Cassandra Patterns

Overview

Apache Cassandra is a distributed NoSQL database designed for high availability and linear scalability. It uses CQL (Cassandra Query Language) and follows a partition-row data model optimized for write-heavy workloads.

Capabilities

• Linear scalability by adding nodes
• CQL (SQL-like query language)
• Partition key design for even data distribution
• Clustering columns for sorted storage within partitions
• Tunable consistency levels (ONE, QUORUM, ALL, etc.)
• Multi-datacenter replication
• Compaction strategies for storage optimization
• Lightweight transactions (LWT) for conditional updates

When to Use

Trigger phrases:

• "cassandra patterns"

• "Apache Cassandra patterns — data modeling, CQL, partition keys, clustering, repl"

• Write-heavy workloads requiring high throughput

• Multi-datacenter, globally distributed applications

• Need linear scalability without downtime

• Time-series data or IoT sensor data

• Applications tolerant of eventual consistency

When NOT to Use

• Task is about deployment, not development (use deploy skills)
• Task is about code review, not writing (use review skills)
• You need to understand existing code first (use research skills)
• Task is about testing only (use test skills)
• Requirements are unclear (clarify first)
• Task is trivially simple (single line fix)

Pseudo Code

The cassandra-patterns workflow follows a standard pipeline pattern.

Core flow:

# cassandra-patterns primary flow
input = prepare(raw_data)
result = process(input, config={apache, cassandra, clustering, data, keys})
validate(result)
deliver(result)

Error handling:

on error:
  log(error_details)
  retry_with_backoff(max=3)
  if still_failing: alert_and_escalate()

Schema Design

CREATE KEYSPACE myapp WITH REPLICATION = {
  'class': 'NetworkTopologyStrategy',
  'us-east1': 3,
  'eu-west1': 3
};

CREATE TABLE myapp.users (
  user_id UUID PRIMARY KEY,
  name TEXT,
  email TEXT,
  created_at TIMESTAMP
);

-- Partition key + clustering for time-series
CREATE TABLE myapp.sensor_data (
  sensor_id UUID,
  reading_time TIMESTAMP,
  value DOUBLE,
  PRIMARY KEY (sensor_id, reading_time)
) WITH CLUSTERING ORDER BY (reading_time DESC);

Queries (Node.js)

import { Client } from 'cassandra-driver';

const client = new Client({
  contactPoints: ['node1', 'node2'],
  localDataCenter: 'us-east1',
  keyspace: 'myapp',
});

// Insert
await client.execute(
  'INSERT INTO users (user_id, name, email) VALUES (uuid(), ?, ?)',
  ['Alice', 'alice@example.com'],
  { prepare: true }
);

// Select with partition key
const result = await client.execute(
  'SELECT * FROM sensor_data WHERE sensor_id = ? AND reading_time > ?',
  [sensorId, startDate],
  { prepare: true }
);

// Batch (same partition only)
const batch = [
  { query: 'INSERT INTO sensor_data (sensor_id, reading_time, value) VALUES (?, ?, ?)', params: [id, time1, val1] },
  { query: 'INSERT INTO sensor_data (sensor_id, reading_time, value) VALUES (?, ?, ?)', params: [id, time2, val2] },
];
await client.batch(batch, { prepare: true });

Consistency Levels

import { types } from 'cassandra-driver';

// Strong consistency
await client.execute(query, params, {
  consistency: types.consistencies.quorum,
  prepare: true,
});

// Eventually consistent (faster)
await client.execute(query, params, {
  consistency: types.consistencies.one,
  prepare: true,
});

Lightweight Transactions

-- Conditional insert (IF NOT EXISTS)
INSERT INTO users (user_id, email) VALUES (uuid(), 'alice@example.com') IF NOT EXISTS;

-- Conditional update
UPDATE users SET name = 'Updated' WHERE user_id = ? IF name = 'Old';

Common Patterns

• Partition key design: Choose high-cardinality keys for even distribution; avoid hot partitions
• Clustering columns: Use for sorting data within a partition (time-series, alphabetical)
• Denormalization: Create multiple tables for different query patterns (no JOINs)
• TTL: Use USING TTL 86400 for auto-expiring data (sessions, cache)
• Compaction: STCS (write-heavy), LCS (read-heavy), TWCS (time-series)

How to Use

1. Understand the requirement and existing codebase patterns
2. Design the solution with error handling and testability in mind
3. Implement incrementally with tests for each change
4. Verify against expected outcomes (manual and automated)
5. Document usage, edge cases, and integration points
6. Review with team before merging to shared branches

Red Flags

• Skipping tests to ship faster: Untested code breaks in production when you least expect it
• No error handling in production code: Unhandled errors crash services and lose user data
• Hardcoded configuration values: Hardcoded values prevent environment switching and leak secrets
• Ignoring security implications: Missing input validation, auth bypasses, and injection vulnerabilities
• Over-engineering simple solutions: Premature abstraction adds complexity without proportional benefit

Verification

• Skill output matches expected behavior

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table