Chapter 3: Working with Cassandra in Clojure

In this section

• 3.1 Introduction to Cassandra's Wide-Column Store
  • Understanding Cassandra's Data Model: Keyspaces, Tables, Partitions, and Clusters
    Dive deep into Cassandra's unique data model, exploring keyspaces, tables, partitions, and clusters, and understand how it differs from traditional relational databases.
  • Cassandra Write and Read Path: Understanding Commit Logs, Memtables, and SSTables
    Explore the intricacies of Cassandra's write and read paths, focusing on commit logs, memtables, SSTables, bloom filters, and caching mechanisms.
• 3.2 Setting Up a Cassandra Cluster
  • Single-Node Setup for Cassandra Development: A Comprehensive Guide
    Learn how to set up a single-node Cassandra environment for development, including installation, configuration, and verification using cqlsh.
  • Multi-Node Cluster Setup for Cassandra: Building Scalable and Resilient Data Solutions
    Learn how to configure a multi-node Cassandra cluster, understand the roles of seed nodes, and explore network configurations, replication, and data distribution strategies for scalable data solutions.
• 3.3 Clojure Clients for Cassandra: Comparing Hector and Cassaforte
  • Hector: A Legacy Clojure Client for Cassandra
    Explore Hector, an older Clojure client for Cassandra, its features, limitations, and its relevance in modern NoSQL data solutions.
  • Cassaforte: A Modern Clojure Client for Cassandra
    Explore Cassaforte, a feature-rich Clojure client for Cassandra, supporting the latest Cassandra features and CQL. Learn how to integrate Cassaforte into your Clojure projects and leverage its capabilities for scalable data solutions.
  • Selecting the Right Client Library: Hector vs. Cassaforte for Clojure and Cassandra Integration
    Explore the intricacies of selecting the right client library for integrating Clojure with Cassandra, focusing on Hector and Cassaforte. Understand the features, community support, and compatibility of each library to make informed decisions for your projects.
• 3.4 Performing CRUD Operations with CQL
  • Creating Keyspaces and Tables in Cassandra with Clojure
    Learn how to create keyspaces and tables in Cassandra using CQL and Clojure. Understand schema design, primary keys, partition keys, and clustering columns for optimal query performance.
  • Inserting Data into Cassandra with Clojure: Techniques and Best Practices
    Learn how to efficiently insert data into Cassandra tables using CQL from Clojure, with a focus on batch inserts, prepared statements, and consistency levels.
  • Querying Data in Cassandra with Clojure: Mastering CQL for Scalable NoSQL Solutions
    Explore the intricacies of querying data in Cassandra using CQL with Clojure, focusing on SELECT queries, partition keys, clustering columns, and overcoming Cassandra's querying limitations.
  • Updating and Deleting Data in Clojure and NoSQL: Techniques and Best Practices
    Explore the intricacies of updating and deleting data in NoSQL databases using Clojure, including lightweight transactions, tombstones, and performance optimization.
• 3.5 Managing Data Consistency and Availability
  • Understanding Consistency Levels in Cassandra: Balancing Availability and Consistency
    Explore the intricacies of consistency levels in Apache Cassandra, including ONE, QUORUM, and ALL, and learn how to set them for read and write operations in Clojure applications.
  • Handling Replication in Cassandra with Clojure
    Explore Cassandra's replication mechanisms, data centers, rack awareness, and their impact on consistency and durability in NoSQL databases using Clojure.
• 3.6 Case Study: Implementing Time-Series Data Storage
  • Designing a Schema for Time-Series Data: Optimizing NoSQL for High-Volume, Efficient Queries
    Explore strategies for designing a schema optimized for time-series data using NoSQL databases, focusing on partitioning, clustering, and efficient querying.
  • Ingesting High-Velocity Data: Strategies for High Throughput in Clojure and NoSQL
    Explore strategies for handling high-velocity data ingestion using Clojure and NoSQL databases, focusing on asynchronous writes, batching, and performance tuning.
  • Efficient Time-Series Data Querying in NoSQL with Clojure
    Explore techniques for querying time-series data efficiently using NoSQL databases and Clojure, focusing on time range retrieval, clustering, filtering, and pagination.