Handling SQL-Like Tasks in Cassandra

Since Cassandra does not support many traditional SQL features, we need to redesign our approach to handle tasks efficiently. Below is a detailed breakdown of how to handle common SQL tasks in Cassandra.

1. Handling Joins (JOIN Operations)

?? Problem:

• Cassandra does not support JOIN because it is designed for denormalization.
• Instead of joining tables, we store related data together in a single table.

? Solution: Denormalization (Pre-Joining Data)

Instead of having separate users and orders tables:

?? SQL Approach (Using Joins)

SELECT users.name, orders.product, orders.price 
FROM users 
JOIN orders ON users.id = orders.user_id 
WHERE users.id = 1;
        

? Cassandra Approach (Embed Orders Inside Users Table)

CREATE TABLE user_orders (
    user_id UUID,
    user_name TEXT,
    order_id UUID,
    product TEXT,
    price DECIMAL,
    PRIMARY KEY (user_id, order_id)  -- Partition by user_id, order_id is clustering column
);
        

?? Now we can retrieve all user orders in one query!

SELECT * FROM user_orders WHERE user_id = some_uuid;
        

?? Takeaway: Instead of JOIN, we use denormalization to store data together for efficient retrieval.

2. Handling Aggregations (SUM, AVG, COUNT, GROUP BY)

?? Problem:

• Cassandra does not support aggregations efficiently.
• COUNT(*) works but is very slow on large datasets.

? Solution: Precomputed Aggregations

Instead of running queries like:

SELECT COUNT(*) FROM orders WHERE user_id = 1;
        

We maintain a separate table to store precomputed counts:

CREATE TABLE order_counts (
    user_id UUID PRIMARY KEY,
    total_orders INT
);
        

Whenever an order is placed, we update the count manually:

UPDATE order_counts SET total_orders = total_orders + 1 WHERE user_id = some_uuid;
        

?? This makes COUNT(*) instant instead of scanning millions of rows!

3. Handling Auto-Increment (Serial ID)

?? Problem:

• Cassandra does not support AUTO_INCREMENT or SERIAL.

? Solution: Use UUIDs or Time-Based IDs

Instead of:

INSERT INTO users (id, name) VALUES (DEFAULT, 'Alice');
        

We generate a UUID in Python before inserting:

import uuid
user_id = uuid.uuid4()
session.execute("INSERT INTO users (id, name) VALUES (%s, %s)", (user_id, 'Alice'))
        

?? Alternative: Use TimeUUIDs (now()) for time-ordered data:

INSERT INTO users (id, name) VALUES (now(), 'Alice');
        

4. Handling Filtering on Non-Primary Key Columns

?? Problem:

• Cassandra does not allow WHERE age > 25 unless the column is indexed.
• Secondary indexes are slow on large datasets.

? Solution 1: Use Clustering Columns

Instead of:

SELECT * FROM users WHERE age > 25;
        

We redesign the table to store age as a clustering column:

CREATE TABLE users_by_age (
    age INT,
    user_id UUID,
    name TEXT,
    PRIMARY KEY (age, user_id)
) WITH CLUSTERING ORDER BY (user_id DESC);
        

Now we can efficiently query:

SELECT * FROM users_by_age WHERE age > 25;
        

? Solution 2: Use Materialized Views

Instead of creating multiple tables manually, Materialized Views can auto-maintain an indexed version:

CREATE MATERIALIZED VIEW users_by_age AS 
SELECT age, user_id, name FROM users 
WHERE age IS NOT NULL 
PRIMARY KEY (age, user_id);
        

Now, we can query it directly:

SELECT * FROM users_by_age WHERE age > 25;
        

?? Use materialized views only when necessary to avoid write amplification!

5. Handling Full-Text Search

?? Problem:

• Cassandra does not support LIKE '%keyword%' searches.

? Solution: Use External Search Engines

Use Apache Solr or Elasticsearch alongside Cassandra.

Alternatively, store tokenized data for searching:

CREATE TABLE product_search (
    product_id UUID PRIMARY KEY,
    name TEXT,
    search_tokens SET<TEXT>
);
        

Now we store a product as:

INSERT INTO product_search (product_id, name, search_tokens) 
VALUES (uuid(), 'iPhone 13', {'iphone', 'apple', 'smartphone', '13'});
        

To search for "iphone", we do:

SELECT * FROM product_search WHERE search_tokens CONTAINS 'iphone';
        

?? Pre-tokenizing data allows for faster text search!

6. Handling Transactions (BEGIN TRANSACTION)

?? Problem:

• Cassandra does not support multi-row ACID transactions.

? Solution: Use Lightweight Transactions (LWT)

For operations that need atomicity:

INSERT INTO users (id, email) VALUES (uuid(), '[email protected]') IF NOT EXISTS;
        

This ensures only one record is created even in concurrent writes.

?? Use LWT only when necessary as they impact performance!

7. Handling ORDER BY on Non-Clustering Columns

?? Problem:

• Cassandra only allows ORDER BY on clustering columns.

? Solution: Use Clustering Keys

Instead of:

SELECT * FROM users ORDER BY name;
        

We create:

CREATE TABLE users_sorted (
    id UUID,
    name TEXT,
    PRIMARY KEY (id, name)
) WITH CLUSTERING ORDER BY (name ASC);
        

Now we can efficiently sort users by name.

8. Handling Foreign Keys (FOREIGN KEY Constraints)

?? Problem:

• Cassandra does not support Foreign Keys.

? Solution: Maintain Data Consistency at Application Level

Instead of enforcing constraints:

ALTER TABLE orders ADD CONSTRAINT fk_user FOREIGN KEY (user_id) REFERENCES users(id);
        

We ensure foreign keys manually by:

1. Writing to both users and orders at the same time.
2. Checking if a user exists before inserting an order.

?? Use an external tool like Apache Kafka for consistency enforcement.

?? Final Takeaway

• Cassandra sacrifices flexibility for scalability and speed.
• Schema design must be query-driven (plan your queries first).
• Use denormalization and materialized views for efficient access.
• Think in terms of distributed systems instead of relational databases.

Would you like me to show hands-on implementation with Python and Cassandra Driver? ??