Sql how does union work

Question: I need to compare two dates and return the count of a field based on the date values. For example, I have a date field in a table called last updated date. For example, you could try the following:. To address real-world data requirements, we may need to combine result sets from multiple data sources so that we could do data analysis or create new datasets.

The datasets may be identical but there are chances that they reference different tables. Is there a way to combine the data in a single query? Are Set Operators a viable option? An operator is a symbol or a keyword defines an action that is performed on one or more expressions in the Select statement. The Union operator combines the results of two or more queries into a distinct single result set that includes all the rows that belong to all queries in the Union.

In this operation, it combines two more queries and removes the duplicates. When looking at Union vs Union All we find they are quite similar, but they have some important differences from a performance results perspective. The Union operator combines the results of two or more queries into a single result set that includes all the rows that belong to all queries in the Union.

In simple terms, it combines the two or more row sets and keeps duplicates. For the same dataset from the aforementioned example, the intersect operator output is given below. For the same dataset from the aforementioned example, the Except operator output is given below.

Note: It is very easy to visualize a set operator using a Venn diagram, where each of the tables is represented by intersecting shapes. The intersections of the shapes, where the tables overlap, are the rows where a condition is met. You can download the sample AdventireWorks database here. In this example, the result set includes a distinct set of rows from the first set and second set.

This may be for tables with similar data within the same database or maybe there is a need to combine similar data across databases or even across servers. There is a big difference in how these work as well as the final result set that is returned, but basically these commands join multiple datasets that have similar structures into one combined dataset.

This operation will allow you to join multiple datasets into one dataset and will remove any duplicates that exist. This operation again allows you to join multiple datasets into one dataset, but it does not remove any duplicate rows. Because this does not remove duplicate rows this process is faster, but if you don't want duplicate records you will need to use the UNION operator instead. Tip: If you don't have the exact same columns in all queries use a default value or a NULL value such as:.

Let's take a look at a few simple examples of how these commands work and how they differ. As you will see the final resultsets will differ, but there is some interesting info on how SQL Server actually completes the process.

This is probably not something you would do, but this helps illustrate the differences of these two operators. When this query is run the result set has rows. This is the rows returned 3 times. Hint: you will have to use the tutorial. And you'll want to group by status and dataset.

Work-related distractions for every data enthusiast. Python Tutorial Learn Python for business analysis using real-world data. Start Now.