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 (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Optimism measures the count of unique addresses that have sent at least one transaction on the Optimism network within a rolling 30-day window. This metric provides insights into user engagement and adoption trends on the Optimism Layer 2 blockchain. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across different dimensions while maintaining accuracy for large datasets.

## 2. Data sources

- **Optimism blockchain transactions**: Raw transaction records from the Optimism network containing sender addresses, recipient addresses, transaction hashes, block timestamps, gas usage data, and fee information
- **HyperLogLog functions**: Probabilistic counting algorithms for efficient unique user aggregation across time windows
- **Date generation utilities**: System functions for creating complete date ranges and handling time-based calculations

## 3. Step-by-step calculation

### Component A: Daily active users extraction
- **Purpose**: Identify unique users who sent transactions each day on Optimism
- **Logic**: Extract distinct `from_address` values from Optimism transaction records, grouped by day. Each unique address that sent at least one transaction on a given day is counted as a daily active user. Users are classified with the type "Transaction sender" and categorized by chain, product dimensions, and business segments
- **Key fields**: `from_address` (transaction sender), `block_timestamp` (transaction time), `chain` (blockchain identifier), `product_id` (product classification)

### Component B: HyperLogLog initialization for daily data
- **Purpose**: Convert daily user sets into HyperLogLog data structures for efficient counting
- **Logic**: Transform each day's unique user addresses into HyperLogLog sketches using 15-bit precision. Group the HLL structures by timestamp, chain, user type, and product dimensions. This probabilistic approach enables efficient merging of user sets across time windows while maintaining high accuracy
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (daily granularity), `user_type` (transaction sender classification)

### Component C: Date spine and rolling window creation
- **Purpose**: Create complete time series and establish 30-day rolling windows for MAU calculation
- **Logic**: Generate a continuous date spine covering the full analysis period to ensure no missing days. For each date in the spine, create a 30-day window by joining with the previous 29 days plus the current day. This approach ensures MAU is calculated consistently even when certain days have no transaction activity
- **Key fields**: `timestamp` (reference date), `joinday` (each day within the 30-day window)

### Component D: Rolling window HLL aggregation
- **Purpose**: Combine daily HyperLogLog structures across 30-day windows
- **Logic**: Join the rolling windows with daily HLL data, matching each window day to its corresponding daily user data. Merge HLL sketches across all days within each 30-day window using HyperLogLog merge operations. This creates a single HLL structure representing all unique users active within the 30-day period
- **Key fields**: `hll` (merged HyperLogLog across window), dimensional grouping fields for aggregation levels

### Component E: Dimensional breakdown aggregation
- **Purpose**: Calculate MAU across different analytical dimensions
- **Logic**: Aggregate the windowed HLL structures across four distinct dimensions: project level (all Optimism data combined), chain level (by blockchain), user type level (by transaction sender classification), and product level (by product classification). For each dimension, merge the relevant HLL sketches and extract the final unique user count
- **Key fields**: `dimensions` (aggregation level), `chain_id`, `product_id`, `user_type`, `user_mau` (final count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing MAU for a specific date, dimension type, and dimension value combination
- **Currency conversion**: No currency conversion required as this is a user count metric
- **Time aggregation**: Daily MAU values calculated using rolling 30-day windows, with each day's MAU representing unique users from that day and the previous 29 days
- **Final calculation**: Extract unique user counts from merged HyperLogLog structures using the HLL extract function, apply schema standardization through the finalize metric macro, and fill any missing time periods with zero values to maintain complete time series

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

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

OP MainnetOP

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown