Monad Data: Index a Parallel-EVM L1 at Full Throughput

Index Monad's contract events, token transfers, and decoded transactions in one pipeline, delivered to your own database. Real-time and complete historical data, no node to run.

Monad bills itself as an institutional-grade blockchain for open financial markets, a Layer 1 that runs Ethereum bytecode unchanged while pushing throughput far past what a single-threaded EVM allows. Built by ex-Jump Trading engineers, it pairs MonadBFT consensus with optimistic parallel execution and a custom storage layer to target roughly 10,000 transactions per second with sub-second finality. Because Solidity contracts, EVM addresses, and existing tooling deploy out of the box, Monad reads as Ethereum to your code and as something much faster to your infrastructure. The team frames it at monad.xyz.

That speed is exactly where the data problem starts. A chain designed to clear thousands of transactions a second emits logs, traces, and decoded calls at a rate that overwhelms naive scraping. Indexing Co covers Monad in a single pipeline, decodes the raw chain into the shape you want, and delivers it to your own PostgreSQL, warehouse, or webhook. Real-time and full history, with no node to operate.

Source Monad L1 blocks

Ingest blocks, logs, traces

Decode ABIs to events

Transform your JavaScript

Deliver your database

Why Monad data is hard

High TPS Breaks Raw RPC Monad is engineered for thousands of transactions per second, and a single eth_getLogs sweep across busy block ranges returns far more events than a generic endpoint will serve in one response. You end up shrinking block windows, hitting result caps, and stitching paginated calls just to read a single contract without timing out or getting throttled.

Parallel Execution and Ordering Monad runs optimistic parallel execution under the hood, scheduling independent transactions concurrently while a dependency tracker preserves the same final state as serial EVM. Your dataset still needs strict per-block, per-log ordering and deterministic indices, so the indexer has to reconstruct a clean linear history from a chain that was never executed in a single line.

Volume Backfill From Genesis Backfilling a high-throughput L1 means replaying enormous block ranges packed with events, not the thin history of a quiet chain. Doing it by hand against an archive node is slow, fragile, and easy to leave with silent gaps where a window was dropped or a batch quietly failed partway through.

Real-Time at Sub-Second Blocks With MonadBFT producing a block every 300 milliseconds and finalizing in about 600 milliseconds, staying at the chain head is a moving target. A consumer that polls too slowly falls behind; one that polls too aggressively burns its rate limit. Keeping a live, ordered feed that never lags the tip is its own engineering problem.

What Indexing Co delivers for Monad

The DIY Path

Stand up and maintain the whole stack yourself.

Run a Monad archive node or wire up a third-party RPC.
Page through eth_getLogs around result caps to capture every event.
Reconstruct strict ordering from parallel-executed blocks.
Build volume backfill, reorg handling, and real-time delivery from scratch.

Indexing Co

One pipeline sources, decodes, and delivers Monad to your store.

Monad contract events, token transfers, and decoded txs in a single pipeline.
Decoded data, shaped by your JavaScript transform.
Lands in your PostgreSQL, BigQuery, warehouse, or webhook.
Real-time delivery plus complete backfilled history. No node, no RPC babysitting.

What you can index

EVM data type	On Monad, for example
Contract events and logs	DEX swaps, lending market borrows and liquidations
Token transfers (ERC-20, ERC-721)	USDC and USDT0 payments, NFT mints
Decoded transactions and traces	Smart-wallet user operations, contract deployments
Protocol and account state	Pool reserves, staking positions, MON balances

A transform, not a subgraph

You write the transform in JavaScript, not AssemblyScript, and change it without re-indexing from block zero. Indexing Co runs your function over each block server-side: decode the logs you care about and return rows for your destination.

// Indexing Co transform: reshape each block into rows for your database
function transform(block) {
  const results = [];
  const signatures = [
    'event Transfer(address indexed from, address indexed to, uint256 value)'
  ];

  for (const tx of block.transactions || []) {
    for (const log of tx.receipt?.logs || []) {
      const event = utils.evmDecodeLogWithMetadata(log, signatures);
      if (!event) continue;
      results.push({
        chain: block._network,
        block: Number(block.number),
        tx_hash: tx.hash,
        contract_address: log.address?.toLowerCase(),
        from: event.decoded.from,
        to: event.decoded.to,
        value: event.decoded.value,
      });
    }
  }
  return results;
}

Monad with Indexing Co

Run it yourself if

You want full control of node infrastructure and have the team to operate a Monad archive node, page through high-volume logs, reconstruct ordering, and build backfill and reorg handling in-house.

Use Indexing Co if

You want Monad contract events, token transfers, and decoded transactions delivered to your own database, with real-time plus complete history and no infrastructure to run.

Get a Demo Open the Console

FAQ

Do I need to run a Monad node?

No. Indexing Co sources Monad blocks, logs, and traces for you, including full historical data, so you never operate an archive node or page through a rate-limited RPC endpoint.

Can I index any protocol on Monad?

Yes. Point a pipeline at any Monad contract or set of contracts, from DEXes and lending markets to your own deployments, and we decode the events against the right ABI. Because Monad is fully EVM bytecode-compatible, standard Solidity ABIs apply.

How does Indexing Co keep up with Monad's throughput?

The pipeline is built to absorb high-TPS chains. It batches and parallelizes ingestion so it stays at the chain head despite sub-second blocks, instead of relying on single eth_getLogs sweeps that hit result caps on busy ranges.

Where does the Monad data go?

Into your own destination: PostgreSQL, BigQuery, a warehouse, or a webhook. You define the output schema and own the storage.

Can I get complete historical Monad data?

Yes. We backfill from your chosen start block and merge it with real-time data, so your Monad dataset is gap-free from genesis forward if you want it, even across high-volume block ranges.

Get Started

Index Monad in minutes. Set up your first Monad contract or token pipeline without running a node.