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 addresses (monthly) Methodology

## 1. Overview

BSC Active Addresses Monthly measures the number of unique wallet addresses that initiated at least one transaction within a 30-day rolling period across the BSC ecosystem. This metric combines activity from both BSC mainnet and its associated opBNB Layer 2 scaling solution to provide a comprehensive view of unique user engagement. The calculation uses a rolling 30-day window approach with HyperLogLog data structures to efficiently handle large-scale unique address counting across the multi-chain BSC ecosystem.

## 2. Data sources

- **BSC mainnet transactions**: Native BSC blockchain transaction records containing transaction sender addresses, timestamps, and transaction metadata
- **opBNB L2 transactions**: Layer 2 scaling solution transaction records that are attributed to the BSC ecosystem, including sender addresses and block timestamps
- **Transaction metadata**: Additional transaction details including block numbers, transaction hashes, and gas information for context and validation

## 3. Step-by-step calculation

### Component A: Daily active addresses extraction
- **Purpose**: Identifies unique addresses that sent transactions each day across BSC mainnet and opBNB L2
- **Logic**: Extracts all transaction sender addresses from BSC mainnet transactions and opBNB L2 transactions. Truncates block timestamps to day boundaries to group transactions by date. Combines both data sources using union to create a comprehensive set of daily active addresses. All addresses are classified as 'Transaction sender' type users to distinguish from other potential user types like contract deployers or token recipients
- **Key fields**: `from_address` as the unique address identifier, `block_timestamp` truncated to day level, `chain` identifier distinguishing BSC from opBNB, `user_type` set to 'Transaction sender'

### Component B: HyperLogLog sketch creation
- **Purpose**: Creates efficient data structures for counting unique addresses across large datasets
- **Logic**: Converts daily sets of unique addresses into HyperLogLog sketches with precision parameter 15. Groups addresses by timestamp, chain, user type, and product identifier. Uses HLL initialization to create compact probabilistic data structures that enable efficient unique counting operations across rolling time windows
- **Key fields**: `hll` sketches containing compressed unique address information, dimensional grouping fields for aggregation

### Component C: Rolling window construction
- **Purpose**: Creates 30-day sliding windows for monthly active address calculation
- **Logic**: Generates a continuous datespine covering all days from the earliest transaction date to the current day minus one. For each date in the datespine, creates joins with the previous 29 days to establish 30-day rolling periods. This ensures consistent time windows even when certain days have no transaction activity. Joins the datespine with daily HLL sketches to create windowed datasets
- **Key fields**: `timestamp` for the end date of each 30-day window, `joinday` representing individual days within each window

### Component D: Multi-dimensional aggregation
- **Purpose**: Calculates monthly active addresses across different analytical dimensions
- **Logic**: Processes the windowed data to create four different aggregation levels. For project-level analysis, merges all HLL sketches regardless of chain or user type. For chain-level analysis, groups by individual chain identifiers while merging across other dimensions. For user-type analysis, aggregates by transaction sender classification. For product-level analysis, groups by BSC product identifier. Uses HLL merge operations to combine sketches while preserving unique counting accuracy
- **Key fields**: `dimensions` indicating the aggregation level, `chain_id` for chain-specific analysis, `user_type` for user classification, `product_id` for product-specific metrics

### Final aggregation
- **Combining components**: Unions the four dimensional aggregations (project_dbt_tag, chain, user_type, product_id) into a single result set containing all analytical perspectives
- **Currency conversion**: Not applicable for address counting metrics
- **Time aggregation**: Uses 30-day rolling windows where each day represents the count of unique addresses active in that day and the previous 29 days
- **Final calculation**: Extracts integer counts from merged HyperLogLog sketches using the extract function. Filters results to include only 'chain' and 'project_dbt_tag' dimensions. Renames the `user_mau` field to `active_addresses_monthly` to reflect the metric's purpose. Applies chain availability filters to ensure data quality and applies the finalize metric macro with zero-fill strategy for missing dates

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

Unique addresses active over a rolling 30-day window.

BNB ChainBNB

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown