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#AGGREGATE DATE UP TO A DATE SQL CODE#

We hope these query walkthroughs gave you some ideas for your own questions, but keep in mind that different databases support different SQL functions, so make a habit of consulting your database’s documentation when working through your queries. (Given that the Sample Database is random, our responses don’t match reality very well-how often do people wait 173 days between account setup and purchase?) Further reading DATEDIFF takes a time period (like “day,” “week,” “month”), and returns the number of periods between two timestamps. We used the DATEDIFF function to find the number of days between account creation and their first order, then stored the result as days_before_first_order. To sum up: we’ve grabbed the customer’s created_at date, and joined the query to our CTE. | ID | ACCOUNT_CREATION | FIRST_ORDER_DATE | DAYS_BEFORE_FIRST_ORDER | Then we’ll use this table and join it to itself, but with an offset: its rows shifted by one week. We’ll call the results table order_count_by_week. Essentially CTEs are a way to assign a variable to interim results, which we can then treat as though the results were an actual table in the database (like Orders or Table). We’ll make the query above a Common Table Expression (CTE) using the WITH keyword.

Subtract the previous week’s order count total from each week’s total to get the week-over-week change.

Join that CTE to itself by offsetting the join by 1 week.

Wrap our results in a Common Table Expression (CTE).

The challenge here is that, in order to perform the subtraction, we need to somehow get each week’s count in the same row as the previous week’s count. What we need to do now is take the order count from each week (which we’ll refer to as w1), and subtract it from the count for the previous week (which we’ll call w2). We’ll use these results to build the rest of the query. For example, using the AS keyword, we’ll change Count(*) to display as accounts_created. We’ll also use aliases for the results to give the columns more specific names.

#AGGREGATE DATE UP TO A DATE SQL CODE#

By the way, SQL is case insensitive, so you can case your code however you like ( date_trunc works too, or DaTe_TrUnc if you’re querying sarcastically). For our purpose here, we’ll write DATE_TRUNC('week', created_at), which will return the date for the Monday of each week. That first text argument is the time period, in this case ‘week’, but we could specify different granularities, like month, quarter, or year (check your database’s documentation on DATE_TRUNC to see the options). DATE_TRUNC takes two arguments: text and a timestamp, and it returns a timestamp. To see which week each created_at date falls in, we’ll use the DATE_TRUNC function.ĭATE_TRUNC lets you round (“truncate”) your timestamps to the granularity you care about: week, month, and so on. We’ll need to group these account creations, but instead of grouping them by date, we’ll need to group them by week. Also referred to as the user’s ‘join date’”. Since we’re interested in when a customer signed up for an account, we’ll need the created_at field, which according to our data reference is “The date the user record was created. Use the Data Reference sidebar to look up info about tables. We can SELECT * FROM people LIMIT 1 to see the list of fields, or simply click on the book icon to see metadata about the tables in the database we’re working with. Here we’re looking to return two columns: Example: how many people created an account each week?

We often want to ask questions like: how many customers signed up each month? Or how many orders were placed each week? Here we’ll go through a table of results, count the rows, and group those counts by a period of time. How many days between when a customer created an account and when they placed their first order? How did the count of orders this week compare with last week? How many people created an account each week? But even if you’re just getting started, you should be able to pick up a few tips. We’re assuming this isn’t your first SQL query, and you’re looking to level up.

We’ll use the Sample Database included with Metabase so you can follow along, and stick to some common SQL functions and techniques that work across many databases. We’ll walk through three common scenarios when working with dates in SQL. Example: how many days between when a customer created an account and when they placed their first order?.Example: how did orders compare to last week?.Example: how many people created an account each week?.