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Blockchains (L1)

Sonic

# Sonic Active addresses (daily) Methodology

## 1. Overview

The active addresses daily metric for Sonic blockchain counts the number of unique addresses that initiated at least one transaction on a given day. This metric serves as a proxy for daily user activity on the Sonic network, providing insight into network engagement and adoption trends. The calculation identifies transaction senders as active addresses and uses efficient HyperLogLog algorithms for scalable distinct counting across large transaction volumes.

## 2. Data sources

- **Sonic blockchain transactions**: Raw transaction records from the Sonic blockchain containing sender addresses, transaction hashes, and block timestamps
- **Sonic network block data**: Timestamp information for transaction ordering and daily boundary determination

## 3. Step-by-step calculation

### Component A: User identification and fact table creation
- **Purpose**: Identify and standardize active users from raw Sonic transactions
- **Logic**: Extract transaction sender addresses from Sonic blockchain transactions and classify them as active users. Each transaction record provides a `from_address` field representing the transaction initiator, which becomes the user identifier. The data is standardized with consistent typing and includes metadata categorizing Sonic as a Layer 1 blockchain in the blockchains market sector
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for transaction identification, `from_address` as the user address, `chain` identifier, and `user_type` classification as "Transaction sender"

### Component B: Daily user deduplication  
- **Purpose**: Create distinct daily user records with proper time boundaries
- **Logic**: Process the user fact data to create daily distinct records by truncating block timestamps to day boundaries using daily granularity. For each day, identify unique user addresses that initiated transactions, removing duplicate occurrences of the same address within the same day. The process includes incremental processing logic with lookback periods to ensure data freshness and completeness
- **Key fields**: `timestamp` truncated to daily boundaries, `user_address` for deduplication, and dimensional metadata including `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog sketch generation
- **Purpose**: Create efficient data structures for large-scale distinct counting
- **Logic**: Build HyperLogLog sketches from the daily distinct user records to enable efficient distinct counting at scale. Initialize HLL sketches for each day using the user addresses as input, with a precision parameter of 15 bits for accuracy. This approach allows for memory-efficient counting of distinct users even with millions of addresses
- **Key fields**: Daily `timestamp`, dimensional breakdowns, and `hll` sketches containing compressed distinct user information

### Component D: Dimensional aggregation
- **Purpose**: Create multiple dimensional views of daily active users
- **Logic**: Aggregate the HLL sketches across different dimensional breakdowns to provide various analytical perspectives. Generate four distinct dimensional views: project-level aggregation for overall metrics, chain-level breakdown for network-specific analysis, user-type-level breakdown for user classification analysis, and product-level breakdown for product-specific metrics. Each breakdown merges the daily HLL sketches and extracts the final distinct count
- **Key fields**: `user_dau` as the extracted distinct count, `dimensions` field indicating the breakdown type, and relevant dimensional identifiers like `chain_id`, `user_type`, or `product_id`

### Component E: Metric transformation and finalization
- **Purpose**: Convert user DAU metric to active addresses terminology and apply final processing
- **Logic**: Transform the user DAU metric specifically for blockchain analysis by renaming it to "active addresses daily" to reflect blockchain-specific terminology where addresses represent active users. Filter the results to include only chain-level and project-level dimensional breakdowns. Apply the finalization macro which handles time series completion by filling missing days with zero values, ensuring continuous daily data coverage
- **Key fields**: `active_addresses_daily` as the final metric value, `timestamp` for daily boundaries, `dimensions` for breakdown categorization, and `chain_id` for chain identification

### Final aggregation
- **Combining components**: The pipeline flows sequentially from raw transactions through user identification, daily deduplication, HLL sketch creation, dimensional aggregation, and final metric transformation. Each step builds upon the previous one in a linear dependency chain
- **Currency conversion**: Not applicable for this address counting metric
- **Time aggregation**: Daily boundaries are established by truncating block timestamps to day-level granularity, with missing days filled with zero values to maintain continuous time series
- **Final calculation**: The final active addresses daily value represents the distinct count of unique sender addresses that initiated at least one transaction on Sonic blockchain within each calendar day, extracted from HyperLogLog sketches and filtered for relevant dimensional breakdowns

*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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