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

Stellar is a distributed ledger for transfer and store of value (a blockchain). Stellar was founded by Jed McCaleb launched in 2014.

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

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Blockchains (L1)

Stellar

# Stellar Active users (weekly) Methodology

## 1. Overview

The Stellar Weekly Active Users (WAU) metric measures the number of unique users who submitted at least one transaction on the Stellar network within any 7-day rolling window. This metric uses HyperLogLog (HLL) sketches for efficient approximate distinct counting and provides breakdowns across multiple dimensions including chain, user type, and product categories.

## 2. Data sources

- **Stellar blockchain transactions**: Raw transaction records from the Stellar network containing transaction hashes, block timestamps, and source account addresses
- **Stellar network timestamps**: Block-level timestamps for temporal aggregation and time series construction
- **Transaction metadata**: Source account addresses that identify transaction senders as active users

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identify unique active users for each day from Stellar transaction data
- **Logic**: Extract user addresses from Stellar transaction records by parsing the `source_account` field from each transaction. Data is split into two time ranges - historical data (pre-2025) and recent data (2025 onward) for processing efficiency. Transactions are aggregated daily using `block_timestamp` truncated to day boundaries. Each unique `source_account` represents one daily active user, classified as 'Transaction sender' user type with 'stellar' as the chain identifier.
- **Key fields**: `timestamp` (daily), `user_address` (from source_account), `transaction_hash`, `user_type` (set to 'Transaction sender'), `chain` (set to 'stellar')

### Component B: HyperLogLog sketch creation
- **Purpose**: Create HLL sketches from daily active user data for efficient distinct counting in rolling windows
- **Logic**: Transform the daily active user data into HyperLogLog sketches using `hll_count.init()` with precision parameter 15. This enables approximate distinct counting that scales efficiently for large datasets. Sketches are grouped by timestamp, chain, user type, and product dimensions to preserve breakdown capabilities.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component C: Rolling window construction  
- **Purpose**: Build 7-day rolling windows for WAU calculation using a complete date spine
- **Logic**: Generate a complete date spine covering the full time range from minimum DAU timestamp to current date minus one day. For each date in the spine, create joins with the 6 preceding days to form 7-day windows. This ensures no dates are skipped even if no transactions occurred. Join the windowed dates with the HLL sketches from daily data to create the rolling window dataset.
- **Key fields**: `timestamp` (window end date), `joinday` (dates within each 7-day window), `hll` (sketches for each day in window)

### Component D: Dimensional breakdowns
- **Purpose**: Calculate WAU across different analytical dimensions
- **Logic**: Create separate aggregations for four different breakdowns: project-level (project_dbt_tag), chain-level, user type-level, and product-level. For each breakdown, merge the HLL sketches within each 7-day window using `hll_count.merge_partial()`, then extract the final count using `hll_count.extract()`. Non-relevant dimension fields are set to null for each breakdown to maintain consistent schema.
- **Key fields**: `dimensions` (breakdown type), `user_wau` (unique user count), dimension-specific identifiers (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with consistent schema, preserving the different analytical perspectives
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily time series with 7-day rolling window calculations, using complete date spine to ensure no gaps in time series
- **Final calculation**: Extract final unique user counts from merged HLL sketches representing the number of distinct users active within each 7-day period, formatted using the `finalize_metric_user_wau` macro for standardized output structure

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

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

StellarXLM

Active users (weekly)

Active users (weekly)

Competitive landscape