Squid SDK tips and troubleshooting

Use this guide when a squid works but is slow, consumes unpredictable resources, or behaves differently after a restart or reorg.

Symptom	First check
Historical sync is slow	Run the same processor configuration with an empty batch handler to separate ingestion time from processing and database time.
Memory rises suddenly	Check for mutable global state, unbounded collections, and operations that grow faster than linearly with the batch size.
Results differ between clean syncs	Remove any correctness state that survives only in process memory and compare clean runs again.
`block.transactions` or another collection is empty	Add a data request or related-data flag. `setFields()` only controls fields on items that were requested.
An external consumer sees duplicate or replaced blocks	Treat messages from unfinalized blocks as provisional and reconcile them by block height and hash.
A processor appears stuck	Enable processor debug logs for a short reproduction near the chain head.
The squid fails with a specific error message	Look it up in Common errors.

Separate ingestion from processing

Before changing RPC settings or scaling the database, measure where time is spent. Temporarily replace the body of the batch handler with a block counter:

import {run} from '@subsquid/batch-processor'
import {createLogger} from '@subsquid/logger'
import {TypeormDatabase} from '@subsquid/typeorm-store'

const log = createLogger('sqd:processor')

run(
  dataSource,
  new TypeormDatabase({ stateSchema: 'squid_ingestion_probe' }),
  async (ctx) => {
    log.info({ blocks: ctx.blocks.length }, 'ingestion probe')
  }
)

Run the probe over the same block range and in the same environment as the slow run:

If throughput improves substantially, optimize transformations and database access. Start with batch processing and query optimization.
If throughput stays similar, investigate the data source, network connection, RPC rate limits, and RPC provider. If the source is unreliable rather than slow, a fallback data source with an RPC standby keeps the squid indexing through outages — see High availability.

An empty handler still advances processor status. Use a disposable database or a unique stateSchema, as shown above. Do not replace a production handler and reuse its processor status.

Compare like with like. A historical range served by the SQD Network and a near-head range served in real time have different performance characteristics.

Request only data you use

Narrow requests by contract, topic, and block range whenever possible. Disable optional fields that are not used — this reduces data transfer and the amount of data held in each batch. For factory contracts, consider the two-pass indexing pattern so that most historical requests can use a known address list. setFields() controls the shape of returned items. It does not request transactions, logs, traces, or state diffs by itself. For example:

const dataSource = new DataSourceBuilder()
  .setFields({
    transaction: {
      input: true,
    },
  })
  .addLog({
    where: {
      address: [CONTRACT_ADDRESS],
      topic0: [EVENT_TOPIC],
    },
    include: {
      transaction: true,
    },
  })
  .build()

Here, include: { transaction: true } requests each matching log’s parent transaction and adds it to block.transactions. The transaction selector in setFields() only adds input to those requested transaction items. Request related collections such as transaction logs, traces, or state diffs explicitly when they are also required.

Make batch handlers deterministic

Batch boundaries are an implementation detail and can change between runs. The same block range can arrive as several different batch groupings.

Do not let results depend on mutable module-level or global state carried from one batch to the next.
Persist correctness-critical state in the database, or derive it again from the current batch and durable data.
Keep in-memory caches bounded and safe to rebuild after a restart.
Test important changes with multiple clean syncs and compare the resulting rows or a stable export.

Batch handlers should also be safe to retry. TypeormDatabase can run a handler more than once for the same data when a transaction is retried or the chain reorganizes. See the typeorm-store transaction notes.

Keep batch work bounded

Processors can receive large batches, and the number of blocks and selected items per batch varies. Keep memory and CPU use close to linear in the number of selected items:

collect IDs in a Set, then load matching rows with one findBy() call;
update entities in a Map, then save them as an array;
avoid nested scans over the complete batch;
clear temporary collections at the end of each handler call;
load test with realistic event density, not only a short quiet range.

Do not replace batching with a database read or write per event. The batch-processing patterns show how to load and persist entities in groups.

Run periodic work intentionally

By default, a processor fetches only blocks that contain requested items. Add includeAllBlocks() when logic must run on every block:

const dataSource = new DataSourceBuilder()
  // ...data requests...
  .includeAllBlocks()
  .build()

run(dataSource, new TypeormDatabase(), async (ctx) => {
  for (const block of ctx.blocks) {
    if (block.header.height % 100 === 0) {
      await updatePeriodicState(ctx, block)
    }
  }
})

includeAllBlocks() accepts an optional block range, so limit it when the requirement is historical only. Fetching every block increases work. If the periodic work performs multiple EVM state reads, use Multicall.

Make external side effects reorg-safe

When a reorg replaces unfinalized blocks, a compatible database rolls back SQL changes made for orphaned blocks and the processor runs the handler again on the canonical chain. Messages already sent to Kafka, HTTP requests, and other external effects are not rolled back. For effects derived from unfinalized blocks:

include block height and block hash in the external record;
make writes idempotent;
let consumers replace or retract provisional data when the canonical hash at a height changes;
delay irreversible actions until the required confirmation or finality threshold has been reached.

