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.

website

twitter

github

etherscan

Blockchains (L2)

Energy Web X

# Energy Web X Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique addresses that performed at least one transaction on the EnergyWeb blockchain within any 7-day rolling window. This metric answers the business question "How many unique users are actively transacting on EnergyWeb each week?" and uses HyperLogLog sketches for efficient approximate distinct counting across rolling time windows. The calculation provides multiple dimensional breakdowns including project-level, chain-level, user type-level, and product-level aggregations.

## 2. Data sources

The calculation relies on the following raw data sources:
- **EnergyWeb blockchain extrinsics**: Transaction data from the EnergyWeb blockchain containing transaction timestamps, transaction IDs, and sender addresses
- **Chain availability configuration**: Data quality filters that ensure completeness and reliability of EnergyWeb blockchain data

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies unique user addresses and their transaction activity from EnergyWeb blockchain data
- **Logic**: Extracts transaction senders from EnergyWeb extrinsics, treating each transaction sender as an active user. Each transaction is timestamped and associated with metadata including chain identifier (`energyweb`), market sector (`blockchains_l2`), business segment (`blockchain`), and user type (`Transaction sender`). Product ID is set to null for EnergyWeb users.
- **Key fields**: `block_timestamp` for transaction timing, `id` as the user address, chain and business classification fields

### Component B: Daily active users calculation  
- **Purpose**: Calculates daily distinct active users and creates HyperLogLog sketches for efficient aggregation
- **Logic**: Groups user activity by day using timestamp truncation to daily granularity. Counts distinct user addresses per day, creating the foundation for weekly aggregation. Applies incremental processing filters to only process new or updated data. Creates HyperLogLog sketches with 15-bit precision for each day's user set to enable efficient approximate distinct counting across time windows.
- **Key fields**: `timestamp` truncated to day, `timestamp_grain` set to 'day', `chain` identifier, `user_type`, `product_id`, and `user_dau` containing the user address

### Component C: Date spine and rolling window creation
- **Purpose**: Creates a complete time series and defines 7-day rolling windows for WAU calculation  
- **Logic**: Generates a continuous date spine from the earliest available data to yesterday to ensure no gaps in the time series. For each date in the spine, creates a 7-day rolling window by generating an array of the current date plus the previous 6 days. This approach ensures every day has a complete 7-day lookback period for accurate WAU calculation.
- **Key fields**: `timestamp` for the target date, `joinday` for each day within the 7-day window

### Component D: HyperLogLog sketch merging
- **Purpose**: Combines daily user sets across 7-day windows to calculate weekly active users
- **Logic**: Joins the rolling windows with daily HLL sketches, matching each day in the 7-day window to its corresponding daily user data. Merges HLL sketches across all days within each 7-day window using HLL merge operations to create a single sketch representing all unique users active during that week. Extracts the final count from the merged HLL sketch to get the approximate weekly active user count.
- **Key fields**: `hll` containing the HyperLogLog sketch data, with merging performed using `hll_count.merge_partial()` and final extraction using `hll_count.extract()`

### Component E: Dimensional breakdowns
- **Purpose**: Creates multiple views of WAU data across different business dimensions
- **Logic**: Generates four separate dimensional breakdowns from the weekly user data: (1) Project-level aggregation combining all dimensions into a single project view, (2) Chain-level breakdown showing WAU by blockchain, (3) User type-level breakdown showing WAU by user classification, (4) Product-level breakdown showing WAU by product (though product ID is null for EnergyWeb). Each breakdown groups and aggregates the HLL sketches appropriately while setting unused dimension fields to null.
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id`, `product_id`, `user_type` as applicable, and `user_wau` containing the final count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a single result set containing WAU metrics across all dimension combinations
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamps with 7-day rolling windows, where each day's WAU represents unique users active in that day plus the previous 6 days
- **Final calculation**: The final WAU value is the approximate distinct count extracted from merged HyperLogLog sketches, representing unique addresses that performed at least one transaction within any 7-day rolling period. Zero-filling is applied using the finalize_metric_user_wau macro to ensure complete time series with zeros for periods with no activity.

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

Unique revenue-generating users over a rolling 7-day window.

Energy Web XEWT

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown