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Blockchains (L2)

peaq

# peaq Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Peaq measures the number of unique addresses that have sent at least one transaction on the Peaq blockchain within a rolling 7-day window. This metric provides insight into user engagement and activity levels on the Peaq network, supporting both native Substrate transactions and Ethereum Virtual Machine (EVM) transactions. The calculation uses HyperLogLog probabilistic counting to efficiently handle large user sets across multiple dimensional breakdowns.

## 2. Data sources

The WAU calculation relies on the following raw blockchain data sources:
- **Peaq extrinsics**: Native Substrate transactions on the Peaq blockchain containing transaction sender addresses and block timestamps
- **Peaq events**: EVM transaction events from the ethereum pallet, specifically `Executed` events that contain transaction sender addresses and execution timestamps
- **Blockchain metadata**: Chain identifiers, market sector classifications, and business segment categorizations applied to all transactions

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Extracts all user addresses that have sent transactions on Peaq, supporting both native and EVM transaction types
- **Logic**: Combines data from two sources - native Peaq extrinsics using the `id` field as the user address, and EVM transactions from ethereum pallet events using the first element of `event_data` array as the user address. Each transaction record includes the block timestamp, chain identifier ('peaq' for native, 'Peaq EVM' for EVM), and standardized metadata fields
- **Key fields**: `block_timestamp` (transaction timing), `user_address` (transaction sender), `chain` (transaction type identifier), `user_type` (always 'Transaction sender'), and classification fields for market sector and business segment

### Component B: Daily active user calculation  
- **Purpose**: Calculates daily distinct active users by aggregating unique addresses per day
- **Logic**: Groups transaction data by truncating `block_timestamp` to day boundaries, then counts distinct `user_address` values for each day. Creates records with daily granularity that include chain, user type, and product dimensions for downstream aggregation
- **Key fields**: `timestamp` (day-truncated date), `timestamp_grain` ('day'), `chain`, `user_type`, `product_id`, and `user_dau` (unique user address for that day)

### Component C: HyperLogLog initialization
- **Purpose**: Converts daily user counts into probabilistic HyperLogLog sketches for efficient weekly aggregation
- **Logic**: Initializes HyperLogLog counters with precision parameter 15 for each daily user count, grouped by timestamp, chain, user type, and product dimensions. This enables approximate counting of unique users across overlapping time windows without storing individual user addresses
- **Key fields**: `hll` (HyperLogLog sketch containing daily user information), maintaining all dimensional grouping fields from the daily calculation

### Component D: Rolling window creation
- **Purpose**: Creates 7-day sliding windows for each date to calculate weekly active users
- **Logic**: Uses a datespine to generate continuous daily timestamps, then creates join tables that map each date to the previous 6 days plus itself (7 total days). This ensures every date has a complete 7-day lookback window, even if some days have no activity
- **Key fields**: `timestamp` (target date), `joinday` (each of the 7 days in the window for that target date)

### Component E: Window aggregation
- **Purpose**: Merges HyperLogLog sketches across the 7-day window to count unique weekly active users
- **Logic**: Joins the rolling windows with daily HLL data, then merges HyperLogLog sketches for all days within each 7-day window. Groups by target timestamp and all dimensional fields, using HLL merge operations to combine probabilistic user counts while avoiding double-counting users who were active on multiple days
- **Key fields**: `timestamp` (target date), dimensional groupings (`chain_id`, `user_type`, `product_id`), and merged `hll` sketches containing 7-day user information

### Final aggregation
- **Combining components**: Extracts final user counts from merged HyperLogLog sketches using `hll_count.extract()`, then creates separate dimensional breakdowns for project-level (overall), chain-level, user-type-level, and product-level WAU metrics
- **Currency conversion**: Not applicable - this is a user count metric
- **Time aggregation**: Uses rolling 7-day windows calculated daily, with incremental processing that includes safety margins and lookback periods to ensure data completeness
- **Final calculation**: The final WAU value represents the approximate count of unique user addresses that sent at least one transaction within the 7-day period ending on each date, broken down by various dimensions and combined using UNION ALL operations

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

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

peaqPEAQ

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown