Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Darwinia Network is a decentralized blockchain for cross-chain interoperability and decentralized finance infrastructure powered by Polkadot Relay chain.

RING holders own and govern the protocol. Darwinia Network’s investors include HashKey, Waterdrip Capital, SevenX Ventures, etc.

website

twitter

discord

Blockchains (L2)

Darwinia Network

# Darwinia Network Active addresses (monthly) Methodology

## 1. Overview

The Darwinia active addresses monthly metric measures the number of unique addresses that have been active on the Darwinia blockchain network within a rolling 30-day window, aggregated monthly. This metric answers the question: "How many distinct users are actively transacting on Darwinia each month?" The calculation treats user addresses as active addresses, using a sophisticated approach that combines native Darwinia transactions with EVM-compatible transactions to capture complete network activity.

## 2. Data sources

The calculation relies on raw blockchain transaction data from the Darwinia network:

- **Native Darwinia extrinsics**: Transaction records from the native Darwinia Substrate blockchain, containing transaction sender addresses (`id` field) and block timestamps
- **Darwinia EVM events**: Parsed ethereum execution events from the Darwinia EVM compatibility layer, specifically `ethereum` pallet `Executed` method events with address data in the first event data element
- **Blockchain metadata**: Chain identifiers, market sector classifications, and business segment categorizations for the Darwinia ecosystem

The data covers both the native Darwinia chain and the EVM-compatible Darwinia chain (Darwinia EVM), providing comprehensive coverage of network activity across both execution environments.

## 3. Step-by-step calculation

### Component A: User identification and unification
- **Purpose**: Creates a unified dataset of all transaction senders across both native Darwinia and EVM-compatible chains
- **Logic**: Combines two data streams - native Darwinia extrinsics using the `id` field as the user address, and Darwinia EVM events where addresses are extracted from the first element of `event_data` for ethereum `Executed` events. Both sources are labeled with their respective chain identifiers (`darwinia` and `Darwinia EVM`), classified under the `blockchains_l2` market sector and `blockchain` business segment, with all users categorized as `Transaction sender` type
- **Key fields**: `block_timestamp` for temporal analysis, `user_address` as the unique identifier, `chain` for network differentiation, and classification fields for market segmentation

### Component B: Daily active users calculation  
- **Purpose**: Calculates the distinct count of active addresses per day across the Darwinia network
- **Logic**: Truncates block timestamps to day granularity and creates distinct records for each unique `user_address` per day. This eliminates multiple transactions from the same address on the same day, counting each address only once per day regardless of transaction volume
- **Key fields**: `timestamp` truncated to day level, `user_dau` representing each unique daily active address, chain and classification metadata

### Component C: HyperLogLog sketch creation
- **Purpose**: Creates probabilistic data structures for efficient unique counting across large datasets
- **Logic**: Transforms daily active user data into HyperLogLog sketches with precision parameter 15, grouping by timestamp, chain, user type, and product dimensions. This enables efficient storage and merging of unique user counts while maintaining statistical accuracy
- **Key fields**: `hll` sketches containing encoded unique user information, dimensional breakdowns for aggregation flexibility

### Component D: Rolling window construction
- **Purpose**: Establishes 30-day rolling windows for monthly active user calculation
- **Logic**: Creates a datespine covering all possible days and generates join relationships for each day to include the previous 29 days plus the current day (30 total). This ensures consistent window sizes and handles missing days properly. The window slides daily, creating overlapping 30-day periods
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 30-day lookback period

### Component E: Monthly active users aggregation
- **Purpose**: Calculates unique users across 30-day rolling windows using probabilistic counting
- **Logic**: Merges HyperLogLog sketches across the 30-day windows to count unique addresses while avoiding double-counting users active on multiple days. Creates multiple dimensional breakdowns including project-level (`project_dbt_tag`), chain-level, user type-level, and product-level aggregations. Uses HLL merge operations to combine sketches and extract final unique counts
- **Key fields**: `user_mau` containing the unique monthly active user count, `dimensions` indicating the aggregation level, various dimensional fields for filtering and analysis

### Final aggregation
- **Combining components**: Selects only the `chain` and `project_dbt_tag` dimensional breakdowns from the MAU calculation, as these are the relevant aggregation levels for the active addresses monthly metric
- **Currency conversion**: Not applicable - this metric counts addresses rather than financial values
- **Time aggregation**: Uses monthly partitioning with 30-day rolling windows, ensuring each monthly timestamp represents unique addresses active in the preceding 30 days
- **Final calculation**: The `user_mau` values are directly used as `active_addresses_monthly` since each unique user address represents one active address. The finalization macro applies schema standardization and fills missing periods with zero values to ensure complete time series data

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

Unique addresses active over a rolling 30-day window.

Darwinia NetworkRING

Archived

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown