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 Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have initiated at least one transaction across the Polkadot ecosystem within a 30-day rolling window. This metric provides insight into user engagement and adoption across Polkadot's multi-chain architecture, including the relay chain and key parachains. The calculation uses HyperLogLog probabilistic counting to efficiently handle large-scale address deduplication across multiple chains while maintaining accuracy.

## 2. Data sources

The metric draws from extrinsics (transaction) data across six Polkadot ecosystem chains:
- **Polkadot relay chain extrinsics**: Core relay chain transaction records containing sender addresses and timestamps
- **Asset Hub extrinsics**: Parachain transaction data for asset transfers and operations  
- **Bridge Hub extrinsics**: Cross-chain bridge transaction records
- **Collectives parachain extrinsics**: Governance and collective decision-making transaction data
- **People parachain extrinsics**: Identity and reputation system transaction records
- **Coretime parachain extrinsics**: Computational resource allocation transaction data

Each extrinsics table contains `block_timestamp`, `extrinsic_hash`, and sender `id` fields that identify when transactions occurred and which addresses initiated them.

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Extract and standardize user activity records from raw extrinsics across all Polkadot chains
- **Logic**: Each chain's extrinsics table is processed to extract the sender address (`id` field), transaction timestamp (`block_timestamp`), and transaction hash (`extrinsic_hash`). Records are standardized with consistent chain identifiers, market classification as 'blockchains_l1', business segment as 'blockchain', and user type as 'Transaction sender'. Each chain receives specific identifiers: 'polkadot' for relay chain, 'Polkadot AssetHub' for Asset Hub, 'Polkadot BridgeHub' for Bridge Hub, 'Polkadot Collectives' for Collectives, 'Polkadot People' for People, and 'Polkadot Coretime' for Coretime
- **Key fields**: `user_address` (sender ID), `block_timestamp`, `chain`, `product_id`, `user_type`

### Component B: Daily user aggregation  
- **Purpose**: Create daily unique user counts by deduplicating addresses within each day and chain
- **Logic**: Transaction timestamps are truncated to daily boundaries using day-level truncation. Within each day-chain combination, user addresses are deduplicated using distinct operations. The union operation combines data from all six chains while maintaining chain-specific attribution. Each record includes the daily timestamp, chain identifier, and unique user address
- **Key fields**: `timestamp` (daily), `chain`, `user_address`, `timestamp_grain` ('day')

### Component C: Rolling monthly active user calculation
- **Purpose**: Calculate 30-day rolling monthly active users using probabilistic counting across multiple dimensional breakdowns
- **Logic**: Daily user data is processed through HyperLogLog probabilistic counting with precision level 15 for efficient large-scale deduplication. A datespine generates all calendar days, then creates 30-day sliding windows by joining each day with the previous 29 days. For each window endpoint, HyperLogLog sketches from the 30-day period are merged and counted to produce monthly active user estimates. The calculation produces separate dimensional breakdowns: project-level aggregation across all chains ('project_dbt_tag'), individual chain breakdowns ('chain'), user type breakdowns ('user_type'), and product-specific breakdowns ('product_id')
- **Key fields**: `user_mau` (HyperLogLog count), `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`

### Final aggregation
- **Combining components**: The monthly active user calculation with 'chain' and 'project_dbt_tag' dimensions is filtered and mapped directly to the active addresses schema
- **Currency conversion**: No currency conversion required as this metric counts addresses rather than monetary values
- **Time aggregation**: Uses 30-day rolling windows with daily granularity, where each day represents the count of unique addresses active in the trailing 30-day period
- **Final calculation**: The `user_mau` field is renamed to `active_addresses_monthly` and processed through the finalization macro which applies consistent schema formatting, fills missing periods with zeros, and applies chain availability filters based on data freshness requirements

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

Unique addresses active over a rolling 30-day window.

PolkadotDOT

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown