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Blockchains (L1)

Berachain

# Berachain Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction on the Berachain network within a rolling 7-day period. This metric provides insights into user engagement and network adoption by tracking distinct wallet addresses that actively send transactions. The calculation uses a rolling window approach where each day's value represents the count of unique addresses that were active in that day plus the previous 6 days.

## 2. Data sources

- **Berachain transaction records**: Raw transaction data from the Berachain blockchain containing transaction hashes, timestamps, sender addresses, and other transaction metadata
- **Transaction traces**: Individual transaction execution records that include the originating address (`from_address`) for each transaction

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Extract and classify transaction senders from raw Berachain transaction data
- **Logic**: Each transaction record is processed to identify the sender address (`from_address`) along with the block timestamp (`block_timestamp`). All sender addresses are classified as 'Transaction sender' user type since this metric focuses on addresses that initiate transactions rather than receive them. The data is enriched with blockchain metadata including chain identifier ('berachain'), market sector ('blockchains_l1'), and business segment ('blockchain')
- **Key fields**: `from_address` (transaction sender), `block_timestamp` (transaction time), `transaction_hash` (unique transaction identifier)

### Component B: Daily active user calculation  
- **Purpose**: Aggregate unique transaction senders by day using efficient distinct counting
- **Logic**: Transaction data is grouped by day using timestamp truncation to day granularity. For each day, the system calculates the distinct count of unique sender addresses using HyperLogLog (HLL) sketches for memory-efficient processing of large datasets. This creates a daily snapshot of unique active addresses with associated metadata including chain, user type, and product classifications
- **Key fields**: `timestamp` (day-level granularity), `user_dau` (distinct daily active addresses), `chain` (blockchain identifier), `user_type` (classification as transaction sender)

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active address calculation
- **Logic**: A date spine is generated covering all days from the earliest transaction date to yesterday. For each day in this spine, a 7-day lookback window is created by generating an array of the current day plus the previous 6 days. This creates overlapping windows where each day is associated with its corresponding 7-day period. The daily HLL sketches are then joined to this window structure to enable rolling aggregations
- **Key fields**: `timestamp` (current day), `joinday` (each day within the 7-day window), `hll` (HyperLogLog sketch for distinct counting)

### Component D: Weekly active user aggregation
- **Purpose**: Calculate weekly active addresses across multiple dimensional breakdowns
- **Logic**: For each 7-day window, the HLL sketches from all 7 days are merged together and the distinct count is extracted to get the weekly active user count. This calculation is performed across four different aggregation levels: project_dbt_tag level (overall network), chain level (by blockchain), user_type level (by address classification), and product_id level (by product). Each aggregation uses HLL merge operations to combine the daily sketches and extract the final distinct count
- **Key fields**: `user_wau` (weekly active addresses count), `dimensions` (aggregation level), `chain_id` (blockchain identifier), `user_type` (address classification)

### Final aggregation
- **Combining components**: The weekly active user calculations from different dimensional breakdowns are combined using union operations. The final model filters this combined dataset to include only 'chain' and 'project_dbt_tag' dimensions, which represent blockchain-level and overall network-level metrics respectively
- **Currency conversion**: No currency conversion is required as this metric counts addresses rather than monetary values
- **Time aggregation**: Each day's value represents a rolling 7-day window ending on that day. The metric uses day-level timestamps with 'day' timestamp grain for daily reporting of weekly rolling values
- **Final calculation**: The `user_wau` field is renamed to `active_addresses_weekly` to reflect the final metric name. Missing data points are filled with zeros using the fill_zeros strategy to ensure complete time series data. Chain availability filters are applied to ensure data is only included for periods when the blockchain was active and data collection was operational

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

Unique active addresses over a rolling 7-day window.

BerachainBERA

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape