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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Blockchains (L1)

Conflux Core Space

# Conflux Core Space Active addresses (daily) Methodology

## 1. Overview

Daily active addresses for Conflux measures the number of unique addresses that initiated at least one transaction on the Conflux blockchain per day. This metric serves as a fundamental indicator of network activity and user engagement. The calculation identifies unique transaction senders (addresses) within each 24-hour period, providing a count of distinct addresses that were actively transacting on the network.

## 2. Data sources

The calculation relies on the following data sources:
- **Conflux transaction records**: Raw transaction data from the Conflux blockchain containing transaction details including sender addresses, block timestamps, and transaction hashes
- **Chain availability configuration**: Data quality rules that determine when chain data is considered complete and reliable for metric calculations

## 3. Step-by-step calculation

### Component A: Transaction user extraction
- **Purpose**: Identifies all unique addresses that sent transactions on Conflux
- **Logic**: Extracts the `sender` address from each Conflux transaction record along with the `block_timestamp` and `transaction_hash`. Each sender address is classified as a "Transaction sender" user type and associated with the Conflux chain metadata including market sector classification as "blockchains_l1" and business segment as "blockchain"
- **Key fields**: `sender` (renamed to `user_address`), `block_timestamp`, `transaction_hash`, `chain`, `user_type`

### Component B: Daily user aggregation  
- **Purpose**: Groups transaction senders into daily time periods to create daily user records
- **Logic**: Truncates `block_timestamp` to day boundaries using day-level granularity, then creates distinct records for each unique `user_address` per day. This removes intraday duplicates so each address is counted only once per day regardless of how many transactions they sent
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain`, `user_address` (as `user_dau`), chain and user type metadata

### Component C: HyperLogLog sketch creation and aggregation
- **Purpose**: Efficiently counts unique users across multiple dimensions while supporting aggregation
- **Logic**: Initializes HyperLogLog sketches for each day using the `user_dau` addresses with a precision parameter of 15. Creates separate aggregations for different dimensional breakdowns including project-level (`project_dbt_tag`), chain-level (`chain`), user-type-level (`user_type`), and product-level (`product_id`). Merges HLL sketches within each dimension grouping and extracts the final unique user count using `hll_count.extract`
- **Key fields**: `hll` (HyperLogLog sketch), `user_dau` (unique user count), `dimensions`, `chain_id`, `product_id`, `user_type`

### Final aggregation
- **Combining components**: The HLL-aggregated user counts from different dimensions are combined using union operations to create a comprehensive dataset with multiple dimensional views of the same underlying user activity
- **Currency conversion**: No currency conversion required as this is a count-based metric
- **Time aggregation**: Data is aggregated at daily boundaries with timestamps truncated to day-level granularity
- **Final calculation**: The `user_dau` field is renamed to `active_addresses_daily` to reflect that for blockchain metrics, daily active users and daily active addresses represent the same concept - unique addresses that performed transactions. The finalize metric macro applies standardized schema formatting and fills missing days with zero values to ensure complete time series. Chain availability filters ensure only reliable, complete data periods are included in the final results

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

Unique active addresses over a fixed 24-hour window.

Conflux Core SpaceCFX

Archived

Active addresses (daily)

Active addresses (daily)

Competitive landscape