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Blockchains (L2)

Unichain

# Unichain Active addresses (daily) Methodology

## 1. Overview

Daily active addresses measures the number of unique addresses that initiated at least one transaction on the Unichain blockchain each day. This metric serves as a key indicator of network adoption and user engagement, tracking how many distinct users are actively interacting with the Unichain ecosystem on a daily basis. The calculation treats each transaction sender address (`from_address`) as an active address and counts unique addresses per day using efficient probabilistic counting methods.

## 2. Data sources

- **Unichain transaction records**: Complete transaction data from the Unichain blockchain including transaction hashes, timestamps, sender addresses (`from_address`), and other transaction metadata
- **Chain availability configuration**: Data defining the active operational periods for Unichain to ensure metrics only include data from supported time ranges

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Extract and classify active users from raw Unichain transaction data
- **Logic**: Process all Unichain blockchain transactions and extract the `from_address` field from each transaction, treating this as the active user address. Each transaction sender is classified with metadata including chain identifier as 'unichain', market sector as 'blockchains_l2', business segment as 'blockchain', and user type as 'Transaction sender'. The `block_timestamp` is preserved to enable daily aggregation in subsequent steps.
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` as the user identifier, `transaction_hash` for transaction tracking, and classification fields for dimensional analysis

### Component B: Daily active user calculation  
- **Purpose**: Aggregate unique active users by day using probabilistic counting
- **Logic**: Group transactions by day using timestamp truncation to create daily boundaries. For each day, collect all unique `user_address` values (derived from transaction senders) and initialize HyperLogLog sketches with precision 15 for efficient distinct counting. This approach enables accurate estimation of unique daily active users while supporting flexible aggregation across different dimensions. Apply incremental processing logic to optimize performance by only reprocessing recent days when new data arrives.
- **Key fields**: `timestamp` truncated to daily boundaries, `user_address` for distinct counting, `chain` and other dimension fields for grouping, `user_dau` as the HLL-encoded user identifier

### Component C: Multi-dimensional aggregation
- **Purpose**: Calculate daily active users across different analytical dimensions
- **Logic**: Process the daily HLL sketches and create separate aggregations for different analytical perspectives. Generate four distinct breakdowns: project-level aggregation (combining all dimensions), chain-level aggregation (grouping by Unichain specifically), user type aggregation (grouping by 'Transaction sender' classification), and product-level aggregation (though product_id is null for blockchain-level metrics). For each dimension, merge the HLL sketches using `hll_count.merge_partial` and extract the final distinct count using `hll_count.extract`. Set unused dimension fields to null to maintain consistent schema across all aggregation levels.
- **Key fields**: `timestamp` for daily boundaries, `dimensions` to identify the aggregation type, `chain_id` for chain-specific aggregations, `user_dau` as the final distinct user count, and various dimension fields for grouping

### Component D: Active addresses mapping
- **Purpose**: Convert daily active user counts to active addresses metric
- **Logic**: Take the calculated `user_dau` values from the multi-dimensional aggregation and directly map them to `active_addresses_daily`. This mapping treats active users and active addresses as equivalent concepts for blockchain metrics, since each active user is represented by a unique address that initiated transactions. Filter to include only 'chain' and 'project_dbt_tag' dimensions to focus on the most relevant analytical perspectives for active addresses reporting.
- **Key fields**: `user_dau` becomes `active_addresses_daily`, `timestamp` for daily boundaries, `dimensions` for aggregation level identification, `chain_id` for chain-specific data

### Final aggregation
- **Combining components**: The final metric directly uses the DAU calculation output, mapping `user_dau` values to `active_addresses_daily` without additional mathematical operations
- **Time aggregation**: Daily boundaries are established using timestamp truncation to day level, ensuring consistent 24-hour periods in UTC timezone
- **Schema standardization**: Apply the `finalize_metric_active_addresses_daily` macro to ensure consistent field names, data types, and handling of missing days by filling gaps with zero values
- **Chain availability filtering**: Apply availability filters to exclude data from periods when the Unichain network was not fully operational or supported
- **Final calculation**: The final active addresses daily value equals the distinct count of transaction sender addresses for each day, calculated using HyperLogLog estimation and aggregated across the specified dimensions

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

Unique active addresses over a fixed 24-hour window.

UnichainUNI

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown