Count distinct case when redshift

The COUNT function has three variations. COUNT ( expression ) computes the number of rows with non-NULL values in a specific column or expression. However, one can still count distinct items in a window by using another method.

Trying to count cumulative distinct entities. CASE is used to specify a result when there are multiple conditions. There are two types of CASE expressions: simple and searched.

You can do runtime conversions between compatible data types by using the CAST and CONVERT functions. Certain data types require an explicit conversion to other data types using the CAST or CONVERT function. You can take the max value of dense_rank() to get the distinct count of A partitioned by B. I use a CASE statement to perform a COUNT all the time.

Usually I do this to set a condition, usually time based. No problem, you think: select count(1) from items where width ! If some of the widths or heights are null, they won’t be counted! Surely that wasn’t your intention. For each row returne return only the unique members of a set. The problem is counting only the distinct alternative account numbers when looking at a main account level.

For example, if they have an event logged in the table within the last days of any particular date then they would be counted as an active user. I want to count the distinct users based on a rolling range of days. Understanding the differences between these methods is a great way to wrap our head around how data warehouses work. ROW_NUMBER, RANK and DENSE_RANK Analytical Functions.

Rows with equal values receive the same rank with next rank value skipped. The rank analytic function is used in top n analysis. In your case , in the expression you have case statement rite. Something went wrong on our end.

Instead of doing all that work, we can compute the distincts in advance, which only needs one hash set. Distinct count with case statement. This metric counts the number of distinct users who performed at least one tracked event during the specified time period. Or a user could be active days in a row, but should only be counted once when the window covers any part of that date range.

In order to ensure your database’s optimal performance the key factor lies in the uniform data distribution into these nodes and slices. The UNION and UNION ALL operation combines the of two similar sub-queries into a single result set that contains the rows that are returned by both SELECT statement. Data types of the column that you are trying to combine should match.

These operations are logical OR. Redshift UNION and UNION ALL. SELECT Stack Exchange Network Stack Exchange network consists of 1QA communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Each block can be read in parallel.

Data example of input and output would have likely changed my suggestion. Also, I did not Count anything, but summed a variable. With notes about the condition that it might work in the lack of details about your data. Getting the opposite effect of returning a COUNT that includes the NULL values is a little more complicated. So this is a peculiar case for postgres.

One reason is because distinct aggregates can be more complicated to plan when you have a case like: SELECT COUNT ( distinct x), COUNT ( distinct y). Some databases have solutions for that problem, but postgres does not. AS (SELECT COUNT ( DISTINCT node) nodenum FROM stv_slices), slices AS (SELECT COUNT ( DISTINCT slice) slices FROM stv_slices s WHERE node = 0), disks AS (SELECT COUNT (p. owner) disks FROM stv_partitions p WHERE p.