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Blockchains (L1)

Plasma

# Plasma Active users (weekly) Methodology

## 1. Overview

The Plasma Weekly Active Users (WAU) metric measures the number of unique users who have sent at least one transaction on the Plasma blockchain within any 7-day rolling window. This metric provides insights into weekly user engagement and activity patterns on the Plasma network. The calculation uses HyperLogLog data structures for efficient computation of unique user counts across overlapping time windows and provides breakdowns across multiple dimensions including chain, user type, and product levels.

## 2. Data sources

- **Plasma blockchain transactions**: Raw transaction records from the Plasma blockchain containing transaction metadata, sender addresses, timestamps, and transaction hashes
- **Chain availability configuration**: Internal configuration data used to filter for data completeness and quality assurance

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique users and calculates daily active users (DAU) as the foundation for WAU calculation
- **Logic**: Extracts transaction sender addresses from Plasma blockchain transaction records. Each transaction sender (`from_address`) is classified as a 'Transaction sender' user type. Transactions are grouped by day using timestamp truncation, and distinct user addresses are counted per day. The data includes chain identifier ('plasma'), market sector ('blockchains_l1'), and business segment ('blockchain') classifications.
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` as the user identifier, `transaction_hash` for transaction tracking, `user_type` set to 'Transaction sender'

### Component B: HyperLogLog encoding for efficient counting
- **Purpose**: Converts daily user sets into HyperLogLog data structures for memory-efficient unique user counting across time windows
- **Logic**: Takes the daily active user counts and initializes HyperLogLog sketches with precision parameter 15. This allows for approximate but highly accurate counting of distinct users while using minimal memory, essential for calculating overlapping rolling windows efficiently.
- **Key fields**: `hll` containing the encoded user set, `timestamp` for daily boundaries, dimensional fields for chain, user type, and product breakdowns

### Component C: Seven-day rolling window creation
- **Purpose**: Generates all possible 7-day rolling windows for WAU calculation using a datespine approach
- **Logic**: Creates a complete datespine covering all days from the earliest available data to yesterday. For each day in the datespine, generates the corresponding 7-day lookback period (6 days prior plus the current day). This ensures no time periods are skipped and provides consistent WAU values even for days with no activity.
- **Key fields**: `timestamp` for the reporting date, `joinday` for each day within the 7-day window

### Component D: Rolling window aggregation
- **Purpose**: Combines daily HyperLogLog sketches across 7-day windows to calculate weekly unique users
- **Logic**: Joins the rolling window datespine with daily HLL data to collect all user sketches within each 7-day period. For each reporting date, merges HLL sketches from the corresponding 7-day window using HyperLogLog merge operations. Extracts the final unique user count from the merged HLL sketch.
- **Key fields**: `timestamp` for reporting date, merged `hll` structures, dimensional attributes preserved from daily data

### Component E: Dimensional breakdowns
- **Purpose**: Calculates WAU across multiple analytical dimensions for comprehensive reporting
- **Logic**: Creates four distinct dimensional views of the WAU data:
  - Project-level: Overall Plasma ecosystem WAU with no dimensional filtering
  - Chain-level: WAU broken down by blockchain (Plasma in this case)
  - User type-level: WAU segmented by user classification (Transaction sender)
  - Product-level: WAU broken down by product identifiers (null for Plasma as it's treated as a single product)
Each breakdown merges HLL sketches within the appropriate dimensional grouping and extracts unique user counts.
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id`, `product_id`, `user_type` for dimensional filtering, `user_wau` as the final count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple analytical views
- **Time aggregation**: WAU is calculated for each day representing the unique users active in the 7-day period ending on that day
- **Missing value handling**: The finalization process fills missing days with zero values to ensure complete time series data
- **Final calculation**: The `user_wau` field contains the count of unique users who sent at least one transaction within the 7-day rolling window, calculated using HyperLogLog approximate counting for efficiency and accuracy

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

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

PlasmaXPL

Active users (weekly)

Active users (weekly)

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