Three NoSQL Data Modeling Mistakes to Avoid


NoSQL is used for the most performance-sensitive workloads, but getting the data model right with NoSQL is challenging.

Here are three mistakes to avoid:

1. Not Addressing Large Partitions

Large partitions arise when developers scale their distributed databases. Such partitions start introducing performance problems across the cluster’s replicas.

So, how can these issues be addressed? A simple way to address this issue is to rethink the primary key. The primary key determines how data will be distributed across the cluster to enhance performance and resource usage. An ideal partition key must have high cardinality and roughly even distribution.

2. Emerging Hot Spots

Hot spots can arise due to large partitions. Hot spots emerge when a problematic data access pattern causes an imbalance in how data is accessed in the cluster.

Another reason for hot spot occurrence is when an application fails to impose limits on the client side and potentially allows tenants to spam a given key. For instance, bots in a messaging app that frequently spam messages in a channel.

Also Read: SQL Vs. NoSQL- Which is Better

3. Collection Misuse

Developers do not always use collections, but when they do, they use them inaccurately. Collections help store or denormalize a small amount of data. They are stored in a single cell, making serialization/deserialization costly.

When using collections, define whether the field is frozen or non-frozen. A frozen collection can only be written as a whole. This means the elements cannot be appended or removed.

A non-frozen collection can be appended. Due to this, the non-frozen collection is mainly misused, leading to performance issues.


To overcome these mistakes, carefully analyze the app’s data usage patterns. At the same time, identify an ideal data modeling approach best suited for specific usage patterns. Furthermore, select the right data modeling option to address technical bottlenecks.

Read More: NoSQL Data Modeling Mistakes that Ruin Performance

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