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

Subsocial is a social platform powered by Polkadot Relay chain.

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Subsocial

# Subsocial Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Subsocial measures the number of unique users who have performed transactions on the Subsocial blockchain over a rolling 30-day window. This metric uses a HyperLogLog-based approach to efficiently calculate unique user counts across multiple dimensions including chain, user type, and product breakdowns. The calculation builds from Daily Active Users and uses a sliding window methodology to ensure consistent MAU values even on days with no activity.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Subsocial blockchain extrinsics**: Native Subsocial blockchain transaction records containing transaction IDs that serve as user identifiers, along with block timestamps for temporal analysis
- **Subsocial blockchain events**: Event logs from the Subsocial blockchain, specifically filtered for EVM-related activity where `event_pallet` equals 'evm' and `event_method` equals 'Executed', with user addresses extracted from event data payloads
- **Chain availability configuration**: Internal configuration data that determines which blockchain networks are active and should be included in metric calculations

## 3. Step-by-step calculation

### Component A: User identification and extraction
- **Purpose**: Identifies and extracts unique user addresses from Subsocial blockchain activity across both native and EVM transaction types
- **Logic**: The system processes two distinct data streams from the Subsocial blockchain. For native Subsocial transactions, user addresses are extracted directly from the extrinsics data using the `id` field as the user identifier. For EVM-compatible transactions, the system filters events where `event_pallet` is 'evm' and `event_method` is 'Executed', then extracts user addresses by parsing the first element of the `event_data` JSON array. All identified users are classified with the user type 'Transaction sender'. Both data streams are filtered to include only transactions with block timestamps up to a specified cutoff date and are assigned chain identifiers ('subsocial' for native transactions, 'subsocialevm' for EVM transactions)
- **Key fields**: `block_timestamp` for temporal analysis, `id` for native user identification, `event_data` array for EVM user extraction, `user_address` as the standardized user identifier, `chain` for blockchain network identification

### Component B: Daily active user calculation
- **Purpose**: Aggregates identified users into daily unique counts to establish the foundation for MAU calculation
- **Logic**: User activity data is grouped by day using timestamp truncation to the day level. The system calculates distinct user addresses for each day, creating Daily Active User counts segmented by chain, user type, market sector, business segment, and product dimensions. The calculation applies incremental processing logic to handle only recent data updates and includes chain availability filtering to ensure only active blockchain networks contribute to the metrics. Each daily record contains the timestamp grain set to 'day' and preserves all dimensional attributes for downstream aggregation
- **Key fields**: `timestamp` truncated to day level, `timestamp_grain` set to 'day', `chain` identifier, `user_type` classification, `user_dau` containing the unique user address, dimensional attributes for segmentation

### Component C: HyperLogLog sketch creation and windowing
- **Purpose**: Converts daily user data into efficient cardinality estimation structures and creates rolling 30-day windows for MAU calculation
- **Logic**: Daily Active User data is transformed into HyperLogLog sketches using a precision parameter of 15 bits to enable efficient approximate counting of unique users. A datespine is generated covering the full time range of available data, creating a continuous timeline that prevents gaps in the calculation window. For each day in the datespine, a 30-day lookback window is created by generating timestamps for each of the previous 29 days plus the current day. This windowing approach uses joint tables to associate each calculation date with its corresponding 30-day period, ensuring that every day has a complete window even when some days within the window have zero activity. The HyperLogLog sketches from the daily data are then mapped to their respective window positions
- **Key fields**: `hll` containing the HyperLogLog sketch, `timestamp` for the calculation date, `joinday` for window date associations, dimensional breakdowns preserved throughout the windowing process

### Component D: Multi-dimensional MAU aggregation
- **Purpose**: Calculates Monthly Active Users across multiple dimensional breakdowns to provide comprehensive analytics coverage
- **Logic**: The system processes the windowed HyperLogLog data through four parallel aggregation paths. The project-level aggregation merges all HLL sketches across the 30-day window without any dimensional filtering, providing an overall MAU count for the entire Subsocial ecosystem. The chain-level aggregation groups by chain identifier to show MAU for each blockchain network separately. The user-type aggregation segments users by their classification type. The product-level aggregation breaks down MAU by product identifier when available. Each aggregation path merges the HyperLogLog sketches using partial merge operations and then extracts the final cardinality estimate to produce the MAU count. Dimensions not relevant to each aggregation path are set to null values to maintain consistent schema structure
- **Key fields**: `user_mau` containing the final unique user count, `dimensions` indicating the breakdown type, `chain_id`, `product_id`, and `user_type` for dimensional segmentation, `timestamp` and `timestamp_grain` for temporal context

### Final aggregation
- **Combining components**: All dimensional breakdown results are combined using union operations to create a comprehensive dataset containing MAU calculations at project, chain, user type, and product levels within a single unified structure
- **Currency conversion**: Not applicable for this user-based metric as no financial values are involved in the calculation
- **Time aggregation**: The calculation uses a rolling 30-day window approach where each day represents the MAU for the trailing 30-day period ending on that date, ensuring continuous coverage without gaps
- **Final calculation**: The final MAU value for any given date represents the count of unique users who performed at least one transaction within the 30-day period ending on that date, calculated using HyperLogLog cardinality estimation from merged daily user sketches across the rolling window period

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

Unique revenue-generating users over a rolling 30-day window.

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Active users (monthly)

Active users (monthly)

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