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Introduction

2 min to complete

Introduction

In Scylla, as opposed to relational databases, the data model is based around the queries and not just around the domain entities. When creating the data model, we take into account both the conceptual data model and the application workflow: which queries will be performed by which users and how often.

One of the main goals of data modeling in Scylla is to return results fast. To achieve that, we want:

  • Even data distribution: data should be evenly spread across the cluster so that every node holds roughly the same amount of data. Scylla determines which node should store the data based on hashing the partition key. Therefore, choosing a suitable partition key is crucial. More on this later on.
  • To minimize the number of partitions accessed in a read query: To make reads faster, we’d ideally have all the data required in a read query stored in a single Table. Although it’s fine to duplicate data across tables, in terms of performance, it’s better if the data needed for a read query is in one table.

Things we should NOT focus on:

  • Avoiding data duplication: To get efficient reads, we sometimes have to duplicate data. More about that and denormalization later in this lesson. In a later session, we learn how to avoid duplication in some cases using Secondary Indexes.
  • Minimizing the number of writes: writes in Scylla aren’t free, but they are very efficient and “cheap.” Scylla is optimized for high write throughput. Reads, while still very fast, are usually more expensive than writes and are harder to fine-tune. We’d usually be ready to increase the number of writes to increase read efficiency. Keep in mind that the number of tables also affects consistency. 

In this lesson and future lessons, we will use an example based around a Veterinary Clinic named 4Paws Clinic. In this clinic, each animal that is admitted has a connected heart rate monitor, which logs heart rate and other vital information every five seconds.

 

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