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).

github

twitter

discord

etherscan

website

Blockchains (L1)

Kusama

# Kusama Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that initiated at least one transaction (extrinsic) on the Kusama network within a 30-day rolling window. This metric tracks user engagement and network adoption by counting distinct transaction senders over monthly periods. The calculation uses a rolling 30-day window approach with HyperLogLog probabilistic counting for efficient aggregation of unique addresses across time periods.

## 2. Data sources

- **Kusama blockchain extrinsic data**: Complete records of all extrinsic operations (transactions, calls, and other network operations) executed on the Kusama blockchain
- **Transaction sender addresses**: Unique wallet addresses that initiated extrinsics on the Kusama network
- **Block timestamp data**: Precise timing information for when each extrinsic was included in Kusama blocks
- **Extrinsic identifiers**: Unique identifiers for each extrinsic operation to ensure accurate transaction tracking

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Extract and standardize user addresses from raw Kusama extrinsic data
- **Logic**: Pull all extrinsic records from Kusama blockchain data, extracting the sender address (`id` field) as the user address. Each record is classified as a "Transaction sender" user type and tagged with Kusama chain metadata including market sector ("blockchains_l2") and business segment ("blockchain"). Block timestamps are preserved for temporal analysis.
- **Key fields**: `block_timestamp` for timing, `user_address` for unique address identification, `chain` set to "kusama", `user_type` set to "Transaction sender"

### Component B: Daily active user calculation  
- **Purpose**: Calculate daily unique active addresses on Kusama
- **Logic**: Aggregate user address data by truncating timestamps to daily granularity and counting distinct user addresses per day. Filter data to include only transaction senders, preserving chain, user type, and product metadata for dimensional analysis. Apply incremental processing to handle new data efficiently.
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing unique user addresses, dimensional metadata for breakdowns

### Component C: Monthly active user aggregation with HyperLogLog
- **Purpose**: Calculate monthly active users using a 30-day rolling window approach
- **Logic**: Initialize HyperLogLog data structures for each daily user set to enable efficient unique counting across time windows. Create a datespine covering all relevant dates, then generate 30-day rolling windows by cross-joining each date with the previous 29 days. Merge HyperLogLog sketches across each 30-day window to calculate unique user counts. Create multiple dimensional breakdowns including chain-level, project-level, user-type-level, and product-level aggregations. Use union operations to combine all dimensional views into a single result set.
- **Key fields**: `timestamp` for window end dates, `user_mau` for monthly active user counts, `dimensions` for breakdown categories, `chain_id` for chain-specific data

### Component D: Final metric conversion
- **Purpose**: Convert monthly active users to active addresses monthly metric
- **Logic**: Select monthly active user data specifically for chain-level and project-level dimensions (`dimensions` in "chain" and "project_dbt_tag"). Rename the `user_mau` field to `active_addresses_monthly` to match the target metric naming convention. Apply standardization using finalize metric macros to ensure consistent schema and fill any missing time periods with zeros.
- **Key fields**: `active_addresses_monthly` as the final metric value, `timestamp` for time series, `chain_id` for network identification

### Final aggregation
- **Combining components**: Data flows sequentially from raw extrinsic extraction through daily aggregation to monthly rolling window calculation, with each component building on the previous step's output
- **Currency conversion**: Not applicable for this address-counting metric
- **Time aggregation**: Uses 30-day rolling windows where each day represents the count of unique addresses active in the current day plus the previous 29 days, providing smooth monthly trend analysis
- **Final calculation**: The final metric value represents the count of unique Kusama addresses that initiated at least one extrinsic within the 30-day period ending on each timestamp, calculated using HyperLogLog merge operations for accuracy and efficiency

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

Unique addresses active over a rolling 30-day window.

KusamaKSM

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape