twitter

discord

website

github

Blockchains (L2)

peaq

# peaq Active users (monthly) Methodology

## 1. Overview

The Peaq Monthly Active Users (MAU) metric measures the number of unique users who performed transactions on the Peaq blockchain ecosystem within a 30-day rolling window. This metric tracks user engagement across both native Peaq transactions and EVM-compatible transactions, providing insights into the protocol's user adoption and activity levels. The calculation uses HyperLogLog algorithms for efficient distinct user counting and provides breakdowns across multiple dimensions including chain, user type, and product.

## 2. Data sources

The metric relies on the following raw blockchain data sources:
- **Native Peaq transactions**: Extrinsic records from the Peaq blockchain containing transaction metadata and sender identifiers
- **EVM transaction events**: Parsed blockchain event logs from Peaq's Ethereum pallet, specifically `Executed` events that capture EVM-compatible transactions with user addresses extracted from event data
- **Chain metadata**: Classification data identifying Peaq and Peaq EVM as Layer 2 blockchain networks in the blockchain business segment

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique transaction senders and aggregates them into daily active user counts
- **Logic**: Extracts user addresses from two sources - native Peaq extrinsics using the `id` field as the transaction sender, and EVM transactions by parsing the first element of `event_data` from Ethereum pallet `Executed` events. Transaction timestamps are truncated to daily granularity, and users are classified as "Transaction sender" type. Both native Peaq and Peaq EVM chains are included with appropriate market sector and business segment classifications.
- **Key fields**: `block_timestamp` truncated to day level, `chain` identifier (peaq/Peaq EVM), `user_address` as the unique identifier, `user_type` set to "Transaction sender"

### Component B: Daily active user (DAU) preparation with HyperLogLog
- **Purpose**: Converts daily user lists into HyperLogLog sketches for efficient aggregation
- **Logic**: Takes the daily user aggregations and creates HyperLogLog sketches using `hll_count.init` with precision 15. This enables efficient computation of distinct users across longer time periods without storing all individual user addresses. Users are grouped by timestamp, chain, user type, and product dimensions.
- **Key fields**: `timestamp` at daily granularity, `hll` sketch containing compressed user data, dimensional fields for breakdowns

### Component C: Rolling window creation
- **Purpose**: Establishes the 30-day rolling window framework for MAU calculation
- **Logic**: Creates a datespine covering all days from the earliest DAU data to the previous day. For each date in the spine, generates a 30-day lookback period using `generate_timestamp_array` with 29 days prior to the current date. This creates joinday records that map each target date to all days in its 30-day window.
- **Key fields**: `timestamp` as the target date, `joinday` as each day within the 30-day window

### Component D: Window data assembly
- **Purpose**: Combines the rolling window framework with actual DAU data
- **Logic**: Joins the 30-day window structure with the HyperLogLog daily user data. For each target timestamp, collects all available HLL sketches from the corresponding 30-day period. This creates multiple records per target date - one for each day with available data within the window.
- **Key fields**: `timestamp` as target date, `hll` sketches from each day in the window, dimensional breakdowns preserved

### Final aggregation
- **Combining components**: HyperLogLog sketches from all days within each 30-day window are merged using `hll_count.merge_partial`, then extracted to get the final distinct user count with `hll_count.extract`
- **Dimensional breakdowns**: Four separate aggregation paths create MAU values by project level (overall), chain level, user type level, and product level, with unused dimensions set to null and combined via union operations
- **Time aggregation**: Daily boundary logic ensures each day's MAU represents unique users active in the trailing 30-day period ending on that date
- **Final calculation**: The MAU value represents the approximate count of distinct users who performed at least one transaction within the 30-day rolling window, calculated efficiently using merged HyperLogLog sketches rather than exact distinct counting

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

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

peaqPEAQ

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown