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 (daily) Methodology

## 1. Overview

Active addresses daily measures the number of unique addresses that submitted at least one extrinsic (transaction) across the Polkadot ecosystem on each day. This metric answers the business question: "How many unique users are actively interacting with Polkadot and its parachains daily?" The calculation aggregates distinct user addresses from extrinsic records across six major Polkadot chains and parachains, using HyperLogLog sketches to efficiently count unique addresses while avoiding double-counting users who are active on multiple chains.

## 2. Data sources

The metric relies on the following raw blockchain data sources:

- **Polkadot relay chain extrinsic records**: Core Polkadot blockchain transaction records containing user-submitted state changes
- **Polkadot Asset Hub extrinsic records**: Transaction records from the Asset Hub parachain for asset transfers and management
- **Polkadot Collectives parachain extrinsic records**: Governance and collective decision-making transaction records
- **Polkadot Bridge Hub extrinsic records**: Cross-chain bridge operation transaction records  
- **Polkadot People parachain extrinsic records**: Identity and people-related transaction records
- **Polkadot Coretime parachain extrinsic records**: Coretime marketplace and allocation transaction records

Each data source contains extrinsic metadata including block timestamps, transaction hashes, and submitter account IDs that serve as user addresses.

## 3. Step-by-step calculation

### Component A: Chain-specific user extraction

- **Purpose**: Extract active user addresses from extrinsic records for each Polkadot chain and parachain
- **Logic**: For each of the six chains (relay chain, Asset Hub, Collectives, Bridge Hub, People, Coretime), the system extracts the submitter account ID from each extrinsic record. The `block_timestamp` field is converted to timestamp format and the extrinsic submitter's `id` field is cast as the user address. Each record includes chain identification, market sector classification as 'blockchains_l1', business segment as 'blockchain', user type as 'Transaction sender', and appropriate product IDs for each chain
- **Key fields**: `block_timestamp` for timing, `id` as the user address, `extrinsic_hash` as transaction identifier, and chain-specific metadata fields

### Component B: Daily user deduplication per chain

- **Purpose**: Aggregate and deduplicate user addresses by day for each individual chain
- **Logic**: User addresses are deduplicated within each chain using DISTINCT operation, with timestamps truncated to daily boundaries using `timestamp_trunc`. The process creates daily snapshots of unique users per chain, preserving chain identity, user type, market sector, business segment, and product ID classifications. The resulting `user_dau` field contains the distinct user address for each chain-day combination
- **Key fields**: Daily `timestamp`, `chain` identifier, `user_address` as the deduplicated user identifier, and classification metadata

### Component C: Cross-chain user aggregation

- **Purpose**: Union all chain-specific daily user data while maintaining the ability to count users distinctly across chains
- **Logic**: Daily user data from all six chains is combined using UNION ALL operations, creating a comprehensive dataset of all daily active users across the Polkadot ecosystem. The data maintains chain identity and other dimensional attributes to enable various aggregation breakdowns. Incremental processing logic ensures efficient updates by only processing recent data based on partition boundaries
- **Key fields**: `timestamp` for daily boundaries, `chain` for source identification, `user_dau` containing user addresses, and dimensional classification fields

### Component D: HyperLogLog dimensional aggregation

- **Purpose**: Create distinct user count aggregations across multiple dimensional breakdowns using HyperLogLog sketches
- **Logic**: The system applies HyperLogLog (HLL) algorithms to efficiently count distinct users across four dimensional breakdowns. First, daily HLL sketches are initialized for each chain-day combination using `hll_count.init` with 15-bit precision. Then separate aggregations are created for: overall project level (project_dbt_tag dimension), individual chain level, user type level, and product ID level. Each aggregation uses `hll_count.merge_partial` to combine sketches and `hll_count.extract` to get final distinct counts, ensuring users active on multiple chains are counted only once in cross-chain aggregations
- **Key fields**: `user_dau` containing the distinct user count, `dimensions` indicating the aggregation level, `chain_id` for chain-specific breakdowns, and classification fields for other dimensional breakdowns

### Final aggregation

- **Combining components**: The four dimensional aggregation breakdowns (project_dbt_tag, chain, user_type, product_id) are combined using UNION ALL, creating a unified dataset with different aggregation levels identified by the `dimensions` field
- **Currency conversion**: No currency conversion is required as this metric counts addresses rather than monetary values
- **Time aggregation**: Daily boundaries are established through timestamp truncation to day level, with missing days filled with zero values through the finalization macro
- **Final calculation**: The `user_dau` field is renamed to `active_addresses_daily` to reflect blockchain terminology where active users are measured as active addresses. The final metric represents the count of unique addresses that submitted at least one extrinsic on each day, filtered for 'chain' and 'project_dbt_tag' dimensions and subject to chain availability filters

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

Unique active addresses over a fixed 24-hour window.

PolkadotDOT

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown