Validators process transactions and receive fees from users (supply-side fees).

BNB Chain is a smart contract platform for decentralized applications (a blockchain). BNB Chain was founded by Changpeng Zhao and launched in 2020.

BNB holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue).

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Blockchains (L1)

BNB Chain

# BNB Chain Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for BSC measures the number of unique addresses that initiated at least one transaction within any 30-day rolling period across the BSC ecosystem. This metric encompasses both BSC Layer 1 (mainnet) and OpBNB Layer 2 users, providing a comprehensive view of blockchain activity. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across rolling 30-day windows while maintaining accuracy at scale.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:
- **BSC transaction records**: Complete transaction history from Binance Smart Chain Layer 1, capturing transaction senders (`from_address`), timestamps, and transaction metadata
- **OpBNB transaction records**: Complete transaction history from OpBNB Layer 2 (BSC's optimistic rollup), capturing transaction senders (`from_address`), timestamps, and transaction metadata

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identifies unique user addresses that sent transactions each day across BSC L1 and OpBNB L2
- **Logic**: Extracts `from_address` values from BSC mainnet transactions and OpBNB Layer 2 transactions, truncating `block_timestamp` to daily boundaries. Users are deduplicated within each day and labeled as "Transaction sender" user type. Both chains contribute to the same 'bsc' product ecosystem
- **Key fields**: `from_address` as `user_dau`, `block_timestamp` truncated to day granularity, `chain` identifier, `user_type` as "Transaction sender"

### Component B: HyperLogLog initialization  
- **Purpose**: Converts daily unique user lists into HyperLogLog data structures for efficient set operations
- **Logic**: Takes the daily unique users and initializes HLL sketches with precision parameter 15, enabling memory-efficient storage and merging of large user sets while maintaining counting accuracy
- **Key fields**: `hll` structures containing encoded user address sets, grouped by `timestamp`, `chain`, `user_type`, and `product_id`

### Component C: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for each date to calculate MAU
- **Logic**: Generates a complete datespine covering all dates from the earliest data to yesterday, then creates join relationships where each date connects to its current day plus the preceding 29 days. This ensures every date has access to exactly 30 days of user data for MAU calculation
- **Key fields**: `timestamp` for the MAU calculation date, `joinday` representing each day within the 30-day window

### Component D: HLL aggregation and merging
- **Purpose**: Combines HLL structures across 30-day windows to count unique users
- **Logic**: Joins the rolling window structure with daily HLL data, then merges HLL sketches from all 30 days using `hll_count.merge_partial()`. This probabilistic approach efficiently counts unique users across the full window without storing individual addresses
- **Key fields**: Merged `hll` structures representing unique users over 30-day periods

### Component E: Dimensional breakdown calculation
- **Purpose**: Calculates MAU across multiple business dimensions
- **Logic**: Creates four separate dimensional views: overall project level (project_dbt_tag), by individual chain, by user type, and by product. Each dimension groups the merged HLL data appropriately and extracts final unique counts using `hll_count.extract()`. Unused dimension fields are set to NULL for each breakdown
- **Key fields**: `dimensions` indicating the breakdown type, `user_mau` containing the final unique user count, dimension-specific identifiers (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive view with multiple aggregation levels in a single result set
- **Currency conversion**: Not applicable - this metric counts users, not financial values
- **Time aggregation**: Each date represents a 30-day rolling window ending on that date, calculated daily to provide continuous MAU tracking
- **Final calculation**: The final `user_mau` value represents the estimated count of unique addresses that sent at least one transaction within the 30-day window ending on the given date, across the BSC ecosystem (BSC L1 + OpBNB L2)

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

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

BNB ChainBNB

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown