When is data relational

For example data used on a website could be stored in a relational database, but on-demand loaded into a graph structure which is then cached in an in-memory key-value store. What matters is again how your application use the data, not the form of the data itself. An object store will be faster at loading an object graph stored as a unit, but will be much slower at ad-hoc querying across many objects, or updating properties on many object.

It seems to me the author is making a good point in that if your code is for example getting the Number of customers in Spain for some bit of logic, you shouldn't populate a list of customers with all the customers in spain and then count the customer objects.

Obviously you cant tell from the customer data structure itself whether it will be used like that. If this includes thing likes like aggregates or 'All X related to Y' then your 'data' is not suitable for the atomic NoSql approach.

Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. How do I know my data is relational or object oriented in nature? Ask Question. Asked 10 years, 5 months ago. Active 6 years, 6 months ago. Viewed 3k times. If your data is relational in nature, the overhead of a relational database is worth it.

Improve this question. Community Bot 1. Gulshan Gulshan 9, 10 10 gold badges 54 54 silver badges 89 89 bronze badges. Tell us more about your data FrustratedWithFormsDesigner I think he's looking for general guidelines. The line that talks about "key-value stores that will allow you to hold elegant, self-contained data structures in huge quantities and access them at lightning speed" seems to describe the "objects" data that should be used in NoSQL - basically it sounds like "self-contained" chunks of data with no references or relations to other chunks of data I can't give good examples of this because it's not something I am used to working with at least not in this context.

Just got this link. Hope it has hints to answer- highscalability. Add a comment. Active Oldest Votes. Codd in Currently, it is the most widely used data model. A relation, also known as a table or file , is a subset of the Cartesian product of a list of domains characterized by a name.

And within a table, each row represents a group of related data values. A row , or record, is also known as a tuple. The columns in a table is a field and is also referred to as an attribute. You can also think of it this way: an attribute is used to define the record and a record contains a set of attributes.

A database is composed of multiple tables and each table holds the data. Figure 7. A database stores pieces of information or facts in an organized way.

Understanding how to use and get the most out of databases requires us to understand that method of organization. The principal storage units are called columns or fields or attributes. These house the basic components of data into which your content can be broken down. When deciding which fields to create, you need to think generically about your information, for example, drawing out the common components of the information that you will store in the database and avoiding the specifics that distinguish one item from another.

Look at the example of an ID card in Figure 7. A domain is the original sets of atomic values used to model data. Since most joins are equijoins we usually drop that specification. The output of an inner join is a new data frame that contains the key, the x values, and the y values. We use by to tell dplyr which variable is the key:.

The most important property of an inner join is that unmatched rows are not included in the result. An inner join keeps observations that appear in both tables. An outer join keeps observations that appear in at least one of the tables. There are three types of outer joins:. This observation has a key that always matches if no other key matches , and a value filled with NA. The left join should be your default join: use it unless you have a strong reason to prefer one of the others.

However, this is not a great representation. So far all the diagrams have assumed that the keys are unique. This section explains what happens when the keys are not unique. There are two possibilities:. One table has duplicate keys. This is useful when you want to add in additional information as there is typically a one-to-many relationship.

This reflects that the key is a primary key in y and a foreign key in x. Both tables have duplicate keys. This is usually an error because in neither table do the keys uniquely identify an observation.

When you join duplicated keys, you get all possible combinations, the Cartesian product:. So far, the pairs of tables have always been joined by a single variable, and that variable has the same name in both tables. You can use other values for by to connect the tables in other ways:.

For example, the flights and weather tables match on their common variables: year , month , day , hour and origin. This is like a natural join, but uses only some of the common variables. For example, flights and planes have year variables, but they mean different things so we only want to join by tailnum. Note that the year variables which appear in both input data frames, but are not constrained to be equal are disambiguated in the output with a suffix.

This will match variable a in table x to variable b in table y. The variables from x will be used in the output. For example, if we want to draw a map we need to combine the flights data with the airports data which contains the location lat and lon of each airport. Each flight has an origin and destination airport , so we need to specify which one we want to join to:.

Compute the average delay by destination, then join on the airports data frame so you can show the spatial distribution of delays. You might want to use the size or colour of the points to display the average delay for each airport. Add the location of the origin and destination i. What happened on June 13 ?

Display the spatial pattern of delays, and then use Google to cross-reference with the weather. The advantages of the specific dplyr verbs is that they more clearly convey the intent of your code: the difference between the joins is really important but concealed in the arguments of merge.

Joining different variables between the tables, e. As this syntax suggests, SQL supports a wider range of join types than dplyr because you can connect the tables using constraints other than equality sometimes called non-equijoins.

Filtering joins match observations in the same way as mutating joins, but affect the observations, not the variables. There are two types:. Semi-joins are useful for matching filtered summary tables back to the original rows. Now you want to find each flight that went to one of those destinations.

You could construct a filter yourself:. How would you construct the filter statement that used year , month , and day to match it back to flights?

Instead you can use a semi-join, which connects the two tables like a mutating join, but instead of adding new columns, only keeps the rows in x that have a match in y :. This means that filtering joins never duplicate rows like mutating joins do:.