twitter

website

github

Blockchains (L2)

Nodle

# Nodle Active addresses (daily) Methodology

## 1. Overview

Daily active addresses measures the number of unique addresses that initiated at least one transaction on the Nodle blockchain within a 24-hour period. This metric tracks user engagement and network activity by counting distinct transaction senders per day. For blockchain networks like Nodle, active addresses are equivalent to daily active users (DAUs), as each unique address represents a user interacting with the network.

## 2. Data sources

- **Nodle blockchain extrinsics**: Transaction records from the Nodle blockchain containing transaction metadata, sender information, and block timestamps
- **Chain availability configuration**: Internal configuration data that defines valid data collection time windows for different blockchain networks

## 3. Step-by-step calculation

### Component A: User fact extraction
- **Purpose**: Extract and standardize user transaction data from raw Nodle blockchain extrinsics
- **Logic**: Pull all extrinsic (transaction) records from Nodle blockchain data. Extract the transaction sender ID as the user address and standardize the block timestamp. Each extrinsic represents a user interaction with the blockchain. Apply standard metadata classification including chain identifier, market sector, and business segment labels.
- **Key fields**: `block_timestamp` (when transaction occurred), `id` (transaction sender, becomes `user_address`), `extrinsic_id` (unique transaction identifier)

### Component B: Daily user activity calculation  
- **Purpose**: Calculate daily distinct active users from transaction-level data
- **Logic**: Group all user transactions by day using timestamp truncation to daily boundaries. For each day, identify all unique user addresses that initiated at least one transaction. This creates a daily snapshot of active users on the Nodle network. Apply incremental processing logic to efficiently handle new data by looking back from the maximum partition date.
- **Key fields**: `timestamp` (truncated to day), `user_address` (distinct transaction senders per day), `chain` (always 'nodle'), `user_type` (always 'Transaction sender')

### Component C: HyperLogLog aggregation
- **Purpose**: Efficiently aggregate distinct user counts across multiple dimensions using probabilistic data structures
- **Logic**: Convert daily user lists into HyperLogLog sketches with precision 15 for scalable distinct counting. Create separate aggregations by different dimensions: project level (overall Nodle metrics), chain level (Nodle chain specifically), user type level (transaction senders), and product level (currently null for Nodle). Use HLL merge operations to combine sketches and extract final distinct counts for each dimensional breakdown.
- **Key fields**: `user_dau` (distinct user count from HLL extraction), `dimensions` (aggregation level: 'project_dbt_tag', 'chain', 'user_type', 'product_id'), `hll` (HyperLogLog sketch for efficient merging)

### Final aggregation
- **Combining components**: Select the user DAU values from dimensional aggregations, specifically filtering for 'chain' and 'project_dbt_tag' dimensions which represent the primary active addresses metrics
- **Currency conversion**: Not applicable - this is a count-based metric measuring user activity rather than financial values  
- **Time aggregation**: Daily boundaries are established by truncating block timestamps to day level. Each day represents a complete 24-hour period of blockchain activity
- **Final calculation**: Rename `user_dau` field to `active_addresses_daily` to reflect blockchain context where active users are equivalent to active addresses. Apply the finalize macro to standardize schema, fill missing days with zero values, and apply chain availability filters to ensure data quality and completeness within valid time windows

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

Unique active addresses over a fixed 24-hour window.

NodleNODL

Archived

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown