Validators process transactions and receive fees from users (supply-side fees).

Kusama is a security & interoperability platform for blockchains and applications. Kusama was founded by Gavin Wood and launched in 2019.

KSM holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue).

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Blockchains (L1)

Kusama

# Kusama Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique addresses that submitted at least one extrinsic (transaction) on the Kusama blockchain within any 7-day rolling window. This metric helps assess user engagement and activity patterns on the Kusama parachain ecosystem. The calculation uses HyperLogLog sketches for memory-efficient distinct counting and provides dimensional breakdowns across different user classifications.

## 2. Data sources

- **Kusama extrinsics data**: Raw transaction data from the Kusama blockchain including extrinsic IDs, block timestamps, and transaction details
- **Transaction sender addresses**: Unique addresses that initiated extrinsics on the Kusama network, representing active users
- **Blockchain timestamps**: Block-level timestamps from the Kusama network used for daily and weekly time window calculations

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique active users per day and creates the foundation for weekly calculations
- **Logic**: Extracts all extrinsic records from Kusama blockchain data and identifies unique sender addresses (`id` field) for each day. Truncates block timestamps to daily granularity to group transactions by day. Classifies all users as "Transaction sender" type and assigns chain identifier as "kusama" with market sector "blockchains_l2" and business segment "blockchain"
- **Key fields**: `block_timestamp` (truncated to day), `chain` identifier, `user_type` classification, `user_address` (sender addresses), `product_id` (null for Kusama)

### Component B: Daily active user calculation with HLL
- **Purpose**: Calculates daily active users (DAU) and initializes HyperLogLog sketches for efficient distinct counting
- **Logic**: Groups the user identification data by day, chain, user type, and product dimensions. Creates distinct counts of active users for each day and initializes HyperLogLog sketches with 15-bit precision to enable efficient merging for weekly calculations. Maintains dimensional attributes for subsequent breakdowns
- **Key fields**: `timestamp` (daily), `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch of daily active users)

### Component C: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for WAU calculation using a datespine approach
- **Logic**: Generates a complete datespine covering all days from the earliest available data to present. For each day in the datespine, creates join keys for the current day plus the 6 preceding days, forming 7-day rolling windows. This ensures no missing days affect the rolling window calculations and provides consistent WAU values even when daily activity is sparse
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 7-day window for joining)

### Component D: Window aggregation and HLL merging  
- **Purpose**: Combines daily HLL sketches across 7-day windows to calculate weekly active users
- **Logic**: Joins the rolling window framework with daily HLL data to collect all daily sketches within each 7-day period. Merges HyperLogLog sketches across the 7 days using HLL merge operations to create a consolidated sketch representing unique users across the entire week. Maintains dimensional attributes for breakdown calculations
- **Key fields**: `timestamp` (week ending date), dimensional attributes (`chain_id`, `user_type`, `product_id`), `hll` (merged weekly sketch)

### Component E: Dimensional breakdowns
- **Purpose**: Creates multiple views of WAU data across different dimensional cuts
- **Logic**: Generates four separate breakdowns of the weekly active user data: 1) Project-level aggregation (overall Kusama WAU), 2) Chain-level breakdown (by chain identifier), 3) User-type-level breakdown (by user classification), 4) Product-level breakdown (by product identifier). Each breakdown extracts final WAU counts from merged HLL sketches and sets non-relevant dimensions to null
- **Key fields**: `dimensions` (breakdown type), `user_wau` (extracted count from HLL), dimensional identifiers based on breakdown type

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set with consistent schema, allowing analysis across multiple dimensional cuts in one dataset
- **Currency conversion**: Not applicable - this is a user count metric without currency values
- **Time aggregation**: Uses daily timestamps with 7-day rolling windows, where each date represents the WAU ending on that day
- **Final calculation**: Extracts integer user counts from HyperLogLog sketches using `hll_count.extract()` function, applies zero-fill strategy for missing days using the `finalize_metric_user_wau` macro, and filters incremental updates to include appropriate lookback periods for data consistency

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

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

KusamaKSM

Active users (weekly)

Active users (weekly)

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