Ifyou’re using DynamoDB — or planning to — you’ve probably faced a few challenges along the way.
I’ve been there.
When I started working with DynamoDB, I made a ton of mistakes.
It wasn’t until I fixed these that I was able to build powerful, scalable, and efficient databases that could handle real-world demands.
The good news is you don’t have to repeat my mistakes.
Let’s look at the top three mistakes I’ve seen (and made myself) with DynamoDB, and how to avoid them.
Mistake 1: Not Designing Based on Access Patterns
If you take away one lesson from this article, let it be this:
DynamoDB isn’t SQL.
Designing a DynamoDB table the same way you would an SQL table is one of the worst mistakes you can make.
With SQL, you can design normalized tables and then rely on complex queries to fetch the data together. But DynamoDB is a NoSQL database that’s optimized for speed and scalability, and its structure is heavily dependent on how your app accesses data.
Here’s the key:
Start by identifying your app’s most common access patterns. These are the specific ways your app retrieves or writes data. Once you know those patterns, design your table’s primary keys (partition key and sort key) to match them.
For example, imagine an e-commerce application database. If your app frequently retrieves all orders for a single customer, use a customerID as your partition key and orderDate as your sort key.
By aligning your table design with your access patterns, you’ll be able to improve performance and save yourself headaches down the road.
Mistake 2: Overusing Scans
When you first start with DynamoDB, Scans can feel like an easy way out. You need to filter data across multiple attributes, so you write a Scan query and let it sift through your table.
It seems to work wonders, until your database grows and the query latency rises exponentially and so does your monthly bill.
Here’s why Scans are a problem.
Scans read every item in your table to find the data you’re looking for. For small datasets, this might seem harmless, but as your table grows, Scans become painfully slow and expensive.
Instead of relying on Scans, focus on query optimization through good primary key design.
Use partition keys to target specific chunks of your data.
Use sort keys to narrow your results further (enable powerful filtering).
For example, say you’re building a to-do list app and you want to retrieve tasks by status (e.g., “completed”). Instead of scanning the table to filter by status, you can design your primary key like this:
Partition key: userID
Sort key: status#taskID
This allows you to query tasks by user and status, avoiding the inefficiencies of Scans entirely.
Mistake 3: Not Using Conditional Writes
DynamoDB handles writes a bit differently than you might expect if you’re coming from an SQL background.
Specifically, DynamoDB will overwrite data if you write an item with an existing primary key.
There’s no error, no warning — just overwriting. And if you’re not careful, this can lead to data loss and unexpected bugs in your app.
So what is the fix?
Conditional Writes.
With Conditional Writes, you can add logic to your write operations. For example, you can say:
“Write this item only if it doesn’t already exist.”
“Update this record only if its version hasn’t changed since I last read it.”
Here’s a practical example:
Imagine you’re building a user registration system, and you want to ensure each email is unique. Without Conditional Writes, if two users submit the same email at the same time, one could overwrite the other. With Conditional Writes, you can add a condition like:
“Only create this user if the Email attribute doesn’t already exist”.
This simple step can save you from unexpected bugs, especially in applications where data integrity is critical.
Conclusion
DynamoDB is an incredibly powerful tool, but it requires a shift in mindset. Unlike SQL databases, it forces you to think about how your app interacts with data before you even start designing your tables.
To summarize, the top three mistakes to avoid are:
Not designing your database based on access patterns
Overusing Scans for filtering
Skipping Conditional Writes to prevent overwrites
Once you get these right, you’ll discover DynamoDB is a lot simpler than you originally thought.
Top comments (1)
Mistake 4: using a NoSQL database (like DynamoDB) when a SQL database would have been a better choice (which means in the vast majority of use cases) ;-)