A Polygon log backfill rebuilds application state from contract events. The difficult part is making progress durable when a query fails or a recent block changes. This playbook covers a worker that reads bounded ranges, commits events with a checkpoint and reconciles recent history after a reorg. For endpoint selection and initial client checks, use the Polygon RPC setup guide; this article focuses on the indexing job that runs after connection.
Define the event dataset before scanning
Record the Polygon network, contract address, ABI or event signature, start block and destination table. Include chain identity in every stored event and cursor. Confirm that the endpoint serves the historical range and supports the necessary log filters. A transaction-history API is a different interface from raw contract logs; an indexer should be explicit about which events it needs and what it can reconstruct from them.
Choose a bounded range and adapt to limits
Start with a modest block range for eth_getLogs, including the contract address and relevant topics. If the response is too large or the provider rejects the range, split it and retry the smaller ranges. Increase the range only after observing consistent behavior. Save both error and request context so an operational limit is distinguishable from an invalid filter. Avoid unbounded loops that immediately repeat a throttled request.
cursor = loadCheckpoint(network, contract)
while cursor.block < targetBlock:
range = nextBoundedRange(cursor.block + 1, targetBlock)
logs = fetchLogs(contract, topics, range)
validateRangeAndBlockHashes(logs, range)
commitEventsAndCheckpoint(logs, range.end)The checkpoint must represent a fully committed range. If the worker stops between fetching and storage, the next run should be able to read that range again without creating duplicate application effects. Rate limits and available history are provider-specific; check the Crypto Chief Polygon RPC page and test the exact request shape before a large import.
Commit events and the checkpoint together
Use a transaction in your storage layer to persist the fetched events and advance the cursor together. Identify EVM log observations using chain, block hash, transaction hash and log index, and retain whether each observation is currently canonical. Validate the block hash associated with the end of the range. Do not advance a cursor merely because an HTTP request returned a successful status code; inspect the JSON-RPC result and verify that processing completed.
Keep recent updates reversible
Separate finalized application effects from recent provisional effects according to your confirmation policy. Store the block hashes needed to compare the saved cursor with current chain history. If a saved block is no longer canonical, find a common ancestor, mark affected observations as removed and rebuild derived state from that point. Avoid treating a webhook notification as a permanent fact until the underlying chain state meets the policy.
Join live ingestion to the backfill with an overlap
When the historical worker approaches the live head, start the live consumer with an overlap rather than trying to switch at one exact instant. Read the overlapping block range again and deduplicate stored observations. Persist a replay cursor for the live consumer too, because a connection can drop after the last successful notification. Event streaming can reduce repeated polling, while bounded RPC reads remain useful for filling a verified gap.
Reconcile before declaring the import complete
Compare a sample of stored transaction receipts and logs with direct RPC reads at the same blocks. Confirm the start block, last committed block and current canonical hash. Rebuild a derived balance or event count from the stored source events and compare it with your application table. Record any excluded contracts or unavailable ranges so downstream users understand the dataset’s scope.
Monitor progress and test restart behavior
Track the last committed block, distance from the chosen target, range size, retry count and failed ranges. Test a process restart after fetch, a failure during storage, duplicate responses, a provider range limit and a changed recent block hash. The recovery test passes when it resumes from a durable checkpoint and produces the same canonical dataset, without repeating irreversible application actions.
Frequently Asked Questions
Why does a Polygon log query fail over a large block range?
The endpoint may limit block ranges, result counts, response sizes or execution time. First inspect the actual error and confirm the filter is valid. Split the range, retain a checkpoint and retry with bounded backoff. A failed large query does not by itself mean the historical data is unavailable.
Can I resume a backfill after switching RPC providers?
Yes, if the replacement serves the required historical methods and range. Validate the saved network and canonical block hash, replay an overlap and deduplicate before advancing. Keep both providers at a common block when comparing responses; their latest heads can differ.
Do blockchain webhooks eliminate the need for a backfill?
Webhooks can deliver new observations, but your application still needs a way to recover events missed before a subscription began or during a delivery gap. Confirm the service’s replay capability. If replay is unavailable or incomplete, use bounded RPC queries from your last verified checkpoint.