Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Heima is a specialized identity and data verification blockchain powered by Polkadot Relay chain.

HEI holders own and govern the protocol. Heima’s investors include LD Capital, Digital Renaissance, Signum Capital, and others.

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Blockchains (L2)

Heima

# Heima Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Heima measures the count of unique addresses that performed transactions or executed smart contract operations on the Heima blockchain within any rolling 7-day period. This metric uses HyperLogLog probabilistic counting for efficient computation of distinct users across time windows and provides dimensional breakdowns by chain, user type, and product to enable comprehensive user activity analysis.

## 2. Data sources

- **Heima blockchain extrinsics**: Transaction records from the Heima blockchain containing sender addresses, block timestamps, and transaction metadata
- **Heima EVM events**: Parsed event logs from Heima's EVM compatibility layer, specifically `Executed` events from the `ethereum` pallet that contain user addresses and execution timestamps

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all unique user addresses that performed blockchain activities on Heima
- **Logic**: Extracts user addresses from two data sources - transaction senders from extrinsics data and addresses from `Executed` events in the ethereum pallet. Each activity record includes the user address, block timestamp, chain identifier ('heima' for native chain, 'heimaevm' for EVM layer), and classification as 'Transaction sender' user type. All users are categorized under 'blockchains_l2' market sector and 'blockchain' business segment
- **Key fields**: `user_address` (sender or extracted from event data), `block_timestamp`, `chain` identifier, `user_type` classification

### Component B: Daily active user aggregation  
- **Purpose**: Converts raw user activity into daily distinct active user counts using probabilistic counting
- **Logic**: Groups user activities by day using timestamp truncation to day boundaries. Creates distinct daily active user records with each unique user-day combination. Initializes HyperLogLog sketches for each user to enable efficient distinct counting across time windows. Applies incremental processing logic to only recalculate recent periods when updating
- **Key fields**: `timestamp` (day-truncated), `timestamp_grain` ('day'), `chain`, `user_type`, `product_id`, `user_dau` (user address for counting)

### Component C: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for each day to calculate weekly active users
- **Logic**: Generates a complete datespine of consecutive days from the earliest activity date to yesterday. For each day in the datespine, creates a 7-day window by generating an array of the target day plus the 6 preceding days. Joins this window structure with the daily user data to associate each day with its corresponding 7-day period of user activity
- **Key fields**: `timestamp` (target day), `joinday` (each day in the 7-day window), HLL sketches from daily data

### Component D: Weekly active user calculation
- **Purpose**: Computes distinct weekly active users using HyperLogLog merge operations
- **Logic**: For each day and its 7-day window, merges the HyperLogLog sketches from all days in the window using HLL merge operations. Extracts the final distinct count from the merged HLL sketch to get the number of unique users active within that 7-day period. This approach efficiently handles overlapping time windows and ensures accurate distinct user counting across the rolling periods
- **Key fields**: Merged HLL sketches, extracted distinct user counts (`user_wau`)

### Component E: Dimensional breakdowns
- **Purpose**: Provides multiple perspectives on weekly active user metrics
- **Logic**: Creates four separate dimensional views of the WAU data - project-level aggregation (all chains and user types combined), chain-level breakdown (separate WAU for 'heima' vs 'heimaevm'), user type breakdown (currently all 'Transaction sender' type), and product-level breakdown (currently null as no products are defined). Each breakdown maintains the same timestamp and user count structure but nullifies non-relevant dimensional fields
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_wau` count

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set with consistent schema, creating a comprehensive view that supports analysis at multiple granularities
- **Time boundaries**: Uses daily timestamp truncation with 7-day rolling windows, ensuring each day represents users active in that day plus the 6 preceding days
- **Data completeness**: Applies gap-filling logic through the finalization macro to ensure missing days are represented with zero values rather than null, maintaining complete time series for analysis and reporting
- **Final calculation**: Each record represents the count of distinct users active within a 7-day window ending on the specified timestamp, calculated using HyperLogLog probabilistic counting for computational efficiency

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

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

HeimaHEI

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown