Example Savings

Let’s assume:

• Your database processes 10M rows daily with poorly optimized queries.

• After optimization, you reduce query execution time by 70% and storage size by 30%.

• Before Optimization:

  • Query Cost: $1,000/month

  • Storage Cost: $300/month

  • Total: $1,300/month

• After Optimization:

  • Query Cost: $300/month (70% reduction)

  • Storage Cost: $210/month (30% reduction)

  • Total: $510/month

🎉 Savings: ~60% of your SQL costs ($790/month)!

1. Missing Indexes

Demo Input:

sales Table:

---

Bad Code:

SELECT * FROM sales WHERE product_name = 'Yoga Mat';

• What Happens: The database performs a full table scan, checking each row in the sales table for product_name = 'Yoga Mat'.

• Output:

---

Fixed Code:

CREATE INDEX idx_product_name ON sales(product_name);

SELECT * FROM sales WHERE product_name = 'Yoga Mat';

• What Happens Now: The database uses the index to directly locate rows where product_name = 'Yoga Mat'.

• Output:

---

Metrics:

• Query Execution Time: Reduced time by eliminating the full table scan.

• Index Efficiency: Demonstrated faster row lookups using an index.

Impact:

• 🚀 Reduced query time by 80% (from 5 seconds to 1 second).

• 🔍 Improved query efficiency for 1M+ rows, making operations scalable.

---

2. Missing Indexes on Joined Columns

Demo Input:

sales Table:

categories Table:

---

Bad Code:

SELECT sales.product_name, categories.category_name
FROM sales
JOIN categories ON sales.category_id = categories.category_id;

• What Happens: The database performs a full table scan on both sales and categories, comparing each row to find matches.

• Output:

---

Fixed Code:

CREATE INDEX idx_category_id_sales ON sales(category_id);
CREATE INDEX idx_category_id_categories ON categories(category_id);

SELECT sales.product_name, categories.category_name
FROM sales
JOIN categories ON sales.category_id = categories.category_id;

• What Happens Now: The database uses indexes on category_id to quickly match rows between sales and categories.

• Output:

---

Metrics:

• Query Execution Time: Reduced join time significantly by leveraging indexed lookups.

• Index Efficiency: Improved the efficiency of matching rows between two large tables.

Impact:

• 🚀 Reduced query time by 75%, making joins scalable for 1M+ rows.

• 🔗 Enhanced performance for queries combining 2+ tables, crucial for reporting and analytics.

---

3. Inefficient Use of Joins or Subqueries

Demo Input:

sales Table:

sales_details Table:

---

Bad Code:

SELECT product_name,
       (SELECT SUM(quantity * price_per_unit)
        FROM sales_details
        WHERE sales.sale_id = sales_details.sale_id) AS total_revenue
FROM sales;

• What Happens: For every row in sales, the subquery is executed separately to calculate the revenue. This leads to repeated scans of sales_details.

• Output:

---

Fixed Code:

SELECT sales.product_name,
       SUM(sales.quantity * sales_details.price_per_unit) AS total_revenue
FROM sales
JOIN sales_details ON sales.sale_id = sales_details.sale_id
GROUP BY sales.product_name;

• What Happens Now: The join processes all rows in a single scan, and the aggregation computes total_revenue in one step.

• Output:

---

Metrics:

• Query Execution Time: Reduced query time by eliminating repeated subquery execution.

• Query Complexity: Simplified logic by replacing a subquery with a join.

Impact:

• 🚀 Reduced execution time by 80% for large datasets.

• 📊 Improved scalability for queries involving millions of rows across multiple tables.

---

4. Retrieving Unnecessary Columns or Rows

Demo Input:

sales Table:

---

Bad Code:

SELECT * FROM sales;

• What Happens: Fetches all columns and all rows, even if only a subset is needed for analysis.

• Output:

---

Fixed Code:

SELECT product_name, SUM(quantity * price_per_unit) AS total_revenue
FROM sales
WHERE order_date >= '2024-01-01' AND order_date <= '2024-01-03'
GROUP BY product_name;

• What Happens Now: Fetches only the required columns and rows, with filters applied to the order_date and aggregation for total_revenue.

• Output:

---

Metrics:

• Data Processing Volume: Reduced unnecessary data retrieval by focusing on relevant columns and rows.

• Query Execution Time: Improved performance by narrowing the dataset scope.

Impact:

• 🚀 Reduced data processed by 60% (all columns → 2 columns).

• 📉 Improved query time from 5 seconds to 1 second, enhancing performance for large datasets.

---

5. Lack of Partitioning for Large Tables

Demo Input:

sales Table (Large Table Example):

---

Bad Code:

SELECT product_name, SUM(quantity * price_per_unit) AS total_revenue
FROM sales
WHERE order_date >= '2024-01-01' AND order_date <= '2024-12-31'
GROUP BY product_name;

• What Happens: The query scans the entire table, processing all rows, even if most rows don’t match the date range.

• Output: Correct result, but performance is poor due to the lack of partitioning.

---

Fixed Code:

-- Create partitioned table
CREATE TABLE sales_partitioned (
    sale_id SERIAL PRIMARY KEY,
    product_name VARCHAR(255),
    category_id INT,
    quantity INT,
    order_date DATE,
    price_per_unit NUMERIC
) PARTITION BY RANGE (order_date);

-- Create partitions
CREATE TABLE sales_2024_q1 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-01-01') TO ('2024-04-01');
CREATE TABLE sales_2024_q2 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-04-01') TO ('2024-07-01');
CREATE TABLE sales_2024_q3 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-07-01') TO ('2024-10-01');
CREATE TABLE sales_2024_q4 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-10-01') TO ('2025-01-01');

-- Query with partition pruning
SELECT product_name, SUM(quantity * price_per_unit) AS total_revenue
FROM sales_partitioned
WHERE order_date >= '2024-01-01' AND order_date <= '2024-03-31'
GROUP BY product_name;

• What Happens Now: The database only scans the relevant partition (sales_2024_q1), drastically reducing the rows processed.

• Output:

---

Metrics:

• Data Processing Volume: Reduced rows scanned by narrowing the scope to relevant partitions.

• Query Execution Time: Improved performance by processing smaller partitions instead of the entire table.

Impact:

• 🚀 Reduced rows scanned by 75% (10M rows → 2.5M rows per quarter).

• 📉 Query time improved by 80%, making it scalable for tables with 100M+ rows.

---

6. Redundant Joins or Aggregations Across Massive Datasets

Demo Input:

sales Table:

categories Table:

---

Bad Code:

SELECT sales.product_name, 
       categories.category_name, 
       SUM(sales.quantity * sales.price_per_unit) AS total_revenue
FROM sales
JOIN categories ON sales.category_id = categories.category_id
JOIN categories AS duplicate_categories ON sales.category_id = duplicate_categories.category_id
GROUP BY sales.product_name, categories.category_name;

• What Happens: The unnecessary second join to categories (duplicate_categories) creates redundant data scans. Aggregation across these redundant rows increases query complexity and execution time.

• Output:

• Problem: While the output may look correct, query performance suffers significantly due to redundant operations.

---

Fixed Code:

SELECT sales.product_name, 
       categories.category_name, 
       SUM(sales.quantity * sales.price_per_unit) AS total_revenue
FROM sales
JOIN categories ON sales.category_id = categories.category_id
GROUP BY sales.product_name, categories.category_name;

• What Happens Now: The redundant join is eliminated, reducing query complexity and execution time.

• Output:

---

Metrics:

• Query Complexity: Reduced unnecessary joins and redundant row scans.

• Data Processing Volume: Minimized rows scanned and aggregated, leading to better performance.

Impact:

• 🚀 Reduced rows processed by 50%, improving performance for large datasets.

• 📉 Query execution time improved by 60%, making it scalable for millions of rows.

---

Here’s the explanation for missing indexes or over-indexing, including metrics and impact:

---

7. Missing Indexes or Over-Indexing

Demo Input:

sales Table:

---

Case 1: Missing Indexes

Bad Code:

SELECT * FROM sales WHERE order_date >= '2024-01-01' AND order_date <= '2024-01-31';

• What Happens: The database performs a full table scan, checking each row in sales to filter by order_date.

• Output: The query is slow, especially for large datasets (e.g., millions of rows).

---

Fixed Code:

CREATE INDEX idx_order_date ON sales(order_date);

SELECT * FROM sales WHERE order_date >= '2024-01-01' AND order_date <= '2024-01-31';

• What Happens Now: The database uses the index to quickly locate rows in the specified date range.

• Output: Query execution is much faster for the same result.

Metrics:

• Query Execution Time: Improved significantly by adding a relevant index.

Impact:

• 🚀 Reduced query execution time by 80% for large tables.

• 📉 Improved performance for filtering operations on time-sensitive data.

---

Case 2: Over-Indexing

Bad Code:

CREATE INDEX idx_product_name ON sales(product_name);
CREATE INDEX idx_order_date ON sales(order_date);
CREATE INDEX idx_category_id ON sales(category_id);
CREATE INDEX idx_quantity ON sales(quantity);

• What Happens: Too many indexes increase overhead for write operations like INSERT, UPDATE, and DELETE, as the database must update all relevant indexes whenever a row is modified.

• Output: Write performance becomes slower, especially for tables with frequent updates or high transaction volume.

---

Fixed Code:

CREATE INDEX idx_order_date ON sales(order_date);

-- Only keep essential indexes based on query patterns.

• What Happens Now: Only the necessary indexes remain, balancing query performance and write speed.

Metrics:

• Index Efficiency: Achieved balance between query speed and write performance.

Impact:

• 🚀 Reduced index update overhead, improving write operations by 50%.

• ⚡ Maintained fast query performance for common queries while avoiding unnecessary overhead.

---

8. Queries Using Non-Indexed Columns in WHERE Clauses

Demo Input:

sales Table:

---

Bad Code:

SELECT * FROM sales WHERE product_name = 'Yoga Mat';

• What Happens: The product_name column is not indexed, so the database performs a full table scan, checking each row to match product_name = 'Yoga Mat'.

• Output:

---

Fixed Code:

CREATE INDEX idx_product_name ON sales(product_name);

SELECT * FROM sales WHERE product_name = 'Yoga Mat';

• What Happens Now: The database uses the index on product_name to quickly find matching rows.

• Output:

---

Metrics:

• Query Execution Time: Eliminates the need for a full table scan, drastically improving performance.

• Index Efficiency: Demonstrates the power of indexing for frequently filtered columns.

Impact:

• 🚀 Reduced query time by 90% for filtering on large datasets.

• 📉 Scalable for millions of rows, ensuring efficient lookups on indexed columns.

---

Key Takeaway:

• Problem: Non-indexed columns in WHERE clauses result in full table scans, leading to slow queries.

• Solution: Add indexes to columns frequently used in filters to enable faster lookups.

Here’s the explanation for overly complex logic that could be pre-computed, including metrics and impact:

---

9. Overly Complex Logic That Could Be Pre-Computed

Demo Input:

sales Table:

---

Bad Code:

SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue, 
       AVG(quantity) AS avg_quantity, 
       MAX(price_per_unit) AS max_price
FROM sales
WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31'
GROUP BY product_name;

• What Happens: Every time this query is executed, the database performs complex aggregations (SUM, AVG, MAX) on a large dataset, even if the data hasn’t changed.

• Output:

---

Fixed Code:

1. Pre-Compute Results Using a Materialized View:

CREATE MATERIALIZED VIEW sales_summary AS
SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue, 
       AVG(quantity) AS avg_quantity, 
       MAX(price_per_unit) AS max_price
FROM sales
WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31'
GROUP BY product_name;

2. Query the Materialized View Instead:

SELECT * FROM sales_summary;

• What Happens Now: The aggregation logic is pre-computed and stored in the sales_summary materialized view. Queries only read the pre-aggregated results.

• Output:

---

Metrics:

• Query Execution Time: Reduced drastically for subsequent queries by pre-computing results.

• Pipeline Efficiency: Eliminated repeated aggregations on the same data.

Impact:

• 🚀 Reduced query execution time by 90% for subsequent queries.

• ⚡ Enabled real-time performance for reports that involve frequent aggregations.

---

Key Takeaway:

• Problem: Repeating complex calculations (aggregations) for every query wastes resources and slows performance.

• Solution: Use materialized views or pre-computed tables to store aggregated results, making subsequent queries faster.

---

10. Redundant Subqueries

Demo Input:

sales Table:

---

Bad Code:

SELECT product_name, 
       (SELECT SUM(quantity * price_per_unit) FROM sales AS s WHERE s.product_name = sales.product_name) AS total_revenue,
       (SELECT MAX(price_per_unit) FROM sales AS s WHERE s.product_name = sales.product_name) AS max_price
FROM sales
GROUP BY product_name;

• What Happens: The subquery for total_revenue and max_price is executed repeatedly for each row, leading to unnecessary computational overhead.

• Output:

---

Fixed Code:

SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue,
       MAX(price_per_unit) AS max_price
FROM sales
GROUP BY product_name;

• What Happens Now: The subqueries are removed, and aggregations are performed directly in a single query using GROUP BY.

• Output:

---

Metrics:

• Query Execution Time: Eliminated repeated subquery execution for faster results.

• Query Complexity: Simplified logic by using direct aggregations.

Impact:

• 🚀 Reduced execution time by 85% (subqueries removed).

• ⚡ Improved performance for datasets with 1M+ rows, making operations scalable.

---

Key Takeaway:

• Problem: Subqueries executed repeatedly for each row in the outer query result in redundant computations and slow performance.

• Solution: Combine aggregations and calculations into a single query using GROUP BY or JOIN.

---

11. Poor Data Validation During Ingestion

Demo Input:

Raw Data (Before Ingestion):

---

Bad Code (No Validation):

INSERT INTO sales (sale_id, product_name, category_id, quantity, price_per_unit)
VALUES 
    (1, 'Yoga Mat', 101, 3, 20.00),
    (2, 'Lipstick', 102, 5, 15.50),
    (3, 'Dumbbells', NULL, -2, 25.00),
    (4, 'Perfume', 'INVALID', 1, 'INVALID'),
    (5, 'Jump Rope', 101, 7, 10.00);

• What Happens: Invalid data (e.g., NULL category_id, negative quantity, non-numeric price_per_unit) is ingested without validation. This leads to data quality issues during analysis.

• Output:

---

Fixed Code (With Validation):

1. Add Constraints to the Table:

CREATE TABLE sales (
    sale_id SERIAL PRIMARY KEY,
    product_name VARCHAR(255) NOT NULL,
    category_id INT NOT NULL,
    quantity INT CHECK (quantity > 0),
    price_per_unit NUMERIC CHECK (price_per_unit > 0)
);

2. Validate Data Before Insertion:

INSERT INTO sales (sale_id, product_name, category_id, quantity, price_per_unit)
VALUES 
    (1, 'Yoga Mat', 101, 3, 20.00),
    (2, 'Lipstick', 102, 5, 15.50),
    (5, 'Jump Rope', 101, 7, 10.00);  -- Skipping invalid rows

• What Happens Now: Only valid rows are ingested into the table, preventing bad data from polluting the dataset.

• Output:

---

Metrics:

• Data Accuracy: Improved by rejecting invalid data during ingestion.

• Data Reduction: Prevented bad data from increasing the dataset size unnecessarily.

Impact:

• ✅ Increased data quality by 95%, ensuring accuracy for analysis.

• 🚫 Eliminated 100% of invalid rows during ingestion.

---

Key Takeaway:

• Problem: Poor validation during data ingestion allows bad data (e.g., NULLs, invalid formats, negative values) into the database.

• Solution: Use constraints and pre-ingestion validation checks to maintain high-quality data.

Here’s the explanation for redundant columns or duplicate records, including metrics and impact:

---

12. Redundant Columns or Duplicate Records

Demo Input:

Raw Data (Before Cleanup):

---

Bad Code (No Cleanup):

SELECT * FROM sales;

• What Happens: Redundant columns like total_price (can be derived as quantity * price_per_unit) and sale_id_duplicate increase storage size unnecessarily. Duplicate rows (e.g., Yoga Mat) further pollute the dataset.

• Output:

---

Fixed Code:

1. Remove Redundant Columns:

ALTER TABLE sales DROP COLUMN total_price;
ALTER TABLE sales DROP COLUMN sale_id_duplicate;

2. Remove Duplicate Records:

DELETE FROM sales
WHERE sale_id IN (
    SELECT sale_id
    FROM (
        SELECT sale_id, ROW_NUMBER() OVER (PARTITION BY sale_id, product_name ORDER BY sale_id) AS row_num
        FROM sales
    ) t
    WHERE t.row_num > 1
);

3. Calculate Derived Values in Queries:

SELECT sale_id, 
       product_name, 
       category_id, 
       quantity, 
       price_per_unit, 
       (quantity * price_per_unit) AS total_price
FROM sales;

• What Happens Now: Redundant columns are removed, and duplicates are eliminated, reducing storage and improving data quality. Derived values are computed on demand.

• Output:

---

Metrics:

• Data Reduction: Reduced dataset size by removing redundant columns and duplicate rows.

• Data Accuracy: Improved data integrity by eliminating duplicate records.

Impact:

• 🚀 Reduced storage requirements by 30% (columns removed).

• 📉 Eliminated 100% of duplicate rows, ensuring reliable analytics.

---

Key Takeaway:

• Problem: Redundant columns and duplicate records waste storage space and create inconsistencies in analysis.

• Solution: Remove redundant columns, eliminate duplicates, and compute derived values on demand.

---

13. Live Queries on Large Datasets

Demo Input:

sales Table (Large Dataset):

---

Bad Code (Live Query Without Optimization):

SELECT product_name, SUM(quantity * price_per_unit) AS total_revenue
FROM sales
WHERE order_date >= '2024-01-01' AND order_date <= '2024-12-31'
GROUP BY product_name;

• What Happens: The query scans the entire sales table (10M rows) to calculate total_revenue every time it is executed. This is resource-intensive and causes significant delays for live dashboards or reports.

• Output:

• Problem: Even though the output is correct, the query time is unacceptably high for live dashboards.

---

Fixed Code (Use Pre-Computed Results):

1. Create a Materialized View:

CREATE MATERIALIZED VIEW sales_summary AS
SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue
FROM sales
WHERE order_date >= '2024-01-01' AND order_date <= '2024-12-31'
GROUP BY product_name;

2. Query the Materialized View:

SELECT * FROM sales_summary;

3. Schedule Regular Refreshes:

REFRESH MATERIALIZED VIEW sales_summary;

• What Happens Now: The aggregation is pre-computed and stored in the sales_summary materialized view, making live queries instantaneous.

• Output:

---

Metrics:

• Refresh Frequency: Time taken to refresh pre-computed results periodically.

• Query Execution Time: Improved significantly for live queries by reducing on-demand computation.

Impact:

• 🚀 Reduced live query execution time by 90% (10 seconds → 1 second).

• 📉 Improved scalability for dashboards with frequent updates, handling datasets with 10M+ rows efficiently.

---

Key Takeaway:

• Problem: Live queries on large datasets are resource-intensive, leading to delays in dashboards or reports.

• Solution: Use materialized views or pre-computed tables to store aggregated results and refresh them periodically.

---

14. Over-Reliance on Non-Optimized Dynamic Calculations

Demo Input:

sales Table:

---

Bad Code (Dynamic Calculations for Every Query):

SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue,
       COUNT(*) AS total_sales,
       (SUM(quantity * price_per_unit) / COUNT(*)) AS avg_revenue_per_sale
FROM sales
WHERE order_date >= '2024-01-01' AND order_date <= '2024-12-31'
GROUP BY product_name;

• What Happens: For every query execution, the database recalculates SUM, COUNT, and avg_revenue_per_sale dynamically, scanning and processing all rows within the date range.

• Output:

• Problem: While the output is correct, dynamic calculations require repeated computations on the dataset, leading to slow performance, especially for large tables.

---

Fixed Code (Pre-Compute Metrics):

1. Create Pre-Computed Aggregates Table:

CREATE TABLE sales_metrics AS
SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue,
       COUNT(*) AS total_sales,
       (SUM(quantity * price_per_unit) / COUNT(*)) AS avg_revenue_per_sale
FROM sales
WHERE order_date >= '2024-01-01' AND order_date <= '2024-12-31'
GROUP BY product_name;

2. Query the Pre-Computed Table:

SELECT * FROM sales_metrics;

• What Happens Now: The metrics are calculated once and stored in the sales_metrics table. Queries now fetch results from the pre-computed data instead of performing dynamic calculations.

• Output:

---

Metrics:

• Pipeline Efficiency: Reduced end-to-end query time for commonly requested metrics.

• Query Execution Time: Improved performance by storing pre-computed metrics.

Impact:

• 🚀 Reduced query time by 90% for recurring metric calculations.

• ⚡ Enabled scalability for dashboards with real-time updates using pre-aggregated data.

---

Key Takeaway:

• Problem: Over-reliance on dynamic calculations wastes computational resources by performing repetitive operations.

• Solution: Pre-compute and store frequently used metrics in a dedicated table or materialized view.

Here’s the explanation for missing values or inconsistent data entry, including metrics and impact:

---

15. Missing Values or Inconsistent Data Entry

Demo Input:

Raw Data (Before Cleaning):

---

Bad Code (No Validation or Cleaning):

SELECT * FROM sales;

• What Happens: Missing values in category_id, price_per_unit, and order_date remain in the dataset, along with inconsistent or invalid values (INVALID, negative quantity).

• Output:

---

Fixed Code (Cleaning Missing and Invalid Data):

1. Handle Missing and Invalid Values:

-- Replace NULLs with default values
UPDATE sales
SET category_id = 0
WHERE category_id IS NULL;

UPDATE sales
SET price_per_unit = 0
WHERE price_per_unit IS NULL;

-- Remove invalid rows
DELETE FROM sales
WHERE quantity <= 0 OR product_name = 'INVALID';

2. Validate Data Before Insertion:

-- Add constraints for validation
ALTER TABLE sales
ADD CONSTRAINT chk_quantity_positive CHECK (quantity > 0);

ALTER TABLE sales
ADD CONSTRAINT chk_price_positive CHECK (price_per_unit > 0);

ALTER TABLE sales
ALTER COLUMN order_date SET NOT NULL;

• What Happens Now: Missing values are replaced or removed, and invalid data is prevented from entering the dataset.

• Output (After Cleaning):

---

Metrics:

• Data Accuracy: Improved by replacing or removing missing and invalid values.

• Data Reduction: Eliminated invalid rows, reducing dataset size.

Impact:

• ✅ Increased data quality by 95%, enabling reliable analytics.

• 🚫 Prevented 100% of invalid records from entering the database.

---

Key Takeaway:

• Problem: Missing values and inconsistent data lead to errors and unreliable results during analysis.

• Solution: Use default values for missing entries, remove invalid rows, and enforce constraints for data integrity.

Here’s the explanation for single-threaded processing or poorly optimized queries, including metrics and impact:

---

17. Single-Threaded Processing or Poorly Optimized Queries

Demo Input:

sales Table (Large Dataset):

---

Bad Code (Single-Threaded and Inefficient):

SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue
FROM sales
WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31'
GROUP BY product_name;

• What Happens: This query runs on a single thread and performs a full table scan, processing 10M+ rows on one core of the database server. The lack of partitioning or parallelization makes the query slow.

• Output:

• Problem: The output is correct, but query execution time is unnecessarily high.

---

Fixed Code (Parallel Processing and Query Optimization):

1. Enable Parallel Processing (Database-Specific Setting):

   • For PostgreSQL, set the following configurations in postgresql.conf:

       max_parallel_workers_per_gather = 4  # Adjust based on CPU cores
       parallel_setup_cost = 0.1           # Reduce setup cost for parallelism
       parallel_tuple_cost = 0.1          # Reduce tuple processing cost
     

2. Use Table Partitioning:

CREATE TABLE sales_partitioned (
    sale_id SERIAL PRIMARY KEY,
    product_name VARCHAR(255),
    category_id INT,
    quantity INT,
    price_per_unit NUMERIC,
    order_date DATE
) PARTITION BY RANGE (order_date);

-- Create partitions for each quarter
CREATE TABLE sales_2024_q1 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-01-01') TO ('2024-04-01');
CREATE TABLE sales_2024_q2 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-04-01') TO ('2024-07-01');
CREATE TABLE sales_2024_q3 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-07-01') TO ('2024-10-01');
CREATE TABLE sales_2024_q4 PARTITION OF sales_partitioned FOR VALUES FROM ('2024-10-01') TO ('2025-01-01');

3. Rewrite the Query to Use Partition Pruning:

SELECT product_name, 
       SUM(quantity * price_per_unit) AS total_revenue
FROM sales_partitioned
WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31'
GROUP BY product_name;

• What Happens Now: The database processes each partition in parallel, and query execution is distributed across multiple threads.

• Output:

---

Metrics:

• Query Execution Time: Drastically reduced by enabling parallel processing and optimizing data access.

• Data Processing Volume: Distributed workload ensures efficient handling of large datasets.

Impact:

• 🚀 Reduced query execution time by 80% (e.g., from 10 seconds to 2 seconds).

• ⚡ Scalability improved for 10M+ rows, enabling real-time analytics for large datasets.

---

Key Takeaway:

• Problem: Single-threaded processing and inefficient full table scans lead to slow performance on large datasets.

• Solution: Use parallel processing, table partitioning, and database configuration tuning to optimize query performance.

Here’s the explanation for storing redundant data or using inefficient data types, including metrics and impact:

---

18. Storing Redundant Data or Using Inefficient Data Types

Demo Input:

sales Table (Before Optimization):

---

Problems Identified:

1. Redundant Data:

   • category_name is stored in the sales table, though it can be derived from a separate categories table using category_id.

   • This increases storage size unnecessarily and creates data consistency risks (e.g., typos or mismatches).

2. Inefficient Data Types:

   • category_id stored as an INT when the range of values (101, 102, etc.) fits into a smaller data type (SMALLINT or TINYINT).

   • price_per_unit stored as FLOAT, which can lead to precision issues for financial data.

---

Bad Schema (No Normalization, Inefficient Data Types):

CREATE TABLE sales (
    sale_id SERIAL PRIMARY KEY,
    product_name VARCHAR(255),
    category_id INT,
    category_name VARCHAR(255), -- Redundant column
    quantity INT,
    price_per_unit FLOAT,       -- Inefficient type for financial data
    order_date DATE
);

• What Happens:

  • Data redundancy increases storage requirements and risks inconsistencies.

  • Inefficient data types lead to wasted memory and precision errors.

---

Fixed Schema (Normalization and Efficient Data Types):

1. Separate categories Table:

CREATE TABLE categories (
    category_id SMALLINT PRIMARY KEY,  -- More storage-efficient type
    category_name VARCHAR(255) NOT NULL
);

INSERT INTO categories (category_id, category_name) VALUES
(101, 'Fitness'),
(102, 'Beauty');

2. Optimize sales Table:

CREATE TABLE sales (
    sale_id SERIAL PRIMARY KEY,
    product_name VARCHAR(255) NOT NULL,
    category_id SMALLINT,             -- Smaller data type
    quantity INT CHECK (quantity > 0), -- Validation to ensure positive values
    price_per_unit NUMERIC(10, 2),    -- Precise type for financial data
    order_date DATE NOT NULL,
    FOREIGN KEY (category_id) REFERENCES categories(category_id)
);

3. Query Data Using Joins:

SELECT s.sale_id, s.product_name, c.category_name, s.quantity, s.price_per_unit, s.order_date
FROM sales s
JOIN categories c ON s.category_id = c.category_id;

• What Happens Now: Data is normalized, redundant columns are removed, and efficient data types are used.

• Output (Result of Query):

---

Metrics:

• Storage Optimization: Reduced storage size by removing redundant columns and using smaller data types.

• Data Accuracy: Eliminated risks of inconsistencies due to redundant data.

Impact:

• 🚀 Reduced storage requirements by 30% (e.g., redundant category_name column removed).

• 📉 Improved query performance by optimizing data retrieval using normalized tables.

• ✅ Increased precision in financial data with NUMERIC(10, 2).

---

Key Takeaway:

• Problem: Storing redundant data and using inefficient data types waste storage and create consistency risks.

• Solution: Normalize the schema, remove redundant columns, and choose efficient data types tailored to the data range and precision requirements.

---

19. Lack of Connection Pooling or Inefficient Query Design

Problem Overview:

• Connection pooling: Re-using established database connections to handle multiple queries, rather than creating and tearing down connections for each request.

• Inefficient query design: Executing multiple queries where a single optimized query could suffice.

---

Example Scenario: Lack of Connection Pooling

Demo Input: Imagine a web application processing 100 simultaneous requests to fetch sales data for specific products.

Inefficient Code (No Connection Pooling):

import psycopg2

for request in requests:  # Simulating 100 user requests
    connection = psycopg2.connect(
        dbname="ecommerce", user="user", password="password", host="localhost"
    )
    cursor = connection.cursor()
    cursor.execute("SELECT product_name, SUM(quantity) FROM sales WHERE product_name = %s GROUP BY product_name", (request["product_name"],))
    result = cursor.fetchall()
    cursor.close()
    connection.close()

• What Happens:

  • For every request, a new database connection is created and closed.

  • This increases overhead on both the client and the database server.

  • If connections exceed the database’s limits, queries fail or get queued, leading to poor performance.

---

Fixed Code (Using Connection Pooling):

from psycopg2.pool import SimpleConnectionPool

# Create a connection pool (shared by all requests)
connection_pool = SimpleConnectionPool(
    1, 20, dbname="ecommerce", user="user", password="password", host="localhost"
)

for request in requests:  # Simulating 100 user requests
    connection = connection_pool.getconn()  # Reuse pooled connections
    cursor = connection.cursor()
    cursor.execute("SELECT product_name, SUM(quantity) FROM sales WHERE product_name = %s GROUP BY product_name", (request["product_name"],))
    result = cursor.fetchall()
    cursor.close()
    connection_pool.putconn(connection)  # Return the connection to the pool

• What Happens Now:

  • Connections are re-used, reducing the overhead of establishing and closing connections.

  • The application can handle more concurrent queries efficiently.

  • Query execution is faster and more reliable.

---

Example Scenario: Inefficient Query Design

Inefficient Code:

-- Separate queries for each product
SELECT SUM(quantity) AS total_quantity FROM sales WHERE product_name = 'Yoga Mat';
SELECT SUM(quantity) AS total_quantity FROM sales WHERE product_name = 'Lipstick';

• What Happens: Each query scans the sales table independently, increasing the total query time.

Fixed Code:

-- Combine into a single query
SELECT product_name, SUM(quantity) AS total_quantity
FROM sales
WHERE product_name IN ('Yoga Mat', 'Lipstick')
GROUP BY product_name;

• What Happens Now:

  • A single query processes multiple products, reducing redundant scans of the sales table.

  • Improved performance by minimizing query overhead.

---

Metrics:

• Query Execution Time: Reduced by avoiding repeated table scans.

• Connection Overhead: Decreased by reusing connections through pooling.

Impact:

• 🚀 Reduced query execution time by 70% for simultaneous requests.

• 📉 Improved system throughput by 50%, enabling handling of 100+ concurrent requests efficiently.

---

Key Takeaway:

• Problem: Opening and closing database connections for every query leads to high overhead, while poorly designed queries result in redundant data processing.

• Solution: Use connection pooling for efficient connection management and optimize query design to minimize redundant operations.

RECAP

---

1. Reduce Query Execution Time

• How It Saves Costs:

  • Faster queries use fewer CPU cycles and memory.

  • In cloud-based databases like AWS RDS, Google BigQuery, or Azure SQL, you're often charged based on compute time and IOPS (Input/Output Operations Per Second).

• Optimizations That Help:

  • Use indexes for frequent queries.

  • Avoid redundant joins, subqueries, and full table scans.

  • Partition large tables to limit the scope of queries.

---

2. Minimize Storage Costs

• How It Saves Costs:

  • Redundant data, inefficient data types, and poor schema design increase storage requirements.

  • Cloud providers charge for storage (e.g., AWS S3, PostgreSQL on RDS, BigQuery).

• Optimizations That Help:

  • Normalize tables to remove redundant data.

  • Use efficient data types (e.g., SMALLINT instead of INT, NUMERIC for precision).

  • Compress data where applicable.

---

3. Optimize Concurrent Connections

• How It Saves Costs:

  • Excessive connections can cause throttling or increase server size requirements.

  • Providers may charge based on the database instance tier or number of concurrent queries.

• Optimizations That Help:

  • Use connection pooling to reduce the need for new connections.

  • Limit unnecessary or duplicate queries.

---

4. Efficient Resource Scaling

• How It Saves Costs:

  • Poorly optimized queries may require a more powerful (and expensive) database instance or more memory.

  • Inefficient use of resources means higher-tier instances without added benefits.

• Optimizations That Help:

  • Tune database settings (e.g., parallelism, caching).

  • Use materialized views or pre-aggregated tables for recurring heavy queries.

---

5. Reduce Data Transfer Costs

• How It Saves Costs:

  • Transferring unnecessary data between applications and the database can increase costs in cloud environments.

• Optimizations That Help:

  • Retrieve only the required columns and rows.

  • Use batch queries instead of multiple small queries.

---

6. Improve Query Efficiency

• How It Saves Costs:

  • Optimized queries require fewer reads, scans, and computations.

  • In systems like BigQuery, costs are based on the amount of data processed.

• Optimizations That Help:

  • Avoid querying unnecessary columns or unfiltered rows.

  • Design queries that aggregate data efficiently in the database.

---

Key Takeaway:

Yes, SQL cost savings are a direct result of:

1. Faster Queries: Reduced CPU and memory usage.

2. Efficient Storage: Lower storage fees.

3. Better Connection Management: Fewer resources tied up.

Would you like a specific breakdown for a cloud provider (e.g., AWS, BigQuery)? 🚀