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 (daily) Methodology

## 1. Overview

This metric calculates the daily count of unique active addresses on the Kusama blockchain. An active address is defined as any address that initiated at least one transaction (extrinsic) on a given day. This metric helps assess daily user engagement and network activity on the Kusama blockchain by counting distinct transaction senders.

The calculation uses HyperLogLog (HLL) probabilistic data structures to efficiently count distinct addresses while enabling aggregation across multiple dimensions without double-counting users.

## 2. Data sources

- **Kusama blockchain extrinsics**: Raw transaction data from the Kusama blockchain network, containing transaction details, sender addresses, block timestamps, and extrinsic IDs
- **Blockchain transaction records**: Complete transaction history including all user-initiated operations on the Kusama network

## 3. Step-by-step calculation

### Component A: Raw user identification
- **Purpose**: Extract and standardize user address data from raw Kusama blockchain transactions
- **Logic**: The system processes raw Kusama extrinsics data to identify transaction senders. Each extrinsic record contains a sender address (`id` field), block timestamp, and unique extrinsic identifier. The data is standardized with consistent field types and blockchain metadata including chain name ('kusama'), market sector classification ('blockchains_l2'), and business segment ('blockchain'). All transaction senders are classified as 'Transaction sender' user type.
- **Key fields**: `block_timestamp` for timing, `id` as the user address, `extrinsic_id` for transaction identification

### Component B: Daily user aggregation
- **Purpose**: Create daily distinct user records from raw transaction data
- **Logic**: Transaction timestamps are truncated to daily boundaries using day-level granularity. The system extracts distinct combinations of date, chain, user type, market sector, business segment, product, and user address. This creates one record per unique user per day, regardless of how many transactions they performed. Incremental processing logic ensures efficient updates by only processing recent data based on configurable lookback periods.
- **Key fields**: `timestamp` truncated to day level, `user_address` for distinct counting, metadata fields for dimensional breakdowns

### Component C: HyperLogLog sketch creation
- **Purpose**: Enable efficient distinct user counting across multiple dimensions
- **Logic**: The system initializes HyperLogLog sketches with precision 15 for each combination of date, chain, user type, and product. HLL is a probabilistic algorithm that allows approximate distinct counting with low memory usage. Each user address is hashed into the HLL sketch, creating a compact representation of the distinct user set for that dimension combination.
- **Key fields**: `hll` sketch containing compressed user address information, dimension keys for grouping

### Component D: Multi-dimensional aggregation
- **Purpose**: Calculate distinct user counts across different analytical dimensions
- **Logic**: The system creates four separate breakdowns by merging HLL sketches and extracting final counts. Project-level aggregation combines all users across the entire Kusama project. Chain-level breakdown provides counts per blockchain (in this case, just Kusama). User-type breakdown segments by transaction sender categories. Product-level breakdown allows for future product-specific analysis. Each breakdown unions the results into a unified dataset with dimension labels and appropriate null values for unused dimension fields.
- **Key fields**: `user_dau` as the final distinct count, `dimensions` indicating the breakdown type, `chain_id`, `product_id`, `user_type` for dimensional context

### Final aggregation
- **Combining components**: The multi-dimensional user DAU results are filtered to include only 'chain' and 'project_dbt_tag' dimensions, which represent the most relevant breakdowns for active address analysis
- **Currency conversion**: No currency conversion required as this metric counts addresses rather than monetary values
- **Time aggregation**: Data is aggregated at daily boundaries with timestamp truncation to day level
- **Final calculation**: The `user_dau` field is directly relabeled as `active_addresses_daily` to align with blockchain terminology where users are identified by their addresses. The finalization macro applies zero-filling for missing days and standardizes the schema with chain availability filtering to ensure data quality and completeness.

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

Unique active addresses over a fixed 24-hour window.

KusamaKSM

Active addresses (daily)

Active addresses (daily)

Competitive landscape