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Blockchains (L1)

Plasma

# Plasma Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Plasma measures the number of unique addresses that have sent at least one transaction on the Plasma blockchain within a rolling 30-day period. This metric provides insight into user engagement and adoption trends by tracking how many distinct users interact with the network over monthly timeframes. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct users across large datasets and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

The calculation relies on the following data sources:
- **Plasma blockchain transactions**: Raw transaction records from the Plasma blockchain containing transaction metadata, sender addresses, and timestamps
- **Daily active user metrics**: Pre-processed daily snapshots of unique transaction senders
- **HyperLogLog data structures**: Probabilistic sketches used for efficient distinct user counting across large datasets

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identifies unique addresses that sent transactions each day on Plasma
- **Logic**: Processes raw Plasma transaction records to extract distinct sender addresses (`from_address`) per day. Each transaction sender is classified as user type "Transaction sender" and associated with the Plasma chain. Transactions are grouped by day using timestamp truncation to identify daily active users.
- **Key fields**: `block_timestamp` for daily grouping, `from_address` as the user identifier, `transaction_hash` for transaction tracking, `chain` set to "plasma"

### Component B: HyperLogLog initialization for daily users
- **Purpose**: Creates probabilistic data structures for efficient distinct user counting
- **Logic**: Takes daily active users and creates HyperLogLog sketches grouped by timestamp, chain, user type, and product dimensions. Each daily user set is converted into an HLL sketch with precision 15, enabling efficient merging and counting operations while maintaining accuracy for large user sets.
- **Key fields**: `timestamp` for daily boundaries, `chain_id` for blockchain identification, `user_type` for user classification, `product_id` for product segmentation, `hll` containing the probabilistic user sketch

### Component C: Rolling window generation
- **Purpose**: Creates a 30-day rolling window structure for MAU calculations
- **Logic**: Generates a datespine of all days from the earliest user activity to the current date. For each day in the spine, creates join relationships with the current day plus the previous 29 days, resulting in 30-day windows. This approach ensures complete coverage even when some days have no user activity.
- **Key fields**: `timestamp` for the target date, `joinday` representing each day in the 30-day window

### Component D: Window-based HLL merging
- **Purpose**: Combines daily HLL sketches across 30-day windows
- **Logic**: Joins the rolling window structure with daily HLL data to collect all relevant daily sketches for each 30-day period. For each target date, gathers HLL sketches from that date and the previous 29 days, grouped by chain, user type, and product dimensions.
- **Key fields**: `timestamp` for the MAU calculation date, dimensional grouping fields for breakdowns, `hll` sketches ready for merging

### Component E: Dimensional MAU breakdowns
- **Purpose**: Calculates MAU at different aggregation levels
- **Logic**: Creates separate MAU calculations for four different dimensional breakdowns:
  - Project-level MAU: Overall user count across all dimensions
  - Chain-level MAU: User count broken down by blockchain
  - User type-level MAU: User count broken down by user classification
  - Product-level MAU: User count broken down by product segments
Each breakdown merges relevant HLL sketches using `hll_count.merge_partial()` and extracts the final distinct count using `hll_count.extract()`.
- **Key fields**: `dimensions` indicating breakdown type, dimensional fields populated only for relevant breakdowns (others set to null), `user_mau` containing the final count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations, creating a comprehensive dataset with MAU values at different aggregation levels
- **Time aggregation**: Daily MAU calculations represent 30-day rolling windows, with each date showing unique users from that date and the previous 29 days
- **Final calculation**: The HyperLogLog merge and extract operations provide the final distinct user count for each 30-day window, maintaining accuracy while efficiently handling large user sets across multiple dimensions

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

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

PlasmaXPL

Active users (monthly)

Active users (monthly)

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