Miners process transactions and receive fees from users (supply-side fees).

Dogecoin is a distributed ledger for transfer and store of value (a blockchain). Dogecoin was founded by Billy Markus & Jackson Palmer and launched in 2013.

DOGE holders own and govern the protocol. They currently do not take a cut of the total transaction fees paid by users (revenue).

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Blockchains (L1)

Dogecoin

# Dogecoin Active addresses (monthly) Methodology

## 1. Overview

Dogecoin monthly active addresses measures the number of unique addresses that have sent (spent) Dogecoin transactions within a trailing 30-day period. This metric tracks user engagement and network activity by identifying addresses that actively participate in the Dogecoin network as transaction senders. The calculation uses a rolling 30-day window approach with HyperLogLog probabilistic counting to efficiently handle large-scale address deduplication across the entire blockchain history.

## 2. Data sources

The calculation relies exclusively on raw Dogecoin blockchain data:
- **Dogecoin transaction records**: Complete transaction history including transaction hashes, timestamps, and input/output structures
- **UTXO (Unspent Transaction Output) data**: Transaction outputs containing recipient address information stored in `scriptPubKey` address arrays
- **Transaction input references**: Input data containing references to previous transactions (via transaction hash and output index) that identify which outputs are being spent
- **Blockchain timestamps**: Transaction block timestamps used for daily time boundaries and rolling window calculations

## 3. Step-by-step calculation

### Component A: Address resolution from transaction outputs
- **Purpose**: Create a lookup table that maps transaction outputs to their recipient addresses
- **Logic**: Parse each Dogecoin transaction's outputs and extract recipient addresses from the `scriptPubKey.addresses` JSON arrays. For each output, extract the first address from the addresses array and combine it with the transaction hash and output index to create a unique mapping. The output index is determined by either an explicit `n` field in the output JSON or the array offset position.
- **Key fields**: `transaction_hash`, `output_index`, `user_address`, `block_timestamp`

### Component B: User activity identification through input resolution
- **Purpose**: Identify which addresses are actively sending Dogecoin by resolving transaction inputs to actual user addresses
- **Logic**: For each transaction, parse the inputs array to extract references to previous transactions (via `txid` and `vout` fields). Each input reference points to a specific output from a previous transaction that is being spent. Join these input references with the address resolution lookup table using transaction hash and output index to determine which user address is spending each input. This identifies the sending addresses for each transaction.
- **Key fields**: `input_transaction_hash`, `input_output_index`, `user_address`, `block_timestamp`

### Component C: Daily active user aggregation
- **Purpose**: Group active addresses by day and combine historical and recent data
- **Logic**: Aggregate resolved user addresses by day using timestamp truncation to day boundaries. The data is processed in two segments: historical data (pre-2019-01-01) and recent data (2019-01-01 onwards) for processing efficiency. Both segments are unioned together to create a complete daily active user dataset covering all blockchain history. Each record represents a unique address that was active on a specific day.
- **Key fields**: `timestamp`, `user_address`, `chain`, `user_type`

### Component D: HyperLogLog sketch creation for efficient distinct counting
- **Purpose**: Convert daily user data into probabilistic data structures for efficient large-scale distinct counting
- **Logic**: Transform daily active user addresses into HyperLogLog sketches with precision 15, which allows for approximate distinct counting with high accuracy while using minimal memory. Group addresses by day and additional dimensions (chain, user type, product ID) and create HLL sketches for each group. This enables efficient merging and counting across large datasets without storing individual addresses.
- **Key fields**: `timestamp`, `timestamp_grain`, `chain_id`, `hll` (HyperLogLog sketch)

### Component E: Rolling 30-day window generation
- **Purpose**: Create sliding 30-day windows for each day to calculate trailing monthly activity
- **Logic**: For each day in the dataset, generate a 30-day lookback period (current day minus 29 days through current day, inclusive). Use a datespine approach to ensure complete date coverage without gaps. Join each day with its corresponding 30-day window to create combinations of target dates and their lookback periods. This ensures consistent monthly calculations even when there are days with no activity.
- **Key fields**: `timestamp` (target date), `joinday` (days within the 30-day window)

### Component F: Monthly active user calculation with HLL merging
- **Purpose**: Calculate monthly active users by merging HLL sketches across 30-day windows
- **Logic**: For each target day's 30-day window, collect all HLL sketches from days within that window and merge them using HLL partial merge operations. Extract the final distinct count from the merged sketch to get the number of unique addresses active within the trailing 30-day period. This produces daily values where each day shows the count of unique addresses active in the 30 days ending on that date.
- **Key fields**: `timestamp`, `user_mau` (monthly active users count)

### Component G: Multi-dimensional breakdown aggregation
- **Purpose**: Calculate monthly active users across different analytical dimensions
- **Logic**: Generate MAU metrics across multiple breakdown dimensions including chain-level (for the Dogecoin chain), project-level aggregation (project_dbt_tag dimension), user-type-level (transaction sender classification), and product-level (null for Dogecoin as it has no sub-products). Each dimension provides a different analytical view of the same underlying user activity data. Union all dimensional breakdowns to create a comprehensive metrics dataset.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_mau`

### Final aggregation
- **Combining components**: The monthly active user data flows directly into the final active addresses metric through a simple field renaming operation
- **Currency conversion**: Not applicable - this is a count-based metric with no currency values
- **Time aggregation**: Daily granularity with each day representing unique addresses active in the trailing 30-day period ending on that date
- **Final calculation**: The `user_mau` field is renamed to `active_addresses_monthly` and filtered to include only chain-level and project-level dimensions. The finalize_metric macro applies schema standardization, fills missing dates with zero values, and applies chain availability filtering to ensure data completeness and consistency.

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

Unique addresses active over a rolling 30-day window.

DogecoinDOGE

Active addresses (monthly)

Active addresses (monthly)

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