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Blockchains (L2)

Nodle

# Nodle Active users (weekly) Methodology

## 1. Overview

Weekly active users (WAU) measures the number of distinct users who performed at least one transaction on the Nodle blockchain within any 7-day rolling window. This metric uses HyperLogLog data structures to efficiently count distinct users across time periods and provides breakdowns by different dimensions including chain, user type, and product ID.

## 2. Data sources

- **Nodle blockchain extrinsics**: Raw transaction records from the Nodle blockchain containing block timestamps, extrinsic IDs, and sender addresses
- **Chain availability data**: Configuration data used to filter results based on data completeness and quality standards

## 3. Step-by-step calculation

### Component A: User transaction extraction
- **Purpose**: Extract and standardize user transaction data from raw Nodle blockchain records
- **Logic**: Raw extrinsic records are filtered and transformed to identify unique user addresses and their transaction timestamps. Each record represents a transaction sender on the Nodle blockchain. The data is classified with market sector as "blockchains_l2", business segment as "blockchain", and user type as "Transaction sender"
- **Key fields**: `block_timestamp` (transaction time), `id` (mapped to `user_address`), `extrinsic_id` (transaction identifier), with chain set to "nodle"

### Component B: Daily active user calculation
- **Purpose**: Calculate daily active users as the foundation for weekly aggregation
- **Logic**: Transaction data is grouped by day using timestamp truncation to day granularity. Distinct user addresses are identified for each day, creating a daily active user count. The data retains dimensional attributes including chain, user type, and product identifiers
- **Key fields**: `timestamp` (truncated to day), `user_address` (for distinct counting), `chain`, `user_type`, `product_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Create efficient data structures for distinct user counting across time windows
- **Logic**: Daily active user data is converted into HyperLogLog sketches using `hll_count.init()` with precision parameter 15. This allows for approximate but highly efficient distinct counting when merging multiple days. Sketches are grouped by timestamp, chain, user type, and product ID dimensions
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for each date to calculate weekly active users
- **Logic**: A complete date spine is generated covering the full data range to ensure no missing days. For each date in the spine, a 7-day window is created by joining with the 6 preceding days. This creates a table where each date is associated with all days in its 7-day window, enabling proper WAU calculation even with missing data
- **Key fields**: `timestamp` (target date), `joinday` (each day within the 7-day window)

### Component E: Weekly active user aggregation
- **Purpose**: Calculate weekly active users by merging HLL sketches within 7-day windows
- **Logic**: HyperLogLog sketches from each 7-day window are merged using `hll_count.merge_partial()` and then extracted to get the final distinct user count using `hll_count.extract()`. This provides an approximate but accurate count of unique users who were active within any 7-day period
- **Key fields**: `user_wau` (weekly active user count), dimensional attributes preserved from daily data

### Component F: Dimensional breakdowns
- **Purpose**: Provide multiple views of WAU data across different dimensions
- **Logic**: Four separate breakdowns are created: project-level (all dimensions set to null), chain-level (grouped by `chain_id`), user type-level (grouped by `user_type`), and product-level (grouped by `product_id`). Each breakdown maintains the same timestamp and WAU calculation but nullifies non-relevant dimensions using `safe_cast(null as string)`
- **Key fields**: `dimensions` (indicates breakdown type), relevant dimensional field retained, others set to null

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using `union all` to create a single table with multiple views of the same underlying WAU data
- **Time aggregation**: Data is calculated at daily granularity with 7-day rolling windows, maintaining day-level timestamp precision
- **Standardization**: The `finalize_metric_user_wau` macro applies final schema standardization, sets the data identifier as "nodle", and implements a fill strategy of "fill_zeros" for missing data points
- **Final calculation**: The metric represents the count of distinct users active within any 7-day rolling window ending on each date, calculated using HyperLogLog approximation for efficiency

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

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

NodleNODL

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown