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🟣 Deep Dive into Apache Cassandra

Apache Cassandra is a highly scalable, distributed NoSQL database designed to handle large volumes of structured data across many commodity servers. It provides high availability, fault tolerance, and eventual consistency with no single point of failure.

📌 Overview

✅ NoSQL: Schema-optional, wide-column store
🌍 Distributed: Peer-to-peer architecture, no master-slave
⚡ Highly Available: Designed for zero downtime
🌱 Scalable: Horizontally scales linearly with minimal effort

🧠 Core Concepts

Concept	Description
Node	Basic storage unit in the cluster
Cluster	Collection of nodes
Data Center	Logical grouping of nodes (can represent physical DCs)
Keyspace	Top-level namespace (like database)
Table	Stores data in rows with flexible columns
Partition Key	Determines data distribution across nodes
Replication Factor	Number of copies of data stored across nodes

⚙️ Architecture

🔁 Peer-to-Peer Model

All nodes are equal; no single point of failure
Nodes gossip to discover and communicate with each other

🔄 Consistent Hashing & Token Ring

Each node owns a range of tokens
Data is distributed based on hash of the partition key
Easy to scale: just add nodes, and token ranges are redistributed

🧱 Storage Engine

Write path uses commit log + memtable
Periodically flushed to SSTables
Uses LSM Tree (Log-Structured Merge Tree) to optimize writes

🧮 Data Model

Cassandra uses a wide-column model (similar to Bigtable).

CREATE TABLE users_by_country (
  country text,
  user_id uuid,
  name text,
  email text,
  PRIMARY KEY (country, user_id)
);

country is the partition key
user_id is the clustering column

// Insert data
INSERT INTO users_by_country (country, user_id, name, email)
VALUES ('US', uuid(), 'Alice', 'alice@example.com');

// Query by partition
SELECT * FROM users_by_country WHERE country = 'US';

🔐 Consistency & Availability

Cassandra offers tunable consistency:

Level	Description
ONE	A single node responds
QUORUM	Majority of replicas respond
ALL	All replicas respond

You choose consistency level per read/write depending on needs.

Rule of thumb: R + W > RF ensures strong consistency.

⚙️ Write Path

Client writes to commit log (durable)
Data written to memtable
Memtable is flushed to disk as SSTable
Background compaction merges SSTables

📖 Read Path

Check Bloom filters to avoid unnecessary reads
Look into memtable, then row cache, then SSTables
Merge results and return to client

🧪 Use Cases

✅ Time-series data
✅ Real-time analytics
✅ IoT backends
✅ Recommendation engines
✅ User activity/event tracking

📈 Performance and Scaling

Scale reads and writes by adding nodes
No need to shard data manually
Local quorum reads improve performance in multi-DC setups
Writes are fast, but reads can be slower compared to in-memory databases

🛠️ Operations and Tools

Task	Tool / Command
Monitoring	`nodetool`, Prometheus + Grafana
Backup	`nodetool snapshot`
Repairs	`nodetool repair`
Adding Nodes	Automatic data rebalance
Compaction	Periodic SSTable merge
Cassandra Shell	`cqlsh` (Cassandra Query Language shell)

🌐 Multi-Region & High Availability

Supports multiple data centers
Can use local quorum for latency-sensitive operations
NetworkTopologyStrategy allows specifying replication per DC

🔐 Security

Authentication and authorization (RBAC)
SSL/TLS encryption for node-to-node and client-to-node
Audit logging and role-based access

🧠 Best Practices

✅ Choose good partition keys to avoid hot spots
✅ Use QUORUM for strong consistency
✅ Regularly repair data (anti-entropy repair)
✅ Avoid large partitions (> 100k rows)
✅ Don’t use Cassandra like a relational DB — no joins!

📚 Learning Resources

✅ Summary

Capability	Cassandra
Availability	⭐⭐⭐⭐⭐
Horizontal Scalability	⭐⭐⭐⭐⭐
SQL-Like Query	⭐⭐⭐
ACID Compliance	❌ (eventual consistency)
Multi-Region Support	✅
Tunable Consistency	✅
Best For	Write-heavy workloads, large-scale distributed systems

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