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    <p>With larger datasets, <a href="https://www.postgresql.org/docs/current/static/tutorial-window.html" rel="noreferrer"><strong>window functions</strong></a> are the most efficient way to perform these kinds of queries -- the table will be scanned only once, instead of once for each date, like a self-join would do. It also looks a lot simpler. :) PostgreSQL 8.4 and up have support for window functions.</p> <p>This is what it looks like:</p> <pre><code>SELECT created_at, sum(count(email)) OVER (ORDER BY created_at) FROM subscriptions GROUP BY created_at; </code></pre> <p>Here <code>OVER</code> creates the window; <code>ORDER BY created_at</code> means that it has to sum up the counts in <code>created_at</code> order.</p> <hr> <p><strong>Edit:</strong> If you want to remove duplicate emails within a single day, you can use <code>sum(count(distinct email))</code>. Unfortunately this won't remove duplicates that cross different dates.</p> <p>If you want to remove <em>all</em> duplicates, I think the easiest is to use a subquery and <code>DISTINCT ON</code>. This will attribute emails to their earliest date (because I'm sorting by created_at in ascending order, it'll choose the earliest one):</p> <pre><code>SELECT created_at, sum(count(email)) OVER (ORDER BY created_at) FROM ( SELECT DISTINCT ON (email) created_at, email FROM subscriptions ORDER BY email, created_at ) AS subq GROUP BY created_at; </code></pre> <p>If you create an index on <code>(email, created_at)</code>, this query shouldn't be too slow either.</p> <hr> <p>(If you want to test, this is how I created the sample dataset)</p> <pre><code>create table subscriptions as select date '2000-04-04' + (i/10000)::int as created_at, 'foofoobar@foobar.com' || (i%700000)::text as email from generate_series(1,1000000) i; create index on subscriptions (email, created_at); </code></pre>
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    1. COThis is great intgr, only that my subscriptions table contains a lot of duplicate email rows. So what `over` is doing is `sum`-ming the `count` numbers, but I still need to recalculate the unique emails on every subsequent date.
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    2. COI updated my answer with a `DISTINCT ON` subquery. It's still a lot faster than Andriy's answer -- can process a million rows within a few seconds -- but perhaps more complicated.
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    3. CONice tip on the generate_series function!
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