Learn Introspection Queries in GraphQL

What is Introspection in GraphQL

GraphQL stands out with its strong type system. Because of the strong typed system, GraphQL is able to provide an introspection system to query the schema. The introspection system in GraphQL provides a way for clients to discover the resources that are available in a GraphQL schema. The introspection system is a feather in the hat for GraphQL. It has plenty of uses and we will see some of them below.

Why is Introspection Useful?

Provides clients a deep view of the schema

Through introspection queries, the client can get a complete view of the resources in the GraphQL schema. This can save the client tons of time going back and forth with documentation. Instead they can introspect the schema and understand the resources available to them in depth.

Build Awesome Tools

The introspection system allows the community to build awesome tools for GraphQL. Some of the tools that we use today that owe their existence to the introspection system are GraphiQL, GraphQL Playground, to name a few. These tools help clients to explore the GraphQL schema, in an easy and user-interactive way.

Self Documentation

These GraphQL tools like GraphQL Playground use the introspection system, to provide features like self documentation of the API, auto completion and code generation.

Introspection Queries

Let’s dive into some introspection queries and learn how to write them.

To demonstrate and learn GraphQL introspection queries, I am going to use the GitHub API that is available to the public. You can follow along by opening https://developer.github.com/v4/explorer/. Make sure you are signed in to your GitHub account to run the introspection queries.

On the GitHub GraphQL explorer, we can start typing our queries on the left side and hit play to see the JSON response on the right side. We can also browse through the documentation of the API on the right side.

Query schema’s type

All the fields in the introspection system are prefixed with two underscores. Let’s query the __schema field and learn about the types supported in the schema.

query getSchmemaTypes {
  __schema {
    types {
      name
    }
  }
}

The JSON response to this query is quite lengthy from the GitHub schema, below is the snapshot of the response.

{
  "data": {
    "__schema": {
      "types": [
        {
          "name": "AcceptEnterpriseAdministratorInvitationInput"
        },
        {
          "name": "AcceptEnterpriseAdministratorInvitationPayload"
        },
        {
          "name": "AcceptTopicSuggestionInput"
        },
        {
          "name": "AcceptTopicSuggestionPayload"
        }
        ......
       ]
    }
  }
}

Query supported queries and mutations

Using introspection we can also retrieve the supported queries and mutations from a schema. You can also use the isDeprecated field to learn if specific queries/mutations are deprecated. This will be useful information for the client before consuming the API. Within the ___schema _field, query for queryType and mutationType.

query getSupportedQueries {
  __schema {
    queryType {
      fields{
        name
      }
    }
  }
}

The JSON response below shows the queries supported by the API.

{
  "data": {
    "__schema": {
      "queryType": {
        "fields": [
          {
            "name": "codeOfConduct"
          },
          {
            "name": "codesOfConduct"
          },
          {
            "name": "enterprise"
          },
          {
            "name": "enterpriseAdministratorInvitation"
          },
          {
            "name": "enterpriseAdministratorInvitationByToken"
          },
          {
            "name": "license"
          },
          {
            "name": "licenses"
          },
          {
            "name": "marketplaceCategories"
          },
          {
            "name": "marketplaceCategory"
          },
          {
            "name": "marketplaceListing"
          },
          {
            "name": "marketplaceListings"
          },
          {
            "name": "meta"
          },
          {
            "name": "node"
          },
          {
            "name": "nodes"
          },
          {
            "name": "organization"
          },
          {
            "name": "rateLimit"
          },
          {
            "name": "relay"
          },
          {
            "name": "repository"
          },
          {
            "name": "repositoryOwner"
          },
          .....
      }
    }
  }
}

#graphql #database #programming #developer

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Learn Introspection Queries in GraphQL
Ahebwe  Oscar

Ahebwe Oscar

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How model queries work in Django

How model queries work in Django

Welcome to my blog, hey everyone in this article we are going to be working with queries in Django so for any web app that you build your going to want to write a query so you can retrieve information from your database so in this article I’ll be showing you all the different ways that you can write queries and it should cover about 90% of the cases that you’ll have when you’re writing your code the other 10% depend on your specific use case you may have to get more complicated but for the most part what I cover in this article should be able to help you so let’s start with the model that I have I’ve already created it.

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let’s just get into this diagram that I made so in here:

django queries aboutDescribe each parameter in Django querset

we’re making a simple query for the myModel table so we want to pull out all the information in the database so we have this variable which is gonna hold a return value and we have our myModel models so this is simply the myModel model name so whatever you named your model just make sure you specify that and we’re gonna access the objects attribute once we get that object’s attribute we can simply use the all method and this will return all the information in the database so we’re gonna start with all and then we will go into getting single items filtering that data and go to our command prompt.

Here and we’ll actually start making our queries from here to do this let’s just go ahead and run** Python manage.py shell** and I am in my project file so make sure you’re in there when you start and what this does is it gives us an interactive shell to actually start working with our data so this is a lot like the Python shell but because we did manage.py it allows us to do things a Django way and actually query our database now open up the command prompt and let’s go ahead and start making our first queries.

#django #django model queries #django orm #django queries #django query #model django query #model query #query with django

Jerad  Bailey

Jerad Bailey

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Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Delbert  Ferry

Delbert Ferry

1622102394

What is a GraphQL query? GraphQL query examples using Apollo Explorer

How to execute GraphQL queries

We know what queries look like and we know what to expect in response when we write them. The next question is: how do we execute them?

It’s important to know that since GraphQL queries are just plain strings, we can use a variety of approaches to fetch data. Among the many options, some that come to mind are:

  • curl
  • fetch
  • GraphQL client libraries (like Apollo Client)

#graphql #graphql query #apollo explorer

sophia tondon

sophia tondon

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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Jackson  Crist

Jackson Crist

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Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning