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D.1 Understanding Clusters |
Often when programming there is a need to combine multiple variables into a single named object. Doing so makes it easier to keep track of your variables and adds clarity to your code. In SheerPower, we call this object a CLUSTER. Some programming languages call these a struct or a vector.
Each cluster is given a name and a list of variables associated with that name. These variables are sometimes also called cluster fields or fields.
Simple clusters have no rows. They are one-dimensional and are typically used to store related information about a single overall concept. For example, below is a cluster that stores the types of food that one has at meal time:
Scalar Cluster:
A scalar cluster in SheerPower is a single, structured
data object that groups together related variables under one name.
It's similar to a record or struct in other programming languages.
Each variable within the cluster, known as a cluster
member or field, holds a single value.
A scalar cluster is one-dimensional and represents a single
instance of data, such as information about a person or a product.
Optionally, each field can be explicitly declared with a data type,
such as int, real, or string.
Example:
employee->name$ = "John Doe"
employee->age = 30
employee->position$ = "Manager"
employee->salary = 70_000
In this example, employee is a scalar cluster that
holds information about a single employee. It includes fields
for name$, age, position$,
and salary. The field salary has been
explicitly declared as a data type of real.
Cluster Array:
A cluster array, on the other hand, is a collection of multiple scalar clusters, arranged
like rows in a spreadsheet. Each row in the cluster array represents an individual record or row, and each
record contains multiple fields. This structure allows you to manage large sets of related data,
similar to how you would use a database table or a spreadsheet.
Example:
add cluster student
student->name$ = "Alice"
student->age = 20
student->grade$ = "A"
add cluster student
student->name$ = "Bob"
student->age = 21
student->grade$ = "B"
In this example, student is a cluster array, where each add cluster
statement adds a new row to the array, representing a different student. You can then access,
manipulate, and iterate through these rows.
Key Differences:
- Single vs. Multiple Records: A scalar cluster holds a single record of related fields, whereas a cluster array holds multiple records, each with the same set of fields.
- Data Organization: Scalar clusters are used when you need to manage a single entity's data, while cluster arrays are ideal for handling multiple entities with similar data structures.
- Iteration: Cluster arrays allow you to iterate over multiple rows, making them suitable for tasks that involve working with lists of data, like databases or spreadsheets.
- Use Case: Use a scalar cluster for simpler, individual data objects and a cluster array for more complex datasets that require storing and managing multiple records.
The print cluster CLUSTERNAME, list statement shows the names of each cluster member and their value.
By default print cluster CLUSTERNAME outputs the cluster fields in CSV format.
When declaring a cluster, you can specify initial values for fields. These values will be set when the cluster is created, providing a convenient way to initialize the cluster with default data.
SheerPower handles NULL or undefined values in clusters by treating them as empty strings or zero values, depending on the data type. When performing operations like sorting or printing, these NULL values are handled gracefully, but developers should be aware of their presence and account for them in logic where necessary.
Summary: Understanding Clusters in SheerPower
Clusters in SheerPower are versatile data structures designed to simplify data management and enhance code clarity, particularly for business applications. They allow developers to group related variables under one name, making data handling efficient and intuitive.
Key Features of Clusters:
- Reduces complexity in managing related variables.
- Improves code readability and maintainability.
- Enables seamless data handling for both single entities and large datasets.
- Handles NULL or undefined values gracefully.
print cluster statement, streamlining
integration with other data systems.Benefits of Using Clusters:
- Reduces complexity in managing related variables.
- Improves code readability and maintainability.
- Enables seamless data handling for both single entities and large datasets.
- Handles NULL or undefined values gracefully.
Whether managing simple data objects or complex datasets, SheerPower clusters offer a powerful combination of simplicity and flexibility. Their ability to adapt to various programming needs makes them a key tool for efficient and elegant code design.
