Network participants process transactions and receive fees from users (supply-side fees).

Polkadot is a security & interoperability platform for blockchains and applications. Polkadot was founded by Gavin Wood and launched in 2020.

DOT holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). Polkadot’s investors include Polychain, IOSG Ventures, Hashkey, etc.

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Blockchains (L1)

Polkadot

# Polkadot Transaction count Methodology

## 1. Overview

The Polkadot transaction count metric measures the daily number of unique transactions across the entire Polkadot ecosystem. This metric aggregates transaction activity from six major Polkadot chains: the relay chain, Asset Hub, Collectives, Bridge Hub, People chain, and Coretime chain. The calculation counts distinct extrinsics (Polkadot's term for transactions) per day across all chains, providing a comprehensive view of network activity throughout the Polkadot ecosystem.

## 2. Data sources

The metric relies on extrinsics data from six Polkadot ecosystem chains:
- **Polkadot relay chain extrinsics**: Transaction records from the main Polkadot relay chain containing cross-chain coordination and governance transactions
- **Asset Hub extrinsics**: Transaction records from Polkadot's Asset Hub parachain handling asset transfers and management
- **Collectives extrinsics**: Transaction records from the Collectives parachain managing governance bodies and technical committees  
- **Bridge Hub extrinsics**: Transaction records from the Bridge Hub parachain facilitating cross-chain bridges and interoperability
- **People chain extrinsics**: Transaction records from the People parachain managing identity and social features
- **Coretime extrinsics**: Transaction records from the Coretime parachain handling computational resource allocation

Each extrinsics dataset contains blockchain transaction records with timestamps, unique transaction identifiers, and transaction hashes representing all state changes submitted to each respective chain.

## 3. Step-by-step calculation

### Component A: Individual chain transaction extraction
- **Purpose**: Standardize and prepare transaction data from each Polkadot chain
- **Logic**: For each of the six chains, extract transaction records from extrinsics data. Cast the `block_timestamp` field to ensure proper timestamp formatting, map `extrinsic_hash` to `transaction_hash` for consistency, and use `extrinsic_id` as the `transaction_index` for unique identification. Add a chain identifier to distinguish transactions from different chains in later aggregation steps.
- **Key fields**: `block_timestamp` for timing, `extrinsic_id` as the unique transaction identifier, `extrinsic_hash` for transaction reference, and chain-specific identifiers

### Component B: Daily transaction counting per chain
- **Purpose**: Count unique daily transactions for each individual chain
- **Logic**: For each chain's transaction data, truncate `block_timestamp` to daily boundaries to establish date-based aggregation periods. Group transactions by day and count distinct values of `transaction_index` to ensure each unique extrinsic is counted only once per day, even if it appears multiple times in the raw data. Add standardized business classification fields including chain identifier, market sector, business segment, and product identifier to maintain chain-level granularity.
- **Key fields**: `timestamp` truncated to day level, `transaction_count` as distinct count of `transaction_index`, chain classification metadata

### Component C: Cross-chain aggregation  
- **Purpose**: Combine daily transaction counts from all six Polkadot chains
- **Logic**: Union the daily transaction count results from all six chains using union all operations to preserve all records and maintain chain-level detail. Each chain's data retains its unique chain identifier and product identifier, allowing for both ecosystem-wide totals and chain-specific analysis. The union preserves the daily timestamp boundaries established in the previous step.
- **Key fields**: Combined `timestamp`, `transaction_count`, and `chain_id` from all chains

### Final aggregation
- **Combining components**: Apply finalization processing to validate the schema structure, ensure all required business classification fields are present, and identify any missing date periods across chains
- **Gap filling**: Use a fill-zeros strategy to populate missing dates with zero transaction counts, ensuring continuous daily time series data even when individual chains had no transaction activity
- **Chain availability filtering**: Apply availability filtering to exclude data from time periods before each chain became operational, preventing artificial zero counts during pre-launch periods
- **Final calculation**: The final metric represents the sum of distinct daily transaction counts across all active Polkadot ecosystem chains, with each chain's contribution tracked separately but combinable for ecosystem-wide totals

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Confirmed transactions added to the blockchain.

PolkadotDOT

Transaction count

Transaction count

Competitive landscape

Chain breakdown