Building Applications With Cassandra: A Very Quick Guide

Cassandra Overview

Cassandra as an open-source NoSQL database has gained popularity in cloud and big data applications. Inspired by DynamoDB, it also has good latency, tunable consistency, easy to achieve scalability, and high-availability with cluster setup.

Our team’s been using Cassandra as the backend for an application we’ve been shipping to customers. We chose it for its high-availability setup, and good performance. We used to store time-series data and some simple configuration data as Key-value pairs. So it felt like a natural choice. And in our experience over time, it has proven to be highly capabable at serving our purposes.

With impressive availability, scalability, and read/write performance, Casandra also comes with its limitations. We cannot design data models the same way we did with traditional relational databases with SQL interface. And it doesn’t come with many of the guarantees from traditional databases, like consistency level, transactions, cascading deletion, etc. Like other NoSQL databases, Cassandra was designed to optimize batch write operations with good read and write latency. It fits applications without too much update/delete operations, especially ones with no high amounts of transactions.

So the best use cases for Cassandra can be:

  • You have a high volume of data with availability concerns.
  • Most data is sequential read/write or append, e.g.: logs, time-series, IoT applications, track records, messages, etc.
  • You don’t have complex data relations between data entities that requires high amount of transactions.

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Paper Reading: Ray: A Distributed Framework for Emerging AI Applications

Paper link:


Ray is a new and grossing distributed programming framework, with an ambitious plan to be the foundation of emerging AI/ML applications. In its own words, it aims to “provide a universal API for distributed computing”. Which means it needs to provide a programming interface that’s flexible enough for new applications, and a backend system designed to scale for elastic computing needs with some good performance. This paper (OSDI 18’) explains its API and architecture design to fulfill this goal. And I’ve found some very interesting points.

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