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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Energy Web X

# Energy Web X Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have initiated at least one transaction on the Energy Web blockchain within a rolling 30-day period. This metric provides insights into user engagement and network activity by tracking distinct transaction senders over monthly time windows. For blockchain protocols like Energy Web, active users are equivalent to active addresses since each unique sender address represents an active participant in the network.

## 2. Data sources

The calculation relies on the following data sources:
- **Energy Web blockchain transaction data**: Raw extrinsics data from the Energy Web blockchain containing transaction records, sender addresses, and execution timestamps
- **Blockchain transaction metadata**: Transaction timing information used to establish daily and monthly activity windows

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique active users (addresses) per day from raw blockchain transactions
- **Logic**: Raw Energy Web extrinsics are processed to extract transaction sender addresses and timestamps. Each sender address is classified as a "Transaction sender" user type within the "blockchains_l2" market sector. Daily boundaries are established by truncating timestamps to day level, and distinct sender addresses are counted for each day to create daily active user (DAU) records.
- **Key fields**: `block_timestamp` for timing, `id` field as the sender address, and truncated daily timestamps for aggregation boundaries

### Component B: Monthly active user calculation with sliding windows
- **Purpose**: Calculates monthly active users using rolling 30-day windows and efficient distinct counting
- **Logic**: A datespine is created covering all days from the earliest transaction to the current date. For each day, a 30-day rolling window is established (from 29 days prior to the current day). HyperLogLog sketches are initialized for each day's unique users, enabling efficient approximate distinct counting. The rolling windows are created by joining each date with all dates in its 30-day window, then merging HyperLogLog sketches to count unique users across the entire window.
- **Key fields**: `timestamp` for the window endpoint, `hll` sketches containing user address data, and `user_mau` as the final distinct count

### Component C: Dimensional breakdowns
- **Purpose**: Creates multiple views of monthly active users across different analytical dimensions
- **Logic**: The monthly active user data is aggregated into four dimensional breakdowns: project-level (combining all data under 'project_dbt_tag'), chain-level (segmented by Energy Web chain), user-type level (segmented by transaction sender type), and product-level (though Energy Web has null product segmentation). Each breakdown uses HyperLogLog merge operations to maintain accurate distinct counts while enabling different analytical perspectives.
- **Key fields**: `dimensions` field indicating the breakdown type, `chain_id` for chain-specific metrics, `user_type` for user classification, and `user_mau` for the aggregated counts

### Final aggregation
- **Combining components**: The monthly active user data is filtered to include only chain-level and project-level dimensions, which are the relevant breakdowns for active addresses analysis
- **Address conversion**: Monthly active user counts are directly mapped to active addresses since each unique user represents a unique blockchain address (`user_mau` becomes `active_addresses_monthly`)
- **Time aggregation**: Monthly boundaries are maintained from the MAU calculation, with each timestamp representing the end date of a 30-day rolling window
- **Final calculation**: The metric value equals the count of unique addresses that initiated at least one transaction within any 30-day rolling period, calculated using HyperLogLog distinct counting for computational efficiency

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

Unique addresses active over a rolling 30-day window.

Energy Web XEWT

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown