Migrating Netflix to GraphQL Safely | by Netflix Expertise Weblog

By Jennifer Shin, Tejas Shikhare, Will Emmanuel

In 2022, a significant change was made to Netflix’s iOS and Android purposes. We migrated Netflix’s cell apps to GraphQL with zero downtime, which concerned a complete overhaul from the shopper to the API layer.

Till lately, an inner API framework, Falcor, powered our cell apps. They’re now backed by Federated GraphQL, a distributed strategy to APIs the place area groups can independently handle and personal particular sections of the API.

Doing this safely for 100s of thousands and thousands of consumers with out disruption is exceptionally difficult, particularly contemplating the numerous dimensions of change concerned. This weblog put up will share broadly-applicable methods (past GraphQL) we used to carry out this migration. The three methods we are going to talk about at the moment are AB Testing, Replay Testing, and Sticky Canaries.

Earlier than diving into these methods, let’s briefly study the migration plan.

Earlier than GraphQL: Monolithic Falcor API applied and maintained by the API Staff

Earlier than shifting to GraphQL, our API layer consisted of a monolithic server constructed with Falcor. A single API workforce maintained each the Java implementation of the Falcor framework and the API Server.

Created a GraphQL Shim Service on prime of our current Monolith Falcor API.

By the summer season of 2020, many UI engineers had been prepared to maneuver to GraphQL. As a substitute of embarking on a full-fledged migration prime to backside, we created a GraphQL shim on prime of our current Falcor API. The GraphQL shim enabled shopper engineers to maneuver shortly onto GraphQL, determine client-side considerations like cache normalization, experiment with completely different GraphQL purchasers, and examine shopper efficiency with out being blocked by server-side migrations. To launch Part 1 safely, we used AB Testing.

Deprecate the GraphQL Shim Service and Legacy API Monolith in favor of GraphQL providers owned by the area groups.

We didn’t need the legacy Falcor API to linger without end, so we leaned into Federated GraphQL to energy a single GraphQL API with a number of GraphQL servers.

We might additionally swap out the implementation of a discipline from GraphQL Shim to Video API with federation directives. To launch Part 2 safely, we used Replay Testing and Sticky Canaries.

Two key elements decided our testing methods:

• Purposeful vs. non-functional necessities
• Idempotency

If we had been testing practical necessities like information accuracy, and if the request was idempotent, we relied on Replay Testing. We knew we might take a look at the identical question with the identical inputs and constantly anticipate the identical outcomes.

We couldn’t replay take a look at GraphQL queries or mutations that requested non-idempotent fields.

And we undoubtedly couldn’t replay take a look at non-functional necessities like caching and logging consumer interplay. In such instances, we weren’t testing for response information however total conduct. So, we relied on higher-level metrics-based testing: AB Testing and Sticky Canaries.

Let’s talk about the three testing methods in additional element.

Netflix historically makes use of AB Testing to guage whether or not new product options resonate with prospects. In Part 1, we leveraged the AB testing framework to isolate a consumer section into two teams totaling 1 million customers. The management group’s visitors utilized the legacy Falcor stack, whereas the experiment inhabitants leveraged the brand new GraphQL shopper and was directed to the GraphQL Shim. To find out buyer affect, we might evaluate numerous metrics similar to error charges, latencies, and time to render.

We arrange a client-side AB experiment that examined Falcor versus GraphQL and reported coarse-grained high quality of expertise metrics (QoE). The AB experiment outcomes hinted that GraphQL’s correctness was lower than par with the legacy system. We spent the following few months diving into these high-level metrics and fixing points similar to cache TTLs, flawed shopper assumptions, and so on.

Wins

Excessive-Degree Well being Metrics: AB Testing offered the peace of mind we would have liked in our total client-side GraphQL implementation. This helped us efficiently migrate 100% of the visitors on the cell homepage canvas to GraphQL in 6 months.

Gotchas

Error Prognosis: With an AB take a look at, we might see coarse-grained metrics which pointed to potential points, nevertheless it was difficult to diagnose the precise points.

The subsequent section within the migration was to reimplement our current Falcor API in a GraphQL-first server (Video API Service). The Falcor API had grow to be a logic-heavy monolith with over a decade of tech debt. So we had to make sure that the reimplemented Video API server was bug-free and equivalent to the already productized Shim service.

We developed a Replay Testing device to confirm that idempotent APIs had been migrated accurately from the GraphQL Shim to the Video API service.

The Replay Testing framework leverages the @override directive out there in GraphQL Federation. This directive tells the GraphQL Gateway to route to 1 GraphQL server over one other. Take, as an example, the next two GraphQL schemas outlined by the Shim Service and the Video Service:

The GraphQL Shim first outlined the certificationRating discipline (issues like Rated R or PG-13) in Part 1. In Part 2, we stood up the VideoService and outlined the identical certificationRating discipline marked with the @override directive. The presence of the equivalent discipline with the @override directive knowledgeable the GraphQL Gateway to route the decision of this discipline to the brand new Video Service quite than the previous Shim Service.

The Replay Tester device samples uncooked visitors streams from Mantis. With these sampled occasions, the device can seize a dwell request from manufacturing and run an equivalent GraphQL question towards each the GraphQL Shim and the brand new Video API service. The device then compares the outcomes and outputs any variations in response payloads.

Notice: We don’t replay take a look at Personally Identifiable Info. It’s used just for non-sensitive product options on the Netflix UI.

As soon as the take a look at is accomplished, the engineer can view the diffs displayed as a flattened JSON node. You may see the management worth on the left facet of the comma in parentheses and the experiment worth on the fitting.

/information/movies/0/tags/3/id: (81496962, null)

/information/movies/0/tags/5/displayName: (Série, worth: “S303251rie”)

We captured two diffs above, the primary had lacking information for an ID discipline within the experiment, and the second had an encoding distinction. We additionally noticed variations in localization, date precisions, and floating level accuracy. It gave us confidence in replicated enterprise logic, the place subscriber plans and consumer geographic location decided the shopper’s catalog availability.

Wins

• Confidence in parity between the 2 GraphQL Implementations
• Enabled tuning configs in instances the place information was lacking as a result of over-eager timeouts
• Examined enterprise logic that required many (unknown) inputs and the place correctness may be arduous to eyeball

Gotchas

• PII and non-idempotent APIs ought to not be examined utilizing Replay Checks, and it could be worthwhile to have a mechanism to forestall that.
• Manually constructed queries are solely nearly as good because the options the developer remembers to check. We ended up with untested fields just because we forgot about them.
• Correctness: The thought of correctness may be complicated too. For instance, is it extra right for an array to be empty or null, or is it simply noise? In the end, we matched the present conduct as a lot as doable as a result of verifying the robustness of the shopper’s error dealing with was troublesome.

Regardless of these shortcomings, Replay Testing was a key indicator that we had achieved practical correctness of most idempotent queries.

Whereas Replay Testing validates the practical correctness of the brand new GraphQL APIs, it doesn’t present any efficiency or enterprise metric perception, such because the total perceived well being of consumer interplay. Are customers clicking play on the identical charges? Are issues loading in time earlier than the consumer loses curiosity? Replay Testing additionally can’t be used for non-idempotent API validation. We reached for a Netflix device known as the Sticky Canary to construct confidence.

A Sticky Canary is an infrastructure experiment the place prospects are assigned both to a canary or baseline host for your entire period of an experiment. All incoming visitors is allotted to an experimental or baseline host primarily based on their gadget and profile, just like a bucket hash. The experimental host deployment serves all the shoppers assigned to the experiment. Watch our Chaos Engineering discuss from AWS Reinvent to study extra about Sticky Canaries.

Within the case of our GraphQL APIs, we used a Sticky Canary experiment to run two cases of our GraphQL gateway. The baseline gateway used the present schema, which routes all visitors to the GraphQL Shim. The experimental gateway used the brand new proposed schema, which routes visitors to the newest Video API service. Zuul, our main edge gateway, assigns visitors to both cluster primarily based on the experiment parameters.

We then gather and analyze the efficiency of the 2 clusters. Some KPIs we monitor intently embrace:

• Median and tail latencies
• Error charges
• Logs
• Useful resource utilization–CPU, community visitors, reminiscence, disk
• Gadget QoE (High quality of Expertise) metrics
• Streaming well being metrics

We began small, with tiny buyer allocations for hour-long experiments. After validating efficiency, we slowly constructed up scope. We elevated the share of buyer allocations, launched multi-region exams, and finally 12-hour or day-long experiments. Validating alongside the best way is important since Sticky Canaries affect dwell manufacturing visitors and are assigned persistently to a buyer.

After a number of sticky canary experiments, we had assurance that section 2 of the migration improved all core metrics, and we might dial up GraphQL globally with confidence.

Wins

Sticky Canaries was important to construct confidence in our new GraphQL providers.

• Non-Idempotent APIs: these exams are appropriate with mutating or non-idempotent APIs
• Enterprise metrics: Sticky Canaries validated our core Netflix enterprise metrics had improved after the migration
• System efficiency: Insights into latency and useful resource utilization assist us perceive how scaling profiles change after migration

Gotchas

• Destructive Buyer Impression: Sticky Canaries can affect actual customers. We would have liked confidence in our new providers earlier than persistently routing some prospects to them. That is partially mitigated by real-time affect detection, which is able to routinely cancel experiments.
• Quick-lived: Sticky Canaries are meant for short-lived experiments. For longer-lived exams, a full-blown AB take a look at ought to be used.

Expertise is continually altering, and we, as engineers, spend a big a part of our careers performing migrations. The query is just not whether or not we’re migrating however whether or not we’re migrating safely, with zero downtime, in a well timed method.

At Netflix, now we have developed instruments that guarantee confidence in these migrations, focused towards every particular use case being examined. We lined three instruments, AB testing, Replay Testing, and Sticky Canaries that we used for the GraphQL Migration.

This weblog put up is a part of our Migrating Important Site visitors sequence. Additionally, try: Migrating Important Site visitors at Scale (part 1, part 2) and Ensuring the Successful Launch of Ads.