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Optimizing Distributed Joins: Google Cloud Spanner and DataStax Astra DB

Distributed joins are commonly considered too expensive to use for real-time transaction processing. That is because, besides joining data, they also frequently require moving or shuffling data between nodes in a cluster, which can significantly affect query response times and database throughput. However, there are certain optimizations that can completely eliminate the need to move data to enable faster joins. In this article, we first review the four types of distributed joins, including shuffle join, broadcast join, co-located join, and pre-computed join. We then demonstrate how leading fully managed Relational and NoSQL databases, namely Google Cloud Spanner and DataStax Astra DB, support optimized joins that are suitable for real-time applications.

Four Types of Distributed Joins

Joins are used in databases to combine related data from one or more tables or datasets. Data is usually combined based on some condition that relates columns from participating tables. We call columns used in a join condition join keys and assume they are always related by equality operators.

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Data Modeling in Cassandra and Astra DB

What does it take to build an efficient and sound data model for Apache Cassandra and DataStax Astra DB? Where would one start? Are there any data modeling rules to follow? Can it be done consistently time and time again? The answers to these and many other questions can be found in the Cassandra data modeling methodology.

In this post, we present a high-level overview of the data modeling methodology for Cassandra and Astra DB, and share over half a dozen complete data modeling examples from various real-life domains. We apply the methodology to create Cassandra and Astra DB data models for IoT, messaging data, digital library, investment portfolio, time series, shopping cart, and order management. We even provide our datasets and queries for you to try.

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5 Data Models for IoT

Apache Cassandra is a rock-solid choice for managing IoT and time series data at scale. The most popular use case of storing, querying, and analyzing time series generated by IoT devices in Cassandra is well-understood and documented. In general, a time series is stored and queried based on its source IoT device. However, there exists another class of IoT applications that require quick access to the most recent data generated by a collection of IoT devices based on a known state. The question that such applications need to answer is: Which IoT devices or sensors are currently reporting a specific state? In this blog post, we focus on this question and provide five possible data modeling solutions to efficiently answer it in Cassandra.

Introduction

The Internet of Things (IoT) is generating massive amounts of time series data that needs to be stored, queried, and analyzed. Apache Cassandra is an excellent choice for this task: not only because of its speed, reliability, and scalability but also because its internal data model has built-in support for time-ordered data.