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Blockchains (L2)

Superseed

# Superseed Active addresses (daily) Methodology

## 1. Overview

The Superseed active addresses daily metric measures the number of unique blockchain addresses that initiated at least one transaction on the Superseed network each day. This metric serves as a key indicator of network activity and user engagement, treating each transaction sender as an active address. The calculation employs efficient HyperLogLog algorithms for scalable distinct counting across multiple aggregation dimensions.

## 2. Data sources

- **Superseed blockchain transactions**: Raw transaction records from the Superseed blockchain containing transaction metadata including block timestamps, transaction hashes, and sender addresses
- **Chain availability data**: Configuration data used to filter results based on data availability and completeness across different time periods

## 3. Step-by-step calculation

### Component A: User fact table creation
- **Purpose**: Standardize and classify transaction data for user analysis
- **Logic**: Extract transaction records from Superseed blockchain data and cast all fields to appropriate data types. Each transaction sender address is classified as a "Transaction sender" user type. Additional metadata is added including chain identifier as "superseed", market sector classification as "blockchains_l2", and business segment as "blockchain". Product identification is set to null for this blockchain-level analysis.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for uniqueness, `from_address` as the user address, `user_type` for classification

### Component B: Daily user aggregation
- **Purpose**: Calculate distinct active users per day with incremental processing
- **Logic**: Group transaction data by day using timestamp truncation to daily boundaries. For each day, identify all distinct user addresses that sent transactions. The calculation uses incremental processing with a lookback window to ensure data freshness while maintaining efficiency. Only the most recent days are recalculated when new data arrives.
- **Key fields**: `timestamp` truncated to day level, `user_address` for distinct counting, `chain` and other dimensional attributes

### Component C: Multi-dimensional aggregation
- **Purpose**: Aggregate distinct users across different analytical dimensions using HyperLogLog
- **Logic**: Create HyperLogLog sketches for each user address to enable efficient distinct counting across large datasets. Aggregate these sketches across four different dimensions: project level (all users combined), chain level (Superseed chain), user type level (transaction senders), and product level (null for blockchain analysis). Each dimension creates a separate aggregation view with appropriate grouping fields. HyperLogLog merge operations combine sketches and extract final distinct counts.
- **Key fields**: `hll` sketches for distinct counting, `dimensions` field indicating aggregation level, `user_dau` as extracted distinct count

### Component D: Active addresses mapping
- **Purpose**: Convert user DAU metrics to active addresses format and apply dimensional filtering
- **Logic**: Filter the multi-dimensional user aggregations to include only chain-level and project-level breakdowns, as these are most relevant for active addresses reporting. Rename the `user_dau` field to `active_addresses_daily` to reflect the metric definition. This mapping treats each unique user address as an active address, establishing equivalence between active users and active addresses for blockchain analysis.
- **Key fields**: `active_addresses_daily` (renamed from `user_dau`), `dimensions` filtered to "chain" and "project_dbt_tag"

### Final aggregation
- **Combining components**: The pipeline flows sequentially from raw transactions through user fact creation, daily aggregation, multi-dimensional HyperLogLog aggregation, and final filtering and renaming
- **Time aggregation**: Daily boundaries are established using timestamp truncation, with incremental processing ensuring efficient updates for recent dates
- **Data quality**: Chain availability filters ensure only periods with complete data are included in final results
- **Final calculation**: The metric represents the exact count of distinct blockchain addresses that initiated transactions each day, aggregated at both chain and project levels using probabilistic counting for scalability

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

Unique active addresses over a fixed 24-hour window.

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Active addresses (daily)

Active addresses (daily)

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