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 (daily) Methodology

## 1. Overview

Daily Active Users (DAU) for Stellar measures the count of unique addresses that initiated transactions on the Stellar blockchain within a 24-hour period. This metric provides insight into network usage and user engagement by tracking transaction senders as active users. The calculation uses HyperLogLog algorithms for efficient unique user counting and provides breakdowns across multiple dimensions including chain, user type, and product categories.

## 2. Data sources

- **Stellar transaction records**: Raw blockchain transaction data containing transaction hashes, source account addresses, and block timestamps
- **Block timestamp data**: Stellar network block timing information used for daily boundary determination
- **Transaction metadata**: Additional transaction context including account identifiers and transaction types

## 3. Step-by-step calculation

### Component A: Historical user extraction
- **Purpose**: Processes historical Stellar transactions (pre-2025) to identify daily active users
- **Logic**: Extracts transaction records from Stellar blockchain data, focusing on the `source_account` field which represents the transaction initiator. Truncates `block_timestamp` to day-level granularity to establish daily boundaries. Filters data to include only transactions before 2025-01-01 for historical processing efficiency.
- **Key fields**: `source_account` becomes `user_address`, `block_timestamp` truncated to `timestamp`, `transaction_hash` for deduplication

### Component B: Recent user extraction  
- **Purpose**: Processes recent Stellar transactions (2025 onwards) with incremental updates
- **Logic**: Applies the same extraction logic as historical data but focuses on recent transactions from 2025-01-01 forward. Uses incremental processing with lookback days to ensure data freshness and handle late-arriving transactions. Maintains the same field mapping and daily truncation approach.
- **Key fields**: `source_account` becomes `user_address`, `block_timestamp` truncated to `timestamp`, `transaction_hash` for deduplication

### Component C: Base metric compilation
- **Purpose**: Combines historical and recent user data into unified daily active user records
- **Logic**: Unions the historical and recent user datasets, assigning consistent metadata including 'stellar' as the `chain` identifier and 'Transaction sender' as the `user_type` classification. Sets `product_id` to null as Stellar operates as a unified network. Maps `user_address` to the `user_dau` field for unique counting.
- **Key fields**: `user_address` mapped to `user_dau`, standardized `chain` and `user_type` values, daily `timestamp` boundaries

### Component D: HyperLogLog initialization
- **Purpose**: Applies probabilistic counting algorithm to enable efficient unique user aggregation
- **Logic**: Initializes HyperLogLog sketches for each day's user addresses using a precision parameter of 15 bits. Groups by timestamp, timestamp grain, chain, user type, and product to maintain dimensional structure while preparing for unique counting across multiple breakdown scenarios.
- **Key fields**: HLL sketch of `user_dau` addresses, dimensional grouping keys preserved

### Final aggregation
- **Combining components**: Creates four dimensional breakdown views: project-level aggregate (total DAU), chain-level breakdown, user type breakdown, and product breakdown. Each view uses HLL merge and extract functions to calculate unique user counts while maintaining appropriate dimensional filters.
- **Currency conversion**: Not applicable for user counting metrics
- **Time aggregation**: Daily boundaries established through timestamp truncation, with consistent day-level granularity maintained throughout the pipeline
- **Final calculation**: HyperLogLog extract function converts probabilistic sketches to final unique user counts. The `finalize_metric_user_dau` macro standardizes output schema, applies chain availability filters, and fills missing days with zero values to ensure complete time series coverage.

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

Unique revenue-generating users over a fixed 24-hour window.

StellarXLM

Active users (daily)

Active users (daily)

Competitive landscape