4.3 Column-Family Stores (e.g., Cassandra)

Cassandra's column-family data model excels at handling large-scale, distributed data. It's optimized for write-heavy workloads and offers high scalability. The model organizes data into keyspaces and column families, providing flexible schemas and fast write throughput.

Interacting with Cassandra involves using CQL, a SQL-like language for data operations. Cluster management is key, with peer-to-peer architecture ensuring high availability. Proper configuration of replication strategies and consistency levels is crucial for balancing data integrity and system performance.

Column-Family Data Model and Cassandra

Column-family data model advantages

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• Designed for distributed storage systems handles large-scale data across multiple nodes
• Optimized for write-heavy workloads (logging systems) and scalability supports high write throughput
• Organizes data into keyspaces (databases) and column families (tables) with flexible schemas
  • Each row has a unique key and can contain varying columns allows for schema evolution
• Provides high write throughput by storing data in a log-structured manner enables fast writes
• Enables horizontal scalability by easily adding new nodes to the cluster accommodates increasing data volume
• Offers fault tolerance and high availability through automatic data replication across nodes ensures cluster remains operational even with node failures

Cassandra data model design

• Denormalizes and duplicates data across multiple tables optimizes for query patterns and avoids joins
  • Example: Duplicate user information in both "users" and "orders" tables for fast reads
• Selects partition keys that evenly distribute data across the cluster prevents hot spots (timestamp)
• Defines clustering columns to optimize for common query patterns and range scans orders data within partitions
  • Example: Cluster by "order_date" for efficient time-based queries
• Utilizes materialized views to maintain additional tables with different partition keys and clustering columns
  • Automatically updates materialized views when the base table is modified improves read performance for specific queries
  • Example: Create a materialized view of "orders" table with "product_id" as partition key for fast product-based lookups

Interacting with Cassandra and Cluster Management

CQL for data operations

• Uses CQL (Cassandra Query Language) similar to SQL syntax for data manipulation and retrieval
• Creates keyspaces (

  CREATE [KEYSPACE](https://www.fiveableKeyTerm:keyspace)

  ) and tables (

  CREATE TABLE

  ) to define schema
• Inserts new rows using

  INSERT

  statement and modifies existing rows with

  UPDATE

  statement
• Retrieves data using

  SELECT

  statement with filtering (

  WHERE

  clause) and ordering (

  ORDER BY

  clause)
  • Example:

    SELECT * FROM users WHERE age > 18 ORDER BY last_name

• Performs atomic operations using

  BEGIN BATCH

  and

  APPLY BATCH

  statements ensures data consistency
• Implements lightweight transactions with

  IF NOT EXISTS

  and

  IF

  clauses for conditional updates
  • Example:

    INSERT INTO users (id, name) VALUES (1, 'John') IF NOT EXISTS

Cassandra cluster management

1. Deploys Cassandra as a peer-to-peer distributed system with no single point of failure ensures high availability
2. Assigns node roles:
   • Seed nodes bootstrap new nodes and maintain cluster membership
   • Data nodes store and replicate data across the cluster
3. Configures replication strategies based on deployment requirements:
   • SimpleStrategy for single data center deployments
   • NetworkTopologyStrategy for multi-data center deployments allows specifying replication factors per data center
4. Tunes consistency levels for read and write operations balances consistency and availability
   • Example:

     [QUORUM](https://www.fiveableKeyTerm:quorum)

     consistency level requires majority of replicas to respond for successful operation
5. Monitors cluster health using tools like

   nodetool

   and performs regular maintenance tasks (repairs, compactions)
6. Handles node additions, removals, and replacements to maintain cluster stability and performance