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

Arbitrum One is an L2 smart contract platform for decentralized applications on Ethereum. Arbitrum was founded by Offchain Labs and launched in 2021.

ARB tokenholders own and govern the protocol. They currently run the only sequencer and take a cut of the total transaction fees paid by users (revenue). Arbitrum’s investors include Polychain, Alameda Research, Coinbase Ventures, etc.

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Arbitrum One

# Arbitrum One Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Arbitrum measures the approximate number of unique addresses that have sent at least one transaction on the Arbitrum network within any 30-day rolling window. This metric provides insight into user engagement and network adoption by tracking transaction-sending activity over monthly periods. The calculation uses HyperLogLog probabilistic counting for efficient aggregation of large user sets across rolling time windows.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Arbitrum blockchain transactions**: Complete transaction records from the Arbitrum network, including transaction metadata such as sender addresses, recipient addresses, block timestamps, gas prices, and gas usage amounts
- **Chain availability metadata**: Information about when Arbitrum network data became reliably available for analysis, used to filter out periods with incomplete data coverage

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identifies unique users (addresses) that sent transactions each day on Arbitrum
- **Logic**: Processes all Arbitrum blockchain transactions and extracts the `from_address` field as the transaction sender. Groups transactions by day using timestamp truncation to daily boundaries. Each unique `from_address` per day represents one Daily Active User (DAU). Users are classified with type 'Transaction sender' and associated with market sector 'blockchains_l2' and business segment 'blockchain'
- **Key fields**: `from_address` (user identifier), `block_timestamp` (truncated to day), `user_type` ('Transaction sender'), `chain` ('arbitrum')

### Component B: HyperLogLog sketch creation  
- **Purpose**: Converts daily user sets into probabilistic data structures for efficient counting
- **Logic**: Takes the daily active users and converts each day's user set into a HyperLogLog sketch using 15-bit precision. This allows for approximate but highly efficient counting of unique users when aggregating across multiple days
- **Key fields**: `hll` (HyperLogLog sketch of daily users), `timestamp` (daily boundary), `timestamp_grain` ('day')

### Component C: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for MAU calculation using a datespine approach
- **Logic**: Generates a complete sequence of calendar days from the earliest available data to yesterday. For each day, creates associations with the previous 29 days plus the current day (30 total days) using timestamp array generation. This ensures no gaps in the time series and provides the foundation for rolling window aggregation
- **Key fields**: `timestamp` (target date for MAU calculation), `joinday` (each day within the 30-day window)

### Component D: Window-based HLL aggregation
- **Purpose**: Collects HLL sketches for all days within each 30-day rolling window
- **Logic**: Joins the rolling window structure with the daily HLL sketches. For each target date, gathers the HLL sketches from all 30 days in the corresponding window. This creates a dataset where each row represents one day's HLL sketch within a specific 30-day MAU calculation window
- **Key fields**: `timestamp` (MAU calculation date), `hll` (individual daily HLL sketch), dimensional attributes

### Component E: Dimensional breakdowns
- **Purpose**: Creates MAU calculations across different analytical dimensions
- **Logic**: Groups the windowed HLL data by different dimensional combinations and merges the HLL sketches within each group. Generates four separate breakdowns: (1) Overall project level with no dimensional filtering, (2) By chain identifier, (3) By user type classification, (4) By product identifier. Each breakdown merges HLL sketches using partial merge functions and extracts the final unique count estimate
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_mau` (extracted count)

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using union operations to create a unified dataset with different analytical perspectives on the same underlying MAU data
- **Currency conversion**: Not applicable - this is a user count metric, not a financial metric
- **Time aggregation**: Uses 30-day rolling windows where each day's MAU represents unique users from that day plus the previous 29 days. No currency conversion needed
- **Final calculation**: The final MAU value for each day is the approximate unique count extracted from the merged HyperLogLog sketches of all daily user sets within the 30-day window. The `finalize_metric_user_mau` macro applies schema standardization and fills any missing dates with zero values to ensure complete time series coverage

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

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

Arbitrum OneARB

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown