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The DGS framework makes it easy to create GraphQL services with Spring Boot.The framework provides an easy-to-use annotation based programming model,and all the advanced features needed to build and run GraphQL services at scale.

The DGS framework is now based on Spring Boot 3.0, so get started by creating a new Spring Boot 3.0 application if you don't have one already.Note that you can still use the DGS framework with Spring Boot 2.7 by using a 5.5.x release.The 6.x release of the framework requires Spring Boot 3.

The DGS Framework has been updated to deeply integrate with Spring GraphQL.For more details on the motivation and implementation, please refer to the docs hereFor the time being, we will offer 2 flavors of the DGS Framework - one with the vanilla version, and a version that integrates with spring-graphql via different starters.There are no breaking changes to users as the changes are mostly internal to the framework and the spring-graphql integration should be a drop-in replacement for the existing framework.For this reason, we encourage new and existing DGSs to use our spring-graphql starter as much as possible, as this will be the default offering in the future.You can read more about the motivation behind integrating with spring-graphql and the details of the integration here.

Add the platform BOM to your Gradle or Maven configuration. The com.netflix.graphql.dgs:graphql-dgs-platform-dependencies dependency is a platform/BOM dependency, which aligns the versions of the individual modules and transitive dependencies of the framework.

Add the DGS starter. The com.netflix.graphql.dgs:graphql-dgs-spring-graphql-starter is a Spring Boot starter that includes everything you need to get started building a DGS that uses Spring GraphQL.

Add the relevant Spring Boot starter for the web flavor you want to use. This would one of org.springframework.boot:spring-boot-starter-web or org.springframework.boot:spring-boot-starter-webflux depending on the stack you are using.

The DGS framework is designed for schema first development.The framework picks up any schema files in the src/main/resources/schema folder.Create a schema file in: src/main/resources/schema/schema.graphqls.

With the new Spring-GraphQL integration, it is technically possible to mix and match the DGS/Spring-GraphQL programming models.However, to maintain consistency in your codebase and to take full advantage of DGS features, we recommend sticking with the DGS programming model.Not all DGS features are applicable to Spring-GraphQL data fetchers in the current integration and would therefore not work as expected.Refer to our Known Gaps and Limitations section for more details.

Note that unlike with REST, you have to specifically list which fields you want to get returned from your query.This is where a lot of the power from GraphQL comes from, but a surprise to many developers new to GraphQL.

If you are an Intellij user, there is a plugin available for DGS.The plugin supports navigation between schema files and code and many hints and quick fixes.You can install the plugin from the Jetbrains plugin repository here.

In the Quick Start guide, we got a reference implementation of Hollow up and running, with a mock data model that can be easily modified to suit any use case. After reading this section, you'll have an understanding of the basic usage patterns for Hollow, and how each of the core pieces fit together.

Hollow manages datasets which are built by a single producer, and disseminated to one or many consumers for read-only access. A dataset changes over time. The timeline for a changing dataset can be broken down into discrete data states, each of which is a complete snapshot of the data at a particular point in time.

Note that the example code above is writing data to local disk. This is a great way to start testing. In a production scenario, data can be written to a remote file store such as Amazon S3 for retrieval by consumers. See the reference implementation and the quick start guide for a scalable example using AWS.

Once the data has been populated into a producer, that producer's state engine is aware of the data model, and can be used to automatically produce a client API. We can also initialize the data model from a brand new state engine using our POJOs:

After this code executes, a set of Java files will be written to the location /path/to/java/api/files. These java files will be a generated API based on the data model defined by the schemas in our state engine, and will provide convenient methods to access that data.

Your BlobRetriever and AnnouncementWatcher implementations should be mirror your Publisher and Announcer interfaces. Here, we're publishing and retrieving from local disk. In production, we'll be publishing to and retrieving from a remote file store. We'll discuss in more detail how to integrate with your specific infrastructure in Infrastructure Integration.

The producer, needs to communicate this updated dataset to consumers. We're going to create a brand new state, and the entirety of the data for the new state must be added to the state engine in a new cycle. When the cycle runs, a new data state will be published, and the new data state's (automatically generated) version identifier will be announced.

Let's take a closer look at what the above code does. The same HollowProducer which was used to produce the snapshot blob is used -- it already knows everything about the prior state and can be transitioned to the next state.
When creating a new state, all of the movies currently in our dataset are re-added again. It's not necessary to figure out which records were added, removed, or modified -- that's Hollow's job.

When the producer runs a cycle, it announces the latest version. The AnnouncementWatcher implementation provided to the HollowConsumer will listen for changes to the announced version -- and when updates occur notify the HollowConsumer by calling triggerAsyncRefresh(). See the source of the HollowFilesystemAnnouncementWatcher, or the two separate examples in the reference implementation.

If it is known what changes are applied to a dataset then incremental production may be utilized. This can bemore efficient than providing the whole dataset on each cycle. An incremental producer is built in a similar mannerto a producer:

This incremental producer runs a cycle for the changes to set of movies (those which are new, have changed, or have been removed). Other than that an incremental producer behaves the same as a producer and a consumer will not know thedifference.

Retrieval from an index is extremely cheap, and indexing is (relatively) expensive. You should create your indexes when the HollowConsumer is initialized and share them thereafter. Indexes will automatically stay up-to-date with the HollowConsumer.

When we add these movies to the dataset, Hollow will traverse everything referenced by the provided records and add them to the state as well. Consequently, both a type Movie and a type Actor will exist in the data model after the above code runs.

Laurence Fishburne starred in both of these films. Rather than creating two Actor records for Mr. Fishburne, a single record will be created and assigned to both of our Movie records. This deduplication happens automatically by virtue of having the exact same data contained in both Actor inputs.

From time to time, we need to redeploy our producer. When we first create a HollowProducer and run a cycle it will not be able to produce a delta, because it does not know anything about the prior data state. If no action is taken, a new state with only a snapshot will be produced and announced, and clients will load that data state with an operation called a double snapshot, which has potentially undesirable performance characteristics.

In the above code, we first initialize the data model by providing the set of classes we will add during the cycle.
After that, we restore by providing our BlobRetriever implementation, along with the version which should be restored. The HollowProducer will use the BlobRetriever to load the desired state, then use it to restore itself.
In this way, a delta can be produced at startup, and consumers will not have to load a snapshot to get up-to-date.

Before restoring, we must always initialize our data model. When a data model changes between deployments, Hollow will automatically merge records of types which have changed. In order to do this correctly, Hollow needs to know about the current data model before the restore operation begins.

Netflix support told me to go onto the Virgin Media website and click on the 'Netflix Account Recovery' button, however this is either taking me to sandbox.netflix.com which apparently is not correct or giving an NSES-500 error. Netflix support said Virgin Media are the only ones who can assist here.

Hi @Prudyuk,

Thank you for your post and welcome to our community forums. We're here to help.

I'm very sorry to hear you're having some trouble with your Netflix account. Would you mind expanding on what's happened exactly?

Thanks,

Falcor includes a Router hiding the actual data stores and directing the calls to the appropriate back-end services responsible for retrieving the data. Also, when data is retrieved it is cached to avoid subsequent trips to the database. Falcor can also batch multiple requests, making a single network request, and does not issue duplicate database requests if there is already one in the process.

Netflix generates awesome shows and movies that can be watched on your TV, Smart TVs, PlayStation, Xbox, and so on, and even available to watch instantly or download for later on phone/tablet. As the biggest American entertainment provider of Internet streaming media and video-on-demand online and DVD by mail, NetFlix attracts an increasingly growing number of worldwide customers to download the software in order to watch the most recently released TV shows and movies anytime and anywhere that is totally personalized for you.

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