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.

twitter

etherscan

website

Blockchains (L2)

opBNB

# opBNB Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have sent at least one transaction on the OpBNB network within a rolling 30-day period. This metric provides insight into user engagement and network activity by tracking distinct addresses that actively participate in transactions. For blockchain networks, active addresses serve as a proxy for active users, since each transaction originates from a unique address.

The calculation uses a rolling 30-day window approach with HyperLogLog probabilistic counting to efficiently estimate cardinality of unique addresses across the time period.

## 2. Data sources

- **OpBNB transaction records**: Raw blockchain transaction data from the OpBNB network containing transaction metadata, timestamps, and sender addresses
- **Transaction sender addresses**: The `from_address` field from OpBNB transactions, representing the originating address for each transaction

## 3. Step-by-step calculation

### Component A: User facts extraction
- **Purpose**: Extract and standardize user address data from raw OpBNB transactions
- **Logic**: Process raw OpBNB transaction records to identify transaction senders. Each transaction's `from_address` is classified as a user address with the user type "Transaction sender". The model adds metadata including chain identifier ('opbnb'), market sector ('blockchains_l2'), and business segment ('blockchain'). Timestamps are preserved from the original `block_timestamp` field.
- **Key fields**: `block_timestamp` for timing, `from_address` as the user address, `transaction_hash` for transaction identification, and classification metadata

### Component B: Daily active user calculation  
- **Purpose**: Aggregate unique user addresses per day to create daily active user counts
- **Logic**: Group transaction data by day using timestamp truncation to daily granularity. For each day, identify all distinct user addresses that sent transactions, creating a daily snapshot of active addresses. This creates the foundation dataset where each record represents a unique address active on a specific day.
- **Key fields**: `timestamp` truncated to day, `user_address` for the unique address, and dimensional metadata including `chain`, `user_type`, and `product_id`

### Component C: Rolling 30-day window creation
- **Purpose**: Create overlapping 30-day windows for each date to calculate rolling monthly active addresses
- **Logic**: Generate a complete datespine covering the analysis period, then for each date create a 30-day lookback window including the current day plus the previous 29 days. This windowing approach ensures that every day has a corresponding 30-day period for MAU calculation, accounting for days with no activity. Each window represents the addresses that could be considered "monthly active" for that specific date.
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 30-day window

### Component D: HyperLogLog aggregation
- **Purpose**: Efficiently count unique addresses across 30-day windows using probabilistic data structures
- **Logic**: Convert daily user address lists into HyperLogLog sketches with precision 15, which provides accurate cardinality estimation while being memory efficient. For each 30-day window, merge the HLL sketches from all days within the window using `hll_count.merge_partial()`. Extract the final unique address count using `hll_count.extract()`. This approach handles large numbers of addresses efficiently while maintaining accuracy.
- **Key fields**: `hll` sketches containing compressed address information, `user_mau` as the final unique address count per 30-day window

### Component E: Dimensional breakdown
- **Purpose**: Calculate monthly active addresses across different analytical dimensions
- **Logic**: Aggregate the HLL-based monthly active user counts by four different breakdowns: chain-level (OpBNB network overall), project-level (using dbt project tags), user-type-level (transaction senders), and product-level (if applicable). Each breakdown provides a different analytical perspective on the same underlying address activity data. Results are union-ed together with appropriate dimension labels.
- **Key fields**: `dimensions` field indicating the breakdown type, `user_mau` containing the unique address count for each dimension

### Final aggregation
- **Combining components**: The dimensional MAU results are filtered to include only 'chain' and 'project_dbt_tag' dimensions, representing network-level and project-level views of monthly active addresses
- **Metric renaming**: The `user_mau` field is renamed to `active_addresses_monthly` to reflect that for blockchain metrics, users are represented by their addresses
- **Time aggregation**: Monthly boundaries are maintained through the 30-day rolling window approach, with each day having its own monthly active address count based on the preceding 30-day period
- **Final calculation**: The final metric represents the count of unique addresses that sent at least one transaction within any rolling 30-day period ending on the specified date, calculated using HyperLogLog cardinality estimation for efficiency and accuracy

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

Unique addresses active over a rolling 30-day window.

opBNB

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown