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

Fraxtal is an Ethereum Layer 2 (L2) blockchain built on the OP Stack by Frax Finance. It uses frxETH as the gas token and features a blockspace incentive system called Flox. Fraxtal launched in February 2024.

Frax Finance team owns and governs the protocol. Frax Finance's investors include Dragonfly Capital, Mechanism Capital, Galaxy Digital, etc.

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Blockchains (L2)

Fraxtal

# Fraxtal Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Fraxtal measures the number of unique users who have sent at least one transaction on the Fraxtal blockchain within any 30-day rolling window. This metric uses a rolling window approach where each day's MAU represents the distinct count of users active in that day plus the previous 29 days. The calculation employs HyperLogLog data structures for memory-efficient distinct counting and provides breakdowns across multiple dimensions including project-level, chain-level, user type, and product categories.

## 2. Data sources

- **Fraxtal transaction records**: Raw blockchain transaction data from Fraxtal including transaction timestamps, transaction hashes, and sender addresses (`from_address`)
- **Chain availability metadata**: Data quality and availability information used to filter results during periods when chain data may be incomplete or unreliable

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify all user activity on Fraxtal by extracting transaction senders
- **Logic**: Extract all transaction records from Fraxtal blockchain data and identify each `from_address` as an active user. Each transaction creates a user activity record with the transaction timestamp, transaction hash, sender address, and metadata including chain identifier ('fraxtal'), market sector ('blockchains_l2'), business segment ('blockchain'), and user type ('Transaction sender'). Product ID is set to null as this is chain-level activity rather than application-specific activity.
- **Key fields**: `block_timestamp` for activity timing, `from_address` as the user identifier, `transaction_hash` for transaction reference, and classification metadata

### Component B: Daily active users calculation  
- **Purpose**: Aggregate user activity into daily distinct user counts
- **Logic**: Group user activity records by day using timestamp truncation and count distinct user addresses per day. This creates Daily Active User (DAU) records where each record represents one unique user active on a specific day. Apply chain availability filtering to exclude periods when data quality may be compromised. Use incremental processing to handle only new or changed data efficiently.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` for distinct counting, classification dimensions including `chain`, `user_type`, `product_id`

### Component C: HyperLogLog conversion and window preparation
- **Purpose**: Prepare DAU data for efficient distinct counting across rolling time windows
- **Logic**: Convert DAU data into HyperLogLog sketches using `hll_count.init` function with precision parameter 15 for memory-efficient distinct counting. Create a complete datespine covering all days from the earliest DAU record to the current date to ensure no missing days in the time series. Generate join tables that create 30-day rolling windows by pairing each date with its corresponding 30-day lookback period (current day plus previous 29 days).
- **Key fields**: `hll` sketches for distinct counting, `timestamp` for time boundaries, dimensional breakdowns preserved from DAU data

### Component D: Rolling window aggregation
- **Purpose**: Calculate distinct user counts across 30-day rolling windows for MAU
- **Logic**: Join the datespine with DAU HyperLogLog data using the rolling window join tables, matching each day in the 30-day window with corresponding DAU records. For each target date, collect all HyperLogLog sketches from the 30-day window and merge them using `hll_count.merge_partial` to combine distinct user counts across the period. Extract the final distinct count using `hll_count.extract` to get the MAU value for that date.
- **Key fields**: `timestamp` as the target date, merged `hll` sketches representing user activity across the 30-day window, dimensional metadata for breakdowns

### Component E: Dimensional breakdowns
- **Purpose**: Create MAU calculations across different analytical dimensions
- **Logic**: Generate four separate dimensional breakdowns from the rolling window data. Project-level breakdown aggregates all activity regardless of other dimensions. Chain-level breakdown groups by `chain_id`. User type breakdown groups by `user_type`. Product-level breakdown groups by `product_id`. For each breakdown, set unused dimension fields to null and perform HyperLogLog merge operations to calculate distinct user counts within each dimensional grouping.
- **Key fields**: `dimensions` field indicating the breakdown type, relevant dimensional fields populated while others set to null, `user_mau` as the final distinct user count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set with consistent schema structure
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses rolling 30-day windows where each day represents users active in that day plus the previous 29 days, ensuring complete time series coverage through datespine methodology  
- **Final calculation**: Apply the `finalize_metric_user_mau` macro to standardize the output schema, set the data identifier as 'fraxtal', and implement zero-filling strategy for missing periods to ensure consistent time series reporting

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

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

FraxtalFXTL

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown