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Blockchains (L1)

Berachain

# Berachain Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Berachain measures the count of unique addresses that have sent at least one transaction on the Berachain network within a rolling 30-day window. This metric provides insight into user engagement and adoption trends by tracking unique user activity over time. The calculation uses HyperLogLog data structures to efficiently compute unique user counts across overlapping time windows and provides breakdowns across multiple dimensions including chain, user type, and product levels.

## 2. Data sources

- **Berachain transaction records**: Raw blockchain transaction data including transaction timestamps, transaction hashes, and sender addresses (`from_address`)
- **Chain availability configuration**: Metadata defining when Berachain data becomes available and reliable for metric calculations
- **Table modification timestamps**: Internal tracking data for incremental processing and determining data freshness

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all unique user addresses that have sent transactions on Berachain
- **Logic**: Extracts transaction data from Berachain blockchain records, focusing on the sender address (`from_address`) and block timestamp (`block_timestamp`) for each transaction. Each transaction sender is classified as a "Transaction sender" user type. The component applies chain availability filtering to ensure only reliable data periods are included. All users are categorized under the "blockchains_l1" market sector and "blockchain" business segment, with no specific product differentiation at this level.
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for transaction uniqueness, and standardized categorization fields

### Component B: Daily active user calculation
- **Purpose**: Aggregates unique users per day to create daily active user (DAU) counts
- **Logic**: Takes the user activity data and truncates timestamps to daily granularity using `timestamp_trunc`. For each day, deduplicates user addresses to count unique daily active users. The calculation preserves dimensional breakdowns by chain, user type, and product. Incremental processing applies a lookback period to ensure data completeness and handle late-arriving transactions. The output maintains user addresses as the `user_dau` field for subsequent HyperLogLog processing.
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing unique user addresses, dimensional fields for breakdown analysis

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user lists into HyperLogLog data structures for efficient unique counting
- **Logic**: Creates HyperLogLog sketches from the daily unique user addresses using `hll_count.init` with precision 15. This probabilistic data structure allows efficient merging of user counts across time windows without storing actual user addresses. The sketches are grouped by timestamp, chain, user type, and product to maintain dimensional granularity while enabling fast aggregation operations.
- **Key fields**: `hll` sketches containing encoded user uniqueness data, preserved dimensional grouping fields

### Component D: Rolling window generation
- **Purpose**: Creates a comprehensive datespine covering all required time periods and 30-day windows
- **Logic**: Generates a complete date range from the earliest available data to yesterday using `generate_timestamp_array`. For each date in this range, creates associations with the previous 29 days to form 30-day rolling windows. This ensures every day has a complete 30-day lookback period, even if some days had no transactions. The datespine prevents gaps in the time series that could cause missing MAU calculations.
- **Key fields**: `timestamp` for the calculation date, `joinday` for each day in the 30-day lookback window

### Component E: Window-based HLL merging
- **Purpose**: Combines daily HyperLogLog sketches across 30-day windows to calculate monthly active users
- **Logic**: Joins the rolling window datespine with the daily HLL sketches, matching each calculation date with its corresponding 30-day lookback period. For each day, collects all HLL sketches from the current day and previous 29 days. Applies incremental processing filters to optimize computation by only processing recent time periods when running incrementally.
- **Key fields**: Window-aligned `timestamp`, merged `hll` sketches ready for final extraction, preserved dimensional attributes

### Component F: Dimensional breakdown calculation
- **Purpose**: Calculates MAU across different dimensional hierarchies
- **Logic**: Processes the windowed HLL data through four separate dimensional breakdowns. Project-level MAU merges all sketches regardless of chain or product. Chain-level MAU groups by individual chains. User-type-level MAU groups by user classification. Product-level MAU groups by specific products. Each breakdown uses `hll_count.merge_partial` to combine sketches within the group, then `hll_count.extract` to get the final unique user count. Unused dimensional fields are set to null for each breakdown.
- **Key fields**: `user_mau` containing the final unique user count, `dimensions` indicating the breakdown type, relevant dimensional identifiers

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with MAU calculations across all dimensional hierarchies
- **Schema standardization**: The `finalize_metric_user_mau` macro applies standardized schema formatting, data type conversions, and fills missing time periods with zero values where appropriate
- **Incremental processing**: Final filtering ensures only recently modified or new data is processed during incremental updates, using a 29-day lookback period plus configurable additional days to account for late-arriving data
- **Final calculation**: Each row contains the unique user count for a specific date, dimensional breakdown, and 30-day rolling window, providing comprehensive MAU metrics across all required analytical dimensions

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

Unique revenue-generating users over a rolling 30-day window.

BerachainBERA

Active users (monthly)

Active users (monthly)

Competitive landscape