Getting Started with the DataQuery Script

By /

Okay, here’s a comprehensive article on “Getting Started with DataQuery Script,” aiming for approximately 5000 words. Because of the sheer length, I’ll break it down into logical sections and provide substantial detail, examples, and explanations in each.

Getting Started with DataQuery Script: A Comprehensive Guide

Table of Contents

  1. Introduction: What is DataQuery Script?

    • 1.1 Defining DataQuery Script
    • 1.2 Why Use DataQuery Script? (Advantages)
    • 1.3 Core Concepts
    • 1.4 Use Cases
    • 1.5 Comparison with other query languages (SQL, etc.)

  2. Setting Up Your Environment

    • 2.1 Choosing Your Editor/IDE
    • 2.2 Installing Necessary Libraries/Packages (Python focus)
    • 2.3 Connecting to Your Data Source (Databases, Files, APIs)
      • 2.3.1 Database Connections (Example: PostgreSQL, MySQL)
      • 2.3.2 File Connections (CSV, JSON, Excel)
      • 2.3.3 API Connections (RESTful APIs)
    • 2.4 Basic Configuration and Environment Variables

  3. Basic Syntax and Structure

    • 3.1 DataQuery Script’s Syntax Philosophy
    • 3.2 Comments
    • 3.3 Data Types
      • 3.3.1 Numeric Types
      • 3.3.2 String Types
      • 3.3.3 Boolean Types
      • 3.3.4 Date and Time Types
      • 3.3.5 Arrays and Lists
      • 3.3.6 Objects/Dictionaries
    • 3.4 Variables and Assignments
    • 3.5 Operators
      • 3.5.1 Arithmetic Operators
      • 3.5.2 Comparison Operators
      • 3.5.3 Logical Operators
      • 3.5.4 Assignment Operators
      • 3.5.5 Other Operators (Membership, Identity)
    • 3.6 Control Flow Statements
      • 3.6.1 if, else if, else
      • 3.6.2 for loops
      • 3.6.3 while loops
      • 3.6.4 break and continue

  4. Data Selection and Filtering

    • 4.1 The SELECT Statement
    • 4.2 Specifying Columns
    • 4.3 The FROM Clause (Data Source)
    • 4.4 The WHERE Clause (Filtering)
      • 4.4.1 Using Comparison Operators
      • 4.4.2 Using Logical Operators (AND, OR, NOT)
      • 4.4.3 Using IN, BETWEEN, LIKE
      • 4.4.4 Handling NULL values
    • 4.5 Aliasing (Columns and Data Sources)
    • 4.6 Distinct selection

  5. Data Transformation and Manipulation

    • 5.1 Built-in Functions
      • 5.1.1 String Functions (Concatenation, Substring, Case Conversion)
      • 5.1.2 Numeric Functions (Rounding, Absolute Value, Mathematical Operations)
      • 5.1.3 Date and Time Functions (Formatting, Extraction, Date Arithmetic)
      • 5.1.4 Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
    • 5.2 User-Defined Functions (UDFs)
      • 5.2.1 Defining a UDF
      • 5.2.2 Calling a UDF
      • 5.2.3 Scope and Parameter Passing
    • 5.3 Data Type Conversion
    • 5.4 Conditional Expressions (Case/When)

  6. Data Aggregation and Grouping

    • 6.1 The GROUP BY Clause
    • 6.2 Using Aggregate Functions with GROUP BY
    • 6.3 The HAVING Clause (Filtering Aggregated Results)
    • 6.4 Combining GROUP BY with other clauses

  7. Working with Multiple Data Sources (Joins)

    • 7.1 Introduction to Joins
    • 7.2 INNER JOIN
    • 7.3 LEFT JOIN (and RIGHT JOIN)
    • 7.4 FULL OUTER JOIN
    • 7.5 CROSS JOIN
    • 7.6 Joining on Multiple Conditions
    • 7.7 Self-Joins

  8. Subqueries and Nested Queries

    • 8.1 What are Subqueries?
    • 8.2 Subqueries in the WHERE Clause
    • 8.3 Subqueries in the SELECT Clause
    • 8.4 Subqueries in the FROM Clause
    • 8.5 Correlated Subqueries
    • 8.6 EXISTS and NOT EXISTS

  9. Data Ordering and Limiting

    • 9.1 The ORDER BY Clause
      • 9.1.1 Ascending and Descending Order
      • 9.1.2 Ordering by Multiple Columns
    • 9.2 The LIMIT Clause (and OFFSET)
      • 9.2.1 Limiting the Number of Rows Returned
      • 9.2.2 Pagination with LIMIT and OFFSET

  10. Working with JSON and Semi-structured Data

    • 10.1 Introduction to JSON
    • 10.2 Accessing JSON Elements
    • 10.3 Filtering and Transforming JSON Data
    • 10.4 Unnesting JSON Arrays
    • 10.5 Working with Nested JSON Objects

  11. Error Handling and Debugging

    • 11.1 Common DataQuery Script Errors
    • 11.2 Debugging Techniques
      • 11.2.1 Print Statements
      • 11.2.2 Using a Debugger
      • 11.2.3 Logging
    • 11.3 Exception Handling (Try-Except Blocks)

  12. Best Practices and Optimization

    • 12.1 Writing Clean and Readable Code
    • 12.2 Optimizing Query Performance
      • 12.2.1 Using Indexes
      • 12.2.2 Avoiding Full Table Scans
      • 12.2.3 Optimizing Joins
      • 12.2.4 Limiting Data Retrieval
    • 12.3 Code Reusability (Functions and Modules)
    • 12.4 Version Control (Git)

  13. Advanced Topics

    • 13.1 Window Functions
    • 13.2 Common Table Expressions (CTEs)
    • 13.3 Recursive Queries
    • 13.4 Working with Geospatial Data
    • 13.5 Integrating with Machine Learning Models

  14. Example Use Cases and Projects

    • 14.1 Data Cleaning and Preparation
    • 14.2 Business Intelligence Reporting
    • 14.3 Data Analysis and Exploration
    • 14.4 Data Migration and Transformation
    • 14.5 Building Data Pipelines

  15. Conclusion and Future of DataQuery Script

1. Introduction: What is DataQuery Script?

1.1 Defining DataQuery Script

DataQuery Script is a hypothetical domain-specific language (DSL) designed for querying and manipulating data from various sources. This article treats it as a language that could exist, borrowing principles from existing query languages and data manipulation tools. The goal is to illustrate the concepts involved in creating and using such a language, rather than describing a specific, real-world implementation. It will draw heavily from SQL, Python’s data manipulation libraries (like Pandas), and concepts from other data processing tools.

1.2 Why Use DataQuery Script? (Advantages)

A well-designed DataQuery Script could offer several advantages:

  • Unified Data Access: A single language to interact with databases, files (CSV, JSON, Excel), and APIs, reducing the need to learn multiple syntaxes.
  • Abstraction: Hide the complexities of underlying data sources, allowing users to focus on the data itself.
  • Expressiveness: Provide a concise and readable syntax for common data operations (filtering, aggregation, transformation).
  • Extensibility: Allow users to define their own functions (UDFs) and integrate with external libraries.
  • Portability: Potentially run queries across different platforms and data sources without modification.
  • Data Governance: Potentially incorporate features for data lineage, auditing, and access control.
  • Declarative nature: Users can focus on what data they want, rather than how to retrieve it.

1.3 Core Concepts

  • Data Source: The origin of the data (database table, file, API endpoint).
  • Query: A statement that describes the desired data operations.
  • Result Set: The data returned by a query.
  • Schema: The structure of the data (column names, data types).
  • Function: A reusable block of code that performs a specific task.
  • Expression: A combination of values, variables, operators, and functions that evaluates to a single value.

1.4 Use Cases

DataQuery Script could be used for a wide range of tasks:

  • Data Analysis: Exploring and summarizing data to gain insights.
  • Reporting: Generating reports and dashboards for business intelligence.
  • Data Cleaning: Correcting errors and inconsistencies in data.
  • Data Transformation: Converting data from one format to another.
  • Data Integration: Combining data from multiple sources.
  • ETL (Extract, Transform, Load): Building data pipelines for data warehousing.

1.5 Comparison with other query languages (SQL, etc.)

  • SQL (Structured Query Language): The standard language for relational databases. DataQuery Script would likely borrow heavily from SQL’s syntax and concepts, but aim to be more general-purpose and support non-relational data sources.
  • Pandas (Python): A powerful data analysis library for Python. DataQuery Script could offer a more declarative and concise syntax compared to Pandas’ procedural approach.
  • LINQ (Language Integrated Query): A .NET framework feature for querying data. Similar to LINQ, DataQuery Script would aim to provide a unified query syntax across different data sources.
  • GraphQL: A query language for APIs. DataQuery Script could potentially be used to query GraphQL APIs, but would also support other data sources.
  • NoSQL Query Languages: Languages like MongoDB’s query language. DataQuery Script would aim to be more general-purpose than database-specific query languages.

2. Setting Up Your Environment

2.1 Choosing Your Editor/IDE

You’ll need a text editor or Integrated Development Environment (IDE) to write and run DataQuery Script. Here are some good options:

  • VS Code (Visual Studio Code): A free, highly customizable, and popular code editor with excellent extension support (including syntax highlighting and debugging for various languages). You could create a custom extension for DataQuery Script.
  • Sublime Text: A fast and lightweight text editor.
  • Atom: Another highly customizable, open-source editor.
  • PyCharm (for Python-based implementation): A powerful IDE specifically for Python development, if your DataQuery Script interpreter is built using Python.
  • Jupyter Notebook/JupyterLab (for interactive exploration): Excellent for interactive data analysis and prototyping, especially if DataQuery Script has a Python-based backend.

2.2 Installing Necessary Libraries/Packages (Python focus)

Since DataQuery Script is hypothetical, we’ll assume a Python-based implementation for this guide. This means you’ll likely use Python libraries to connect to data sources and execute queries. Here’s how to install the necessary packages using pip:

bash
pip install pandas # For data manipulation and file I/O
pip install psycopg2-binary # For PostgreSQL (or psycopg2)
pip install pymysql # For MySQL
pip install sqlalchemy # For database abstraction
pip install requests # For making API requests
pip install openpyxl # For working with Excel files

  • pandas: Essential for handling data in DataFrames, reading/writing files (CSV, JSON, Excel), and performing data manipulation.
  • psycopg2-binary / psycopg2: The most popular PostgreSQL adapter for Python. psycopg2-binary is easier to install, but psycopg2 is generally recommended for production.
  • pymysql: A pure-Python MySQL client.
  • sqlalchemy: Provides a high-level, database-agnostic interface, allowing you to work with different databases using a consistent API. Highly recommended for flexibility.
  • requests: The standard library for making HTTP requests to interact with APIs.
  • openpyxl: For reading and writing more modern Excel files (.xlsx).

2.3 Connecting to Your Data Source (Databases, Files, APIs)

2.3.1 Database Connections (Example: PostgreSQL, MySQL)

We’ll use SQLAlchemy for a more consistent approach.

“`python
from sqlalchemy import create_engine

PostgreSQL

engine_pg = create_engine(‘postgresql://user:password@host:port/database’)

MySQL

engine_mysql = create_engine(‘mysql+pymysql://user:password@host:port/database’)

Example usage (fetching data with Pandas):

import pandas as pd
df = pd.read_sql_query(‘SELECT * FROM my_table’, engine_pg)
print(df)
“`

  • create_engine: Creates a connection engine to the database.
  • Connection String: The string passed to create_engine specifies the database type, username, password, host, port, and database name. The format varies slightly depending on the database.
  • pd.read_sql_query: Executes a SQL query (which could be a DataQuery Script translated to SQL) and returns the result as a Pandas DataFrame.

2.3.2 File Connections (CSV, JSON, Excel)

Pandas makes reading files very straightforward:

“`python
import pandas as pd

CSV

df_csv = pd.read_csv(‘my_data.csv’)

JSON

df_json = pd.read_json(‘my_data.json’)

Excel

df_excel = pd.read_excel(‘my_data.xlsx’, sheet_name=’Sheet1′)

print(df_csv)
print(df_json)
print(df_excel)

“`

  • pd.read_csv, pd.read_json, pd.read_excel: Functions to load different files types.

2.3.3 API Connections (RESTful APIs)

“`python
import requests
import pandas as pd

Example API endpoint (replace with your actual API)

api_url = ‘https://api.example.com/data’

Optional: Add parameters to the request

params = {‘param1’: ‘value1’, ‘param2’: ‘value2’}

Make the API request

response = requests.get(api_url, params=params)

Check for successful response (status code 200)

if response.status_code == 200:
# Parse the JSON response
data = response.json()

# Convert to a Pandas DataFrame (if the data is tabular)
df_api = pd.DataFrame(data)
print(df_api)

else:
print(f”Error: API request failed with status code {response.status_code}”)
“`

  • requests.get: Sends a GET request to the API endpoint.
  • response.status_code: Checks the HTTP status code (200 indicates success).
  • response.json(): Parses the JSON response into a Python dictionary.
  • pd.DataFrame: convert json data to pandas DataFrame.

2.4 Basic Configuration and Environment Variables

It’s good practice to store sensitive information (like database credentials and API keys) in environment variables rather than hardcoding them in your scripts.

“`python
import os
from dotenv import load_dotenv

Load environment variables from a .env file (optional, but recommended)

load_dotenv()

Access environment variables

db_user = os.getenv(‘DB_USER’)
db_password = os.getenv(‘DB_PASSWORD’)
api_key = os.getenv(‘API_KEY’)

Use the variables in your connection string or API requests

engine = create_engine(f’postgresql://{db_user}:{db_password}@host:port/database’)
“`

  • os.getenv: Retrieves the value of an environment variable.
  • .env file: A simple text file to store environment variables locally (don’t commit this to version control!). You’ll need the python-dotenv package (pip install python-dotenv).
  • This provides a more secure and flexible method.

3. Basic Syntax and Structure

3.1 DataQuery Script’s Syntax Philosophy

DataQuery Script (DQS) aims for a syntax that is:

  • Declarative: Focus on what data you want, not how to get it.
  • SQL-inspired: Leverage the familiarity and power of SQL for relational data operations.
  • Pythonic: Adopt Python’s readability and use of indentation (where appropriate).
  • Extensible: Allow user-defined functions and integration with external libraries.
  • Case-Insensitive (for keywords): SELECT, select, and SeLeCt would be treated the same. However, identifiers (table and column names) might be case-sensitive depending on the underlying data source.

3.2 Comments

DQS supports both single-line and multi-line comments:

“`dataqueryscript
— This is a single-line comment

/
This is a
multi-line comment
/

SELECT * FROM my_table; — Comment after a statement
“`

3.3 Data Types

DQS would support a range of data types, similar to SQL and Python:

3.3.1 Numeric Types

  • INT (or INTEGER): Integer numbers.
  • BIGINT: Large integer numbers.
  • FLOAT: Floating-point numbers (with decimal points).
  • DECIMAL (or NUMERIC): Fixed-precision decimal numbers (for financial data, etc.).

3.3.2 String Types

  • VARCHAR (or STRING): Variable-length strings.
  • CHAR: Fixed-length strings.
  • TEXT: Large text strings.

3.3.3 Boolean Types

  • BOOLEAN: TRUE or FALSE.

3.3.4 Date and Time Types

  • DATE: Date values (year, month, day).
  • TIME: Time values (hour, minute, second).
  • DATETIME (or TIMESTAMP): Combined date and time values.
  • INTERVAL: Represents a duration of time.

3.3.5 Arrays and Lists

  • ARRAY<data_type>: Represents an ordered collection of elements of the specified data_type.

3.3.6 Objects/Dictionaries
* OBJECT: Represents a collection of key-value pairs, similar to JSON objects or Python dictionaries.

3.4 Variables and Assignments

“`dataqueryscript
LET my_variable = 10;
LET my_string = ‘Hello, world!’;
LET my_date = DATE(‘2023-10-27’);

SELECT my_variable, my_string, my_date;
“`

  • LET: Keyword to declare and assign a variable.
  • Data types are often inferred, but can optionally be declared.

3.5 Operators

3.5.1 Arithmetic Operators

  • + (Addition)
  • - (Subtraction)
  • * (Multiplication)
  • / (Division)
  • % (Modulo – remainder of division)
  • ^ (Exponentiation)

3.5.2 Comparison Operators

  • = (Equal to)
  • != (Not equal to)
  • <> (Not equal to – alternative)
  • > (Greater than)
  • < (Less than)
  • >= (Greater than or equal to)
  • <= (Less than or equal to)

3.5.3 Logical Operators

  • AND
  • OR
  • NOT

3.5.4 Assignment Operators

  • = (Assignment)
  • += (Add and assign: x += 5 is equivalent to x = x + 5)
  • -= (Subtract and assign)
  • *=, /=, %= (Similar compound assignment operators)

3.5.5 Other Operators (Membership, Identity)

  • IN (Check if a value is within a set of values)
  • BETWEEN (Check if a value is within a range)
  • LIKE (Pattern matching for strings)
  • IS NULL (Check if a value is null)
  • IS NOT NULL (Check if a value is not null)

3.6 Control Flow Statements

3.6.1 if, else if, else

“`dataqueryscript
LET age = 25;

IF age >= 18 THEN
SELECT ‘Adult’;
ELSE IF age >= 13 THEN
SELECT ‘Teenager’;
ELSE
SELECT ‘Child’;
END IF;
“`

  • IF, ELSE IF, ELSE: Keywords for conditional execution.
  • THEN: Indicates the start of the code block to execute if the condition is true.
  • END IF: Marks the end of the IF statement.

3.6.2 for loops

“`dataqueryscript
— Iterate over a list of numbers
FOR i IN [1, 2, 3, 4, 5] LOOP
SELECT i;
END LOOP;

–Iterate over the result of a query.
FOR row IN (SELECT name FROM employees) LOOP
SELECT ‘Employee Name: ‘ || row.name;
END LOOP;
“`

  • FOR, IN, LOOP, END LOOP: Keywords for loop control.

3.6.3 while loops

“`dataqueryscript
LET counter = 0;

WHILE counter < 5 LOOP
SELECT counter;
LET counter = counter + 1;
END LOOP;
“`

  • WHILE, LOOP, END LOOP: Keywords for while loop.

3.6.4 break and continue

“`dataqueryscript
FOR i IN [1, 2, 3, 4, 5] LOOP
IF i = 3 THEN
BREAK; — Exit the loop
END IF;
SELECT i;
END LOOP;

FOR i IN [1, 2, 3, 4, 5] LOOP
IF i = 3 THEN
CONTINUE; — Skip to the next iteration
END IF;
SELECT i;
END LOOP;
“`

  • BREAK: Immediately exits the loop.
  • CONTINUE: Skips the rest of the current iteration and goes to the next iteration.

4. Data Selection and Filtering

4.1 The SELECT Statement

The SELECT statement is the core of data retrieval in DQS. It specifies which columns to retrieve and from which data source.

dataqueryscript
SELECT column1, column2, column3
FROM my_table;

4.2 Specifying Columns

  • Individual Columns: List the column names separated by commas.
  • All Columns: Use the asterisk (*) to select all columns.

dataqueryscript
SELECT * -- Select all columns
FROM my_table;

  • Expressions: You can include expressions in the SELECT list.

dataqueryscript
SELECT column1, column2 * 2 AS doubled_column2
FROM my_table;

4.3 The FROM Clause (Data Source)

The FROM clause specifies the data source (table, file, API endpoint).

“`dataqueryscript
— From a database table
SELECT * FROM employees;

— From a CSV file (using a hypothetical file path syntax)
SELECT * FROM FILE(‘/path/to/my_data.csv’);

— From a JSON file
SELECT * FROM FILE(‘/path/to/my_data.json’);

— From an API endpoint (hypothetical syntax)
SELECT * FROM API(‘https://api.example.com/data’);
“`

4.4 The WHERE Clause (Filtering)

The WHERE clause filters the rows based on a specified condition.

4.4.1 Using Comparison Operators

“`dataqueryscript
SELECT *
FROM employees
WHERE salary > 50000;

SELECT *
FROM products
WHERE price <= 100;

SELECT *
FROM customers
WHERE city = ‘New York’;
“`

4.4.2 Using Logical Operators (AND, OR, NOT)

“`dataqueryscript
SELECT *
FROM employees
WHERE salary > 50000 AND department = ‘Sales’;

SELECT *
FROM products
WHERE price < 10 OR category = ‘Electronics’;

SELECT *
FROM customers
WHERE NOT city = ‘London’;
“`

4.4.3 Using IN, BETWEEN, LIKE

“`dataqueryscript
— IN
SELECT *
FROM employees
WHERE department IN (‘Sales’, ‘Marketing’, ‘Engineering’);

— BETWEEN
SELECT *
FROM products
WHERE price BETWEEN 50 AND 100;

— LIKE (pattern matching)
SELECT *
FROM customers
WHERE last_name LIKE ‘S%’; — Starts with ‘S’

SELECT *
FROM customers
WHERE email LIKE ‘%@example.com’; — Ends with ‘@example.com’
“`

4.4.4 Handling NULL values

“`dataqueryscript
SELECT *
FROM employees
WHERE manager_id IS NULL; — Employees with no manager

SELECT *
FROM products
WHERE description IS NOT NULL; — Products with a description
“`

4.5 Aliasing (Columns and Data Sources)

Aliases provide temporary names for columns or data sources, making queries more readable and concise.

“`dataqueryscript
— Column alias
SELECT
first_name AS given_name,
last_name AS surname
FROM employees;

— Data source alias
SELECT e.employee_id, e.first_name, d.department_name
FROM employees AS e — Alias ‘e’ for the employees table
JOIN departments AS d — Alias ‘d’ for the departments table
ON e.department_id = d.department_id;
“`

  • AS: Keyword for defining aliases.

4.6 Distinct Selection
The DISTINCT keyword is used to return only unique values in the result set. It eliminates duplicate rows based on the selected columns.
dataqueryscript
SELECT DISTINCT department
FROM employees;
This query retrieves a list of unique department names from the employees table, removing any duplicates.

5. Data Transformation and Manipulation

5.1 Built-in Functions

DQS would include a rich set of built-in functions for common data operations.

5.1.1 String Functions

  • CONCAT(string1, string2, ...): Concatenates strings.
  • SUBSTRING(string, start, length): Extracts a substring.
  • LOWER(string): Converts a string to lowercase.
  • UPPER(string): Converts a string to uppercase.
  • TRIM(string): Removes leading and trailing whitespace.
  • LENGTH(string): Returns the length of a string.
  • REPLACE(string, old_substring, new_substring): Replaces occurrences of a substring.

dataqueryscript
SELECT
CONCAT(first_name, ' ', last_name) AS full_name,
LOWER(email) AS lowercase_email,
SUBSTRING(phone_number, 1, 3) AS area_code
FROM employees;

5.1.2 Numeric Functions

  • ROUND(number, decimals): Rounds a number to a specified number of decimal places.
  • ABS(number): Returns the absolute value of a number.
  • FLOOR(number): Returns the largest integer less than or equal to a number.
  • CEIL(number): Returns the smallest integer greater than or equal to a number.
  • SQRT(number): Returns the square root of a number.
  • POWER(base, exponent): Raises a number to a power.

dataqueryscript
SELECT
ROUND(price, 2) AS rounded_price,
ABS(discount) AS absolute_discount
FROM products;

5.1.3 Date and Time Functions

  • NOW(): Returns the current date and time.
  • DATE(datetime): Extracts the date part from a datetime value.
  • TIME(datetime): Extracts the time part from a datetime value.
  • YEAR(date): Extracts the year from a date.
  • MONTH(date): Extracts the month from a date.
  • DAY(date): Extracts the day from a date.
  • DATE_ADD(date, INTERVAL value unit): Adds a time interval to a date.
  • DATE_SUB(date, INTERVAL value unit): Subtracts a time interval from a date.
  • DATE_DIFF(date1, date2): Calculates the difference between two dates.

dataqueryscript
SELECT
NOW() AS current_datetime,
YEAR(hire_date) AS hire_year,
DATE_ADD(order_date, INTERVAL 7 DAY) AS delivery_date
FROM employees
JOIN orders ON employees.employee_id = orders.employee_id;

5.1.4 Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)

Aggregate functions perform calculations on a set of values and return a single value.

