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Blockchains (L2)

Nodle

# Nodle Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Nodle measures the count of unique user addresses that performed at least one transaction (extrinsic) on the Nodle parachain within any 30-day rolling period. This metric provides insights into user engagement and adoption trends for the Nodle blockchain network. The calculation uses HyperLogLog probabilistic data structures to efficiently handle large-scale unique user counting across multiple dimensional breakdowns including project-level, chain-level, user-type-level, and product-level aggregations.

## 2. Data sources

The MAU calculation relies on the following raw data sources:

- **Nodle blockchain extrinsics**: Core transaction records from the Nodle parachain containing sender addresses, timestamps, and transaction metadata. Each extrinsic represents a user interaction with the blockchain.
- **Nodle blockchain transaction logs**: Block-level timestamp data providing temporal context for user activities and enabling daily aggregation boundaries.
- **Chain availability metadata**: Data quality flags and availability windows used to filter out periods with incomplete or unreliable blockchain data.

## 3. Step-by-step calculation

### Component A: User fact extraction
- **Purpose**: Extracts and standardizes user activity data from raw Nodle blockchain extrinsics
- **Logic**: Processes raw Nodle extrinsic records to identify user addresses and transaction timestamps. Each extrinsic sender ID is treated as a unique user address. The component applies standardized classifications including chain identification as 'nodle', market sector as 'blockchains_l2', business segment as 'blockchain', and user type as 'Transaction sender'. Block timestamps are preserved for temporal analysis.
- **Key fields**: `block_timestamp` for temporal analysis, `extrinsic_id` as transaction identifier, `id` field converted to `user_address`, along with standardized dimension fields

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily unique active users from the standardized user activity data
- **Logic**: Groups user activity by truncated daily timestamps to count distinct user addresses per day. Applies incremental processing logic to efficiently update only recent time periods. Includes chain availability filtering to ensure data quality. Preserves dimensional attributes for downstream aggregation including chain, user type, and product identifiers.
- **Key fields**: `timestamp` truncated to day level, `user_address` for uniqueness counting, `chain`, `user_type`, `product_id` for dimensional analysis

### Component C: HyperLogLog sketch preparation
- **Purpose**: Converts daily user counts into HyperLogLog sketches for efficient 30-day window calculations  
- **Logic**: Initializes HyperLogLog sketches from daily active user data with precision parameter of 15 for optimal accuracy-memory tradeoff. Groups data by timestamp, chain, user type, and product dimensions. Creates probabilistic data structures that enable efficient merging across time windows while maintaining cardinality estimation accuracy.
- **Key fields**: `hll` sketch containing compressed user count data, dimensional grouping fields preserved from daily aggregation

### Component D: Date spine and rolling window construction
- **Purpose**: Creates comprehensive temporal framework for 30-day rolling window analysis
- **Logic**: Generates complete date spine covering the entire analysis period from minimum available data date to current date minus one day. For each date in the spine, creates associations with the 29 preceding days to form 30-day rolling windows. This approach ensures no missing dates are skipped and provides consistent window boundaries. Applies incremental processing boundaries when updating existing data.
- **Key fields**: `timestamp` as primary date, `joinday` representing each day within the 30-day window

### Component E: Rolling window HLL aggregation
- **Purpose**: Merges HyperLogLog sketches across 30-day rolling windows to count unique users
- **Logic**: Joins the date spine framework with daily HLL sketches to create comprehensive 30-day windows. For each target date, includes HLL sketches from that date and the 29 preceding days. Preserves dimensional attributes throughout the window construction process. Applies incremental processing filters with safety margins to ensure data completeness.
- **Key fields**: `timestamp` as window end date, `hll` sketches ready for merging, dimensional grouping fields

### Component F: Dimensional breakdown calculation
- **Purpose**: Creates multiple aggregation levels for comprehensive MAU analysis
- **Logic**: Generates four distinct dimensional views of MAU data. Project-level breakdown aggregates across all dimensions for total MAU. Chain-level breakdown groups by chain identifier. User-type-level breakdown segments by user classification. Product-level breakdown analyzes by product identifier. Each breakdown merges HLL sketches within its dimensional grouping and extracts final cardinality estimates. Non-applicable dimensions are set to null for each breakdown type.
- **Key fields**: `dimensions` field identifying breakdown type, `user_mau` containing final unique user count, relevant dimensional identifiers

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple aggregation levels in a single table structure
- **HLL processing**: HyperLogLog sketches are merged using `hll_count.merge_partial()` function within dimensional groups, then extracted to final cardinality estimates using `hll_count.extract()` function
- **Time aggregation**: Rolling 30-day windows ensure consistent temporal boundaries with daily granularity for the final timestamp
- **Final calculation**: The `finalize_metric_user_mau` macro applies standardization including schema validation, missing value handling with zero-fill strategy, and ensures consistent output format across all dimensional breakdowns

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

Unique revenue-generating users over a rolling 30-day window.

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Archived

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown