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 (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Stellar measures the number of unique users who initiated transactions on the Stellar network within any rolling 30-day period. This metric provides insight into user engagement and activity levels on the Stellar blockchain by tracking transaction senders over time. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct users across large datasets and time windows.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Stellar transaction records**: Complete blockchain transaction data including transaction hashes, block timestamps, and source account addresses
- **Transaction metadata**: Detailed transaction information with precise timing and account identification for all Stellar network activity

## 3. Step-by-step calculation

### Component A: User transaction facts
- **Purpose**: Extract and standardize user activity from raw Stellar transactions
- **Logic**: The system processes raw Stellar transaction records to identify active users by extracting the `source_account` field from each transaction. Transactions are grouped by day using the `block_timestamp`, creating daily snapshots of user activity. The data is split into historical (pre-2025) and recent (2025 onwards) partitions for efficient processing. Each user address is classified as a "Transaction sender" user type, representing users who initiated transactions on the Stellar network.
- **Key fields**: `timestamp` (daily), `user_address` (source account), `transaction_hash`, and `timestamp_grain` (set to 'day')

### Component B: Daily active users aggregation  
- **Purpose**: Create daily unique user counts with HyperLogLog optimization
- **Logic**: The system combines historical and recent user transaction data to create a comprehensive daily active user dataset. Each day's data includes all unique user addresses that initiated transactions, classified by chain ('stellar'), user type ('Transaction sender'), and other dimensional attributes. The user addresses are prepared for HyperLogLog processing to enable efficient distinct counting in subsequent aggregation steps.
- **Key fields**: `timestamp`, `chain` (stellar), `user_type` (Transaction sender), `product_id` (null), and `user_dau` (user addresses)

### Component C: HyperLogLog initialization and windowing
- **Purpose**: Apply probabilistic counting and create rolling 30-day windows
- **Logic**: Daily active user data is converted into HyperLogLog sketches using 15-bit precision for efficient distinct counting. A comprehensive datespine is generated covering all days from the earliest transaction date to the current date. For each day in the datespine, the system creates a 30-day rolling window by generating timestamps for the current day plus the previous 29 days. This windowing approach ensures complete coverage without gaps in the time series, even for days with no transaction activity.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (target date), `joinday` (dates within the 30-day window)

### Component D: MAU calculation by dimensions
- **Purpose**: Calculate monthly active users across different analytical dimensions
- **Logic**: The system performs MAU calculations for four different dimensional breakdowns. Each breakdown aggregates the HyperLogLog sketches across the 30-day windows and extracts the final distinct user count. The dimensions include: project-level aggregation (overall MAU), chain-level breakdown (by blockchain), user type breakdown (by user classification), and product-level breakdown (currently null for Stellar). For each dimension, HyperLogLog sketches are merged across the rolling window period and then extracted to produce the final distinct user count.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, and `user_mau` (distinct user count)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive MAU dataset with multiple analytical perspectives
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: MAU is calculated using precise 30-day rolling windows, where each day represents the count of unique users active in that day plus the previous 29 days
- **Final calculation**: The final MAU value represents the distinct count of user addresses that initiated at least one transaction within any 30-day period, calculated using HyperLogLog probabilistic counting for efficiency and accuracy across large datasets

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

Unique revenue-generating users over a rolling 30-day window.

StellarXLM

Active users (monthly)

Active users (monthly)

Competitive landscape