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3:T46c,At last year’s GraphQL Conf, Stephanie Saunders sang the praises of schema mocking in her excellent talk “Sophisticated Schema Mocking”. As Stephanie outlined, tests written using mock schemas have several benefits over ones written with static response mocks (seriously, go back and watch the talk if you haven’t!) Mock schemas are the perfect pair for Mock Service Worker which describes itself as “an API mocking library that allows you to write client-agnostic mocks and reuse them across any frameworks, tools, and environments.” In this talk, I’ll demonstrate how to use mock schemas with MSW using testing tools created by the Apollo Client team. These utilities can be used with any GraphQL client for the web whether it’s Apollo Client, Relay, urql, isograph or even just plain fetch requests to a GraphQL endpoint, and support incremental delivery (@defer/@stream), subscriptions and more. With MSW + your front-end stack of choice + test runner or framework of choice (Jest, Puppeteer, Cypress, Storybook, the list goes on) this talk will teach you how to level up the tests you and your team are writing.4:T482,Paramount is a group of companies including Network Streaming, Paramount+, PlutoTV. Each of these companies has its own distinct tech stack and architecture. One of the goals of the Architectural Team is to simplify the architecture of the entire group by converging and merging the solutions while avoiding a big-bang approach that would have a significant impact on the business. After some investigations, the GraphQL Federated Architecture emerged as a strong candidate to initiate this process using a top-down approach, starting with the Paramount EPG services. In this session, we will describe: - The design of each intermediate EPG convergent architecture: we will outline the necessary steps to reach the final solution using a top-down and phased approach. - The challenges we faced: this includes choosing between code-first and schema-first approaches, modeling the data, addressing performance issues, deploying each subgraph to different infrastructures, and organizing subgraph teams, roles, and responsibilities. - Solutions we tried: we explored various tools and frameworks, such as Apollo and WunderGraph, to address these challenges.5:T428,WebAssembly is reshaping our approach to software execution units and components. With its ecosystem maturing--the ability to deploy with multiple cloud providers, the standardization of WebAssembly Components, and the rise of WASI--its potential is fast becoming a reality. WebAssembly is especially suitable for running server-side code at the edge, where resource constraints and security concerns are paramount. To realize the full potential of running API servers at the edge--lowered latency, reduced costs, and improved scalability--we must rethink our approach to building and deploying servers. GraphQL is particularly well-suited for edge deployments. It not only reduces the number of trips between the client and the server but also allows implementations to optimize the whole query to reduce round trips to the data server. In this session, we will explore how to target a Rust implementation of GraphQL server to WebAssembly. We'll also look into how this fits within the WebAssembly ecosystem and how you can run these servers even in your browser.6:T4ad,Pinterest is too large to simply “rewrite our app” to use GraphQL in one fell swoop. Even migrating an individual screen takes months, at our scale this is quite challenging, like changing a plane’s engine while flying. Is GraphQL adoption destined to be difficult for large companies? Can this process be made more incremental? Unfortunately, the answer seems to be no. It's hard to imagine how components consuming GraphQL data can coexist on a page that makes network requests to a REST endpoint. And yet, we figured it out. And we had a good time, too! We designed Relay-compatible APIs that allow us to read data either from a GraphQL store or from arbitrary objects (e.g. from Redux.) So, engineers can migrate individual components within a larger tree. These components specify the data they need using a fragment, and receive GraphQL-shaped data, regardless of whether the data came from GraphQL or REST. When a component tree is fully migrated, we're able to A/B test the REST and GraphQL endpoints, and only turn on GraphQL when we're sure doing so won't degrade any metrics. And we're about to release this to open source: adopting GraphQL on the front-end has never been easier!7:T4a5,Two aspects of resolvers have an outsized influence on their performance: the size of the execution context, and the way we compute their value. In the Node.js implementation of graphql, promises wrapping primitive values are especially disruptive, since they add a large computing overhead. The context size creates a memory usage baseline that can rise very quickly with even small additions to the context, when there are many concurrent contexts. The execution can create temporary objects, increasing memory usage. Often-run resolvers, such as those responsible for filling out large arrays of objects, can become performance bottlenecks. At Auction.com, our search results page (SRP) requests up to 500 items of roughly 80 fields each. The query resolving these fields was suffering a high latency. We shall examine the tools to instrument our code and identify memory usage and CPU utilization bottlenecks. Our realtime elements (e.g. realtime updates to the status of currently viewed properties) are implemented using a translation of kafka messages to graphql updates. We shall present the tools and procedures to reduce memory usage and CPU usage when fanning out such messages.8:T44f,Many of the most common issues developers run into when building GraphQL APIs (N+1 queries, difficult authorization logic, protecting against arbitrary query complexity) come down to a single problem: when an untrusted client can construct arbitrary queries, lots can go wrong. So what if they just couldn't? The wins of GraphQL aren't in letting *clients* build their own queries but in letting *client developers* build their own queries. When Firebase chose GraphQL as the basis for its new Data Connect product, we introduced "Connectors", a new resource type that bundles a group of predefined GraphQL queries and mutations and exposes them at an endpoint. While trusted servers can execute arbitrary queries, untrusted clients can only use predefined queries and mutations. This approach substantially simplifies the security model of building with GraphQL. Rather than worrying about every possible query, you can build authorization and complexity mechanics around well-known predefined queries. Learn the how, the why, and the possible future of "Connectors" for GraphQL in Firebase and beyond.9:T41c,At Expedia Group, we leverage a "code-first" approach with GraphQL Kotlin to deliver high quality experiences to travelers globally. This means we let our implementations drive our GraphQL schema. However, our developers have a growing appetite to agree on our schema up front before diving into implementation. This talk will explore how we shifted our architecture to use a schema-first approach within a code-first framework. Overall, I aim to dispel the notion that using a code-first solution prevents us from defining schema as a first step. I will explore the advantages of doing schema review at scale, as well as highlight an open source GraphQL-Codegen plugin we built to generate Kotlin classes and interfaces from our schema definitions. I am eager to contribute to the conversation around schema-first vs code-first, which has been the topic of several GraphQLConf talks over the past few years. This approach brings a fresh perspective and should inspire folks to incorporate a schema-first mentality into their GraphQL development cycle.a:T5c1,In this talk, we'll explore the future of GraphQL and why its success hinges on our collective ability to address the elephant in the room of governance. Despite its popularity among developers due to its flexibility and efficiency, GraphQL has yet to fully win over enterprise architects. Their world revolves around REST, model-first abstractions, data lineage, and rigorous compliance standards. To truly gain their trust and drive broader adoption in the enterprise, I'll argue that GraphQL implementations must be metadata-driven, with an emphasis on configuration over code. However, all is not lost! In today’s enterprise landscape, the domain model is predicated on the principle that the teams who own the data should also be responsible for managing it and making it available through agnostic channels. With this in mind, GraphQL is uniquely positioned to excel, thanks to its capabilities in federation and schema stitching. These features enable a paradigm that scales both technically and, more importantly, organizationally, aligning with the goals of managing traceability, lineage, and compliance in a manner that satisfies regulatory demands. In this session, we'll delve into how the future of GraphQL, much like the stability of any nation, is rooted in good governance. We'll examine the priorities of enterprise architects and demonstrate how a governance-focused approach can help GraphQL evolve into a robust data access layer within any business.b:T42b,Real-world observations of GraphQL Federation use. Gartner predicts, "By 2027, 30% of enterprises utilizing GraphQL will employ GraphQL federation, up from less than 5% in 2024." The report states, “Federation enables centralized governance, team autonomy (teams can own their subgraphs), composability, and adaptability to evolving requirements at different speeds.” As GraphQL Federation continues to redefine how enterprises architect and scale their APIs, 2024 has marked a pivotal year in its adoption and evolution. This session will explore the rising trend of GraphQL Federation, highlighting key insights and success stories from diverse customer and partner bases. We will delve into how businesses leverage federation to create more cohesive, efficient, and scalable API landscapes, addressing current challenges and showcasing emerging best practices. Join us to understand the broader impact of GraphQL Federation in the tech industry and gather actionable strategies to enhance your organization's API strategies through advanced GraphQL techniques.c:T4a8,In this session - we’ll explore how GraphQL Clients can provide a road for developers to make full use of Semantic Nullability! GraphQL Spec defines that field errors should result in a null value. This makes null values ambiguous - either null due to error - or null due to nullability. Semantic Nullability allows you to define nullability explicitly - see https://github.com/graphql/graphql-wg/discussions/1410 Therefore, GraphQL Clients need to be able to handle errors differently - giving engineers more confidence in how field states are handled. In this session, we'll cover: 1. Why explicitly handling errors can enable us to move to a new normal where all errors are surfaced explicitly (throw-by-default on error) 2. How using @catch directive allows us to differentiate error nulls from true nulls in product code 3. Steps 1 and 2 will require developers to handle errors explicitly - and this opens the door to Semantic Nullability! I’ll also cover how working on the @catch directive helped push the semantic nullability conversation forward, the rollout at Meta, and what future capabilities can be unlocked. Original proposal: https://github.com/facebook/relay/issues/4416d:T46a,Discovering GraphQL fields and generating queries is a tedious task for developers. They spend a considerable amount of time finding the appropriate fields in large schemas. To solve this problem at Intuit where we have a super graph consisting of millions of lines we implemented a framework that makes use of Generative AI to help developers with attribute discovery and query generation. The benefits of our approach include being able to work with large schemas without the hassle of going through the whole schema and requiring less back and forth communication between consumers and schema owners, which results in a huge boost in developer productivity. We created chunks of the schema and ingested them into a vector store, we then do a retrieval, dynamically build a minimal schema and perform RAG where the LLM is provided with the minimal schema and the user query. The LLM responds with either a list of discovered attributes or GraphQL query. This framework aims to achieve lower latency and less hallucinations by reducing the size of the schema sent to the LLM, this also results in lower costs and higher accuracy.e:T485,We will explore the revolutionary integration of GraphQL with the Oracle Database in this session. The way developers query and interact with the database has been greatly simplified by this integration. We will introduce the concept of Duality Views, a brand-new feature available in Oracle Database 23ai. Using Duality Views, data is still stored in relational tables in a highly efficient normalized format but is accessed by apps in the form of JSON documents. Duality Views are created using an intuitive and flexible GraphQL-like syntax, enhancing developer productivity. We will also demonstrate new capabilities in the database to automatically infer the GraphQL schema from existing relational schemas, making it easy to introspect and visualize the relational schema as well as write GraphQL queries against the database. Finally, we will talk about new GraphQL directives to allow calculations, reshaping, and transformations in a GraphQL query that can be executed on a relational database. Come learn how Oracle's support for GraphQL is transforming data access and providing a powerful and seamless solution for modern application development.f:T473,GraphQL Docs: Beyond the Schema is a presentation focusing on how Technical Writers and DevEx teams can enhance developer experience with interactive documentation. I hope to inspire DevEx teams and Technical Writers to consider GraphQL as more than just "self-documenting." The problem I will present is that I want more than a schema as a developer. I want to know how to use the schema to build my product. The solution to the problem will focus on interactive documentation by defining what makes it interactive, for example, embedded code samples linked to a user's sandbox environment. I will then explore how DevEx teams can implement these elements to create interactive documentation for their GraphQL API. I expect the audience to gain insight into exactly how they can create their own interactive GraphQL documentation and best practices. This presentation will help better the ecosystem by highlighting the pain points of GraphQL documentation as it exists today, emphasizing the need to create interactive documentation for developers working with GraphQL, and enforcing the importance of creating rich developer experiences.10:T428,Platform engineering and internal developer portals have been a growing trend in the tech industry to make developers more efficient. For example, how do we help new developers ship their first feature faster? GraphQL helps Platform API efforts ship features faster, but what about when your schema gets very complex? How can a new developer find what they need quickly? GraphQL already provides a complete and understandable description of the data in our APIs, but what if we provide that context to a LLM? In this talk, we'll journey through GitHub's APIs and explore how a GraphQL schema is a significant advantage in AI-based tooling. We're seeing more AI-based tools generate fetch code based on OpenAPI definitions, and while they may be tempting at first, it could be a decision with unexpected trade-offs. We'll show how to take a standard open-sourced LLM and provide a GraphQL-aware context to generate operations from text input. After this talk, you can safely bring AI to your developer efficiency initiatives with any LLM, 3rd party, or self-hosted!11:T490,GraphQL was conceived as a unified access layer that empowers product teams by providing a common language for exposing and consuming data capabilities. These capabilities were typically implemented in a single schema through a resolver-based model. That poses challenges in environments with diverse microservices managed by various teams however, as found in most large organizations. Recognizing these challenges, we introduced Apollo Federation in 2019 to deliver on the promise of GraphQL within those environments. It respects existing service and team boundaries through a principled schema composition model that supports team collaboration and efficient, query plan-based execution across services. This has allowed GraphQL APIs to effectively scale to large numbers of services and teams. Given the success and common challenges observed, a working group comprising engineers from various organizations has been formed to establish a proposed open standard for federated GraphQL. This initiative aims to unify best practices and design principles. This talk will highlight the key discussions from this group and their implications for the evolving standard.12:T4b0,Why implement GraphQL security? We will set the stage by introducing some examples of critical GraphQL vulnerabilities found in popular softwares. - CVE-2021-41248: This vulnerability in GraphiQL, a GraphQL IDE, relates to schema introspection responses that could lead to XSS attacks. - CVE-2023-38503: In Directus, a real-time API and dashboard for managing SQL database, there was a vulnerability in GraphQL subscriptions where permission filters were not properly checked, leading to unauthorized event notifications. - CVE-2023-34047: A vulnerability in Spring for GraphQL where a batch loader function could be exposed to GraphQL context with security context values from a different session, potentially leading to unauthorized access or information disclosure. Top 10 GraphQL Security Checks - #1 Disable Introspection in Production - #2 Robust Authentication - #3 Limit Query Depths - #4 Rate Limiting - #5 Input Validation - #6 Secure Direct Object References - #7 Error Handling - #8 Query Complexity Analysis - #9 Mass Assignment Checks - #10 Excessive Data Exposure How to automate GraphQL Security? - we will talk about automating the 10 security checks in code and CI/CD15:{"service":"Twitter","url":"https://twitter.com/n1rual"}
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