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

Scroll is an L2 smart contract platform for decentralized applications on Ethereum. Scroll was founded by Sandy Peng, Haichen Shen, and Ye Zhang and launched in 2023.

SCR tokenholders own and govern the protocol. Scroll's investors include Polychain, Qiming Venture Partners, etc.

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Blockchains (L2)

Scroll

# Scroll Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique addresses that initiated at least one transaction on the Scroll blockchain within a 30-day rolling window. This metric provides insight into sustained user engagement and adoption trends for the Scroll Layer 2 network. The calculation uses HyperLogLog probabilistic counting to efficiently aggregate daily active users into monthly totals while maintaining accuracy for large user bases.

## 2. Data sources

The calculation relies on the following data sources:

- **Scroll transaction records**: Complete transaction data from the Scroll blockchain, including transaction timestamps, transaction hashes, and sender addresses (`from_address`)
- **Chain availability metadata**: Information about data availability and quality for different blockchain networks to ensure reliable metric calculation
- **Incremental processing metadata**: System metadata for tracking data freshness and managing efficient incremental updates

## 3. Step-by-step calculation

### Component A: User extraction from transactions
- **Purpose**: Identifies unique user addresses from Scroll blockchain transactions and establishes user classification
- **Logic**: Extracts all transaction records from Scroll blockchain data, casting the sender address (`from_address`) as the user identifier. Each transaction sender is classified with the user type "Transaction sender". Additional metadata is applied including chain identifier ("scroll"), market sector ("blockchains_l2"), and business segment ("blockchain"). The `product_id` field is set to null as Scroll operates as a unified Layer 2 platform
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for uniqueness, `from_address` as the user identifier, and classification fields for dimensional analysis

### Component B: Daily active user calculation  
- **Purpose**: Calculates the count of distinct active users per day from the extracted transaction data
- **Logic**: Groups transaction data by day using timestamp truncation to daily boundaries. For each day, counts distinct user addresses that initiated at least one transaction. Maintains the user type classification and other dimensional attributes. Uses incremental processing to efficiently update only recent days when new data arrives
- **Key fields**: `timestamp` truncated to day level, `user_address` for distinct counting, `chain` and `user_type` for dimensional breakdowns, and `timestamp_grain` set to "day"

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily active user counts into HyperLogLog sketches to enable efficient aggregation into monthly totals
- **Logic**: Initializes HyperLogLog sketches for each day's distinct user set using a precision parameter of 15 bits. Groups data by timestamp, chain, user type, and product dimensions. These probabilistic data structures allow for accurate distinct counting when merging multiple days of data without the computational overhead of exact distinct operations
- **Key fields**: `hll` containing the HyperLogLog sketch, dimensional grouping fields, and daily timestamps

### Component D: Datespine and rolling window creation
- **Purpose**: Ensures complete time coverage and creates 30-day rolling windows for each target date
- **Logic**: Generates a complete datespine covering all dates from the earliest available data to the current date. For each date in the datespine, creates a 30-day lookback window by generating an array of dates from 29 days prior through the target date. Joins this expanded datespine with the daily HyperLogLog data to create a complete dataset for rolling window aggregation, ensuring no dates are skipped even when no transactions occurred
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 30-day window, and all dimensional fields carried forward from the daily data

### Component E: Dimensional aggregations
- **Purpose**: Calculates MAU across different dimensional breakdowns to support various analytical perspectives  
- **Logic**: Creates four separate dimensional views by aggregating the HyperLogLog sketches: project-level (all data combined), chain-level (maintaining chain dimension), user type-level (maintaining user type classification), and product-level (maintaining product dimension). For each dimensional breakdown, merges the HyperLogLog sketches across the 30-day window and extracts the final distinct count estimate
- **Key fields**: `dimensions` field indicating the breakdown type, relevant dimensional fields maintained or nulled as appropriate, and `user_mau` containing the final monthly active user count

### Final aggregation
- **Combining components**: Unions all dimensional breakdown results into a single output table with consistent schema, allowing for flexible querying across different analytical dimensions
- **Time aggregation**: Uses 30-day rolling windows calculated from daily boundaries, ensuring consistent monthly periods regardless of calendar month variations
- **Incremental processing**: Implements efficient updates by processing only recent time periods with appropriate safety margins, using lookback periods to handle late-arriving data
- **Final calculation**: The monthly active user count represents the estimated number of distinct addresses that initiated at least one transaction within any 30-day period ending on the specified date, calculated using HyperLogLog probabilistic counting for computational efficiency

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

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

ScrollSCR

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown