AWS-Online-Tech-Talks
The video discusses how AWS AppSync allows developers to create a single endpoint to connect any type of application to data sources in an AWS account and hide the backend complexity from clients. The presenter goes on to discuss the use of GraphQL with AWS AppSync, introducing the concept of a schema and how it allows for multiple fields to be requested in one query, as well as the use of resolvers for defining business logic to retrieve fields from data sources. The section then covers the benefits of using AWS AppSync with DynamoDB, a NoSQL database that provides consistent performance through horizontal scaling, and the importance of primary and partition keys in data modeling for optimal performance. Finally, the speaker discusses the trade-offs between using DynamoDB with multi-table and single table designs in GraphQL and AppSync, providing sample code to showcase their differing levels of complexity and highlighting the importance of understanding access patterns.
user, which means that the client doesn't need to know where the data is coming from. In addition, with GraphQL, you can request all of the data that you want in a single request, and you can get only the data that you need. This is because GraphQL allows you to be really specific about what data you want to retrieve, and it gives you predictable results because you get back data in the specific shape that you requested.
In this section, the benefits of using GraphQL over traditional RESTful APIs are discussed. GraphQL reduces the number of requests needed to access data by allowing applications to request multiple fields in a single query, and resolves relationships between data sources. This saves on performance and reduces the amount of data that needs to be sent over the network, resulting in a faster and more efficient experience for mobile applications. GraphQL introduces a schema, which is a document that defines the data and how it should be interacted with, and includes types, fields, and operations such as queries, mutations, and subscriptions. The selection set within these operations allows for varying sets of fields to be requested, further optimizing the amount of data sent and increasing the power and flexibility of GraphQL.
In this section, the benefits of using GraphQL with AWS AppSync are discussed. GraphQL allows for a reduction in requests, the control of data fetched using the selection set, and the shape and format of data received using the selection set. Onboarding overhead is also reduced due to the self-documenting schema and introspection capability. AWS AppSync, a managed GraphQL service, provides a GraphQL endpoint that connects any type of application to data sources in your AWS account. AWS AppSync allows automatic connection to DynamoDB, Aurora, OpenSearch, Lambda, and any HTTP endpoint. Real-time updates can also be implemented with AWS AppSync, reducing the need for polling that can be intensive on the client and add pressure on the backend.
In this section of the video, the presenter discusses the benefits of using AWS AppSync and GraphQL APIs for connecting to a single endpoint and hiding backend complexity, allowing clients to focus only on the schema, types, and operations. They explain that working with AppSync and GraphQL involves starting with the schema and then writing resolvers to define the business logic that tells AppSync how to retrieve fields from the data sources configured. The presenter also goes into detail about utilizing pipeline resolvers and direct lambda resolvers, and AppSync's real-time data broadcasting capabilities through subscriptions and secured websockets. Overall, AppSync simplifies the process of implementing pop-up APIs and allows for powerful queries that retrieve a lot of data from multiple sources in a single query.
In this section of the video, the speaker discusses the various features and functionalities of AWS AppSync, a managed GraphQL API service. AppSync allows for pub/sub functionality, subscription filtering, and multiple methods of authorization. It supports AWS web integration and configurable batching, caching with custom keys, and cache and item eviction to reduce latency. AppSync can be used as the gateway to all data sources and functions in a user's AWS account. The section then moves on to discussing DynamoDB, a NoSQL database from AWS that provides consistent performance through horizontal scaling. The video explores the concept of single table design and the factors one should consider when using it with GraphQL and AppSync.
In this section, the speaker discusses the performance of DynamoDB in comparison to traditional relational databases like MySQL, and the consistent performance that comes from the horizontal scaling and focused API of DynamoDB. The speaker emphasizes the importance of the primary key and partition key in DynamoDB's data modeling, and how it helps with consistent performance by allowing fast query requests to the correct partition of data. Additionally, the speaker highlights that DynamoDB is a fully managed database which has increased its popularity in serverless applications and GraphQL APIs. The speaker notes that data modeling with DynamoDB differs from a relational database in that access patterns must be considered first and how this affects the design of the database schema.
In this section, the speaker discusses how to model data with DynamoDB to maximize efficiency. The first step is identifying access patterns and designing the primary key accordingly for the partitioning to work effectively. The query operation is a powerful tool to fetch many records in a single request using the partition key and sort key. Additionally, the concept of single table design is introduced where multiple entity types are stored within the same table using a pre-join approach to fetch heterogeneous items in one efficient request. The primary key values for different types of items must use generic names such as pk and sk because they have different attributes.
In this section, the speaker explains how data modeling works in DynamoDB and how it can be applied to GraphQL APIs. While DynamoDB is schema-less, it's still recommended to enforce a schema in your application code. In terms of creating an AppSync API with DynamoDB data sources, there are two main design options: single table and multi-table design. Multi-table design offers more flexibility in queries and is easier to use in resolvers, making it the default option for most people. However, the speaker later discusses the benefits of single table design and how it can simplify data access and reduce overall costs.
In this section, the speaker discusses the trade-offs of using DynamoDB with multi-table and single table designs in GraphQL and AppSync. Multiple sequential requests in a GraphQL back-end lead to higher latency in multi-table design, but it offers more flexibility than single table design. On the other hand, single table design is more performant and has lower latency since it handles multiple objects in a single request. However, it adds more complexity to resolvers and may be less flexible to clients. The speaker emphasizes that the two designs can be mixed and matched based on the application needs.
In this section, the speaker highlights some low hanging fruit for implementing DynamoDB and GraphQL. They recommend looking for easy stuff first and implementing parts that are often accessed, either retrieved together or individually, and then tackling more complex parts with separate resolvers. The speaker stresses the importance of understanding DynamoDB's access patterns and trade-offs between single and multi-table designs. They also walk through sample codes for multi-table and single table design repos to highlight the differing levels of complexity in the resolvers. Overall, users are advised to make the decision that's right for them, and the speaker invites questions to clarify any doubts.
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