website

twitter

discord

github

Blockchains (L1)

Berachain

# Berachain Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique addresses that initiated at least one transaction on the Berachain network within a rolling 7-day window. This metric provides insight into user engagement and platform adoption by tracking how many distinct users interact with the blockchain over weekly periods. The calculation uses HyperLogLog data structures for efficient distinct user counting across multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following data sources:
- **Berachain transaction records**: Raw blockchain transaction data containing transaction timestamps, hashes, and sender addresses
- **Table modification timestamps**: Metadata used for incremental processing and determining data freshness

## 3. Step-by-step calculation

### Component A: Daily active users extraction
- **Purpose**: Identifies unique users who initiated transactions each day
- **Logic**: Extracts transaction data from Berachain transaction records, focusing on transaction senders as the user population. Each transaction record includes the `block_timestamp`, `transaction_hash`, and `from_address` (sender). Users are classified as "Transaction sender" type and assigned to the 'berachain' chain with 'blockchains_l1' market sector and 'blockchain' business segment. Daily aggregation groups transactions by day using timestamp truncation to day granularity and counts distinct sender addresses
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` as the user identifier, `transaction_hash` for transaction reference

### Component B: HyperLogLog initialization
- **Purpose**: Converts daily user counts into efficient data structures for window-based aggregation
- **Logic**: Takes the daily active user data and initializes HyperLogLog data structures for each day, using precision 15 for accuracy. Groups data by timestamp, timestamp grain, chain, user type, and product dimensions. HyperLogLog structures enable efficient approximate distinct counting when merging data across time windows
- **Key fields**: `timestamp` for daily grouping, `user_dau` containing distinct user addresses, `hll` as the HyperLogLog data structure

### Component C: Datespine and window creation
- **Purpose**: Creates a complete time series and establishes 7-day rolling windows
- **Logic**: Generates a continuous datespine covering all days from the minimum DAU timestamp to the current date minus one day, ensuring no gaps in the time series. For each day in the datespine, creates a 7-day window by generating timestamps for the current day plus the 6 preceding days. This creates joinable date pairs where each day maps to its 7-day lookback window
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 7-day window

### Component D: Window-based user aggregation
- **Purpose**: Combines daily HLL structures across 7-day windows
- **Logic**: Joins the window pairs with daily HLL data to create records for each day within every 7-day window. This creates multiple records per final timestamp, where each record represents one day's contribution to that timestamp's weekly window. The HLL structures from different days can then be merged to count distinct users across the entire 7-day period
- **Key fields**: `timestamp` as window end date, `hll` containing daily user data, dimensional fields for grouping

### Component E: Dimensional breakdowns
- **Purpose**: Calculates WAU across different analytical dimensions
- **Logic**: Creates four separate dimensional views of the data: project-level (project_dbt_tag dimension with all other dimensions null), chain-level (chain dimension populated, others null), user-type-level (user_type dimension populated, others null), and product-level (product_id dimension populated, others null). For each dimension, merges HLL structures across the 7-day window and extracts the final distinct count
- **Key fields**: `dimensions` indicating the breakdown level, dimension-specific fields populated while others are null, `user_wau` as the distinct user count

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single dataset, maintaining the dimensional structure with appropriate null values for non-applicable dimensions
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users active in that day plus the 6 preceding days
- **Final calculation**: Applies the `finalize_metric_user_wau` macro to standardize the metric schema, handle missing days with zero-fill strategy, and format the output according to Token Terminal's metric standards. The final WAU value represents the approximate distinct count of user addresses that initiated at least one transaction within the 7-day window ending on each date

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

Unique revenue-generating users over a rolling 7-day window.

BerachainBERA

Active users (weekly)

Active users (weekly)

Competitive landscape