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Blockchains (L2)

peaq

# peaq Active addresses (daily) Methodology

## 1. Overview

Daily active addresses for Peaq measures the number of unique addresses that performed at least one transaction on the Peaq network within a 24-hour period. This metric tracks user engagement across both Peaq's native substrate blockchain and its EVM-compatible layer. The calculation uses HyperLogLog approximation for efficient processing of large-scale address counting and provides daily snapshots of network activity.

## 2. Data sources

- **Peaq substrate blockchain extrinsics**: Transaction records from Peaq's native substrate blockchain containing sender addresses and block timestamps
- **Peaq EVM ethereum pallet events**: Event logs from Peaq's EVM-compatible layer, specifically `Executed` events from the ethereum pallet that contain user address information
- **Blockchain metadata**: Chain identifiers and market sector classifications for the Peaq network

## 3. Step-by-step calculation

### Component A: Raw user extraction
- **Purpose**: Identifies all unique user addresses that performed transactions on the Peaq network
- **Logic**: Extracts user addresses from two data sources. From substrate extrinsics, the `id` field represents transaction sender addresses. From EVM events, user addresses are extracted from the first element of `event_data` array in ethereum pallet `Executed` events. Both sources classify users as "Transaction sender" type and assign them to the "blockchains_l2" market sector. The component handles both "peaq" (substrate) and "Peaq EVM" (EVM-compatible) chain identifiers.
- **Key fields**: `block_timestamp`, `chain` (peaq/Peaq EVM), `user_address`, `user_type` (Transaction sender)

### Component B: Daily user aggregation  
- **Purpose**: Calculates daily distinct active users by truncating timestamps and deduplicating addresses
- **Logic**: Truncates `block_timestamp` to day-level boundaries using timestamp truncation. Identifies distinct user addresses per day while preserving dimensional attributes including chain, user type, market sector, business segment, and product identifiers. Implements incremental processing with lookback periods for efficient data updates.
- **Key fields**: `timestamp` (day-truncated), `timestamp_grain` (day), `chain`, `user_address` as `user_dau`

### Component C: HyperLogLog dimensional aggregation
- **Purpose**: Creates scalable approximate distinct counting across multiple dimensional breakdowns
- **Logic**: Applies HyperLogLog counting algorithm with precision 15 to handle large-scale distinct address counting efficiently. Creates four dimensional breakdowns: project-level aggregation (project_dbt_tag), chain-level breakdown, user-type-level breakdown, and product-level breakdown. Each breakdown uses HLL initialization, merging, and extraction to calculate approximate distinct counts while maintaining dimensional granularity.
- **Key fields**: `hll` (HyperLogLog sketch), `dimensions` (breakdown type), `user_dau` (extracted distinct count)

### Final aggregation
- **Combining components**: Selects only 'chain' and 'project_dbt_tag' dimensional breakdowns from the multi-dimensional aggregation results
- **Time aggregation**: Daily boundaries are maintained throughout the calculation with no further time aggregation required
- **Schema standardization**: Applies finalization macro to standardize output schema and implement zero-filling strategy for missing dates
- **Final calculation**: Renames the `user_dau` field to `active_addresses_daily` to reflect the semantic meaning of unique addresses rather than users. Applies chain availability filtering to ensure data quality and availability constraints are met.

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

Unique active addresses over a fixed 24-hour window.

peaqPEAQ

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown