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

Conflux is a smart contract platform for decentralized applications (a blockchain). Conflux was founded by David Chow, Fan Long, Ming Wu, and YuanJie Zhang and launched in 2020. Note that this dashboard only includes Conflux Core Space.

CFX holders own and govern the protocol. They currently do not take a cut of the total transaction fees paid by users (revenue).

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Conflux Core Space

# Conflux Core Space Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Conflux measures the number of unique addresses that sent at least one transaction on the Conflux blockchain within any rolling 30-day period. This metric provides insight into user engagement and adoption trends on the Conflux network. The calculation uses HyperLogLog data structures for efficient counting of unique users across large datasets and creates multiple dimensional breakdowns for comprehensive analysis.

## 2. Data sources

- **Conflux blockchain transactions**: Raw transaction records from the Conflux network containing transaction metadata, sender addresses, block timestamps, and transaction hashes
- **Chain availability metadata**: Data quality filters that ensure completeness and reliability of Conflux network data

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique transaction senders and aggregate them into daily active users
- **Logic**: Extract all transaction sender addresses from Conflux blockchain transaction records, truncate block timestamps to daily boundaries, and create distinct user counts per day. Each sender address represents a unique user who initiated at least one transaction on that day.
- **Key fields**: `block_timestamp` (transaction timing), `sender` (user address), `transaction_hash` (transaction identifier), with chain identifier set to conflux and user type classified as "Transaction sender"

### Component B: HyperLogLog sketch creation
- **Purpose**: Create efficient data structures for counting unique users across large datasets
- **Logic**: Convert daily user lists into HyperLogLog sketches with precision parameter 15, enabling approximate but highly accurate unique counting while reducing memory requirements. Group by timestamp, chain, user type, and product dimensions to maintain analytical granularity.
- **Key fields**: `timestamp` (daily boundary), `chain_id` (conflux), `user_type` (transaction sender classification), `product_id` (null for base conflux usage), `hll` (HyperLogLog sketch of users)

### Component C: Date spine and rolling window creation
- **Purpose**: Generate complete time series and create 30-day rolling windows for MAU calculation
- **Logic**: Create a continuous date spine from the earliest available data to the current date minus one day, ensuring no gaps in the time series. For each date in the spine, generate a 30-day lookback window by creating an array of the current day plus the previous 29 days. This creates overlapping windows where each day appears in 30 different MAU calculations.
- **Key fields**: `timestamp` (target date for MAU calculation), `joinday` (each of the 30 days included in the rolling window)

### Component D: Window-based user aggregation
- **Purpose**: Aggregate unique users within each 30-day rolling window
- **Logic**: Join the rolling windows with the daily HyperLogLog sketches, matching each window day with its corresponding daily user data. For each 30-day window, merge all HyperLogLog sketches to count unique users across the entire period, accounting for users who may have been active on multiple days within the window.
- **Key fields**: `timestamp` (MAU calculation date), `hll` (merged HyperLogLog sketch for the 30-day period)

### Component E: Dimensional breakdowns
- **Purpose**: Create multiple analytical views of MAU data for different use cases
- **Logic**: Generate four separate dimensional breakdowns from the windowed data: project-level aggregation (overall Conflux MAU), chain-level breakdown (conflux-specific), user type breakdown (transaction senders), and product-level breakdown (currently null for base usage). Each breakdown extracts unique user counts from the merged HyperLogLog sketches while setting irrelevant dimension fields to null.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type` (populated based on breakdown), `user_mau` (extracted unique user count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, creating a comprehensive view that supports analysis across multiple dimensions while avoiding double-counting users
- **Time aggregation**: Daily MAU calculations using rolling 30-day windows, where each day represents unique users active in that day plus the previous 29 days
- **Incremental processing**: Implements incremental updates with configurable lookback periods and safety margins to ensure data freshness while maintaining efficiency
- **Final calculation**: The MAU value represents the approximate count of unique addresses that sent at least one transaction within any 30-day rolling window, extracted from merged HyperLogLog sketches and standardized through the finalization macro

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

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

Conflux Core SpaceCFX

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape