![]() You’ll begin by performing arithmetic and using various functions with dates and times using only the SELECT statement. In this tutorial, you will learn how to use dates and times in SQL. For instance, you may need to calculate the total hours spent on a certain activity, or perhaps you need to manipulate date or time values using mathematical operators and aggregate functions to calculate their sum or average. The “log p” is due to the sort operation, but since you are grouping your activity to a smaller number of periods, this is typically not an issue.When working with relational databases and Structured Query Language (SQL), there may be times when you need to work with values representing specific dates or times. The complexity of this query should be O(e + p log p) where e is the number of entities, and p is the number of periods. SELECT "month", 0 as ct, 0 as value FROM months_table To make sure all months are accounted for. * Optionally, if you have very sparse data and want Also, you could prefer to prorate their value for the current monthĭate_trunc('month',entity.end_date) as "month", of the month are only removed in the subsequent month. to the close date, so that entities that are active for any portion ![]() Depending on your business logic, you may want to add a month or period ![]() (ORDER BY entity_period.month ROWS UNBOUNDED PRECEDING) as active_valueĭate_trunc('month',entity.start_date) as "month", (ORDER BY entity_period.month ROWS UNBOUNDED PRECEDING) as active_entities, The inner sum is to combine the 2 rows for each month for "new" and "closed" The outer sum is for the running total over months The basic idea is to aggregate your entity table twice, once by start period, and once by end period, and then to use an ordered window function to calculate the net aggregate over time. O(e*d) intermediate result set - Not only do the number of comparisons grow quadratically, but if you have open ended entities like users, the size of the intermediate result set is also quadratic, which can lead to large materialized tables, and hash operations that spill to diskĮasy to compound the problem by one-to-many joining activity - It can be tempting to join event or activity rows to the date table as well, multiplicatively increasing the cardinality of the join As your dataset grows, both in number of entities and dates, these computations grow quadratically.īroadcasting of rows - If your database is an MPP database, in order to do nested loop joins, it must broadcast data around the network to enable doing all of the comparisons in the nested loop join. O(e*d) join - In most databases (assume yours too unless you know otherwise), joins on inequalities result in the database performing a nested loop join, meaning that the number of comparisons it must do is equal to e*d, where e and d are the number of rows in your entity and date tables respectively. ON date_table.month >= date_trunc('month', entity.start_date)ĪND date_table.month <= date_trunc('month', entity.end_date) This is tempting because then for each entity, you have a row for each day the entity was valid, and then you can just aggregate over the date to get your answer. It’s tempting take our entities (tickets, users, subscriptions, etc.) and join a date table on the condition that the date is between the entity’s start and end-date (or the current date for open-ended entities).
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