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Blockchains (L2)

Soneium

# Soneium Active addresses (daily) Methodology

## 1. Overview

Active addresses daily measures the number of unique wallet addresses that sent at least one transaction on the Soneium blockchain within a 24-hour period. This metric provides insight into daily user engagement and adoption on the Soneium Layer 2 network. The calculation identifies unique transaction senders per day using probabilistic counting methods to efficiently handle large datasets while maintaining accuracy.

## 2. Data sources

- **Soneium transaction records**: Raw blockchain transaction data from the Soneium Layer 2 network, containing transaction metadata including sender addresses (`from_address`), timestamps (`block_timestamp`), and transaction hashes
- **Chain availability data**: Token Terminal's internal chain operational status data used to filter out periods when the blockchain was not fully operational
- **Time series completion logic**: Internal logic to ensure consistent daily time series by filling gaps with zero values when no activity occurred

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Extract and identify unique users from raw transaction data
- **Logic**: Process raw Soneium transaction records to identify transaction senders as active users. Each transaction's `from_address` field represents a user who initiated blockchain activity. The data is standardized with consistent metadata including chain identification as 'soneium', market sector as 'blockchains_l2', business segment as 'blockchain', and user type classification as 'Transaction sender'. All user addresses are cast as strings for consistent data typing.
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for transaction reference

### Component B: Daily user aggregation
- **Purpose**: Calculate distinct active users per day with dimensional breakdowns
- **Logic**: Transform transaction-level data into daily user counts by truncating timestamps to day boundaries using `timestamp_trunc(block_timestamp, day)`. Identify distinct user addresses per day while preserving dimensional attributes like chain, user type, and product identification. Apply incremental processing logic with configurable lookback periods to efficiently handle new data. Chain availability filtering ensures only periods when the blockchain was operational are included.
- **Key fields**: Daily truncated `timestamp`, `user_address` for distinct counting, dimensional fields for breakdowns

### Component C: HyperLogLog aggregation
- **Purpose**: Efficiently count distinct users across multiple dimensions using probabilistic counting
- **Logic**: Initialize HyperLogLog sketches with precision 15 for each day and dimensional combination. This probabilistic data structure enables efficient distinct counting while maintaining high accuracy for large datasets. Create four distinct dimensional breakdowns: project-level aggregation across all dimensions, chain-level breakdown, user-type-level breakdown, and product-level breakdown. Merge HyperLogLog sketches and extract final counts for each dimension. Union all dimensional breakdowns into a single result set.
- **Key fields**: `hll` sketches for probabilistic counting, `dimensions` field indicating breakdown type, `user_dau` as the final distinct count

### Final aggregation
- **Combining components**: The HyperLogLog-aggregated user counts are processed through finalization logic that ensures consistent time series completion and applies the fill-zeros strategy for missing days
- **Currency conversion**: No currency conversion required as this metric counts addresses rather than monetary values
- **Time aggregation**: Daily boundaries are established using day-level timestamp truncation, with time series completion filling any missing days with zero values
- **Final calculation**: Filter the multi-dimensional results to include only 'chain' and 'project_dbt_tag' dimensions, then rename the `user_dau` field to `active_addresses_daily`. Apply final chain availability filtering to exclude any periods when the Soneium blockchain was not operational, ensuring data quality and consistency in the reported active address counts.

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

Unique active addresses over a fixed 24-hour window.

Soneium

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown