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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Blockchains (L2)

OP Mainnet

# OP Mainnet Active addresses (weekly) Methodology

## 1. Overview

The Optimism active addresses weekly metric measures the number of unique addresses that executed at least one transaction on the Optimism network during each 7-day period. This metric provides insight into weekly user engagement and adoption on the Optimism layer-2 network. The calculation uses a rolling 7-day window approach with HyperLogLog sketches for efficient approximate distinct counting of large-scale blockchain address data.

## 2. Data sources

The metric relies on the following raw data sources:
- **Optimism transaction records**: Complete transaction history from the Optimism blockchain including sender addresses, recipient addresses, transaction timestamps, gas usage data, and fee information
- **Chain availability configuration**: Token Terminal's internal chain availability filters to ensure data quality and proper coverage across supported blockchain networks

## 3. Step-by-step calculation

### Component A: Transaction data extraction
- **Purpose**: Extracts and standardizes all Optimism blockchain transactions for analysis
- **Logic**: Retrieves all transaction records from Optimism blockchain data, extracting key fields including sender address (`from_address`), recipient address (`to_address`), block timestamp, transaction hash, and gas-related metrics. Each transaction is classified with market sector as 'blockchains_l1' and business segment as 'blockchain'. The component also calculates layer-2 and layer-1 gas fees by converting gas amounts using appropriate decimal adjustments
- **Key fields**: `block_timestamp` for temporal analysis, `from_address` as the transaction sender identifier, `transaction_hash` for uniqueness, `gas_used_amount` and gas pricing data for fee calculations

### Component B: Daily active user identification
- **Purpose**: Identifies unique addresses that were active each day by sending transactions
- **Logic**: Groups transaction data by day using timestamp truncation and extracts distinct sender addresses (`from_address`) for each calendar day. Filters data to focus on transaction senders only, classifying them as 'Transaction sender' user type. Uses incremental processing to handle large transaction volumes efficiently by only processing recent data in subsequent runs
- **Key fields**: `timestamp` truncated to day boundaries, `user_dau` representing unique sender addresses, `chain` identifier, and `user_type` classification

### Component C: Weekly active user aggregation with HyperLogLog
- **Purpose**: Aggregates daily active users into weekly active users using efficient approximate distinct counting
- **Logic**: Creates HyperLogLog sketches for each day's unique addresses to enable scalable distinct counting. Constructs a date spine covering the full time range to ensure no missing days, then builds 7-day sliding windows by joining each date with the previous 6 days. For each 7-day window, merges the HyperLogLog sketches to count approximate distinct addresses across the week. Creates multiple dimensional breakdowns including chain-level aggregation and project tag-level aggregation
- **Key fields**: `timestamp` for the end of each 7-day window, `user_wau` containing the weekly active user count, `dimensions` indicating the aggregation level ('chain' or 'project_dbt_tag')

### Final aggregation: Active addresses transformation
- **Combining components**: Selects the weekly active user counts from the HyperLogLog aggregation component, filtering specifically for chain-level and project-level dimensional breakdowns. Maps the `user_wau` field directly to `active_addresses_weekly` since transaction senders represent active addresses
- **Currency conversion**: No currency conversion required as this metric counts addresses rather than monetary values
- **Time aggregation**: Uses 7-day rolling windows where each timestamp represents the end date of a weekly period, ensuring continuous weekly coverage without gaps
- **Final calculation**: The final weekly active addresses count is the distinct count of addresses that sent at least one transaction during each 7-day period, calculated using HyperLogLog merge operations across daily address sketches. Missing periods are filled with zero values to maintain consistent time series data.

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

Unique active addresses over a rolling 7-day window.

OP MainnetOP

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown