Optimizing Amazon DynamoDB Performance Tips and Best Practices

Introduction

Amazon DynamoDB is a fully managed NoSQL database by AWS, offers scalability, flexibility, and high performance, making it a popular choice for applications requiring low-latency and high-throughput data access. Its seamless integration with AWS services and automatic scaling capabilities makes it an attractive option for developers.
However, designing and optimizing Amazon DynamoDB tables can be challenging, especially for those unfamiliar with its architecture. Developers often face inefficient queries, high read and write costs, improper indexing, and throttling. Understanding these common pitfalls and implementing best practices can help maximize performance, reduce costs, and ensure a smooth experience. In this blog, we will explore common issues encountered using Amazon DynamoDB and practical solutions to overcome them.

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Common issues encountered

Table Design Challenges

Choosing the right primary key is crucial for efficient data retrieval. Many users select attributes with low cardinality (like status or gender) as partition keys, leading to data skew and “hot partitions,” which can significantly impact performance.

How to Improve Table Design

Select a partition key that is unique enough to distribute data evenly across partitions. Combining it with a sort of key allows for efficient querying. For example, a combination of UserID (partition key) and Timestamp (sort key) is ideal for time-series data.

{ 
  "UserID": "user123", 
  "Timestamp": "2025-02-18T10:00:00Z" 
}

{

"UserID": "user123",

"Timestamp": "2025-02-18T10:00:00Z"

}

Limited Use of Indexes

Relying solely on the primary key for querying limits flexibility and often increases complexity. Developers sometimes overlook Global Secondary Indexes (GSIs) and Local Secondary Indexes.

(LSIs), which are essential for querying non-primary key attributes.

Enhancing Query Flexibility with Indexes

Utilize GSIs to query non-primary key attributes and LSIs for additional query patterns while maintaining the same partition key. This improves query performance and reduces complexity. However, be mindful of the additional cost and storage requirements.

{ 
  "TableName": "Orders", 
  "AttributeDefinitions": [ 
    {"AttributeName": "OrderID", "AttributeType": "S"}, 
    {"AttributeName": "CustomerID", "AttributeType": "S"} 
  ], 
  "KeySchema": [ 
    {"AttributeName": "OrderID", "KeyType": "HASH"} 
  ], 
  "GlobalSecondaryIndexes": [ 
    { 
      "IndexName": "CustomerIDIndex", 
      "KeySchema": [ 
        {"AttributeName": "CustomerID", "KeyType": "HASH"} 
      ], 
      "Projection": { 
        "ProjectionType": "ALL" 
      } } ] }

{

"TableName": "Orders",

"AttributeDefinitions": [

{"AttributeName": "OrderID", "AttributeType": "S"},

{"AttributeName": "CustomerID", "AttributeType": "S"}

"KeySchema": [

{"AttributeName": "OrderID", "KeyType": "HASH"}

"GlobalSecondaryIndexes": [

{

"IndexName": "CustomerIDIndex",

"KeySchema": [

{"AttributeName": "CustomerID", "KeyType": "HASH"}

"Projection": {

"ProjectionType": "ALL"

} } ] }

Suboptimal Querying and Scanning

Using Scan operations instead of Queries is a common practice that negatively impacts performance. Scans examine every item in the table, leading to high read capacity unit consumption and slower performance.

Efficient Data Retrieval

Queries are more efficient as they use indexes to retrieve a subset of data. Prefer Queries over Scans whenever possible. Additionally, filters should be used sparingly to minimize the read capacity units consumed.

const params = { 
  TableName: "Orders", 
  KeyConditionExpression: "CustomerID = :customerId", 
  ExpressionAttributeValues: { 
    ":customerId": "user123" 
  } 
}; 
const data = await dynamoDB.query(params).promise();

const params = {

TableName: "Orders",

KeyConditionExpression: "CustomerID = :customerId",

ExpressionAttributeValues: {

":customerId": "user123"

}

};

const data = await dynamoDB.query(params).promise();

Provisioned Throughput Mismanagement

Manually setting provisioned read and write capacity without understanding the workload patterns can lead to throttling or unnecessary costs.

Efficient Capacity Management

For unpredictable workloads, use the on-demand mode to scale automatically based on demand. For consistent traffic, enable Auto Scaling to adjust capacity units dynamically.

{ 
  "TableName": "Orders", 
  "BillingMode": "PAY_PER_REQUEST" 
}

{

"TableName": "Orders",

"BillingMode": "PAY_PER_REQUEST"

}

Or for Auto Scaling:

{ 
  "TableName": "Orders", 
  "ProvisionedThroughput": { 
    "ReadCapacityUnits": 5, 
    "WriteCapacityUnits": 5 
  }, 
  "AutoScalingSettings": { 
    "ReadCapacityAutoScalingSettings": { 
      "MinCapacity": 5, 
      "MaxCapacity": 100, 
      "TargetUtilization": 70 
    }, 
    "WriteCapacityAutoScalingSettings": { 
      "MinCapacity": 5, 
      "MaxCapacity": 100, 
      "TargetUtilization": 70 
    } } }

{

"TableName": "Orders",

"ProvisionedThroughput": {

"ReadCapacityUnits": 5,

"WriteCapacityUnits": 5

"AutoScalingSettings": {

"ReadCapacityAutoScalingSettings": {

"MinCapacity": 5,

"MaxCapacity": 100,

"TargetUtilization": 70

"WriteCapacityAutoScalingSettings": {

"MinCapacity": 5,

"MaxCapacity": 100,

"TargetUtilization": 70

} } }

Handling Large Item Sizes

Storing large items or too many attributes increases read/write costs and storage usage. It also affects query performance.

Optimizing Storage

Large files or unstructured data can be stored in Amazon S3 and saved as a reference (URL) in Amazon DynamoDB. This reduces costs and improves performance.

{ 
  "ProductID": "prod123", 
  "ProductName": "Smartphone", 
  "ImageURL": "https://s3.amazonaws.com/bucket-name/product-images/prod123.jpg" 
}

{

"ProductID": "prod123",

"ProductName": "Smartphone",

"ImageURL": "https://s3.amazonaws.com/bucket-name/product-images/prod123.jpg"

}

Overuse of Transactions

Using DynamoDB Transactions for simple operations adds unnecessary latency and cost because they involve two-phase commits.

When to Use Transactions

Use transactions for atomicity and consistency across multiple items. For simpler operations, use BatchWrite or BatchGet for better performance.

const params = { 
  TransactItems: [ 
    { 
      Update: { 
        TableName: "Accounts", 
        Key: { "AccountID": "acc123" }, 
        UpdateExpression: "SET Balance = Balance - :amount", 
        ExpressionAttributeValues: { ":amount": 100 } 
      } }, 
    { 
      Update: { 
        TableName: "Accounts", 
        Key: { "AccountID": "acc456" }, 
        UpdateExpression: "SET Balance = Balance + :amount", 
        ExpressionAttributeValues: { ":amount": 100 } 
     } } ] }; 
await dynamoDB.transactWrite(params).promise();

const params = {

TransactItems: [

{

Update: {

TableName: "Accounts",

Key: { "AccountID": "acc123" },

UpdateExpression: "SET Balance = Balance - :amount",

ExpressionAttributeValues: { ":amount": 100 }

} },

{

Update: {

TableName: "Accounts",

Key: { "AccountID": "acc456" },

UpdateExpression: "SET Balance = Balance + :amount",

ExpressionAttributeValues: { ":amount": 100 }

} } ] };

await dynamoDB.transactWrite(params).promise();

Inefficient Data Retrieval

Fetching all attributes in queries or scans consumes more read capacity units, increasing costs.

Using Projection Expressions

Only retrieve the required attributes using Projection Expressions to optimize cost and performance.

const params = { 
  TableName: "Orders", 
  KeyConditionExpression: "CustomerID = :customerId", 
  ExpressionAttributeValues: { 
    ":customerId": "user123" 
  }, 
  ProjectionExpression: "OrderID, OrderDate, TotalAmount" 
}; 
const data = await dynamoDB.query(params).promise();

const params = {

TableName: "Orders",

KeyConditionExpression: "CustomerID = :customerId",

ExpressionAttributeValues: {

":customerId": "user123"

ProjectionExpression: "OrderID, OrderDate, TotalAmount"

};

const data = await dynamoDB.query(params).promise();

Conclusion

Amazon DynamoDB is a powerful NoSQL database with high performance and scalability. By avoiding these common issues and implementing best practices, you can optimize your Amazon DynamoDB usage for cost, performance, and reliability.

Start by designing your tables with the right primary keys, leveraging indexes for efficient querying, and monitoring usage with Amazon CloudWatch. Optimize data retrieval with projection expressions, handle large objects smartly, and use transactions wisely.

By paying attention to these aspects, you can avoid costly issues and ensure your Amazon DynamoDB implementation runs efficiently and effectively.

Drop a query if you have any questions regarding Amazon DynamoDB and we will get back to you quickly.

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FAQs

1. What is the impact of storing large items in Amazon DynamoDB?

ANS: – Storing large items increases storage costs and read/write capacity consumption. It also impacts query performance due to the larger data transfer. Using Amazon S3 for large objects and storing references in Amazon DynamoDB is a more efficient approach.

2. Can I use joins in Amazon DynamoDB like in relational databases?

ANS: – No, Amazon DynamoDB doesn’t support joins natively. To achieve similar functionality, design tables using a denormalized schema or perform joins at the application level. You can also use composite keys and GSIs for related queries.

WRITTEN BY Aehteshaam Shaikh

Aehteshaam Shaikh is working as a Research Associate - Data & AI/ML at CloudThat. He is passionate about Analytics, Machine Learning, Deep Learning, and Cloud Computing and is eager to learn new technologies.