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Plasma

# Plasma Active addresses (daily) Methodology

## 1. Overview

The Plasma active addresses daily metric measures the number of unique addresses that performed transactions on the Plasma blockchain each day. This metric serves as a proxy for daily user activity and network engagement by counting distinct transaction senders. The calculation treats each unique address that sends at least one transaction on a given day as an "active address" for that day.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Plasma blockchain transactions**: Complete transaction records from the Plasma blockchain containing sender addresses, timestamps, and transaction metadata
- **Transaction timing data**: Block timestamps that determine when transactions occurred for daily aggregation
- **Address data**: Transaction sender addresses (`from_address`) that represent active users on the network

## 3. Step-by-step calculation

### Component A: User identification from transactions
- **Purpose**: Extract and classify transaction senders as active users
- **Logic**: The system processes raw Plasma blockchain transaction records to identify unique users. Each transaction's sender address (`from_address`) is extracted along with the block timestamp and transaction hash. All transaction senders are classified as "Transaction sender" user type, representing active participants in the network. The data is enriched with blockchain metadata including chain identifier ('plasma'), market sector ('blockchains_l1'), and business segment ('blockchain').
- **Key fields**: `from_address` (renamed to `user_address`), `block_timestamp`, `transaction_hash`, `user_type`

### Component B: Daily user aggregation
- **Purpose**: Calculate unique active users per day using efficient approximate counting
- **Logic**: Transaction timestamps are truncated to day-level granularity to group activity by date. For each day, the system identifies distinct user addresses that performed transactions. To enable efficient aggregation across multiple dimensions, the calculation uses HyperLogLog (HLL) data structures with precision level 15 for approximate distinct counting. This approach allows for accurate user counting while maintaining computational efficiency across different breakdowns.
- **Key fields**: `timestamp` (truncated to day), `user_address`, `hll` (HyperLogLog structure)

### Component C: Multi-dimensional breakdowns
- **Purpose**: Create user count aggregations across different analytical dimensions
- **Logic**: The system generates multiple dimensional views of daily active users using HLL merge operations. Four distinct breakdowns are created: project-level aggregation (project_dbt_tag dimension), chain-level aggregation, user type aggregation, and product-level aggregation. Each breakdown extracts the final user count from merged HLL structures, providing different analytical perspectives on user activity.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_dau` (extracted HLL count)

### Final aggregation
- **Combining components**: The active addresses metric specifically uses the chain-level and project-level dimensional breakdowns from the multi-dimensional user DAU calculations
- **Time aggregation**: Daily boundaries are determined by truncating block timestamps to day level, ensuring consistent daily aggregation across all transactions
- **Metric transformation**: The `user_dau` field from the user aggregation is directly renamed to `active_addresses_daily`, treating daily active users as active addresses
- **Final calculation**: The final active addresses count represents the number of unique transaction sender addresses that performed at least one transaction on the Plasma blockchain during each day, with missing days filled with zero values to ensure complete time series data

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

Unique active addresses over a fixed 24-hour window.

PlasmaXPL

Active addresses (daily)

Active addresses (daily)

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