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Arunkumar Subramani
Arunkumar Subramani

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AN Introduction to GraphQL

What is graphql ?

GraphQL is an open-source query language and runtime for APIs (Application Programming Interfaces). It was developed by Facebook and released publicly in 2015. GraphQL provides a flexible and efficient approach to fetching and manipulating data, allowing clients to request specific data structures and enabling developers to design APIs that precisely match the needs of their applications.

Unlike traditional REST APIs, where the server defines fixed endpoints that return predetermined data structures, GraphQL allows clients to send queries specifying the exact data they require. With GraphQL, clients can request multiple resources and fields in a single query, reducing the number of network requests and minimizing overfetching or underfetching of data.

The core concepts of GraphQL:


GraphQL uses a schema to define the available types, fields, and relationships in the API. The schema serves as a contract between the client and the server, specifying what data can be requested and what mutations can be performed.

In GraphQL, the schema is a crucial component that defines the capabilities and structure of the data that can be requested from a GraphQL API. It acts as a contract between the client and the server, outlining the available types, fields, and relationships in the API.

Here are some key aspects of the GraphQL schema:

  1. Types: The schema consists of various types that represent the objects or entities in your data model. These types can be object types, scalar types, enumeration types, interface types, union types, or input types.

  2. Object Types: Object types define the structure of complex objects in your data model. They consist of fields, each of which has a name and a specific type.

  3. Fields: Fields represent the individual data elements or properties of an object type. Each field has a name and a corresponding type. Fields can be scalar types (e.g., String, Int, Boolean) or other object types.

  4. Scalars: Scalars are basic data types in GraphQL, representing primitive values like strings, numbers, booleans, or dates. Examples of scalar types include String, Int, Float, Boolean, and ID.

  5. Enumerations: Enumerations define a specific set of possible values for a field. They allow you to define a predefined list of options that a field can accept.

  6. Interfaces: Interfaces define a common set of fields that can be implemented by multiple object types. They provide a way to specify shared fields and ensure consistency across related types.

  7. Unions: Unions allow you to specify that a field can return objects of different types. This is useful when a field can represent multiple types of related data.

  8. Input Types: Input types are used for arguments in mutations or queries. They represent a set of input fields that can be passed as arguments to perform operations.

The schema is typically defined using the GraphQL schema definition language (SDL), which provides a human-readable syntax for describing types, fields, and relationships.

By defining the schema, you establish the contract between the client and server, enabling clients to understand the structure of the data and make precise queries or mutations to request or modify the data. The server uses the schema to validate and execute the queries and mutations, ensuring that the requested data adheres to the defined structure.


Clients use GraphQL queries to request data from the server. Queries define the fields and relationships the client wants to retrieve and allow for nested selections and aliasing.

Queries in GraphQL are used to retrieve data from a GraphQL server. They define the structure and fields of the data that clients want to fetch. With queries, clients can precisely specify the data requirements and receive the exact data they need in a single request.

Here are some key aspects of queries in GraphQL:

  1. Defining Queries: Queries are defined using the GraphQL query language. They start with the keyword query followed by the fields and their corresponding subfields that the client wants to fetch.

  2. Fields and Subfields: Fields in a query represent the data that clients want to retrieve. They can be scalar types (such as strings, integers, booleans) or object types (representing related data). Queries can have multiple fields and can include nested subfields to traverse through object relationships.

  3. Aliases: Aliases in queries allow clients to assign different names to the fields they want to fetch. This can be useful when multiple fields of the same type are needed in a query.

  4. Arguments: Queries can include arguments to provide specific parameters for filtering, sorting, or pagination. Arguments are specified within parentheses after the field name and can be of any scalar or input type defined in the schema.

  5. Fragments: Fragments in queries enable reusable selections of fields. They allow clients to define a set of fields and include them in multiple queries, reducing duplication and improving maintainability.

  6. Executing Queries: To execute a query, clients send a GraphQL request to the server containing the query structure and any necessary variables. The server then resolves the query by executing the corresponding resolvers, fetching the requested data, and returning the response to the client.

  7. Error Handling: If an error occurs during the execution of a query (such as invalid arguments or data not found), the server can return an error response with meaningful error messages, helping clients understand and handle the issues gracefully.

GraphQL queries provide a flexible and efficient way for clients to retrieve precisely the data they need. The client has control over the shape and depth of the data fetched, reducing over-fetching and under-fetching of data commonly encountered in REST APIs. Queries allow clients to efficiently fetch related data in a single round trip, improving performance and minimizing network overhead.


Mutations enable clients to modify data on the server. Unlike queries, mutations are used for operations that have side effects, such as creating, updating, or deleting data.

Mutations in GraphQL are a way to modify data on the server. While queries are used for retrieving data, mutations enable clients to perform create, update, and delete operations, also known as CRUD operations.

In GraphQL, mutations are defined as part of the schema and specify the operations that can be performed to modify data. Mutations have a similar structure to queries, with a name and a set of input arguments. However, mutations are executed on the server-side and can have side effects, such as modifying the database or updating the application's state.

Here are some key aspects of mutations in GraphQL:

  1. Defining Mutations: Mutations are defined in the GraphQL schema using the type Mutation block. Inside the Mutation type, you can define individual mutation fields that represent specific operations.

  2. Input Arguments: Mutations can take input arguments, which are used to provide the necessary data for the operation. These arguments can be scalar types or custom input types defined in the schema.

  3. Return Type: Each mutation field defines its return type, indicating what data will be returned after the mutation is executed. It can be a scalar type, an object type, or even a custom type specifically designed for the mutation's response.

  4. Executing Mutations: To execute a mutation, clients send a GraphQL request with the mutation field and the corresponding input arguments. The server then executes the mutation resolver associated with the mutation field and performs the necessary operations, such as creating, updating, or deleting data.

  5. Side Effects: Mutations can have side effects, such as modifying the server's state or triggering external actions. For example, a createUser mutation may create a new user in the database, while an updatePost mutation may update an existing post.

  6. Error Handling: Mutations can also handle and report errors. If an error occurs during the execution of a mutation, the server can return an error response with meaningful error messages to the client.

It's important to note that mutations are not limited to modifying a single resource. They can be used to perform complex operations involving multiple entities and data relationships.

By using mutations, GraphQL provides a standardized and flexible way to modify data on the server. Mutations allow clients to precisely specify the changes they want to make and provide a structured approach to modifying data in a GraphQL API.


GraphQL has a rich type system that allows developers to define custom types, including scalar types (e.g., String, Int, Boolean) and complex object types. Types can have fields and relationships with other types, enabling the construction of complex and hierarchical data structures.

GraphQL provides several built-in scalar data types for defining fields in the schema. Here are the commonly used scalar data types in GraphQL:

  1. String: Represents a sequence of characters.

  2. Int: Represents a signed 32-bit integer value.

  3. Float: Represents a signed double-precision floating-point value.

  4. Boolean: Represents a boolean value (true or false).

  5. ID: Represents a unique identifier, often serialized as a string. It's typically used to represent unique keys or identifiers.

Besides scalar types, GraphQL also allows the definition of complex types:

  1. Object Types: Represents an object with a set of fields. Fields within an object type can have their own types, including scalar types or other object types.

  2. Enum Types: Represents a set of predefined values. Enum types allow defining a specific list of possible values that a field can have.

  3. Interface Types: Represents a collection of fields that other object types can implement. Interfaces define a contract for objects to adhere to, allowing shared fields and ensuring consistency across types.

  4. Union Types: Represents a type that can be one of several object types. Union types allow defining a field that can return different types of objects.

  5. Input Types: Represents a set of input fields that can be used as arguments in mutations or queries. Input types are similar to object types but are used specifically for input data.

Additionally, GraphQL supports list types to represent arrays of values. For example, [Int] represents a list of integers.

These data types can be combined and nested to build complex schemas that accurately represent the data model of your application.


Resolvers are responsible for resolving the data requested in a GraphQL query. They fetch the necessary data from various sources, such as databases or other APIs, and provide the requested data back to the client.

Resolvers in GraphQL are functions that are responsible for resolving the data requested in a GraphQL query. They define how the server retrieves the data from various sources, such as databases, APIs, or other services, and maps it to the fields specified in the query.

Each field in a GraphQL schema can have a corresponding resolver function. When a query is executed, the GraphQL server invokes the appropriate resolvers to fetch the requested data.

Here are some key aspects of resolvers in GraphQL:

  1. Field-Level Resolution: Resolvers are associated with individual fields in the schema. When a query is made, the resolver for each field is responsible for fetching the data for that specific field.

  2. Parent-Child Relationship: Resolvers can establish relationships between fields. For example, if an object type User has a field posts, the resolver for the posts field can fetch the posts associated with that user.

  3. Data Fetching: Resolvers determine how data is fetched from the underlying data sources. This can involve making database queries, calling external APIs, or accessing other services.

  4. Data Transformation: Resolvers can perform data transformations or formatting before returning the data. This allows customization of the data returned to match the structure expected by the client.

  5. Asynchronous Operations: Resolvers can handle asynchronous operations, such as querying a database or making API calls. They can return promises or use async/await syntax to handle asynchronous tasks.

  6. Error Handling: Resolvers can handle errors and provide meaningful error messages when something goes wrong during data fetching or processing. Errors can be thrown within resolvers to indicate specific issues.

Resolvers are typically defined as part of a GraphQL server implementation. Depending on the programming language or framework used, the server implementation provides mechanisms to define resolvers and wire them to the corresponding fields in the schema.

By implementing resolvers, you can control how data is fetched and manipulated in response to GraphQL queries, allowing you to fetch data from multiple sources, perform complex data transformations, and efficiently resolve the data needed for each field in the schema.

Sample Example using graphql with apollo-server to login and get user info from mysql db with node server with authentication:


GraphQL is a modern way to communicate between the client and server in web or mobile applications. It allows the client to ask for exactly the data it needs and get a predictable response from the server. Here's what makes GraphQL special:

  1. Efficiency: With GraphQL, the client can request only the specific data it needs, avoiding unnecessary data transfers and reducing the amount of data sent over the network.

  2. Flexibility: GraphQL gives the client the power to ask for different combinations of data in a single request, making it easier to build dynamic and complex applications.

  3. Ease of Development: GraphQL provides a clear and self-documenting API through its schema. This helps developers understand the available data and build applications faster.

  4. Real-Time Updates: GraphQL supports real-time updates through subscriptions, allowing applications to receive live data updates without repeatedly requesting the server.

Overall, GraphQL simplifies data fetching and makes it more efficient, giving developers greater control and flexibility in building modern applications.

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