GraphQL is not just a query language, it’s a paradigm shift in how we think about APIs and data management
So back in 2012, Facebook was having trouble with their mobile app. It was slow and didn’t work well on weak internet connections. They were using a type of technology called REST API, but it wasn’t working well for them.
So they came up with a new idea called GraphQL. It’s a special way of asking for information from a server. Instead of getting a bunch of information all at once, GraphQL lets the app ask for only the specific information it needs at that moment. This makes the app faster and more efficient, especially on weak internet connections.
Think of it like asking a waiter for your order at a restaurant. Instead of getting a whole menu, you can just ask for the specific food you want to eat right now. This way, you don’t waste time looking at things you don’t want, and the waiter can bring you your food faster!
here is another example :
As you can see, the above query and the result have the same form. Using GraphQL queries enables the server to know what the client has asked for. It also returns only the fields specifically requested.
With GraphQL, you don’t need to know all the technical details about how the different sources work or where the data is stored. You just tell GraphQL what you want, and it figures out how to get it for you.
To help make this process easier, GraphQL uses something called Schema Definition Language (SDL). SDL is like a map that tells GraphQL where to find the information you want. By using SDL, GraphQL knows exactly what you’re looking for and can give you just the data you need, making your app faster and more efficient.
Increased Efficiency: With REST APIs, clients receive all the data defined by the server, even if they don’t need all of it. In contrast, GraphQL allows clients to request only the data they need, making the application more efficient and reducing the amount of data transferred over the network.
By specifying exactly what data is needed, GraphQL reduces the problem of over-fetching (receiving more data than necessary) and under-fetching (not receiving enough data to fully populate the client).
With GraphQL, clients can request multiple resources in a single request, and the server can respond with the exact data the client needs, regardless of where it is stored.
The GraphQL API is strongly typed, meaning that developers can easily understand and interact with the API using tools like GraphQL playgrounds and code editors.
Because clients can specify the exact data they need, changes to the API schema can be made without breaking existing clients.
As you can see above even before designing and implementing their user interfaces, front-end engineers can explore and test the backend APIs they need using this interface: A very powerful collaboration mechanism for the team.
|URL-based queries and parameters
|Schema-based queries and fields
|Pre-defined data structures
|Dynamic response based on query
|Limited to pre-defined endpoints
|Flexible, can request any data
|Can return more data than needed
|Requests only needed data
|May require multiple requests
|Can retrieve multiple resources
|May require versioning of endpoints
Can add fields without breaking clients
|May require multiple API calls and data filtering
|Can simplify client requests and data retrieval
|Can be easily cached using HTTP caching
|Requires a custom caching layer
|Requires polling or WebSockets
Supports real-time updates via subscriptions
Overall, GraphQL can help reduce complexity and improve efficiency in building and consuming APIs, making it a powerful tool for modern application development.
In conclusion, GraphQL is a query language and runtime API that has gained popularity in recent years due to its advantages over traditional REST APIs.
As a result, GraphQL has become a preferred method for building APIs, especially for applications with complex data requirements, mobile applications, and microservices architectures. While REST APIs are still widely used, it’s clear that GraphQL offers a more flexible, efficient, and developer-friendly alternative that is quickly gaining traction in the software development community.
Here are some links to help you getting started and get a deeper insight on GraphQl
https://graphql.org/learn/ (Official documentation)
https://www.howtographql.com/basics/1-graphql-is-the-better-rest/ (Deeper dive on tooling, environment, security etc Frontend/backend)
|Microsoft Azure Cosmos DB
|Fast, reliable graph database built for the cloud
|Native multi-model DBMS for graph, document, key/value and search. All in one engine and accessible with one query language.
|Wide-column store based on ideas of BigTable and DynamoDB
|Globally distributed, horizontally scalable, multi-model database service
|Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings
|Primary database model
|Graph DBMS RDF store
|Document store Graph DBMS Key-value store Search engine
|Wide column store
|Document store Graph DBMS Key-value store Wide column store
|ArangoDB Oasis –The Managed Cloud Service of ArangoDB. Oasis provides fully managed, and monitored cluster deployments of any size, with enterprise-grade security. Get started for free and continue for as little as $0,21/hour.
|• Aiven for Apache Cassandra: Fully managed, open source NoSQL database specifically designed to be highly available, performant, and scalable. Astra DB: Multi-cloud DBaaS built on Apache Cassandra.
|Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.
|Server operating systems
|schema-free and schema-optional
|SQL-like SELECT, DML and DDL statements (CQL)
|SQL-like query language
|APIs and other access methods
|RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin 3.3
Graph API (Gremlin)
GraphQL query language
Java & SpringData
JSON style queries
Graph API (Gremlin)
RESTful HTTP API
Cypher query language
RESTful HTTP API
Spring Data Neo4j
|Supported programming languages
MongoDB client drivers written for various programming languages
|yes using Neo4j Fabric
|Multi-availability zones high availability, asynchronous replication for up to 15 read replicas
|Source-replica replication with configurable replication factor
|selectable replication factor
|Causal Clustering using Raft protocol
|with Hadoop integration
OneShard (highly available, fault-tolerant deployment mode with ACID semantics)
|Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode
|Multi-item ACID transactions with snapshot isolation within a partition
|Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)
|Access rights for users can be defined per object
|Access rights can be defined down to the item level
|Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)
ArangoDB is cluster ready for document, key/value and even for graph-models. With ArangoDB 3.x releases further improvements are being made for performant cluster usage with graphs.
ArangoDB is perfectly suitable for high-availability, high-performance or any other use case a document store might be challenged with.
By reducing development effort and enabling data-model flexibility, ArangoDB is designed for fast development and easy scaling. With the Foxx Microservices Framework, you can build production-ready session services within minutes.
ArangoDB offers the same functionality as Neo4j with more than competitive performance, plus several additional features:
Scalability needs and ArangoDB
ArangoDB is cluster ready for graphs, documents and key/values. ArangoDB is suitable for e.g. recommendation engines, personalization, Knowledge Graphs or other graph-related use cases. ArangoDB provides special features for scale-up (Vertex-centric indices) and scale-out (SmartGraphs).
The ArangoDB Query Language (AQL) is similar to the Structured Query Language (SQL) in its purpose. Both support reading and modifying collection data, however AQL does not support data definition operations, such as creating and dropping databases, collections and indexes.
Though some of the keywords overlap, AQL syntax differs from SQL. For instance, the SQL
WHERE and AQL
FILTER clauses are equivalent in that they both define conditions for returning results. But, SQL uses predefined sequence to determine where the
WHERE clause must occur in the statement. In AQL, clauses execute from left to right, so the position of a
FILTER clause in the query determines its precedence.
Despite such differences, anyone with an SQL background should have no difficulty in learning AQL. If you run into any problems, we have a table below showing SQL functions and commands with their AQL equivalents.Comparing Relational DBs to multi-model ArangoDB. Get the White Paper
Below is a table with the terms of both systems.
|primary key (automatically present on
A Spec that describes a declarative query language that your client can use to ask an API for the exact data they need. and it’s done by creating a strongly typed schema for your API.
It provides ultimate flexibility in how your API can resolve data and client queries validated against your schema.
The GraphQL Solving the following problems :
In REST API unlike GraphQL We have to pass the GET request to multiple endpoints to get the data that we need. and it’s a fixed data structure that returns more files of data than the data that we exactly need.
As you can see above to fetch the user’s Id and User post and followers of that particular user we have to reach multiple endpoints to get the required data and we are facing the issue of over-fetching by receiving data that we do not even need.
with GraphQL since it’s a single endpoint we can retrieve the exact data that we need. That is why GraphQL is very effective when fetching data. Notice that even though the structure of the data has more fields its will only return according to the query request from a client-side.
Under-Fetching generally means that the specific endpoint didn’t provide the sufficient data that we need and again we have to reach out to more endpoints in order to get the required data.
As I mentioned earlier it’s a strongly typed language and it is important to define the data type for each field.
A Query type on a schema that defines operations where clients can perform to access data that resembles the shape of the other type in schema. and it defines how clients can access data.
It is a function that is responsible for returning values for fields that exist on types in a scheme. and resolvers execution depends on the incoming client query.
A Mutation is used to Create, Delete, and Modify the data.
It combines more than one API to compose it in a single GraphQL Umbrella.
A schema that contains a type definition, resolvers, query definition, and mutation definition. A scheme is defined using Schema Definition Language (SDL). And programmatically creating a schema using language construct.
Apollo Server is an open-source tool that allows connecting with any kind of GraphQL client to develop GraphQL API. and its self-documented which means it provides basic information about schema types and fields suggestions which is very helpful while developing queries for a client including Apollo Client.
We can use Apollo Server as :
Enough for theory let’s get our hands dirty with implementing it.
To create a GraphQL API with apollo server we need to install the required packages from npm follow the steps given below
Not that all the types and queries must be enclosed within the template literals followed by the graphql-tag.
The next step is to create resolvers for the query. and the query field name should be the same as the query field name. and it should return the object of data requested from the query.
After that, we have to pass the type definition and resolver to the ApolloServer as a parameter and then provide a specific port to listen.
Now run the js file and you should see the apollo server on the browser and then we can perform the query request as a client to get a required data
once you execute it successfully you will be seeing the page as shown in the above image You should click Query your server button and then you will be redirected to the apollo server studio where you can play around with your GraphQL API.
As you can see you can retrieve specific data that we need.
AppSync is a service provided by AWS(Amazon Web Services) that helps in creating the API using GraphQL. GraphQL is a query language developed by Facebook in 2012 and was made open-source in 2015. AppSync is fully managed by AWS and helps in developing the application at a very fast rate. AppSync helps in creating a single endpoint of GraphQL, and with the help of it, we can query databases like dynamo DB, Ethena, RedShift, RDS, Aurora, and many more. AppSync can also act as a link between the client app and the databases.
Half of the work regarding the back-end will be handled by AppSync which saves most of your time.
AppSync will scale itself according to the traffic and the number of people using it. No matter if it is a small scale application or a large scale e-commerce website
Any change in the database is reflected across all the clients in real-time. This job can be cumbersome if you plan on coding the project yourself. Your application will inherit this splendid feature without putting extra effort into it.
It syncs the app data so that all the requests and queries can be performed even when the device isn’t connected to the Internet. The changes will be reflected as soon as the device gets connected. AppSync will take care of the data conflicts.
All your datasets will be under the roof of Amazon Web Services. AWS AppSync supports all the services provided by Amazon like DynamoDB, Elasticsearch, Aurora serverless, Amazon Cognito, and any other Amazon Service that you would like to implement in your application.
AppSync also allows you to define your security system for your data using multiple concurrent authentication modes.
This is how you create an API on AppSync, You can easily integrate the API in your app using the instructions provided there only.
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Graphene is a python library for creating GraphQL APIs in python easily and with very little code. In Graphene, we follow the code first approach as compare to the schema first approach in Apollo Server or Ariadne. Graphene can be easily integrated with popular web frames such as Django, Flask and bottle, so it makes it very easy to integrate GraphQL with all these frameworks.
So that’s it from the first part, I will be adding more blogs on this series and in the next part we will talk about mutations that is the CRUD operations in GraphQL.
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For more interesting Blogs Visit- Utkarsh Shukla Author