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

Pendulum is a decentralized blockchain for cross-border payments and traditional finance infrastructure powered by Polkadot Relay chain.

PEN holders own and govern the protocol. Pendulum is a subsidiary of SatoshiPay. SatoshiPay's investors include Blue Star Capital, KR1 plc, Stellar Development Foundation, etc.

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Blockchains (L2)

Pendulum

# Pendulum Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that initiated at least one transaction on the Pendulum blockchain within a 30-day rolling window period. This metric provides insight into user engagement and adoption trends on the Pendulum network. The calculation uses HyperLogLog probabilistic sketches to efficiently estimate unique address counts over overlapping time periods, treating each transaction sender address as an active user.

## 2. Data sources

- **Pendulum blockchain extrinsics**: Raw transaction and state change records from the Pendulum blockchain network, containing transaction metadata including sender addresses, block timestamps, and extrinsic identifiers
- **Datespine generation**: Algorithmically generated continuous date ranges to ensure complete time series coverage without gaps

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies user activity events from raw blockchain data
- **Logic**: Extracts transaction data from Pendulum blockchain extrinsics, treating each extrinsic as a user activity event. Maps the `id` field as the user address (transaction sender) and captures the `block_timestamp` for temporal analysis. Classifies all users as "Transaction sender" type and categorizes the data under the "blockchains_l2" market sector and "blockchain" business segment.
- **Key fields**: `block_timestamp` for timing, `id` as `user_address` for identification, `extrinsic_id` for transaction reference

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily unique active users as the foundation for monthly calculations
- **Logic**: Groups blockchain activity by calendar day using timestamp truncation to day level. For each day, counts distinct user addresses that initiated at least one transaction. Creates daily snapshots of active users across multiple dimensions including chain, user type, market sector, business segment, and product level.
- **Key fields**: `timestamp` truncated to day grain, distinct `user_address` as `user_dau`

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user sets into probabilistic data structures for efficient unique counting
- **Logic**: Transforms daily active user lists into HyperLogLog sketches using BigQuery's `hll_count.init` function with 15-bit precision. This probabilistic approach enables efficient calculation of unique users across overlapping time windows without storing individual address lists.
- **Key fields**: `hll` sketches containing compressed user address information

### Component D: Rolling window construction
- **Purpose**: Creates 30-day lookback windows for monthly active user calculations
- **Logic**: Generates a continuous datespine covering the full analysis period, then creates rolling 30-day windows by joining each date with the previous 29 days. This approach ensures complete coverage of all time periods and handles gaps in the underlying transaction data.
- **Key fields**: `timestamp` for current date, `joinday` for historical dates within each 30-day window

### Component E: Monthly active user calculation
- **Purpose**: Estimates unique users across 30-day rolling windows using HyperLogLog merge operations
- **Logic**: For each 30-day window, merges the daily HyperLogLog sketches using `hll_count.merge_partial` to combine user sets while avoiding double-counting. Extracts the final unique user estimate using `hll_count.extract`. Performs this calculation across multiple dimensional breakdowns including project level, chain level, user type level, and product level.
- **Key fields**: Merged `hll` sketches, extracted `user_mau` counts

### Final aggregation
- **Combining components**: Unions the dimensional breakdown results (project, chain, user type, product) into a single comprehensive dataset, filtering to include only chain-level and project-level dimensions for the final active addresses metric
- **Currency conversion**: Not applicable for this counting metric
- **Time aggregation**: Uses 30-day rolling windows updated daily, with results aggregated at monthly grain for reporting purposes
- **Final calculation**: Renames the `user_mau` field to `active_addresses_monthly` to reflect that unique user addresses represent monthly active addresses on blockchain networks. Applies zero-filling strategy for missing data points and applies chain availability filtering to ensure data quality

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

Unique addresses active over a rolling 30-day window.

PendulumPEN

Archived

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

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