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

Energy Web X is energy blockchain ecosystem powered by Polkadot Relay chain.

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Blockchains (L2)

Energy Web X

# Energy Web X Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who have submitted at least one transaction (extrinsic) on the EnergyWeb blockchain within a rolling 30-day window ending on each given day. This metric uses HyperLogLog probabilistic counting to efficiently calculate unique user counts across overlapping time windows. The calculation provides multiple dimensional breakdowns to analyze user activity at different granularity levels.

## 2. Data sources

- **EnergyWeb blockchain extrinsic records**: Raw transaction data from the EnergyWeb network, a Substrate-based blockchain, containing transaction sender addresses and block timestamps
- **Blockchain transaction metadata**: EnergyWeb network activity logs including block timing information and transaction identifiers

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify unique daily active users from EnergyWeb blockchain activity
- **Logic**: Extract user addresses from EnergyWeb extrinsic records, where each extrinsic represents a blockchain transaction. The `id` field from extrinsics identifies the transaction sender address. All users are classified as "Transaction sender" type since EnergyWeb extrinsics represent user-initiated blockchain activity. Timestamps are truncated to day boundaries to aggregate daily activity. Users are associated with the 'energyweb' chain, 'blockchains_l2' market sector, and 'blockchain' business segment.
- **Key fields**: `block_timestamp` for temporal analysis, `id` field as `user_address`, `user_type` set to "Transaction sender"

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily distinct active users with HyperLogLog initialization
- **Logic**: Aggregate the user activity data to create daily distinct user counts. Timestamps are truncated to day granularity using `timestamp_trunc` function. Distinct user addresses are counted per day, segmented by chain, user type, and product dimensions. HyperLogLog sketches are initialized for each daily user count to enable efficient probabilistic counting in downstream calculations.
- **Key fields**: `timestamp` truncated to day, `timestamp_grain` set to 'day', `user_dau` containing distinct user addresses, `hll` for probabilistic counting

### Component C: Datespine and rolling window creation
- **Purpose**: Create a complete time series and establish 30-day rolling windows
- **Logic**: Generate a datespine covering the full date range from the earliest user activity to the current date minus one day. For each date in the datespine, create a 30-day rolling window by generating timestamps from 29 days prior through the current date. This creates joinable date pairs where each target date connects to all dates within its 30-day lookback window.
- **Key fields**: `timestamp` for target dates, `joinday` for dates within each 30-day window

### Component D: MAU window aggregation
- **Purpose**: Calculate monthly active users using the rolling 30-day windows
- **Logic**: Join the rolling window date pairs with the daily HyperLogLog data to create user activity windows. For each target date, collect all HLL sketches from the corresponding 30-day period. Merge the HLL sketches using `hll_count.merge_partial` to combine user counts across the window while maintaining uniqueness. Extract the final unique user count using `hll_count.extract` to get the probabilistic distinct count.
- **Key fields**: `timestamp` for the target date, `hll` sketches merged across the 30-day window, `user_mau` as the extracted unique count

### Component E: Dimensional breakdowns
- **Purpose**: Create multiple analytical perspectives of MAU data
- **Logic**: Generate four separate dimensional views of the MAU calculation: project-level aggregation (combining all dimensions), chain-level breakdown (preserving chain_id dimension), user_type-level breakdown (preserving user_type dimension), and product_id-level breakdown (preserving product_id dimension). Each breakdown aggregates the HLL sketches appropriately while nullifying non-relevant dimensions. All breakdowns are combined using UNION ALL operations.
- **Key fields**: `dimensions` field indicating the breakdown type, dimension-specific fields preserved or nullified based on breakdown type

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set containing project-level, chain-level, user_type-level, and product_id-level MAU calculations
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily time series with each day representing the unique users active in the trailing 30-day period
- **Final calculation**: For each date and dimensional breakdown, the MAU represents the probabilistic distinct count of users who submitted at least one EnergyWeb blockchain transaction within the 30-day window ending on that date

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

Unique revenue-generating users over a rolling 30-day window.

Energy Web XEWT

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown