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Postgres Ep 2 : Amutha Hotel and Issues with Flat Files
Once upon a time in ooty, there was a small business called “Amutha Hotel,” run by a passionate baker named Saravanan. Saravanan bakery was famous for its delicious sambar, and as his customer base grew, he needed to keep track of orders, customer information, and inventory.
Being a techie, he decided to store all this information in a flat file a simple spreadsheet named “HotelData.csv.”
The Early Days: Simple and Sweet
At first, everything was easy. Saravanan’s flat file had only a few columns, OrderID, CustomerName, Product, Quantity, and Price. Each row represented a new order, and it was simple enough to manage. Saravanan could quickly find orders, calculate totals, and even check his inventory by filtering the file.
The Business Grows: Complexity Creeps In
As the business boomed, Saravanan started offering new products, special discounts, and loyalty programs. He added more columns to her flat file, like Discount, LoyaltyPoints, and DeliveryAddress. He once-simple file began to swell with information.
Then, Saravanan decided to start tracking customer preferences and order history. He began adding multiple rows for the same customer, each representing a different order. His flat file now had repeating groups of data for each customer, and it became harder and harder to find the information he needed.
His flat file was getting out of hand. For every new order from a returning customer, he had to re-enter all their information
CustomerName, DeliveryAddress, LoyaltyPoints
over and over again. This duplication wasn’t just tedious; it started to cause mistakes. One day, he accidentally typed “John Smyth” instead of “John Smith,” and suddenly, his loyal customer was split into two different entries.
On a Busy Saturday
One busy Saturday, Saravanan opened his flat file to update the day’s orders, but instead of popping up instantly as it used to, it took several minutes to load. As he scrolled through the endless rows, his computer started to lag, and the spreadsheet software even crashed a few times. The file had become too large and cumbersome for him to handle efficiently.
Customers were waiting longer for their orders to be processed because Saravanan was struggling to find their previous details and apply the right discounts. The flat file that once served his so well was now slowing her down, and it was affecting her business.
The Journaling
Techie Saravanan started to note these issues in to a notepad. He badly wants a solution which will solve these problems. So he started listing out the problems with examples to look for a solution.
His journal continues …
Before databases became common for data storage, flat files (such as CSVs or text files) were often used to store and manage data. The data file that we use has no special structure; it’s just some lines of text that mean something to the particular application that reads it. It has no inherent structure
However, these flat files posed several challenges, particularly when dealing with repeating groups, which are essentially sets of related fields that repeat multiple times within a record. Here are some of the key problems associated with repeating groups in flat files,
1. Data Redundancy
- Description: Repeating groups can lead to significant redundancy, as the same data might need to be repeated across multiple records.
- Example: If an employee can have multiple skills, a flat file might need to repeat the employee’s name, ID, and other details for each skill.
- Problem: This not only increases the file size but also makes data entry, updates, and deletions more prone to errors.
Eg: Suppose you are maintaining a flat file to track employees and their skills. Each employee can have multiple skills, which you store as repeating groups in the file.
EmployeeID, EmployeeName, Skill1, Skill2, Skill3, Skill4 1, John Doe, Python, SQL, Java, 2, Jane Smith, Excel, PowerPoint, Python, SQL
If an employee has four skills, you need to add four columns (Skill1, Skill2, Skill3, Skill4). If an employee has more than four skills, you must either add more columns or create a new row with repeated employee details.
2. Data Inconsistency
- Description: Repeating groups can lead to inconsistencies when data is updated.
- Example: If an employee’s name changes, and it’s stored multiple times in different rows because of repeating skills, it’s easy for some instances to be updated while others are not.
- Problem: This can lead to situations where the same employee is listed under different names or IDs in the same file.
Eg: Suppose you are maintaining a flat file to track employees and their skills. Each employee can have multiple skills, which you store as repeating groups in the file.
EmployeeID, EmployeeName, Skill1, Skill2, Skill3, Skill4 1, John Doe, Python, SQL, Java, 2, Jane Smith, Excel, PowerPoint, Python, SQL
If John’s name changes to “John A. Doe,” you must manually update each occurrence of “John Doe” across all rows, which increases the chance of inconsistencies.
3. Difficulty in Querying
- Description: Querying data in flat files with repeating groups can be cumbersome and inefficient.
- Example: Extracting a list of unique employees with their respective skills requires complex scripting or manual processing.
- Problem: Unlike relational databases, which use joins to simplify such queries, flat files require custom logic to manage and extract data, leading to slower processing and more potential for errors.
Eg: Suppose you are maintaining a flat file to track employees and their skills. Each employee can have multiple skills, which you store as repeating groups in the file.
EmployeeID, EmployeeName, Skill1, Skill2, Skill3, Skill4 1, John Doe, Python, SQL, Java, 2, Jane Smith, Excel, PowerPoint, Python, SQL
Extracting a list of all employees proficient in “Python” requires you to search across multiple skill columns (Skill1, Skill2, etc.), which is cumbersome compared to a relational database where you can use a simple JOIN on a normalized EmployeeSkills table.
4. Limited Scalability
- Description: Flat files do not scale well when the number of repeating groups or the size of the data grows.
- Example: A file with multiple repeating fields can become extremely large and difficult to manage as the number of records increases.
- Problem: This can lead to performance issues, such as slow read/write operations and difficulty in maintaining the file over time.
Eg: You are storing customer orders in a flat file where each customer can place multiple orders.
CustomerID, CustomerName, Order1ID, Order1Date, Order2ID, Order2Date, Order3ID, Order3Date 1001, Alice Brown, 5001, 2023-08-01, 5002, 2023-08-15, 1002, Bob White, 5003, 2023-08-05,
If Alice places more than three orders, you’ll need to add more columns (Order4ID, Order4Date, etc.), leading to an unwieldy file with many empty cells for customers with fewer orders.
5. Challenges in Data Integrity
- Description: Ensuring data integrity in flat files with repeating groups is difficult.
- Example: Enforcing rules like “an employee can only have unique skills” is nearly impossible in a flat file format.
- Problem: This can result in duplicated or invalid data, which is hard to detect and correct without a database system.
Eg: You are storing customer orders in a flat file where each customer can place multiple orders.
CustomerID, CustomerName, Order1ID, Order1Date, Order2ID, Order2Date, Order3ID, Order3Date 1001, Alice Brown, 5001, 2023-08-01, 5002, 2023-08-15, 1002, Bob White, 5003, 2023-08-05,
There’s no easy way to enforce that each order ID is unique and corresponds to the correct customer, which could lead to errors or duplicated orders.
6. Complex File Formats
- Description: Managing and processing flat files with repeating groups often requires complex file formats.
- Example: Custom delimiters or nested formats might be needed to handle repeating groups, making the file harder to understand and work with.
- Problem: This increases the likelihood of errors during data entry, processing, or when the file is read by different systems.
Eg: You are storing customer orders in a flat file where each customer can place multiple orders.
CustomerID, CustomerName, Order1ID, Order1Date, Order2ID, Order2Date, Order3ID, Order3Date 1001, Alice Brown, 5001, 2023-08-01, 5002, 2023-08-15, 1002, Bob White, 5003, 2023-08-05,
As the number of orders grows, the file format becomes increasingly complex, requiring custom scripts to manage and extract order data for each customer.
7. Lack of Referential Integrity
- Description: Flat files lack mechanisms to enforce referential integrity between related groups of data.
- Example: Ensuring that a skill listed in one file corresponds to a valid skill ID in another file requires manual checks or complex logic.
- Problem: This can lead to orphaned records or mismatches between related data sets.
Eg: A fleet management company tracks maintenance records for each vehicle in a flat file. Each vehicle can have multiple maintenance records.
VehicleID, VehicleType, Maintenance1Date, Maintenance1Type, Maintenance2Date, Maintenance2Type V001, Truck, 2023-01-15, Oil Change, 2023-03-10, Tire Rotation V002, Van, 2023-02-20, Brake Inspection, ,
There’s no way to ensure that the Maintenance1Type and Maintenance2Type fields are valid maintenance types or that the dates are in correct chronological order.
8. Difficulty in Data Modification
- Description: Modifying data in flat files with repeating groups can be complex and error-prone.
- Example: Adding or removing an item from a repeating group might require extensive manual edits across multiple records.
- Problem: This increases the risk of errors and makes data management time-consuming.
Eg: A university maintains a flat file to record student enrollments in courses. Each student can enroll in multiple courses.
StudentID, StudentName, Course1, Course2, Course3, Course4, Course5 2001, Charlie Green, Math101, Physics102, , , 2002, Dana Blue, History101, Math101, Chemistry101, ,
If a student drops a course or switches to a different one, manually editing the file can easily lead to errors, especially as the number of students and courses increases.
After listing down all these, Saravanan started looking into solutions. His search goes on…
