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Flare Quick Start

SubQuery TeamAbout 3 min

Flare Quick Start

The goal of this quick start guide is to index all rewards from the Flare FTSO Reward Manager from Flare's Songbird network.

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.

Please initialise a Flare Songbird Network, not Flare Network :::


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

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

We are indexing all RewardClaimed logs from the FTSORewardManager contract, first you will need to import the contract abi defintion from hereopen in new window. You can copy the entire JSON and save as a file ftsoRewardManager.abi.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 RewardClaimed logs, you need to update the datasources section as follows:

  - kind: flare/Runtime
    startBlock: 36036 # Block that this contract was deployed
      # Must be a key of assets
      abi: ftsoRewardManager
      address: "0xc5738334b972745067ffa666040fdeadc66cb925" #
        file: "ftsoRewardManager.abi.json" # Import the correct contract file
      file: "./dist/index.js"
        - handler: handleLog
          kind: flare/LogHandler
              ## Follows standard log filters
              - RewardClaimed(address indexed dataProvider, address indexed whoClaimed, address indexed sentTo, uint256 rewardEpoch, uint256 amount)

The above code indicates that you will be running a handleLog mapping function whenever there is an RewardClaimed log on any transaction from the FTSO Reward Manager contractopen in new window.

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 all rewards and also addresses that those rewards go to/are claimed from:

type Reward @entity {
  id: ID! # Transaction has
  recipient: Address!
  dataProvider: String! @index
  whoClaimed: Address!
  rewardEpoch: BigInt! @index
  amount: BigInt!

type Address @entity {
  id: ID! # accountIDs
  receivedRewards: [Reward] @derivedFrom(field: "recipient")
  claimedRewards: [Reward] @derivedFrom(field: "whoClaimed")

Since we have a many-to-many relationship, we add the @derivedFrom annotation to ensure that we are mapping to the right foreign key.


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 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:

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. 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.ts. Let’s make changes to it, process all RewardClaimed transaction logs, and save them to the GraphQL entities created earlier.

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

import { FlareLog } from "@subql/types-flare";
import { BigNumber } from "@ethersproject/bignumber";
import { Address, Reward } from "../types";

type RewardClaimedLogArgs = [string, string, string, BigNumber, BigNumber] & {
  dataProvider: string;
  whoClaimed: string;
  sentTo: string;
  rewardEpoch: BigNumber;
  amount: BigNumber;

export async function handleLog(
  event: FlareLog<RewardClaimedLogArgs>,
): Promise<void> {
  // See example log in this transaction
  //"flare Event");

  // Ensure that our account entities exist
  const whoClaimed = await Address.get(event.args.whoClaimed.toLowerCase());
  if (!whoClaimed) {
    // Does not exist, create new
    await Address.create({
      id: event.args.whoClaimed.toLowerCase(),

  const whoRecieved = await Address.get(event.args.sentTo.toLowerCase());
  if (!whoRecieved) {
    // Does not exist, create new
    await Address.create({
      id: event.args.sentTo.toLowerCase(),

  // Create the new Reward entity
  const reward = Reward.create({
    id: event.transactionHash,
    recipientId: event.args.sentTo.toLowerCase(),
    dataProvider: event.args.dataProvider,
    whoClaimedId: event.args.whoClaimed.toLowerCase(),
    rewardEpoch: event.args.rewardEpoch.toBigInt(),
    amount: event.args.amount.toBigInt(),


Let’s understand how the above code works.

The function here receives an FlareLog which includes transaction log data in the payload. We extract this data and then first ensure that our account entities (foreign keys) exist. We then instantiate a new Reward 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).


For more information on mapping functions, please refer to our Mappings documentation.

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


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 start:docker


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 {
  rewards(first: 5, orderBy: AMOUNT_DESC) {
    nodes {
  addresses(first: 5, orderBy: RECEIVED_REWARDS_SUM_AMOUNT_DESC) {
    nodes {

You will see the result similar to below:

  "data": {
    "rewards": {
      "nodes": [
          "id": "0xd832d0283f56acbda902066dd47147f510a68fd923296a2162cffcf10c15d8f8",
          "amount": "62306014311508310008",
          "recipientId": "0xc2e6628b5b0277e97c68a47328f8effde9629184",
          "dataProvider": "0x69141E890F3a79cd2CFf552c0B71508bE23712dC",
          "whoClaimedId": "0xc2e6628b5b0277e97c68a47328f8effde9629184"
          "id": "0xd6e84fc6b13f5832e04c8a851a2d3e634e82b029f253000d980ec68dc59e697f",
          "amount": "248122819100600283",
          "recipientId": "0x665574495eb0a4a03291f2fb3f150914dc4009f3",
          "dataProvider": "0x939789ed3D07A80da886A3E3017d665cBb5591dC",
          "whoClaimedId": "0x665574495eb0a4a03291f2fb3f150914dc4009f3"
    "addresses": {
      "nodes": [
          "id": "0xc2e6628b5b0277e97c68a47328f8effde9629184"
          "id": "0x665574495eb0a4a03291f2fb3f150914dc4009f3"


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.