OP Mainnet batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

OP Mainnet is an L2 smart contract platform for decentralized applications on Ethereum. OP Mainnet was founded by Jinglan Wang, Karl Floersch & Kevin Ho and launched in 2021.

Optimism team & investors own and govern the protocol. Optimism’s investors include Paradigm, a16z, IDEO CoLab Ventures, etc.

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OP Mainnet

# OP Mainnet Active users (weekly) Methodology

## 1. Overview

Weekly active users (WAU) for Optimism measures the count of unique addresses that sent at least one transaction on the Optimism blockchain within any 7-day rolling window. This metric provides insight into user engagement and adoption trends by tracking unique transaction senders across weekly periods. The calculation uses HyperLogLog data structures to efficiently count unique users across overlapping time windows without double-counting.

## 2. Data sources

The weekly active users metric relies on the following raw data sources:

- **Optimism blockchain transactions**: Complete transaction records from the Optimism blockchain including transaction hashes, sender addresses, recipient addresses, timestamps, and gas usage information
- **Token metadata**: WETH contract address information (`0x4200000000000000000000000000000000000006`) for standardizing gas fee token references
- **Chain availability data**: Configuration data used for data quality filtering and chain-specific processing rules

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identifies unique transaction senders on a daily basis to establish the foundation for weekly aggregation
- **Logic**: Extracts all unique sender addresses (`from_address`) from Optimism blockchain transactions, grouping by calendar day using the transaction timestamp. Each unique sender address that appears in at least one transaction on a given day is counted as a daily active user. All identified users are classified with the user type 'Transaction sender' since they represent addresses that initiated blockchain transactions.
- **Key fields**: `block_timestamp` truncated to day level for temporal grouping, `from_address` as the user identifier, `chain` set to 'optimism', `user_type` standardized as 'Transaction sender'

### Component B: HyperLogLog conversion and datespine creation
- **Purpose**: Converts daily user sets into efficient HyperLogLog data structures and creates a continuous date spine to handle missing dates
- **Logic**: Transforms the daily active user sets into HyperLogLog structures using a precision of 15 bits for memory-efficient set operations. Simultaneously creates a comprehensive datespine covering the full date range from the earliest available transaction data to the previous day, generating daily timestamps to ensure no dates are missing from the analysis window.
- **Key fields**: `hll` structures containing compressed user sets, continuous `timestamp` series covering the complete analysis period

### Component C: Rolling window construction
- **Purpose**: Builds 7-day rolling windows for each date to calculate weekly active users
- **Logic**: For each date in the datespine, generates a 7-day lookback window that includes the current day plus the 6 preceding days. This creates overlapping weekly windows where each day serves as the end date for its own 7-day period. The window construction uses timestamp array generation to create joinable date ranges that can be matched against the daily HyperLogLog data.
- **Key fields**: `timestamp` as the window end date, `joinday` representing each of the 7 days within the rolling window

### Component D: HyperLogLog merging within windows
- **Purpose**: Combines daily user sets within each 7-day window to count unique users without duplication
- **Logic**: Joins the rolling window dates with the daily HyperLogLog user data, then merges the HLL structures for all days within each 7-day window. The HLL merge operation efficiently combines user sets while automatically handling deduplication of users who were active on multiple days within the same week. This produces a single HLL structure for each weekly window containing all unique users.
- **Key fields**: Merged `hll` structures containing unique users across the 7-day period, maintaining dimension attributes like `chain_id`, `user_type`, and `product_id`

### Component E: Dimensional aggregation
- **Purpose**: Calculates weekly active users across different analytical dimensions for comprehensive reporting
- **Logic**: Processes the merged weekly HLL data through four separate aggregation paths. First aggregates at the project level (project_dbt_tag) combining all chains and user types. Second aggregates by individual chain while combining user types. Third aggregates by user type across all chains. Fourth aggregates by product identifier across chains and user types. Each aggregation uses HLL partial merge functions to combine relevant data subsets.
- **Key fields**: `dimensions` field identifying the aggregation level, dimension-specific identifiers like `chain_id` or `user_type`, merged `hll` structures for each dimensional combination

### Final aggregation
- **Combining components**: The four dimensional aggregations are combined using UNION ALL operations to create a comprehensive dataset containing weekly active user counts for all analytical dimensions in a single unified structure
- **Currency conversion**: Not applicable for user count metrics as no monetary values are involved in the calculation
- **Time aggregation**: Uses 7-day rolling windows where each day represents the end date of a weekly period, ensuring daily granularity of weekly metric reporting with complete overlap between consecutive periods
- **Final calculation**: Extracts the final unique user counts from the merged HyperLogLog structures using the HLL extract function, which provides the estimated count of distinct users within each weekly window. The finalize_metric_user_wau macro applies final formatting, standardizes the schema structure, and fills missing dates with zero values to ensure continuous time series reporting.

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

Unique revenue-generating users over a rolling 7-day window.

OP MainnetOP

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown