Apache Cassandra

Website

cassandra.apache.org

$ Details

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Categories

#Databases #NoSQL Databases #Relational Databases #Big Data

Thanos.io

Website

thanos.io

$ Details

Categories

#Dev Ops #Cloud #DevOps Tools #Prometheus #Monitoring #Metrics

Apache Cassandra features and specs

• Scalability
  Apache Cassandra is designed for linear scalability and can handle large volumes of data across many commodity servers without a single point of failure.
• High Availability
  Cassandra ensures high availability by replicating data across multiple nodes. Even if some nodes fail, the system remains operational.
• Performance
  It provides fast writes and reads by using a peer-to-peer architecture, making it highly suitable for applications requiring quick data access.
• Flexible Data Model
  Cassandra supports a flexible schema, allowing users to add new columns to a table at any time, making it adaptable for various use cases.
• Geographical Distribution
  Data can be distributed across multiple data centers, ensuring low-latency access for geographically distributed users.
• No Single Point of Failure
  Its decentralized nature ensures there is no single point of failure, which enhances resilience and fault-tolerance.

Possible disadvantages of Apache Cassandra

• Complexity
  Managing and configuring Cassandra can be complex, requiring specialized knowledge and skills for optimal performance.
• Eventual Consistency
  Cassandra follows an eventual consistency model, meaning that there might be a delay before all nodes have the latest data, which may not be suitable for all use cases.
• Write-heavy Operations
  Although Cassandra handles writes efficiently, write-heavy workloads can lead to compaction issues and increased read latency.
• Limited Query Capabilities
  Cassandra's query capabilities are relatively limited compared to traditional RDBMS, lacking support for complex joins and aggregations.
• Maintenance Overhead
  Regular maintenance tasks such as node repair and compaction are necessary to ensure optimal performance, adding to the administrative overhead.
• Tooling and Ecosystem
  While the ecosystem for Cassandra is growing, it is still not as extensive or mature as those for some other database technologies.

Thanos.io features and specs

• Global View
  Thanos provides a global view of all your Prometheus metrics, allowing you to query across multiple clusters and data sources seamlessly.
• High Availability
  Thanos enables high availability by providing redundancy and failover for data queries, ensuring continuous access to metric data even if one or more Prometheus instances fail.
• Long-term Storage
  Thanos offers long-term storage for Prometheus metrics, allowing users to store data efficiently beyond the retention period offered by Prometheus.
• Scalability
  Designed to scale horizontally, Thanos can handle increased workloads and large volumes of data, accommodating growing environments.
• Compatibility
  Thanos is fully compatible with Prometheus, which means you can integrate it without changing your existing Prometheus setup.

Possible disadvantages of Thanos.io

• Complexity
  Setting up and managing Thanos can be complex compared to a standalone Prometheus installation, requiring additional components and configurations.
• Resource Intensive
  Thanos can be resource-intensive, necessitating more storage and compute resources to handle data deduplication and global querying effectively.
• Learning Curve
  Developers and operations teams may face a learning curve in adopting Thanos, requiring time to understand its architecture and best practices.
• Network Overhead
  Using Thanos can introduce network overhead due to data being transferred across clusters and remote storage solutions, potentially impacting performance.
• Dependency Management
  Implementing Thanos adds more components and dependencies to an infrastructure, which can lead to increased management and maintenance efforts.

Course Intro | DS101: Introduction to Apache Cassandra™

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