The unfinalized blocks and reorgs guide explains which blocks are considered hot and how database rollback works.

Debug a processor near the failure

Use a narrow debug namespace before enabling all logs:

SQD_DEBUG=sqd:processor*

Debug output can be large. If the issue occurs near the chain head, first sync close to the head with the normal log level, then restart with debug logging for a short reproduction. See overriding the log level and Cloud logging. For slow or oversized GraphQL requests, use the query optimization procedure, add schema indexes, paginate collections, and configure OpenReader DoS protection.

Common errors

Processor errors

`Error: data out-of-bounds` `ethers` errors on EVM

Usually this means that you’re using the decoder on the wrong data. Make sure that the decoder receives only the data you intend it to. Example: suppose you want to add the processing of a Mint event to a squid that is currently processing only Transfer events. You change the processor configuration to get the Mint events for you, but you forget to sort the events in the batch handler and a data item with a Mint event finds its way into a decoder of Transfers.
Another common source of this error is faulty RPC endpoints. If your EVM processor crashes during RPC ingestion on a log with '0x' in its data field, try switching to another RPC provider or, if you are developing locally, to another Ethereum network emulator.

`BAD_DATA` when using a Multicall contract

This error can occur for a variety of reasons, but one common cause is choosing a Multicall deployment that is newer than the oldest blocks that have to be indexed. When batch state queries are performed on historical chain state older than the Multicall deployment, EVM detects that and refuses to run. Solutions:

Use an older Multicall deployment.
Delay your chain state queries until a later block.

These issues are explored in Part 4 of the BAYC tutorial.

Database errors

`QueryFailedError: relation "..." does not exist`

It is likely that the generated migrations in the db/migrations folder are outdated and do not match the schema file. Recreate the migrations from scratch as detailed on this page. The relevant commands are listed in the squid-typeorm-migration reference.

`Query runner already released. Cannot run queries anymore`, or `too late to perform db updates, make sure you haven't forgot to await on db query`

If your squid saves its data to a database, all operations with ctx.store are asynchronous. Make sure you await on all store operations like upsert, update, find, save etc. You may find the require-await eslint rule to be helpful.

`QueryFailedError: invalid byte sequence for encoding "UTF8": 0x00`

PostgreSQL doesn’t support storing NULL (\0x00) characters in text fields. Usually the error occurs when a raw bytes string (like UIntArray or Bytes) is inserted into a String field. If this is the case, use hex encoding, e.g. using util-internal-hex library. For addresses, use appropriate encoding for your chain type.

GraphQL errors

`response might exceed the size limit`

Make sure the input query has limits set or the entities are decorated with @cardinality. We recommend using XXXConnection queries for pagination. For configuring limits and max response sizes, see DoS protection.

Overview

Pipes SDK

Squid SDK

Alternative clients

Squid SDK tips and troubleshooting

Separate ingestion from processing

Request only data you use

Make batch handlers deterministic

Keep batch work bounded

Run periodic work intentionally

Make external side effects reorg-safe

Debug a processor near the failure

Common errors

Processor errors

`Error: data out-of-bounds` `ethers` errors on EVM

`BAD_DATA` when using a Multicall contract

Database errors

`QueryFailedError: relation "..." does not exist`

`Query runner already released. Cannot run queries anymore`, or `too late to perform db updates, make sure you haven't forgot to await on db query`

`QueryFailedError: invalid byte sequence for encoding "UTF8": 0x00`

GraphQL errors

`response might exceed the size limit`

​Separate ingestion from processing

​Request only data you use

​Make batch handlers deterministic

​Keep batch work bounded

​Run periodic work intentionally

​Make external side effects reorg-safe

​Debug a processor near the failure

​Common errors

​Processor errors

​Error: data out-of-bounds ethers errors on EVM

​BAD_DATA when using a Multicall contract

​Database errors

​QueryFailedError: relation "..." does not exist

​Query runner already released. Cannot run queries anymore, or too late to perform db updates, make sure you haven't forgot to await on db query

​QueryFailedError: invalid byte sequence for encoding "UTF8": 0x00

​GraphQL errors

​response might exceed the size limit

Separate ingestion from processing

Request only data you use

Make batch handlers deterministic

Keep batch work bounded

Run periodic work intentionally

Make external side effects reorg-safe

Debug a processor near the failure

Common errors

Processor errors

`Error: data out-of-bounds` `ethers` errors on EVM

`BAD_DATA` when using a Multicall contract

Database errors

`QueryFailedError: relation "..." does not exist`

`Query runner already released. Cannot run queries anymore`, or `too late to perform db updates, make sure you haven't forgot to await on db query`

`QueryFailedError: invalid byte sequence for encoding "UTF8": 0x00`

GraphQL errors

`response might exceed the size limit`