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 (weekly) Methodology

## 1. Overview

Weekly active users (WAU) for Conflux measures the number of unique addresses that sent at least one transaction in a rolling 7-day period. This metric tracks user engagement on the Conflux blockchain by identifying transaction senders as active users. The calculation uses HyperLogLog data structures for efficient cardinality estimation and provides breakdowns across multiple dimensions including project level, chain level, user type, and product level.

## 2. Data sources

- **Conflux transaction records**: Raw Conflux blockchain transaction data containing sender addresses, transaction hashes, and block timestamps
- **Transaction metadata**: Block timestamps for temporal aggregation and transaction identification
- **User classification data**: Transaction sender addresses classified as active users with user type categorization

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identifies unique user addresses from Conflux blockchain transactions
- **Logic**: Extracts sender addresses from raw Conflux transaction records, treating each `sender` address as a user identifier. Each transaction provides a `block_timestamp` for temporal analysis and `transaction_hash` for uniqueness. Users are classified with the type `Transaction sender` and associated with the Conflux chain and blockchain business segment
- **Key fields**: `sender` address as `user_address`, `block_timestamp` for timing, `transaction_hash` for transaction identification

### Component B: Daily active user aggregation
- **Purpose**: Aggregates unique users to daily level using HyperLogLog sketches
- **Logic**: Groups user addresses by day using timestamp truncation to daily boundaries. Creates HyperLogLog sketches initialized with 15-bit precision to efficiently estimate unique user counts per day. Each daily record includes chain identifier, user type classification, and product categorization for dimensional analysis
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, HyperLogLog sketches for cardinality estimation

### Component C: Date spine creation
- **Purpose**: Generates continuous daily timestamp sequence to ensure complete time series
- **Logic**: Creates a complete sequence of daily timestamps from the minimum date in the daily active user data through the current date minus one day. This ensures no missing dates in the final output and provides the foundation for rolling window calculations
- **Key fields**: `timestamp` for each day in the complete time range

### Component D: Rolling window construction  
- **Purpose**: Creates 7-day rolling windows for weekly active user calculation
- **Logic**: For each day in the date spine, generates a 7-day window including that day plus the previous 6 days using date arithmetic. Creates join relationships between each target date and all dates within its 7-day window. This enables aggregation of users across the rolling weekly period
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 7-day window

### Component E: Weekly active user calculation
- **Purpose**: Aggregates unique users across 7-day rolling windows using HyperLogLog merging
- **Logic**: Joins the rolling window structure with daily HyperLogLog sketches. For each 7-day window, merges HyperLogLog sketches from all days in the window to count unique users who were active at least once during the period. Uses HyperLogLog merge operations to avoid double-counting users active on multiple days
- **Key fields**: Merged HyperLogLog sketches representing unique users across the 7-day period

### Final aggregation
- **Combining components**: Creates four separate dimensional breakdowns by grouping and merging HyperLogLog sketches - project level (all dimensions aggregated), chain level (by `chain_id`), user type level (by `user_type`), and product level (by `product_id`). Each breakdown nullifies non-relevant dimensions
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamp boundaries with 7-day rolling windows, ensuring each day represents unique users active in that day plus the previous 6 days
- **Final calculation**: Extracts final user counts from merged HyperLogLog sketches using `hll_count.extract()` function, applies standard metric finalization with zero-filling for missing dates, and combines all dimensional breakdowns into a unified output table

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

Unique revenue-generating users over a rolling 7-day window.

Conflux Core SpaceCFX

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape