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Blockchains (L2)

Superseed

# Superseed Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique addresses that initiated at least one transaction on the Superseed blockchain within a 7-day rolling window. This metric provides insight into user engagement and platform adoption by tracking distinct transaction senders over weekly periods. The calculation uses HyperLogLog probabilistic counting to efficiently handle large datasets and ensure accurate deduplication across the rolling time windows.

## 2. Data sources

- **Superseed blockchain transactions**: Raw transaction records from the Superseed blockchain containing transaction metadata, timestamps, and sender addresses
- **Incremental processing metadata**: Internal system metadata used to optimize incremental data processing and determine update boundaries

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Extract and classify unique user addresses from raw transaction data
- **Logic**: Process all Superseed blockchain transactions to identify unique transaction senders. Each transaction's `from_address` is treated as a user address. All users are classified with the user type 'Transaction sender' since the metric focuses on addresses that actively initiate transactions. The data includes standard blockchain metadata like `block_timestamp`, `transaction_hash`, and categorizes Superseed as a Layer 2 blockchain in the blockchains market sector.
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for transaction reference, and standardized categorization fields for market classification

### Component B: Daily active user calculation
- **Purpose**: Calculate daily distinct active users from the user transaction data
- **Logic**: Aggregate transaction data by truncating timestamps to daily boundaries using `timestamp_trunc` function. Count distinct user addresses per day to determine how many unique users were active on each calendar day. This creates the foundation dataset where each record represents one unique user being active on one specific day. Filter logic applies incremental processing boundaries to optimize performance by only processing recent data when updating existing results.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` for distinct counting, `chain` identifier, `user_type` classification, and dimensional breakdown fields like `product_id`

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily active user records into HyperLogLog sketches for efficient deduplication
- **Logic**: Transform each daily active user record into a HyperLogLog sketch using 15-bit precision. This probabilistic data structure allows efficient merging and counting of distinct users across multiple days without storing individual user addresses. Group records by timestamp, chain, user type, and product dimensions to maintain proper breakdown granularity while creating the sketches.
- **Key fields**: `hll` sketch containing the probabilistic representation of daily active users, along with all dimensional grouping fields

### Component D: Datespine and window creation
- **Purpose**: Ensure complete time coverage and create 7-day rolling windows
- **Logic**: Generate a complete datespine covering all days from the earliest DAU record to the most recent data. For each day in the datespine, create a 7-day window by joining that day with the 6 preceding days. This sliding window approach ensures every day has a complete 7-day lookback period. The window creation uses array generation functions to create the date ranges and joins them with the DAU data to form the rolling windows.
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 7-day window, and all associated dimensional fields

### Component E: Weekly aggregation
- **Purpose**: Aggregate HyperLogLog sketches within each 7-day window to calculate weekly active users
- **Logic**: For each 7-day window, merge all HyperLogLog sketches from the constituent days using `hll_count.merge_partial` function. This combines the probabilistic sketches while maintaining accurate distinct counting across the week. Extract the final count from the merged sketch using `hll_count.extract` to get the estimated number of unique users active during that 7-day period.
- **Key fields**: Merged `hll` sketches and extracted `user_wau` counts representing distinct weekly active users

### Component F: Dimensional breakdowns
- **Purpose**: Create multiple views of the WAU metric across different dimensions
- **Logic**: Generate separate aggregations for different analytical perspectives: project-level view (project_dbt_tag), chain-level view, user type breakdown, and product-level analysis. Each breakdown merges HyperLogLog sketches at the appropriate granularity while setting irrelevant dimension fields to null. This creates a unified dataset that supports analysis across multiple dimensions without data duplication.
- **Key fields**: `dimensions` field indicating the breakdown type, `chain_id`, `product_id`, `user_type` as applicable, and `user_wau` as the final metric value

### Final aggregation
- **Combining components**: Union all dimensional breakdown results into a single comprehensive dataset that supports analysis across project, chain, user type, and product dimensions
- **Data standardization**: Apply the `finalize_metric_user_wau` macro to standardize the output schema, handle missing data by filling gaps with zeros, and ensure consistent formatting across all records
- **Time boundaries**: Use incremental processing logic with configurable lookback periods to optimize performance while ensuring data freshness and accuracy
- **Final calculation**: The final `user_wau` value represents the estimated number of distinct addresses that initiated at least one transaction during the 7-day period ending on each timestamp, calculated using HyperLogLog probabilistic counting with 15-bit precision

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

Unique revenue-generating users over a rolling 7-day window.

SuperseedSUPR

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape