Comparing Transaction Throughputs for 8 blockchains in Google BigQuery with Google Data Studio

Evgeny Medvedev
2 min readFeb 5, 2019


With our recent release of Bitcoin-derived blockchain datasets, BigQuery now contains 8 cryptocurrencies in total including Bitcoin, Bitcoin Cash, Zcash, Litecoin, Dogecoin, Dash, Ethereum, and Ethereum Classic. Below is the graph demonstrating daily transaction counts for those blockchains:

Daily transaction counts. Scroll to bottom for interactive version of the graphs.

Ethereum is clearly leading with almost 600k daily transactions on average in Jan 2019. The highest daily transactions were seen in Jan 2018 with almost 1.2M tx/day, which is around 14 tx/sec on average.

You can also see the Ethereum DAO and the Bitcoin Cash forks on the top graph. Ironically, the increase of the block size in Bitcoin Cash, meant to raise the number of transactions, is followed by an abrupt drop instead.

The second graph is showing Dogecoin followed by Litecoin, Dash, and Zcash in the order of decreasing transaction counts.

You can run the SQLs in BigQuery yourself:

with daily_transactions as (
select date(block_timestamp) as date, count(*) as count
from `bigquery-public-data.crypto_bitcoin.transactions`
group by date
select date_trunc(date, WEEK) as week, cast(avg(count) as INT64) as count
from daily_transactions
group by week
order by week

Run it in BigQuery

Simply substitute bitcoin in the dataset name with one of bitcoin_cash, litecoin, dash, zcash, ethereum, or ethereum_classic, to query the data for the respective chain.

Below is an interactive version of the graphs.

Daily transaction counts (Interactive).

This report is public in Data Studio Make a copy of it to view the SQLs and modify the graphs.

Transaction Throughputs

Let’s compare the throughputs of the 8 blockchains. This query outputs the maximum number of transactions processed in a block divided by the average block time on that day, for each blockchain. Here are the results:

The maximum theoretical values are likely higher, but the numbers above give us a rough approximation of those.

Check out the tools we used to ingest the data to BigQuery, they are open-source:

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