Avalanche Quick Start

SubQuery TeamAbout 4 min

Avalanche Quick Start


The goal of this quick start guide is to index all Pangolin token RewardPaid logs.


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

Now, let's move forward and update these configurations.

Previously, in the 1. Create a New Project section, you must have noted 3 key files. Let's begin updating them one by one.


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

1. 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 all rewards in Pangolin:

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


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

You will find the generated models in the /src/types/models directory.

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 move forward to the next file.

2. Update Your Project Manifest File

The Project Manifest (project.yaml) file works as an entry point to your Avalanche project. It defines most of the details on how SubQuery will index and transform the chain data. For Avalanche, 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

Note that the manifest file has already been set up correctly and doesn’t require significant changes, but you need to change the datasource handlers. This section lists the triggers that look for on the blockchain to start indexing.

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

  - kind: avalanche/Runtime
    startBlock: 7906490 # Block when the first reward is made
      # Must be a key of assets
      abi: erc20
      ## Pangolin reward contract https://snowtrace.io/token/0x88afdae1a9f58da3e68584421937e5f564a0135b
      address: "0x88afdae1a9f58da3e68584421937e5f564a0135b"
        file: "./node_modules/@pangolindex/exchange-contracts/artifacts/contracts/staking-rewards/StakingRewards.sol/StakingRewards.json"
      file: "./dist/index.js"
        - handler: handleLog
          kind: avalanche/LogHandler
            ## Follows standard log filters https://docs.ethers.io/v5/concepts/events/
              - 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 reward contractopen in new window.

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

Next, 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. Delete both the handleBlock and handleTransaction functions as you will only deal with the handleLog function.

The handleLog function receives event data whenever an event matches the filters, which you specified previously in the project.yaml. Let’s make changes to it, process all RewardPaid transaction logs, and save them to the GraphQL entities created earlier.

Update the handleLog function as follows (note the additional imports):

import { PangolinRewards } from "../types";
import { AvalancheLog } from "@subql/types-avalanche";

export async function handleLog(event: AvalancheLog): Promise<void> {
  const { args } = event;
  if (args) {
    const pangolinRewardRecord = new PangolinRewards(

    pangolinRewardRecord.transactionHash = event.transactionHash;
    pangolinRewardRecord.blockHash = event.blockHash;
    pangolinRewardRecord.blockNumber = BigInt(event.blockNumber);

    pangolinRewardRecord.receiver = args.user;
    pangolinRewardRecord.amount = BigInt(args.reward.toString());

    await pangolinRewardRecord.save();

Let’s understand how the above code works.

The function here receives an AvalancheLog which includes transaction log data in the payload. We extract this data and then instantiate a new PangolinRewards entity defined earlier in the schema.graphql file. After that, we add additional information and then use the .save() function to save the new entity (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:


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 quickiest 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:


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:

  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 query 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: 1) {
    nodes {

You will see the result similar to below:

  "data": {
    "pangolinRewards": {
      "nodes": [
          "id": "0x39b4d0a98192d1509c15543caa70cad7e067a08d98f9b4e335ab92c87585cf54-60",
          "receiver": "0x3F8D6e7bA3A842642Fd362C7122BE8d17DC82555",
          "amount": "48537775882115127788",
          "blockNumber": "7906491",
          "blockHash": "0x39b4d0a98192d1509c15543caa70cad7e067a08d98f9b4e335ab92c87585cf54",
          "transactionHash": "0x202891b2c5c62467b08f04816dfe8ecbaf40e967e1926127318ebcb85c76a46d"


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

What's next?

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


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.