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Blockchains (L2)

Superseed

# Superseed Active addresses (monthly) Methodology

## 1. Overview

The SuperSeed active addresses monthly metric measures the number of unique addresses that have initiated at least one transaction on the SuperSeed blockchain network within a rolling 30-day period. This metric provides insight into user engagement and network activity by tracking distinct transaction senders over monthly windows. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct addresses across large transaction datasets, with the final metric representing monthly active users treated as active addresses for blockchain analytics.

## 2. Data sources

The calculation relies on the following raw data sources:

- **SuperSeed blockchain transactions**: Complete transaction records from the SuperSeed blockchain network, including transaction metadata such as block timestamps, transaction hashes, and sender addresses
- **Transaction sender data**: The `from_address` field from each transaction, representing the address that initiated the transaction and serves as the "active address" being counted
- **Block timing data**: Block-level timestamp information (`block_timestamp`) used to determine when transaction activity occurred and establish daily time boundaries
- **Chain metadata**: Blockchain network identification and classification data that categorizes SuperSeed as a layer-2 blockchain in the blockchains market sector

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation

- **Purpose**: Transform raw transaction data into daily distinct active addresses
- **Logic**: Raw SuperSeed blockchain transaction records are processed to extract unique transaction senders for each calendar day. The `from_address` field from each transaction becomes a user address, representing an active address on the network. Block timestamps are truncated to day boundaries using `timestamp_trunc(block_timestamp, day)` to group transactions by calendar day. Additional metadata is enriched, including chain identification ('superseed'), market sector classification ('blockchains_l2'), business segment ('blockchain'), and user type classification ('Transaction sender'). The daily aggregation creates distinct lists of unique addresses active each day, with incremental processing to handle new transaction data efficiently.
- **Key fields**: `block_timestamp` for timing, `from_address` as the active address identifier, `transaction_hash` for transaction uniqueness, and derived fields like `chain` and `user_type`

### Component B: Monthly active user calculation with HyperLogLog

- **Purpose**: Calculate monthly active users using rolling 30-day windows with efficient distinct counting
- **Logic**: Daily distinct addresses are converted into HyperLogLog probabilistic counting structures using `hll_count.init` with a precision of 15 bits for memory-efficient distinct counting. A datespine is generated covering all calendar days from the first transaction date to the current date. For each day in the datespine, a 30-day lookback window is constructed (from day-29 to day) using `generate_timestamp_array`. HyperLogLog structures from all days within each 30-day window are merged using `hll_count.merge_partial` to count distinct addresses across the rolling window. The final distinct count is extracted using `hll_count.extract`, providing the monthly active user count for each day's 30-day window.
- **Key fields**: `timestamp` for daily boundaries, `hll` structures for efficient counting, `user_mau` as the monthly active user count

### Component C: Dimensional breakdown and aggregation

- **Purpose**: Provide monthly active user counts across multiple analytical dimensions
- **Logic**: The monthly active user calculation is broken down into four dimensional views: project-level aggregation (labeled as 'project_dbt_tag') that provides overall project metrics, chain-level aggregation (labeled as 'chain') that maintains SuperSeed chain identification, user type-level aggregation that preserves the 'Transaction sender' classification, and product-level aggregation for potential product-specific analysis (though product_id remains null for SuperSeed). Each dimensional breakdown uses the same HyperLogLog merging logic but groups by different dimensional attributes. All dimensional breakdowns are combined using union operations to create a comprehensive view with multiple analytical perspectives.
- **Key fields**: `dimensions` field indicating the aggregation level, `chain_id` for chain-specific metrics, `user_type` for user classification, `product_id` for product-specific analysis

### Final aggregation

- **Combining components**: The monthly active user data from different dimensional breakdowns is filtered to include only 'chain' and 'project_dbt_tag' dimensions, which are the relevant breakdowns for active addresses analysis. The `user_mau` field is renamed to `active_addresses_monthly` to reflect the blockchain context where monthly active users represent active addresses on the network.

- **Currency conversion**: No currency conversion is required as this metric counts addresses rather than financial values.

- **Time aggregation**: The metric uses rolling 30-day windows calculated daily, where each day represents the count of distinct addresses active in the preceding 30-day period. Monthly boundaries are established using timestamp truncation to month level for incremental processing optimization.

- **Final calculation**: The final metric value represents the count of unique addresses that initiated at least one transaction within a rolling 30-day window ending on each calculation date. The HyperLogLog-based calculation provides approximate but highly accurate distinct counts (typically >99% accuracy) while maintaining computational efficiency for large transaction datasets. The metric is finalized using standardized schema formatting and gap-filling with zero values for periods with no activity.

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

Unique addresses active over a rolling 30-day window.

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Active addresses (monthly)

Active addresses (monthly)

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