Apache Cassandra — Distributed Wide-Column Database at Scale

What Cassandra Does

• Wide-column model — partition key + clustering keys + columns
• CQL query language — SQL-like declarative syntax
• Masterless peer-to-peer — all nodes equal (no single point of failure)
• Tunable consistency — per-query consistency level (ONE, QUORUM, ALL)
• Multi-DC replication — NetworkTopologyStrategy
• Lightweight transactions (LWT) — Paxos-based compare-and-set
• Materialized views — denormalized auto-maintained tables
• TTL — per-cell time-to-live
• Gossip protocol — peer state distribution
• Compaction strategies — STCS, LCS, TWCS for different workloads

Architecture

Peer-to-peer ring: each node owns a range of partition keys determined by consistent hashing. Data is replicated to N nodes. Writes go to a memtable + commit log, flushed to SSTables. Reads merge SSTables + memtable + possibly bloom filters and row cache.

Self-Hosting

# 3-node cluster
version: "3"
services:
  cassandra-node1:
    image: cassandra:5
    environment:
      CASSANDRA_CLUSTER_NAME: tokrepo
      CASSANDRA_SEEDS: cassandra-node1
  cassandra-node2:
    image: cassandra:5
    environment:
      CASSANDRA_CLUSTER_NAME: tokrepo
      CASSANDRA_SEEDS: cassandra-node1
  cassandra-node3:
    image: cassandra:5
    environment:
      CASSANDRA_CLUSTER_NAME: tokrepo
      CASSANDRA_SEEDS: cassandra-node1

Key Features

• Linear horizontal scaling
• Masterless architecture
• Tunable consistency
• Multi-DC replication
• CQL query language
• Secondary indexes
• Materialized views
• Lightweight transactions
• TTL for auto-expiration
• Battle-tested at petabyte scale

Comparison

FAQ

Q: Cassandra vs ScyllaDB? A: Fully API-compatible. ScyllaDB is implemented in C++ with a shard-per-core architecture and performs several times better, but its commercial edition is proprietary. Cassandra is an Apache Foundation project with a more mature ecosystem.

Q: What scenarios is it good for? A: Large-scale time series data, event logs, IoT, message history, and recommendation systems. Not suitable for scenarios requiring complex JOINs or strong transactions (no multi-table JOINs).

Q: Data modeling principles? A: Query-driven. Decide on your query patterns first, then design your table schema. Denormalization and data duplication are the norm — one table per query pattern.

Sources

• Docs: https://cassandra.apache.org/doc
• GitHub: https://github.com/apache/cassandra
• License: Apache 2.0