  • COUNT(*): Counts the number of rows.
  • COUNT(column): Counts the number of non-null values in a column.
  • SUM(column): Calculates the sum of values in a column.
  • AVG(column): Calculates the average of values in a column.
  • MIN(column): Returns the minimum value in a column.
  • MAX(column): Returns the maximum value in a column.

dataqueryscript
SELECT
COUNT(*) AS total_employees,
SUM(salary) AS total_salary,
AVG(salary) AS average_salary,
MIN(hire_date) AS earliest_hire_date,
MAX(salary) AS highest_salary
FROM employees;

5.2 User-Defined Functions (UDFs)

UDFs allow you to create reusable blocks of code to perform custom data transformations.

5.2.1 Defining a UDF

dataqueryscript
CREATE FUNCTION calculate_discount(price DECIMAL, discount_rate DECIMAL)
RETURNS DECIMAL
BEGIN
RETURN price * (1 - discount_rate);
END;

  • CREATE FUNCTION: Keyword to define a UDF.
  • function_name: The name of the function.
  • (parameters): A list of input parameters with their data types.
  • RETURNS data_type: Specifies the data type of the return value.
  • BEGIN ... END: Defines the body of the function.
  • RETURN: Returns the result of the function.

5.2.2 Calling a UDF

dataqueryscript
SELECT
product_name,
price,
calculate_discount(price, 0.10) AS discounted_price -- Calling the UDF
FROM products;

5.2.3 Scope and Parameter Passing

  • Scope: Variables declared inside a UDF are local to that function.
  • Parameter Passing: Parameters are typically passed by value (a copy of the value is passed to the function).

5.3 Data Type Conversion

DQS would provide functions for converting between data types.

  • CAST(expression AS data_type): Converts an expression to a specified data type.
  • TO_CHAR(value, format): Converts a number or date to a string with a specified format.
  • TO_NUMBER(string): Converts a string to a number.
  • TO_DATE(string, format): Converts a string to a date with a specified format.

dataqueryscript
SELECT
CAST(quantity AS DECIMAL) AS decimal_quantity,
TO_CHAR(hire_date, 'YYYY-MM-DD') AS formatted_date
FROM orders;

5.4 Conditional Expressions (Case/When)

The CASE expression allows you to define conditional logic within a query.

dataqueryscript
SELECT
product_name,
price,
CASE
WHEN price > 100 THEN 'High'
WHEN price > 50 THEN 'Medium'
ELSE 'Low'
END AS price_category
FROM products;

  • CASE, WHEN, THEN, ELSE, END: Keywords for the CASE expression.

6. Data Aggregation and Grouping

6.1 The GROUP BY Clause

The GROUP BY clause groups rows with the same values in one or more columns into summary rows.

dataqueryscript
SELECT department, COUNT(*) AS num_employees
FROM employees
GROUP BY department;

This query groups the employees table by the department column and counts the number of employees in each department.

6.2 Using Aggregate Functions with GROUP BY

You almost always use aggregate functions (COUNT, SUM, AVG, MIN, MAX) in conjunction with GROUP BY.

dataqueryscript
SELECT department, AVG(salary) AS average_salary
FROM employees
GROUP BY department;

This query calculates the average salary for each department.

6.3 The HAVING Clause (Filtering Aggregated Results)

The HAVING clause filters the results of a GROUP BY query, similar to how WHERE filters individual rows. You use HAVING to apply conditions to the aggregated values.

dataqueryscript
SELECT department, COUNT(*) AS num_employees
FROM employees
GROUP BY department
HAVING COUNT(*) > 5; -- Only departments with more than 5 employees

6.4 Combining GROUP BY with other clauses

You can combine GROUP BY

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top