Aurora Quick Start (EVM)

SubQuery TeamAbout 6 min

Aurora Quick Start (EVM)

Auroraopen in new window is a next-generation Ethereum compatible blockchain and ecosystem that runs on the NEAR Protocol, and powers the innovations behind Aurora Cloud—the fastest path for Web2 businesses to capture the value of Web3.

Since SubQuery fully supports NEAR and Aurora, you can index data from both execution environments in the same SubQuery project and into the same dataset.

Goals

The goal of this quick start guide is to index transfers and approvals for the Wrapped NEAR smart contractopen in new window on NEAR Aurora.

Note

Before we begin, make sure that you have initialised your project using the provided steps in the Start Here section. Please initialise an NEAR Aurora project

In every SubQuery project, there are 3 key files to update. Let's begin updating them one by one.

Note

The final code of this project can be found hereopen in new window.

1. Your Project Manifest File

Important

We use Ethereum packages, runtimes, and handlers (e.g. @subql/node-ethereum, ethereum/Runtime, and ethereum/*Handler) for NEAR Aurora. Since Aurora is a EVM implementation on NEAR, we can use the core Ethereum framework to index it.

The Project Manifest (project.yaml) file works as an entry point to your Aurora project. It defines most of the details on how SubQuery will index and transform the chain data. For Aurora, there are three types of mapping handlers (and you can have more than one in each project). Note that these are different mapping handlers to that of traditional NEAR projects:

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

Note that the manifest file has already been set up correctly and doesn’t require significant changes, but you need to import the correct contract definitions and update the datasource handlers.

As we are indexing all transfers and approvals for the Wrapped NEAR smart contract, the first step is to import the contract abi definition which can be obtained from hereopen in new window. Copy the entire contract ABI and save it as a file called erc20.abi.json in the /abis 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 we are indexing all transfers and approvals for the Wrapped NEAR smart contract, you need to update the datasources section as follows:

dataSources:
  - kind: ethereum/Runtime # We use ethereum runtime since NEAR Aurora is a layer-2 that is compatible
    startBlock: 42731897 # Block with the first interaction with NEAR https://explorer.aurora.dev/tx/0xc14305c06ef0a271817bb04b02e02d99b3f5f7b584b5ace0dab142777b0782b1
    options:
      # Must be a key of assets
      abi: erc20
      address: "0xC42C30aC6Cc15faC9bD938618BcaA1a1FaE8501d" # this is the contract address for wrapped NEAR https://explorer.aurora.dev/address/0xC42C30aC6Cc15faC9bD938618BcaA1a1FaE8501d
    assets:
      erc20:
        file: "./abis/erc20.abi.json"
    mapping:
      file: "./dist/index.js"
      handlers:
        - handler: handleTransaction
          kind: ethereum/TransactionHandler # We use ethereum runtime since NEAR Aurora is a layer-2 that is compatible
          filter:
            ## The function can either be the function fragment or signature
            # function: '0x095ea7b3'
            # function: '0x7ff36ab500000000000000000000000000000000000000000000000000000000'
            function: approve(address spender, uint256 rawAmount)
        - handler: handleLog
          kind: ethereum/LogHandler # We use ethereum runtime since NEAR Aurora is a layer-2 that is compatible
          filter:
            topics:
              ## Follows standard log filters https://docs.ethers.io/v5/concepts/events/
              - Transfer(address indexed from, address indexed to, uint256
                amount)
              # address: "0x60781C2586D68229fde47564546784ab3fACA982"

The above code indicates that you will be running a handleTransaction and handlelog mapping function whenever there is an approve or Transfer log on any transaction from the Wrapped NEAR contractopen in new window.

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

2. 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 block information such as the id and the blockHeight along with addresses such as to, from, owner and spender, along with the contract address and value as well.

type Transaction @entity {
  id: ID! # Transaction hash
  txHash: String
  blockHeight: BigInt
  to: String!
  from: String!
  value: BigInt!
  contractAddress: String!
}

type Approval @entity {
  id: ID! # Transaction hash
  value: BigInt!
  owner: String!
  spender: String!
  contractAddress: String!
}

Important

When you make any changes to the schema file, please ensure that you regenerate your types directory.

SubQuery makes it easy and type-safe to work with your GraphQL entities, as well as smart contracts, events, transactions, and logs. SubQuery CLI will generate types from your project's GraphQL schema and any contract ABIs included in the data sources.

yarn codegen

npm run-script codegen

This will create a new directory (or update the existing one) src/types which contains generated entity classes for each type you have defined previously in schema.graphql. These classes provide type-safe entity loading, and read and write access to entity fields - see more about this process in the GraphQL Schema. All entities can be imported from the following directory:

import { Approval, Transaction } from "../types";

As you're creating a new EVM based project, this command will also generate ABI types and save them into src/types using the npx typechain --target=ethers-v5 command, allowing you to bind these contracts to specific addresses in the mappings and call read-only contract methods against the block being processed.

It will also generate a class for every contract event to provide easy access to event parameters, as well as the block and transaction the event originated from. Read about how this is done in EVM Codegen from ABIs.

In this example SubQuery project, you would import these types like so.

import {
  ApproveTransaction,
  TransferLog,
} from "../types/abi-interfaces/Erc20Abi";

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.

3. Add a Mapping Function

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

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:

import { Approval, Transaction } from "../types";
import {
  ApproveTransaction,
  TransferLog,
} from "../types/abi-interfaces/Erc20Abi";
import assert from "assert";

export async function handleLog(log: TransferLog): Promise<void> {
  logger.info(`New transfer transaction log at block ${log.blockNumber}`);
  assert(log.args, "No log.args");
  const transaction = Transaction.create({
    id: log.transactionHash,
    txHash: log.transactionHash,
    blockHeight: BigInt(log.blockNumber),
    to: log.args.to,
    from: log.args.from,
    value: log.args.value.toBigInt(),
    contractAddress: log.address,
  });

  await transaction.save();
}

export async function handleTransaction(tx: ApproveTransaction): Promise<void> {
  logger.info(`New Approval transaction at block ${tx.blockNumber}`);
  assert(tx.args, "No tx.args");

  const approval = Approval.create({
    id: tx.hash,
    owner: tx.from,
    spender: await tx.args[0],
    value: BigInt(await tx.args[1].toString()),
    contractAddress: tx.to,
  });

  await approval.save();
}

The handleTransaction function receives a tx parameter of type ApproveTransaction which includes transaction log data in the payload. We extract this data 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 on mapping functions.

4. 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 build

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.

5. Run Your Project Locally with Docker

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 quickest way to do this.

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 start:docker

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.

6. Query your Project

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

Open your browser and head to http://localhost:3000.
You will see a GraphQL playground in the browser and the schemas which are ready to query.
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 query to understand how it works for your new SubQuery starter project. Don’t forget to learn more about the GraphQL Query language.

# Write your query or mutation here
{
  query {
    transactions(first: 2, orderBy: BLOCK_HEIGHT_ASC) {
      totalCount
      nodes {
        id
        txHash
        blockHeight
        to
        from
        value
        contractAddress
      }
    }
  }
}

You will see the result similar to below:

{
  "data": {
    "query": {
      "transactions": {
        "totalCount": 1,
        "nodes": [
          {
            "id": "0x44e9396155f6a90daaea687cf48c309128afead3be9faf20c5de3d81f6f318a6-5",
            "txHash": "0x9fd50776f133751e8ae6abe1be124638bb917e05",
            "value": "12373884174795780000"
          },
          {
            "id": "0x44e9396155f6a90daaea687cf48c309128afead3be9faf20c5de3d81f6f318a6-5",
            "txHash": "0x9fd50776f133751e8ae6abe1be124638bb917e05",
            "value": "12373884174795780000"
          }
        ]
      }
    }
  }
}

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.

Aurora Quick Start (EVM)

# Aurora Quick Start (EVM)

# Goals

# 1. Your Project Manifest File

# 2. Update Your GraphQL Schema File

# 3. Add a Mapping Function

# 4. Build Your Project

# 5. Run Your Project Locally with Docker

# 6. Query your Project

# What's next?

Aurora Quick Start (EVM)

Goals

1. Your Project Manifest File

2. Update Your GraphQL Schema File

3. Add a Mapping Function

4. Build Your Project

5. Run Your Project Locally with Docker

6. Query your Project

What's next?