Avalanche Quick Start - Pangolin Rewards

The goal of this quick start guide is to index all token deposits and transfers from the Avalanche's Pangolin token.

In the earlier Quickstart section , you should have taken note of three crucial files. To initiate the setup of a project from scratch, you can proceed to follow the steps outlined in the initialisation description.

As a prerequisite, you will need to generate types from the ABI files of each smart contract. Additionally, you can kickstart your project by using the EVM Scaffolding approach (detailed here). You'll find all the relevant events to be scaffolded in the documentation for each type of smart contract.

Note

The final code of this project can be found here.

Your Project Manifest File

The Project Manifest file is an entry point to your project. It defines most of the details on how SubQuery will index and transform the chain data.

For EVM chains, there are three types of mapping handlers (and you can have more than one in each project):

• BlockHanders: On each and every block, run a mapping function
• TransactionHandlers: On each and every transaction that matches optional filter criteria, run a mapping function
• LogHanders: On each and every log that matches optional filter criteria, run a mapping function

Important

We use Ethereum packages, runtimes, and handlers (e.g. @subql/node-ethereum, ethereum/Runtime, and ethereum/*Handler) for Avalanche. Since Avalanche's C-chain is built on Ethereum's EVM, we can use the core Ethereum framework to index it.

We are indexing actions from the Pangolin Rewards contract, first you will need to import the contract abi defintion from here. You can copy the entire JSON and save as a file ./abis/PangolinRewards.json in the root directory.

This section in the Project Manifest now imports all the correct definitions and lists the triggers that we look for on the blockchain when indexing.

Since you are going to index all Pangolin Rewards, you need to update the datasources section as follows:

{
  dataSources: [
    {
      kind: EthereumDatasourceKind.Runtime,
      // # Block when the first reward is made
      startBlock: 7906490,
      options: {
        // Must be a key of assets
        abi: "erc20",
        // Pangolin reward contract https://snowtrace.io/token/0x88afdae1a9f58da3e68584421937e5f564a0135b
        address: "0x88afdae1a9f58da3e68584421937e5f564a0135b",
      },
      assets: new Map([["erc20", { file: "./abis/PangolinRewards.json" }]]),
      mapping: {
        file: "./dist/index.js",
        handlers: [
          {
            kind: EthereumHandlerKind.Event,
            handler: "handleLog",
            filter: {
              topics: ["RewardPaid(address user, uint256 reward)"],
            },
          },
        ],
      },
    },
  ],
}

The above code indicates that you will be running a handleLog mapping function whenever there is an RewardPaid log on any transaction from the Pangolin Rewards contract.

Check out our Manifest File documentation to get more information about the Project Manifest (project.ts) file.

Update Your GraphQL Schema File

The schema.graphql file determines the shape of your data from SubQuery due to the mechanism of the GraphQL query language. Hence, updating the GraphQL Schema file is the perfect place to start. It allows you to define your end goal right at the start.

Remove all existing entities and update the schema.graphql file as follows. Here you can see we are indexing two entities, PangolineRewards and Users where receiver is of type User and rewards contains a reverse look up to the receiver field.

type PangolinRewards @entity {
  id: ID! # Id is required and made up of block has and log index
  transactionHash: String!
  blockNumber: BigInt!
  blockHash: String!
  receiver: User!
  amount: BigInt!
}

type User @entity {
  id: ID! # Wallet address
  totalRewards: BigInt!
  rewards: [PangolinRewards]! @derivedFrom(field: "receiver") #This is virtual field
}

Note

Importantly, these relationships can not only establish one-to-many connections but also extend to include many-to-many associations. To delve deeper into entity relationships, you can refer to this section. If you prefer a more example-based approach, our dedicated Hero Course Module can provide further insights.

SubQuery simplifies and ensures type-safety when working with GraphQL entities, smart contracts, events, transactions, and logs. The SubQuery CLI will generate types based on your project's GraphQL schema and any contract ABIs included in the data sources.

yarn

yarn codegen

npm

npm run-script codegen

This action will generate a new directory (or update the existing one) named src/types. Inside this directory, you will find automatically generated entity classes corresponding to each type defined in your schema.graphql. These classes facilitate type-safe operations for loading, reading, and writing entity fields. You can learn more about this process in the GraphQL Schema section.

It will also generate a class for every contract event, offering convenient access to event parameters, as well as information about the block and transaction from which the event originated. You can find detailed information on how this is achieved in the EVM Codegen from ABIs section. All of these types are stored in the src/types/abi-interfaces and src/types/contracts directories.

You can conveniently import all these types:

import { PangolinRewards, User } from "../types";
import { RewardPaidLog } from "../types/abi-interfaces/PangolinRewards";

Check out the GraphQL Schema documentation to get in-depth information on schema.graphql file.

Now that you have made essential changes to the GraphQL Schema file, let’s proceed ahead with the Mapping Function’s configuration.

Add a Mapping Function

Mapping functions define how blockchain data is transformed into the optimised GraphQL entities that we previously defined in the schema.graphql file.

Follow these steps to add a mapping function:

Navigate to the default mapping function in the src/mappings directory. You will be able to see three exported functions: handleBlock, handleLog, and handleTransaction. Replace these functions with the following code (note the additional imports):

import { PangolinRewards, User } from "../types";
import { RewardPaidLog } from "../types/abi-interfaces/PangolinRewards";

async function checkGetUser(id: string): Promise<User> {
  let user = await User.get(id.toLowerCase());
  if (!user) {
    // does not exist, create a new user
    user = User.create({
      id: id.toLowerCase(),
      totalRewards: BigInt(0),
    });
  }
  return user;
}

export async function handleLog(event: RewardPaidLog): Promise<void> {
  logger.info(`New Reward Paid at block ${event.blockNumber}`);
  const { args } = event;
  if (args) {
    const user = await checkGetUser(args.user);

    const pangolinRewardRecord = new PangolinRewards(
      `${event.blockHash}-${event.logIndex}`,
    );

    pangolinRewardRecord.transactionHash = event.transactionHash;
    pangolinRewardRecord.blockHash = event.blockHash;
    pangolinRewardRecord.blockNumber = BigInt(event.blockNumber);
    pangolinRewardRecord.receiverId = user.id;
    pangolinRewardRecord.amount = BigInt(args.reward.toString());

    user.totalRewards += pangolinRewardRecord.amount;
    await user.save();
    await pangolinRewardRecord.save();
  }
}

Let’s understand how the above code works.

The mapping function here receives an RewardPaidLog which includes transaction log data in the payload. We extract this data and first read and confirm that we have a User record via checkGetUser. We then create a new PangolinRewards entity that we defined in our schema.graphql and then save this to the store using the .save() function (Note that SubQuery will automatically save this to the database).

Check out our Mappings documentation to get more information about the Project Manifest (project.ts) file.

Build Your Project

Next, build your work to run your new SubQuery project. Run the build command from the project's root directory as given here:

yarn

yarn build

npm

npm run-script build

Important

Whenever you make changes to your mapping functions, you must rebuild your project.

Now, you are ready to run your first SubQuery project. Let’s check out the process of running your project in detail.

Whenever you create a new SubQuery Project, first, you must run it locally on your computer and test it and using Docker is the easiest and quickiest way to do this.

Run Your Project Locally with Docker

The docker-compose.yml file defines all the configurations that control how a SubQuery node runs. For a new project, which you have just initialised, you won't need to change anything.

However, visit the Running SubQuery Locally to get more information on the file and the settings.

Run the following command under the project directory:

yarn

yarn start:docker

npm

npm run-script start:docker

Note

It may take a few minutes to download the required images and start the various nodes and Postgres databases.

Query your Project

Next, let's query our project. Follow these three simple steps to query your SubQuery project:

1. Open your browser and head to http://localhost:3000.

2. You will see a GraphQL playground in the browser and the schemas which are ready to query.

3. Find the Docs tab on the right side of the playground which should open a documentation drawer. This documentation is automatically generated and it helps you find what entities and methods you can query.

Try the following queries to understand how it works for your new SubQuery starter project. Don’t forget to learn more about the GraphQL Query language.

query {
  pangolinRewards(first: 5) {
    nodes {
      id
      amount
    }
  }
  users(first: 5, orderBy: TOTAL_REWARDS_DESC) {
    nodes {
      id
      totalRewards
      rewards(first: 5) {
        totalCount
      }
    }
  }
}

You will see the result similar to below:

{
  "data": {
    "pangolinRewards": {
      "nodes": [
        {
          "id": "0xa0759b9929d68bc88ad01832484ecc24fd0abbcaf19a92a69a1a8fc1f2f23a71-20",
          "amount": "750406183852"
        },
        {
          "id": "0xf4b8a0948afc4264b876b4431da01a7a96a11f1ce24d73a2a0a71f9a8228b3c9-121",
          "amount": "31106152923645074116"
        },
        {
          "id": "0x8f348fcc2eb78e91e6d212a045356983f4f46ba1843e5e0f763e1e75a1ae8582-33",
          "amount": "612972344478229813"
        },
        {
          "id": "0xbb588aa14c97bad75d34ccbae332af03eab1390678516df01badf4b4f1886d4e-56",
          "amount": "3588963063129"
        },
        {
          "id": "0x477c12a0a4a5642378e58569743b24af200d36f7952d2e6bc4cfd9fa8e96592f-74",
          "amount": "30987822664812021072"
        }
      ]
    },
    "users": {
      "nodes": [
        {
          "id": "0x5da33bcd38fbc3e9632f9f6a198f4f0ef13746b6",
          "totalRewards": "4883581127128396302822",
          "rewards": {
            "totalCount": 2
          }
        },
        {
          "id": "0x79dcf1ef9786255c0f00f506c785bbd878ec184a",
          "totalRewards": "2282547055289881964699",
          "rewards": {
            "totalCount": 1
          }
        },
        {
          "id": "0x695b71dbd30a9f30c1958644086900ac9cd33c85",
          "totalRewards": "1620206587081566430579",
          "rewards": {
            "totalCount": 1
          }
        },
        {
          "id": "0xfd94d62683d8962055b661c4e64e762ed41e5489",
          "totalRewards": "1149590592806652626951",
          "rewards": {
            "totalCount": 1
          }
        },
        {
          "id": "0xab7901b09b67ee05b016456289cf74d362bd6d8c",
          "totalRewards": "882873704607946614381",
          "rewards": {
            "totalCount": 1
          }
        }
      ]
    }
  }
}

Note

The final code of this project can be found here.

What's next?

Congratulations! You have now a locally running SubQuery project that accepts GraphQL API requests for transferring data.

Tip

Find out how to build a performant SubQuery project and avoid common mistakes in Project Optimisation.

Click here to learn what should be your next step in your SubQuery journey.