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 users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Dogecoin measures the number of unique addresses that initiated transactions within a rolling 30-day window. This metric answers the business question: "How many unique users are actively transacting on the Dogecoin network each month?" The calculation builds a 30-day rolling window over Daily Active Users (DAU), using HyperLogLog algorithms for efficient distinct user counting across multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Dogecoin blockchain transactions**: Complete transaction records including inputs, outputs, timestamps, and transaction hashes
- **Transaction input data**: References to previous transaction outputs that are being spent, containing transaction hash and output index (`vout`) fields
- **Transaction output data**: scriptPubKey data containing recipient address information in JSON format
- **Address resolution mapping**: Lookup tables connecting transaction inputs to their corresponding output addresses

## 3. Step-by-step calculation

### Component A: Address resolution from transaction outputs
- **Purpose**: Extract and map recipient addresses from transaction outputs to enable input address resolution
- **Logic**: Parse Dogecoin transaction data to extract all outputs with their corresponding addresses. For each transaction output, extract the recipient address from the `scriptPubKey.addresses` JSON array, taking only the first address when multiple exist. Create a lookup table mapping each transaction hash and output index combination to its recipient address
- **Key fields**: `transaction_hash`, `output_index` (from `n` field or UNNEST offset), `user_address` (from scriptPubKey addresses), `block_timestamp`

### Component B: Historical user identification (pre-2019)
- **Purpose**: Identify transaction senders from historical Dogecoin data before 2019
- **Logic**: Process transactions with timestamps before 2019-01-01. For each transaction input, extract the referenced transaction hash (`txid`) and output index (`vout`). Join with the address resolution lookup table to determine which address originally received the funds being spent, thus identifying the transaction sender
- **Key fields**: `timestamp` (daily truncated), `user_address` (resolved sender address)

### Component C: Recent user identification (2019 onwards)
- **Purpose**: Identify transaction senders from recent Dogecoin data starting from 2019
- **Logic**: Process transactions from 2019-01-01 onwards using the same input resolution logic as historical data. Extract transaction input references, resolve sender addresses through the lookup table, and aggregate by day to identify unique active addresses
- **Key fields**: `timestamp` (daily truncated), `user_address` (resolved sender address)

### Component D: Daily active users aggregation
- **Purpose**: Combine historical and recent user data to create a unified DAU dataset
- **Logic**: Union the historical and recent user datasets to create a complete view of daily active users. Each record represents a unique address that sent at least one transaction on a given day. Add dimensional attributes including chain identifier ('dogecoin'), user type ('Transaction sender'), and timestamp grain ('day')
- **Key fields**: `timestamp`, `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau` (user address)

### Component E: HyperLogLog preparation and windowing
- **Purpose**: Prepare DAU data for efficient MAU calculation using HyperLogLog algorithms
- **Logic**: Initialize HyperLogLog sketches for each day's unique users to enable efficient approximate distinct counting. Create a datespine covering all days in the dataset, then generate 30-day rolling windows by creating join tables that map each day to the previous 29 days. Join the HLL-prepared DAU data with these rolling windows to create the foundation for MAU calculation
- **Key fields**: `timestamp`, `hll` (HyperLogLog sketch), window join dates

### Final aggregation
- **Combining components**: Merge HyperLogLog sketches across the 30-day windows for each dimension (project-level, chain-level, user type-level, product-level) using `hll_count.merge_partial()`, then extract final counts with `hll_count.extract()`
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily boundaries with 30-day rolling windows. Each day's MAU includes unique users from that day plus the previous 29 days
- **Final calculation**: The final MAU value represents the approximate count of distinct addresses that initiated at least one transaction within the 30-day window ending on each date. Results are broken down by different dimensions (overall project, by chain, by user type, by product) and combined using UNION ALL operations

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

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

DogecoinDOGE

Active users (monthly)

Active users (monthly)

Competitive landscape