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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Polkadot

# Polkadot Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that have sent at least one transaction (extrinsic) across the Polkadot ecosystem within a 7-day rolling window. This metric provides insight into user engagement and adoption across Polkadot's multi-chain architecture, including the relay chain and all major parachains. The calculation uses HyperLogLog probabilistic sketches to efficiently handle large-scale distinct address counting across multiple chains and time periods.

## 2. Data sources

- **Polkadot relay chain extrinsic records**: Transaction data from the main Polkadot relay chain containing sender addresses, transaction hashes, and block timestamps
- **Polkadot Asset Hub extrinsic records**: Transaction data from the Asset Hub parachain for asset transfers and management
- **Polkadot Collectives extrinsic records**: Transaction data from the Collectives parachain for governance and collective decision-making
- **Polkadot Bridge Hub extrinsic records**: Transaction data from the Bridge Hub parachain for cross-chain bridge operations
- **Polkadot People parachain extrinsic records**: Transaction data from the People parachain for identity and social features
- **Polkadot Coretime parachain extrinsic records**: Transaction data from the Coretime parachain for computational resource allocation

## 3. Step-by-step calculation

### Component A: User extraction from individual chains
- **Purpose**: Extract daily active users from each Polkadot chain's transaction data
- **Logic**: For each of the six Polkadot chains, process extrinsic records to identify unique sender addresses per day. Each extrinsic record contains an `id` field representing the sender address, `extrinsic_hash` as the transaction identifier, and `block_timestamp` for temporal grouping. The data is standardized with chain-specific identifiers and business metadata including market sector, business segment, and product classifications
- **Key fields**: `block_timestamp` truncated to day boundaries, `user_address` (derived from `id`), `chain` identifier, `user_type` set to "Transaction sender"

### Component B: Daily user aggregation
- **Purpose**: Combine users from all Polkadot chains into a unified daily active user dataset
- **Logic**: Union the standardized user data from all six chains, ensuring distinct user addresses per day across the entire ecosystem. Each record represents a unique address that was active on a specific day on a specific chain. The aggregation maintains chain-level granularity while enabling cross-chain user analysis
- **Key fields**: `timestamp` (day-level), `chain`, `product_id`, `user_dau` (the active address)

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user lists into probabilistic data structures for efficient weekly aggregation
- **Logic**: Transform daily active user datasets into HyperLogLog sketches using BigQuery's HLL functions with precision parameter 15. This creates compact probabilistic representations of user sets that can be efficiently merged across time periods while maintaining approximate distinct count accuracy
- **Key fields**: `hll` sketch containing compressed user address information, grouped by day, chain, user type, and product

### Component D: Sliding window generation
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: Generate a datespine covering all dates in the analysis period, then create sliding windows by joining each date with the previous 6 days using timestamp arrays. This produces a table where each row represents one day in a 7-day window, enabling proper weekly aggregation without gaps in the time series
- **Key fields**: `timestamp` (the end date of the 7-day window), `joinday` (each individual day within the window)

### Component E: Weekly aggregation with HLL merging
- **Purpose**: Calculate weekly active users by merging HLL sketches across 7-day windows
- **Logic**: For each 7-day window, merge all HLL sketches from the constituent days using HLL merge functions, then extract the final distinct count. This approach provides approximate but highly accurate distinct user counts across weekly periods while handling the computational complexity of cross-chain user deduplication
- **Key fields**: `user_wau` (weekly active users extracted from merged HLL sketches)

### Component F: Multi-dimensional breakdowns
- **Purpose**: Provide weekly active user metrics at different aggregation levels
- **Logic**: Calculate weekly active users across four different dimensions: project level (all chains combined), individual chain level, user type level, and product level. Each breakdown aggregates the HLL sketches appropriately, with null values for non-relevant grouping fields
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id`, `product_id`, `user_type` as grouping variables

### Final aggregation
- **Combining components**: The multi-dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple granularities of the same metric
- **Currency conversion**: No currency conversion required as this is a count-based metric
- **Time aggregation**: Weekly boundaries are defined as 7-day rolling windows, with each day representing the end date of its respective weekly period
- **Final calculation**: The `finalize_metric_active_addresses_weekly` macro applies standardized formatting, fills missing periods with zero values, and renames `user_wau` to `active_addresses_weekly` to align with blockchain terminology where users are represented by their addresses

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

Unique active addresses over a rolling 7-day window.

PolkadotDOT

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown