opBNB is built on the OP Stack, the same underlying framework as Optimism. It extends the Ethereum Virtual Machine (EVM) to provide faster and cheaper transactions, while leveraging BNB Smart Chain (BSC) for ecosystem integration.

opBNB is an Ethereum-compatible Layer 2 scaling solution designed to improve the performance and affordability of decentralized applications on BNB Chain. It provides high throughput and low fees, making it suitable for gaming, social, and DeFi applications.

opBNB is developed by BNB Chain, which is supported by contributors from the broader Binance ecosystem.

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Blockchains (L2)

opBNB

# opBNB Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique addresses that initiated at least one transaction on the opBNB blockchain within any rolling 30-day period. This metric provides insight into opBNB's user engagement and adoption trends over time. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across overlapping time windows, enabling accurate MAU computation while handling large datasets efficiently.

## 2. Data sources

The calculation relies on the following raw data sources:

- **opBNB transaction data**: Complete transaction records from the opBNB blockchain including transaction metadata, sender addresses, and execution details
- **Block timestamps**: Precise timing information for when each transaction was included in a block
- **Transaction hashes**: Unique identifiers for each transaction on the opBNB network
- **Sender addresses**: The `from_address` field identifying which address initiated each transaction

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique active users per day from transaction activity
- **Logic**: Extract all transaction sender addresses from opBNB blockchain transaction data. Group transactions by day using block timestamp truncated to daily granularity. Count each unique sender address (`from_address`) as an active user for that day. Classify all users as "Transaction sender" type since opBNB focuses on transaction-based activity. Set the chain identifier as "opbnb", market sector as "blockchains_l2", and business segment as "blockchain". Product identifier remains null as opBNB is treated as a single unified product.
- **Key fields**: `block_timestamp` (truncated to day), `from_address` (user identifier), `user_type` (set to "Transaction sender"), `chain` (set to "opbnb")

### Component B: HyperLogLog conversion for efficient unique counting
- **Purpose**: Convert daily user lists into probabilistic data structures for efficient aggregation
- **Logic**: Transform each day's list of unique user addresses into HyperLogLog (HLL) sketches using precision level 15. This probabilistic approach enables efficient merging of user sets across time periods while maintaining acceptable accuracy for unique count estimation. Each daily HLL sketch represents the set of unique users active on that specific day.
- **Key fields**: `hll` (HyperLogLog sketch containing daily unique users), `timestamp` (day identifier), `chain_id`, `user_type`, `product_id`

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a continuous date spine covering the full analysis period to ensure no days are skipped. For each day in the date spine, create associations with that day and the previous 29 days (forming 30-day windows). This cross-join operation between the date spine and 30-day lookback periods establishes all the day-to-window relationships needed for rolling MAU calculation.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within each window)

### Component D: Window-based HLL aggregation  
- **Purpose**: Merge daily HLL sketches within each 30-day window
- **Logic**: For each 30-day window, collect all the daily HLL sketches that fall within that window period. Join the rolling window structure with the daily HLL data to match each window with its constituent daily sketches. This creates a dataset where each record represents one daily HLL sketch within a specific 30-day window.
- **Key fields**: `timestamp` (window date), `hll` (daily sketch to be merged), dimensional groupings (`chain_id`, `user_type`, `product_id`)

### Component E: Dimensional breakdowns
- **Purpose**: Calculate MAU across different analytical dimensions
- **Logic**: Create separate MAU calculations for four different dimensional breakdowns. For project-level MAU, merge all HLL sketches across all dimensions within each 30-day window. For chain-level MAU, group by chain identifier before merging sketches. For user-type-level MAU, group by user type classification before merging. For product-level MAU, group by product identifier before merging. Each breakdown uses HLL merge operations to combine sketches and extract final unique user counts.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id` (grouping fields), `user_mau` (extracted unique count)

### Final aggregation
- **Combining components**: Union all four dimensional breakdowns (project, chain, user type, product) into a single result set. Each record represents MAU for a specific combination of date, dimension type, and dimension values
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: MAU is calculated daily using trailing 30-day windows. Each day represents the unique user count for that day plus the previous 29 days
- **Final calculation**: Extract unique user counts from merged HLL sketches using the HyperLogLog extract function. Apply the `finalize_metric_user_mau` macro to standardize output format, handle missing dates with zero-filling, and add metadata fields including data source identifier ("opbnb") and metric classification

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

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

opBNB

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown