Validators process transactions and receive fees from users (supply-side fees).

Flow is a smart contract platform for decentralized applications (a blockchain). Flow was founded by Roham Gharegozlou, Dieter Shirley and Mikhael Naayem and launched in 2019.

FLOW holders own and govern the protocol. They currently don’t take a cut of the total transaction fees paid by users (revenue). Flow’s investors include a16z, Coatue, Coinfund, Fabric Ventures, etc.

governance

github

twitter

discord

website

Blockchains (L1)

Flow

# Flow Active users (monthly) Methodology

## 1. Overview

Flow Monthly Active Users (MAU) measures the number of unique addresses that have been active on the Flow blockchain within a rolling 30-day window. This metric combines user activity from both Flow's native Cadence execution environment and its Ethereum-compatible Flow EVM environment to provide a comprehensive view of Flow ecosystem adoption. The calculation uses HyperLogLog probabilistic counting to efficiently aggregate daily active users into monthly unique counts across multiple dimensional breakdowns.

## 2. Data sources

The MAU calculation relies on the following raw data sources:

- **Flow blockchain transaction records**: Native Flow Cadence transaction data including payer addresses, proposal key addresses, gas limits, and fees
- **Flow EVM transaction data**: Ethereum-compatible transaction records on Flow's EVM environment with standard transaction fields including sender addresses
- **Flow collections data**: Transaction collection records containing authorizer address arrays that must be unnested to identify all transaction signers
- **Blockchain metadata**: Block timestamps, block numbers, and transaction hashes for temporal organization and deduplication

## 3. Step-by-step calculation

### Component A: Flow EVM user identification
- **Purpose**: Extract active user addresses from Flow's Ethereum-compatible execution environment
- **Logic**: Process Flow EVM transaction records to identify unique active addresses using the standard `from_address` field from each transaction. Each transaction sender is classified as a user with appropriate chain labeling as 'Flow EVM', market sector as 'blockchains_l1', business segment as 'blockchain', and product as 'Flow EVM'
- **Key fields**: Extract `block_timestamp`, `transaction_hash`, and `from_address` as the primary user identifier

### Component B: Flow user identification  
- **Purpose**: Extract active user addresses from Flow's native Cadence execution environment, accounting for multiple signer roles per transaction
- **Logic**: Process Flow Cadence transactions to identify three types of active addresses: payers who pay transaction fees using the `payer` field, proposers who submit transactions using the `proposal_key_address` field, and authorizers who approve transactions by unnesting the `authorizers` array from collections data. Each address type is treated as an active user with chain labeling as 'flow' and product as 'Flow'
- **Key fields**: Extract `payer`, `proposal_key_address`, and unnested `authorizers` addresses, along with `block_timestamp` and `transaction_hash` for each role

### Component C: Daily user aggregation
- **Purpose**: Combine Flow EVM and Flow users into unified daily active user counts
- **Logic**: Union all extracted user addresses from both execution environments, group by daily timestamp boundaries using `timestamp_trunc` to day granularity, and create distinct user sets for each combination of date, chain, user type, and product. This creates the foundation daily active user (DAU) dataset that feeds into monthly calculations
- **Key fields**: Aggregate `user_address` as `user_dau`, grouped by `timestamp`, `chain`, `user_type`, and `product_id`

### Component D: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into probabilistic data structures for efficient monthly aggregation
- **Logic**: Transform daily unique user sets into HyperLogLog sketches using `hll_count.init` with 15 buckets for approximate counting. This enables efficient merging of user sets across multiple days without storing individual addresses
- **Key fields**: Convert `user_dau` sets into `hll` sketches while preserving dimensional groupings

### Component E: Rolling window construction
- **Purpose**: Create 30-day rolling windows for each target date to support monthly active user calculation
- **Logic**: Generate a complete datespine covering all dates from the earliest data through the current date, then create join relationships where each target date connects to its preceding 29 days plus itself (30 total days). This ensures complete temporal coverage and handles missing data days appropriately
- **Key fields**: Create `timestamp` and `joinday` relationships spanning 30-day periods

### Final aggregation
- **Combining components**: Join the datespine with HyperLogLog sketches to create 30-day user activity windows, then merge HyperLogLog sketches across each window using `hll_count.merge_partial` to combine probabilistic user counts from multiple days
- **Time aggregation**: Use daily timestamp boundaries as the base unit, with 30-day rolling windows calculated from each day backwards to include the current day plus previous 29 days
- **Multi-dimensional calculation**: Generate MAU values across four dimensional breakdowns: project-level (overall), chain-level (Flow EVM vs flow), user-type-level, and product-level (Flow EVM vs Flow)
- **Final calculation**: Extract final unique user counts from merged HyperLogLog sketches using `hll_count.extract`, providing approximate monthly active user counts for each dimensional combination and time period

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

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

FlowFLOW

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown