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Project Optimisation

SubQuery TeamAbout 3 min

Project Optimisation

Performance is a crucial factor in each project. So, how to optimise your SubQuery project to speed it up?

Fortunately, there are several things you can do to improve indexing and query speed.

Optimisation for Production Hosting

If you're looking for advice on how to run high performance SubQuery infrastructure in a production environment, please read Running High Performance SubQuery Infrastructure.

Common Issues and Top Suggestions

  • Avoid using blockHandlers where possible. Using block handlers slows your project down as they can be executed with each and every block. Use them only if you need to and consider adjusting project architecture.
    • If you must use a block handler, ensure that you carefully optimise every code path called by it. As it will be executed on each block the total time that it might take will increase linearly as the chain grows.
    • Use a convenient modulo filter to run a handler only once to a specific block. This filter allows handling any given number of blocks, which is extremely useful for grouping and calculating data at a set interval. For instance, if modulo is set to 50, the block handler will run on every 50 blocks. It provides even more control over indexing data to developers.
  • Always use a dictionary (we can help create one for your new network). You can see examples of how to create a dictionary in the dictionary repositoryopen in new window.
  • Use filter conditions in your mapping handlers (within the project manifest) to reduce the number of events/transactions that need to be processed. Create filters as specific as possible to avoid querying unnecessary data.
  • Set the start block in your project manifest to when the contract was initialised or when the first event/transaction occurs.
  • Use node worker threads to move block fetching and block processing into its own worker thread. It could speed up indexing by up to 4 times (depending on the particular project). You can easily enable it using the -workers=<number> flag. Note that the number of available CPU cores strictly limits the usage of worker threads. Read more here.

Other Improvements

Here is the list of further suggestions for improvements of query and indexing performance.

Indexing Performance Advice

  • Add indexes to entity fields that you plan to filter or sort by for query performance, this is especially important for historical projects. Add the @index or @index(unique: true) annotation after any non-key field. Read more here.
type Transaction @entity {
  id: ID! # Transaction hash
  value: BigInt!
  to: Account! # Foreign key field
  from: Account! # Foreign key field
  contractAddress: String! @index
  • Use parallel/batch processing as often as possible.
    • Use api.queryMulti() to optimise Polkadot API calls inside mapping functions and query them in parallel. This is a faster way than a loop.
    • Use Promise.all(). In case of multiple async functions, it is better to execute them and resolve in parallel.
    • If you want to create a lot of entities within a single handler, you can use store.bulkCreate(entityName: string, entities: Entity[]). You can create them in parallel, no need to do this one by one (see example below). Read more in our advanced access to the store documentation.
  await Promise.all([
    store.bulkCreate("Event", events),
    store.bulkCreate("Extrinsic", calls),
  • Making API calls to query state or to third party APIs can be slow. You could try to minimise calls where possible and to use extrinsic/transaction/event data, or persist data in the store and only update it occasionally.

  • We always recommend enabling a reverse lookup on an entity to a related entity. Attach @derivedFrom annotation to the field and point to its reverse lookup field of another entity. Read more here.

type Account @entity {
  id: ID!
  publicAddress: String!
  sentTransfers: [Transfer] @derivedFrom(field: "from")
  receivedTransfers: [Transfer] @derivedFrom(field: "to")

type Transfer @entity {
  id: ID!
  amount: BigInt
  from: Account!
  to: Account!
  • Optimise your schema design, keep it as simple as possible.

    • Try to reduce unnecessary fields and columns.
    • Create indexes and reverse lookups as needed.
  • Note that JSON.stringify doesn’t support native BigInts. Our logging library will do this internally if you attempt to log an object. We are looking at a workaround for this.

Query Performance Advice

Running High Performance SubQuery Infrastructure

There is more information focussed on the DevOps and configuration of running high performance SubQuery projects here.

Review Project Architecture

If your project requires indexing all the blocks, transactions alongside more specific data, consider dividing it into separate SubQuery projects responsible for different data sources. If such separation is possible it can provide better development experience and efficient workflow. This decision can be compared to a design decision between micro-services and monolith project architecture.

We recommend this approach, because it takes time to index all the blocks and it can slow down your project significantly. If you want to apply some changes to your filters or entities shape you may need to remove your database and reindex the whole project from the beginning.

A common example is creating a large project that indexes everything so you can perform internal analysis on your contracts, and then much smaller and optimised project for indexing the key data for your dApp. The larger project that indexes everything might never change and so you can avoid costly reindexing, while the smaller optimised project will change as your dApp matures and can be reindexed much faster.