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 addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction (extrinsic) on the Kusama blockchain within a rolling 7-day window. This metric answers the question "How many distinct users are actively transacting on Kusama each week?" and provides insight into user engagement and network adoption. The calculation uses HyperLogLog probabilistic data structures to efficiently handle large-scale distinct counting across time windows.

## 2. Data sources

- **Kusama blockchain extrinsics**: Raw transaction data from the Kusama blockchain including all extrinsic types (transactions, calls, and operations)
- **Block timestamp data**: Timing information for each block containing the extrinsics
- **Chain availability configuration**: Data quality filters and chain-specific availability rules

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique transaction senders and aggregate them by day
- **Logic**: Extract the sender address (`id` field) from each Kusama extrinsic and treat them as active users. Each extrinsic represents an on-chain action initiated by a user address. Truncate block timestamps to daily granularity and count distinct user addresses per day. Apply chain availability filters to ensure data quality.
- **Key fields**: `block_timestamp` (transaction timing), `id` (sender address), `extrinsic_id` (unique transaction identifier)

### Component B: HyperLogLog daily sketch creation
- **Purpose**: Create probabilistic data structures for efficient distinct counting
- **Logic**: For each day, initialize HyperLogLog sketches using the distinct user addresses from daily aggregation. HyperLogLog allows efficient approximate distinct counting with controlled error rates, essential for large-scale blockchain data processing. Group sketches by timestamp, chain, user type, and product dimensions.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (daily grain), `chain_id` (blockchain identifier)

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: Generate a complete datespine covering the full time range to avoid gaps in the time series. For each day, create a 7-day lookback window by joining with the 6 preceding days. This approach ensures every day has a complete 7-day window for weekly aggregation, handling missing days gracefully through the datespine approach.
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 7-day window)

### Component D: Weekly active user aggregation with dimensional breakdowns
- **Purpose**: Calculate weekly active users across multiple dimensional views
- **Logic**: For each 7-day window, merge the HyperLogLog sketches from all days in the window and extract the distinct count. Create separate dimensional breakdowns: chain-level aggregation (showing activity for the specific Kusama chain), project-level aggregation (showing activity for the overall Kusama project), user-type aggregation, and product-level aggregation. Each dimension provides different analytical perspectives on the same underlying user activity.
- **Key fields**: `user_wau` (weekly active users), `dimensions` (aggregation level), `chain_id` (for chain-level breakdowns)

### Final aggregation
- **Combining components**: Select weekly active user counts specifically from chain-level and project-level dimensional breakdowns, as these are most relevant for the active addresses metric
- **Currency conversion**: Not applicable for address counting metrics
- **Time aggregation**: Weekly boundaries are created using rolling 7-day windows, where each day represents the end date of a 7-day lookback period
- **Final calculation**: Rename `user_wau` field to `active_addresses_weekly` to reflect the blockchain context where users are represented by addresses. Apply schema finalization with zero-filling strategy to ensure complete time series coverage, filling missing periods with zero values rather than nulls

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

Unique active addresses over a rolling 7-day window.

KusamaKSM

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape