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Blockchains (L1)

XDC Network

# XDC Network Active addresses (daily) Methodology

## 1. Overview

Daily active addresses for XDC Network represents the count of unique addresses that initiated at least one transaction on the XDC blockchain within a 24-hour period. This metric measures network usage by tracking distinct transaction senders, providing insight into the daily engagement level on the XDC Network. The calculation uses HyperLogLog algorithms for efficient approximate distinct counting across large datasets and supports multiple dimensional breakdowns.

## 2. Data sources

The calculation is built entirely from XDC Network's native blockchain data:
- **XDC transaction records**: Raw transaction data from the XDC blockchain including transaction hashes, block timestamps, sender addresses (`from_address`), and transaction metadata
- **XDC block data**: Block timestamp information used for daily time boundary determination

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identify all addresses that sent transactions on XDC Network
- **Logic**: Extract transaction sender addresses from XDC blockchain transaction records. Each transaction record provides the `from_address` field which represents the account that initiated the transaction. The system also captures the `block_timestamp` for temporal grouping and assigns metadata including chain identifier ('xdc'), market sector ('blockchains_l1'), business segment ('blockchain'), product identifier ('xdc'), and user type classification ('Transaction sender'). All sender addresses are treated as potential active users regardless of transaction success or failure.
- **Key fields**: `block_timestamp` for daily grouping, `from_address` as the user identifier, `transaction_hash` for transaction tracking, and classification metadata

### Component B: Daily user deduplication
- **Purpose**: Calculate unique daily users by removing duplicate addresses within each day
- **Logic**: Group all transaction records by day using timestamp truncation to day boundaries. Within each day, apply distinct counting to the `user_address` field (mapped from `from_address`) to ensure each address is counted only once per day regardless of how many transactions they sent. This creates a daily snapshot of unique active addresses with associated metadata including chain, user type, product identifier, and business classification.
- **Key fields**: `timestamp` (truncated to day), `user_address` (deduplicated), `chain`, `user_type`, `product_id`

### Component C: HyperLogLog aggregation setup
- **Purpose**: Convert daily distinct users into HyperLogLog sketches for efficient approximate counting
- **Logic**: Transform the daily distinct user counts into HyperLogLog data structures using 15-bit precision for scalable approximate distinct counting. Each day's unique addresses are converted into an HLL sketch using initialization functions. This approach enables efficient aggregation and merging of distinct counts across different time periods and dimensions without storing individual address lists.
- **Key fields**: HLL sketch representing daily unique users, maintaining timestamp, chain, user type, and product dimensions

### Component D: Multi-dimensional breakdown aggregation
- **Purpose**: Create aggregated views across different analytical dimensions
- **Logic**: Aggregate the HLL sketches across four key dimensions using HLL merging operations. Create separate breakdowns for project-level view (project_dbt_tag dimension with all addresses across XDC), chain-level view (preserving chain identifier), user-type-level view (grouping by transaction sender type), and product-level view (grouping by XDC product identifier). Each breakdown uses HLL merge operations to combine sketches and extract final distinct counts. The results are unioned together to create a comprehensive multi-dimensional dataset.
- **Key fields**: `dimensions` indicating breakdown type, `chain_id`, `product_id`, `user_type`, and `user_dau` (extracted HLL count)

### Final aggregation
- **Combining components**: The final model selects from the multi-dimensional user DAU aggregation, filtering specifically for 'chain' and 'project_dbt_tag' dimensions to focus on the relevant breakdowns for active addresses reporting
- **Currency conversion**: Not applicable - this metric counts addresses rather than monetary values
- **Time aggregation**: Daily boundaries are established using timestamp truncation to day granularity, with incremental processing using monthly partitions and configurable lookback periods for performance optimization
- **Final calculation**: The `user_dau` field is directly mapped to `active_addresses_daily`, representing the count of unique addresses that sent transactions each day. The finalization macro applies data quality checks, fills missing days with zeros using the fill_zeros strategy, and applies chain availability filtering to ensure consistent data coverage across the time series.

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

Unique active addresses over a fixed 24-hour window.

XDC NetworkXDC

Active addresses (daily)

Active addresses (daily)

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