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Blockchains (L1)

Plasma

# Plasma Active users (daily) Methodology

## 1. Overview

Daily Active Users (DAU) for Plasma measures the number of unique addresses that sent at least one transaction on the Plasma blockchain each day. This metric provides insight into daily user engagement and adoption of the Plasma network. The calculation uses HyperLogLog algorithms for efficient distinct counting and provides dimensional breakdowns across chain, user type, and product dimensions.

## 2. Data sources

- **Plasma blockchain transactions**: Raw transaction records from the Plasma blockchain containing transaction metadata, timestamps, and sender addresses
- **Chain availability metadata**: Data availability periods used to filter results based on when reliable chain data is available

## 3. Step-by-step calculation

### Component A: User extraction
- **Purpose**: Extract and classify users from raw Plasma blockchain transaction data
- **Logic**: Select all transaction records from Plasma blockchain transactions and identify the sender address (`from_address`) as the user. Each transaction sender is classified as 'Transaction sender' user type. The component assigns static project metadata including chain identifier as 'plasma', market sector as 'blockchains_l1', and business segment as 'blockchain'. No product-level segmentation is applied (product ID remains null).
- **Key fields**: `block_timestamp` for transaction timing, `transaction_hash` for transaction identification, `from_address` as the user address, and static classification fields

### Component B: Daily user aggregation  
- **Purpose**: Calculate daily distinct active users by aggregating unique addresses per day
- **Logic**: Truncate block timestamps to daily boundaries using day-level time truncation to define activity periods. Select distinct user addresses within each day to eliminate duplicate counting of users who made multiple transactions on the same day. Group results by daily timestamp, chain, user type, and product dimensions to maintain dimensional structure.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` for distinct counting, dimensional grouping fields including `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog aggregation
- **Purpose**: Create efficient distinct user counting with dimensional breakdowns using probabilistic data structures
- **Logic**: Initialize HyperLogLog sketches with 15-bit precision for each combination of timestamp and dimensional attributes. Create four separate dimensional aggregations: total project-level DAU (all dimension fields set to null), DAU broken down by chain (chain field populated, others null), DAU by user type (user type populated, others null), and DAU by product ID (product ID populated, others null). Use HyperLogLog merge and extract operations to compute final distinct counts for each dimensional view.
- **Key fields**: HyperLogLog sketch (`hll`) for probabilistic distinct counting, `dimensions` field indicating breakdown type, dimensional identifier fields (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-level, by chain, by user type, by product ID) into a single result set with consistent schema structure
- **Time aggregation**: Daily boundary logic uses day-level timestamp truncation with zero-filling for missing days to ensure complete time series
- **Data availability filtering**: Apply chain availability filters to exclude periods where Plasma blockchain data may be incomplete or unreliable
- **Final calculation**: Extract final distinct user counts from merged HyperLogLog sketches, providing probabilistic but highly accurate estimates of daily unique active users across all dimensional breakdowns

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

Unique revenue-generating users over a fixed 24-hour window.

PlasmaXPL

Active users (daily)

Active users (daily)

Competitive landscape