Screener

One row per chain in screeners.chains, every figure as of as_of, the last complete day. Each measure has a column per window and aggregation, named <measure>_<window>_<aggregation>: fees_30d_sum is the fees paid to the chain over the 30 days ending on as_of, and app_fees_30d_sum is the fees paid to the apps on it.

Columns

Keys
Aggregations
Measures

The two key columns, then one FLOAT64 column per measure, window and aggregation. Every row is a chain in dimensions.chains, so the join on chain_id always matches.

Column	Type	Description
`as_of`	TIMESTAMP	The last complete day, the same on every row. Every window ends on it.
`chain_id`	STRING	Chain the row belongs to, such as `ethereum`; joins `dimensions.chains`.

A window is 1d, 7d, 30d, 90d, 180d or 365d, and spans that many days ending on as_of, while the max_ columns read the whole history. Which aggregations a measure has follows its over_time in Metric definitions: only a measure whose over_time is sum has sum.

Column	What it holds
`<measure>_<window>_avg`	Mean of the daily values over the window.
`<measure>_<window>_sum`	Total of the daily values over the window. Flows only: the measures ticked under Sum.
`<measure>_<window>_change`	A comparison of two aggregations: the value on `as_of` against the value one window earlier, as `(now - then) / abs(then)`. Null when either value is missing or the earlier one is 0.
`<measure>_<window>_trend`	A comparison of two aggregations: the window’s average against the average of the window before it, as `(avg - prior) / abs(prior)`. Null unless the window before has a value on every one of its days, and null when its average is 0.
`<measure>_max_latest`	The value on `as_of`. Null when there is none.
`<measure>_max_ath`	The highest daily value over the chain’s whole history.
`<measure>_max_atl`	The lowest daily value over the chain’s whole history.

Every measure has the avg, change, trend and max_ columns, and the flows also have sum. The app_ measures are the apps on the chain added together. Price, market cap and developers sit on the project screener, for the project dimensions.chains names in project_id.

Measure	Sum	What it holds
`daily_active_addresses`	✗	Distinct addresses transacting on the chain per day.
`monthly_active_addresses`	✗	Distinct addresses transacting on the chain over a rolling 30 days.
`weekly_active_addresses`	✗	Distinct addresses transacting on the chain over a rolling 7 days.
`app_monthly_active_addresses`	✗	Distinct addresses using the apps’ contracts on the chain over a rolling 30 days, each app’s count added up, so an address using two apps counts twice.
`app_active_loans`	✗	Open loans in the apps on the chain, USD.
`app_assets_staked`	✗	Assets staked with the apps on the chain, USD.
`app_bridged_supply`	✗	Stablecoins bridged onto the chain, USD.
`app_fees`	✓	Fees paid to the apps on the chain, USD.
`app_gas_used`	✓	Gas consumed by transactions calling the apps’ contracts on the chain.
`app_outstanding_supply`	✗	Stablecoin supply issued on the chain, USD.
`app_trading_volume`	✓	Trading volume of the exchanges on the chain, USD. Derivatives venues and aggregators are left out: an aggregator’s volume is counted at the exchange it routes to.
`app_transaction_count_contracts`	✓	Transactions calling the apps’ contracts on the chain.
`app_tvl`	✗	Value locked in the apps on the chain, USD.
`app_monthly_active_users`	✗	Monthly active users of the apps on the chain, each app’s count added up, so a user of two apps counts twice.
`block_time_average`	✗	Average time between blocks.
`bridge_deposits`	✗	USD value of assets held in the chain’s canonical bridge on the chain it settles to.
`daily_contract_deployers`	✗	Distinct addresses deploying contracts.
`contract_deployment_count`	✓	Contracts deployed on the chain.
`cost_of_revenue`	✓	The chain’s direct cost of revenue, such as data posted to the chain it settles to.
`expenses`	✓	What the chain spent, in USD.
`fees`	✓	Fees paid to the chain (gas / sequencer).
`fees_supply_side`	✓	Fees the chain does not retain (pass-throughs to builders / operators); fees = revenue + fees_supply_side. 0 where there are no pass-throughs, e.g. HyperEVM gas.
`validators`	✗	Number of validators securing the chain’s consensus.
`revenue`	✓	Revenue retained by the chain (gas / sequencer fees net of pass-throughs; equals fees where there are none, e.g. HyperEVM).
`staking_market_cap`	✗	USD value of the chain’s native token staked to secure the network.
`token_incentives`	✓	Value of tokens the chain distributed as incentives.
`transaction_count`	✓	Transactions processed by the chain.
`transaction_fee_average`	✗	Average fee per transaction.
`transaction_fee_median`	✗	Median fee per transaction.
`transactions_per_second`	✗	Average transactions per second.

Sample queries

One chain
Top chains by app fees
Chain fees against app fees

Every column of one row shares the same as_of, so the figures read together. fees is what users paid the chain, and app_fees what they paid the apps running on it.

select
    as_of,
    fees_30d_sum,
    app_fees_30d_sum,
    transaction_count_30d_sum,
    app_tvl_max_latest,
    app_tvl_30d_change
from `screeners.chains`
where chain_id = 'ethereum'

app_fees_30d_sum is the fees paid to the apps on the chain over the 30 days ending on as_of. The join to dimensions.chains on chain_id adds the name.

select
    chains.name,
    screener.app_fees_30d_sum,
    screener.transaction_count_30d_sum,
    screener.app_fees_30d_trend
from `screeners.chains` as screener
join `dimensions.chains` as chains
    using (chain_id)
where screener.app_fees_30d_sum is not null
order by screener.app_fees_30d_sum desc
limit 25

Both sides are 30-day totals over the same window, so their ratio compares the gas users paid the chain with the fees they paid its apps. The app fee floor keeps a small base from dominating the ranking.

select
    chains.name,
    screener.fees_30d_sum,
    screener.app_fees_30d_sum,
    safe_divide(screener.fees_30d_sum, screener.app_fees_30d_sum) as chain_to_app_fees
from `screeners.chains` as screener
join `dimensions.chains` as chains
    using (chain_id)
where screener.app_fees_30d_sum > 100000
order by chain_to_app_fees desc
limit 25

Overview

Popular datasets

Market verticals

Chain ecosystems

Core entities

Columns

Sample queries

​Columns

​Sample queries

Columns

Sample queries