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Blockchains (L1)

Berachain

# Berachain Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have sent at least one transaction on the Berachain network within a rolling 30-day period. This metric provides insight into the regular user engagement and adoption of the Berachain blockchain. The calculation uses a rolling 30-day window approach with efficient HyperLogLog probabilistic data structures to handle large-scale address counting while maintaining accuracy.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Berachain transaction records**: Complete blockchain transaction data including transaction hashes, block timestamps, and sender addresses (`from_address`)
- **Chain availability configuration**: Data quality and availability windows for the Berachain network used for filtering

## 3. Step-by-step calculation

### Component A: User facts extraction
- **Purpose**: Identifies unique users from raw blockchain transaction data
- **Logic**: Extracts transaction sender addresses (`from_address`) from Berachain transaction records along with block timestamps and transaction hashes. Each transaction sender is classified as a user with user type "Transaction sender". Additional metadata is added including chain identifier ('berachain'), market sector ('blockchains_l1'), and business segment ('blockchain')
- **Key fields**: `block_timestamp`, `transaction_hash`, `from_address` (renamed to `user_address`), `chain`, `user_type`

### Component B: Daily active users calculation
- **Purpose**: Calculates the count of distinct active users per day
- **Logic**: Groups user activity by day using timestamp truncation to day boundaries. For each day, identifies all unique user addresses that sent transactions, creating distinct daily user records. Filters data based on incremental processing windows for efficient updates
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` ('day'), `user_address` (renamed to `user_dau`), `chain`, `user_type`, `product_id`

### Component C: Monthly active users window creation
- **Purpose**: Builds a rolling 30-day window for MAU calculation using probabilistic counting
- **Logic**: Creates HyperLogLog sketches for each day's distinct users to enable memory-efficient counting across rolling windows. Generates a complete date spine to ensure no days are missing in the 30-day rolling calculation. For each target date, creates join relationships with the previous 29 days to form the rolling window. Uses HyperLogLog initialization and merging to handle overlapping user counts across the rolling period
- **Key fields**: `timestamp`, `hll` (HyperLogLog sketch), `chain_id`, `user_type`, `product_id`

### Component D: Dimensional MAU breakdowns
- **Purpose**: Calculates MAU across multiple dimensional views
- **Logic**: Merges HyperLogLog sketches across the rolling 30-day window and extracts final counts for different dimensional breakdowns: chain-level (aggregated by `chain_id`), project-level (`dimensions` = 'project_dbt_tag'), user-type-level (aggregated by `user_type`), and product-level (aggregated by `product_id`). Each breakdown uses HyperLogLog merge operations to combine sketches and extract distinct user counts
- **Key fields**: `user_mau`, `dimensions`, `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Selects MAU values specifically for chain-level and project-level dimensions (where `dimensions` in ('chain', 'project_dbt_tag'))
- **Metric conversion**: Renames the `user_mau` field to `active_addresses_monthly` since blockchain users are identified by their unique addresses
- **Schema standardization**: Applies the `finalize_metric_active_addresses_monthly` macro to standardize output schema, fill missing periods with zeros, and ensure consistent formatting
- **Data quality filtering**: Applies chain availability filters to ensure only high-quality data periods are included in the final results
- **Final calculation**: The final metric represents the count of unique addresses that have sent at least one transaction within each rolling 30-day period, calculated using probabilistic HyperLogLog counting for scalability and accuracy

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

Unique addresses active over a rolling 30-day window.

BerachainBERA

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape