Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Phala is a specialized privacy-preserving cloud computing blockchain powered by Polkadot Relay chain.

PHA holders own and govern the protocol. Phala’s investors include IOSG Ventures, Incuba Alpha, Candaq Fintech Group, and others.

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Phala

# Phala Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of distinct users who performed at least one transaction on Phala Network within any 7-day rolling window. This metric provides insight into user engagement and platform adoption by tracking unique addresses that submit extrinsics (transactions) on the Phala blockchain over weekly periods. The calculation uses HyperLogLog data structures for efficient distinct user counting across rolling time windows.

## 2. Data sources

- **Phala Network extrinsic records**: Blockchain transaction data containing user addresses, block timestamps, and transaction metadata from the Phala Network
- **Table modification timestamps**: Internal system timestamps used to optimize incremental data processing and determine appropriate lookback periods

## 3. Step-by-step calculation

### Component A: Daily active users foundation
- **Purpose**: Identifies distinct users who performed transactions on each day
- **Logic**: Extracts transaction records from Phala Network extrinsics, truncates block timestamps to day granularity, and identifies unique user addresses per day. Each extrinsic sender is classified as a 'Transaction sender' user type with chain identifier 'phala'. The process applies incremental filtering to only process recent data based on table modification timestamps.
- **Key fields**: `block_timestamp` (transaction time), `user_address` (transaction sender), `chain` (phala), `user_type` (Transaction sender)

### Component B: HyperLogLog aggregation
- **Purpose**: Converts daily user data into efficient data structures for set operations
- **Logic**: Takes the daily active users and creates HyperLogLog sketches with 15-bit precision for each day, grouped by timestamp, chain, user type, and product. HyperLogLog enables efficient approximate distinct counting and merging across time windows without storing individual user addresses.
- **Key fields**: `hll` (HyperLogLog sketch of user addresses), `timestamp` (day), `chain_id`, `user_type`, `product_id`

### Component C: Datespine and window creation
- **Purpose**: Ensures complete time series coverage and creates 7-day rolling windows
- **Logic**: Generates a complete datespine from the earliest available data to the current date using daily intervals. For each date in the spine, creates associations with the current day plus the previous 6 days to form 7-day rolling windows. This approach ensures no dates are skipped and every day has a complete 7-day window for analysis.
- **Key fields**: `timestamp` (current date), `joinday` (each day in the 7-day window)

### Component D: Rolling window aggregation
- **Purpose**: Combines daily HLL sketches across 7-day windows
- **Logic**: Joins the 7-day window structure with daily HyperLogLog data, matching each window day with its corresponding HLL sketch. For each date, this creates a collection of up to 7 HLL sketches representing the daily user sets within the rolling window. Incremental processing applies safety margins to ensure data freshness.
- **Key fields**: `timestamp` (window end date), `hll` (collection of daily HLL sketches)

### Component E: Dimensional breakdowns
- **Purpose**: Calculates WAU across different analytical dimensions
- **Logic**: Merges HyperLogLog sketches within each 7-day window and extracts the final distinct count, creating separate aggregations for four dimensions: overall project level (project_dbt_tag), chain level, user type level, and product level. Each breakdown uses HLL merge operations to combine daily sketches and extract approximate distinct user counts. Non-relevant dimension fields are set to null for each breakdown.
- **Key fields**: `dimensions` (breakdown type), `user_wau` (distinct weekly active user count), dimension-specific identifiers

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive view with multiple analytical perspectives in a single dataset
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each date represents users active within that date and the previous 6 days, ensuring consistent weekly measurement across all time periods
- **Final calculation**: The `user_wau` field contains the approximate distinct count of user addresses that performed at least one transaction within the 7-day window, calculated by merging and extracting HyperLogLog sketches. Schema standardization and zero-filling for missing dates are applied through the finalization macro.

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

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

PhalaPHA

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown