. This strategy can be further refined with semantic awareness about event types. E.g., a top-level navigation to a service worker is likely to generate sub-resource fetch events soon after. Shutting down the original service worker execution context quickly may be wasteful.

It’s all async

Aside from importScripts(), no synchronous APIs are available to service workers. This is by design. Remaining responsive from the shared service worker context requires ensuring that decisions are made quickly and long-running work can be deferred to underlying (asynchronous) systems. No API will be added to the service worker execution context that can potentially create cross-context mutex holds (e.g., Local Storage) or otherwise block (e.g., synchronous XHR). This discipline makes it simpler to reason about what it means to be a poorly-written service worker script.


  • Prioritize the service worker thread. Service worker execution is likely to be a sensitive component of overall application performance. Ensure it isn’t getting unscheduled!
  • Warn on long periods of service worker thread work. Remaining responsive requires yielding to the service worker thread’s event loop. DevTools can provide value by warning developers of long periods of potentially-blocking work that might reduce responsiveness to incoming events. A reasonable, mobile-friendly starting point might be to warn on any function that takes longer than 20ms to return in the fast path (e.g., onfetch or onmessage). Recommend to users that they move long-running work to sub-workers, to other turns (via new promises), or that they cache expensive work such that it can be returned via an async API (e.g. IDB).
  • Plan features with async in mind. As you plan to add new features to the platform that you’d like to have exposed to the service worker context, remember that they’ll need to pass the “async only” test. Using Promises is a good first place to start, and reaching out to the editors of the service worker spec early can’t hurt.
  • Write async tests. Your implementation is going to need to handle a large degree of variability in timing. Testing (and fuzzing) your scheduling behavior to optimize for overall app performance, particularly in the multiple-tab scenario, is key to keeping an eye on the real-world impact of performance tuning.
  • Get smart about return types. When it comes to dealing in async responses, the word “return” is usually phrased as e.respondWith(). What’s replied with is usually an instance of a Promise type, but not all Promises are created equal. Consider the case of e.respondWith(e.default()) and e.respondWith(caches.match(e.request)). Both return types result in a delegation to an underlying mechanism with no further service worker interaction. Since no additional handlers are added to the vended Promise object, the receiving event can know that the service worker is effectively out of the loop for the rest of the operation. This allows savvy implementations to avoid re-entering the service worker and reifying Response objects in the service worker execution context.

Best effort

UAs are free to not start service worker for navigations (or other events), event if they would match a registered and active service worker. If you find yourself under memory pressure or for some reason don’t happen to think a service worker is necessary or beneficial (e.g., it has previously been killed for abusing CPU, doesn’t respond to requests in a timeline way, or habitually times out and has to be clobbered), the UA is free to not start it for a given navigation. This has the knock-on effect that the service worker won’t be consulted for sub-resource requests for the document either.

UAs, obviously, should try to do their best to enable service workers to do their best in serving meaningful content to users, and we recommend always trying to send sub-resource requests to service workers for documents that begin life based on the service worker in question.

Installation is not in the fast path

The first page loaded from an application that calls navigator.serviceWorker.register() cannot, by definition, be served from the service worker itself. This illuminates the async nature of service worker installation. Because service worker installation may take a long time (downloading imported scripts and populating Cache objects), the lifecycle events for the service worker system are designed to allow authors to inform the UA of installation success.

Until installation success a registered service worker will not be sent any performance-sensitive events. The async nature of installation provides an opportunity for implementations to do extra work to keep the fast-path responsive.


  • Prioritize service worker resource fetching appropriately. It may improve performance of the installing document to prioritize resource fetches of the foreground page above those of the installing service worker.
  • Do extra work to optimize service worker loading before activation. UAs have leeway to avoid activating the service worker for navigations until they’re good and ready to do so. Thinks is your chance to optimize the crud out of them.

Racing allowed

Anecdotal evidence suggests that commodity OSes and hardware perform VERY badly in disk I/O. Some systems are so degraded that local (valid) caches fetched from disk may perform worse than requests for equivalent resources from the network. Improvements in networks, the spread of malware, and the spread of poorly-written virus scanners exacerbate the underlying trend towards poorly performing disk I/O.


  • Learn to race on top-level navigations. Adapting to historical (local) I/O performance may help UAs decide if and when to avoid using local resources. Service workers may, at first glance, appear to muddy the waters, but fear not! Nothing about the service worker spec forces an implementer to always send navigations to a matching service worker. In fact, the implementation can attempt to go to the network for a top-level navigation while concurrently attempting to dispatch an onfetch to the matching service worker. Assuming the UA chooses to render the document produced by the winner of the race, the model from then on is clear. Thanks to the page-ownership model of the service worker design, the first system to respond will “control” sub-resource requests. This means that if a service worker is used, all subsequent sub-resource requests should be sent to the service worker (not raced). If the network wins the race, all sub-resource requests will be sent to the network (not the service worker).
  • Learn to disable service workers. In extreme cases, if disk is degraded to the extent that service workers can never start quickly, it may be advantageous to simply disable the service worker subsystem entirely. Thanks to the online-fallback model of service workers, applications should continue to function (to the extent that they can).

Cache objects are HTTP-specific

Objects in self.caches are instances of Cache. These HTTP-like-cache objects deal exclusively in HTTP Response objects. With the exception of programmatic (not automatic) eviction these Cache objects are best thought of as a group of native cache items. Most of the operations available for dealing with them mirror HTTP cache query semantics and will likely be familiar to implementers. You know how to make this stuff fast.


  • Store Response objects like HTTP cache entries. It might be tempting to lean on IDB or other general-purpose storage for handling Cache entries. Browser HTTP caches have seen a decade+ of tuning for the specific use-case of returning HTTP data to renderers. It may be advantageous to lean on this engineering where possible.

Interaction with prefetch and prerender is sane

Speculative pre-parsing of documents and CSS resources (not blocking a parser on <script> elements, etc.) to locate URLs to speculatively load is an important browser optimization. Service workers play well with this optimization since they conceptually sit “behind” the document but in front of the network layer. Sending onfetch events for these resources should happen in the usual way.

Similarly, pre-rendering requests should be sent to service workers. They can help warm-up documents while offline. A UA might decide to only send pre-render requests to URL space with a matching service worker registration when offline to avoid wasting resources.


  • Start DNS and TCP early. It may improve overall performance to begin DNS resolution and TCP warm-up for prefetch-located resources concurrently with dispatching the request to the service worker. This, obviously, requires measurement (and perhaps adaption on a per–service worker version basis) to verify that requests outbound from a service worker usually correspond to the origin of the Request sent to the service worker itself.
  • Pre-fetch to service worker–controlled URL space when offline. Pre-render is a huge boon to app performance when online, but generally isn’t available offline. Service workers can help by providing a “map” of the available world based on service worker registrations with onfetch handlers.