AWS DynamoDB in Detail
Amazon DynamoDB in detail
NoSQL Database
NoSQL databases are a type of database management system that are not based on the traditional relational database model. Unlike relational databases, which store data in tables with predefined schemas, NoSQL databases store data in flexible, schema-less formats. NoSQL databases are designed to handle large volumes of unstructured or semi-structured data, making them well-suited for modern web applications, big data analytics, and other use cases where scalability and flexibility are important.
Key characteristics of NoSQL databases include:
Flexible Schema: NoSQL databases allow for flexible data schemas, meaning that each record (or document, in some NoSQL databases) can have its own unique structure. This flexibility makes it easier to adapt to changing data requirements and schemas.
Scalability: NoSQL databases are designed to scale horizontally, meaning that they can easily handle increasing amounts of data by adding more servers or nodes to the database cluster. This makes them well-suited for applications with rapidly growing data volumes.
High Availability: Many NoSQL databases are designed to provide high availability and fault tolerance, ensuring that applications can continue to operate even in the event of hardware failures or network issues.
Distributed Architecture: NoSQL databases often use a distributed architecture, with data distributed across multiple servers or nodes in a cluster. This allows for parallel processing and improved performance.
Examples of popular NoSQL databases include MongoDB, Cassandra, Redis, and, of course, Amazon DynamoDB.
In summary, NoSQL databases offer a flexible and scalable alternative to traditional relational databases, making them well-suited for modern applications that require flexibility, scalability, and high availability.
DynamoDB Basics
Amazon DynamoDB is a fully managed NoSQL database service provided by Amazon Web Services (AWS). It offers a flexible and scalable database solution for applications that require low-latency, high-performance data storage with seamless scalability. Here are some key concepts to understand about DynamoDB:
Tables: In DynamoDB, data is organized into tables. Each table consists of multiple items, which are analogous to rows in a relational database.
Items: An item is a single data record in a DynamoDB table. Each item is uniquely identified by a primary key, which can consist of one or two attributes: a partition key (also known as a hash key) and an optional sort key (also known as a range key). Items can also contain additional attributes, which are similar to columns in a relational database.
Attributes: Attributes are the individual pieces of data that make up an item. Each attribute has a name and a value. DynamoDB supports various data types for attributes, including string, number, binary, boolean, list, map, and more.
Primary Keys: Every item in a DynamoDB table must have a primary key, which uniquely identifies the item within the table. The primary key can be composed of one or two attributes:
Partition Key
: A single attribute that determines the partition (physical storage location) of the item in the table. Items with the same partition key are stored together and can be retrieved efficiently.
Sort Key (Optional)
: An optional second attribute that is used to order items with the same partition key. It allows for range queries and sorting within a partition.
Secondary Indexes: DynamoDB supports secondary indexes, which allow you to query the data in a table using alternate attributes as keys. There are two types of secondary indexes:
Local Secondary Index (LSI)
: An index with the same partition key as the base table but a different sort key.
Global Secondary Index (GSI)
: An index with a different partition key and optionally a different sort key from the base table. GSIs provide more flexibility but require additional provisioned throughput.
Streams: DynamoDB Streams allow you to capture changes to items in a DynamoDB table in real-time. Each change to an item, such as an insertion, modification, or deletion, is recorded as a stream event, which can be processed asynchronously using AWS Lambda, Amazon Kinesis, or other AWS services.
Understanding these fundamental concepts of DynamoDB is essential for effectively designing, implementing, and querying DynamoDB tables in your applications.
Data Modeling in DynamoDB
Data modeling in Amazon DynamoDB involves designing the structure of your tables and items to efficiently store and retrieve your data. Unlike traditional relational databases, DynamoDB does not enforce a fixed schema, allowing for flexible data modeling based on the specific needs of your application. Here are some key considerations for data modeling in DynamoDB:
Normalization vs. Denormalization: DynamoDB's flexible schema allows you to denormalize your data, meaning that you can store related data together in a single table to optimize read performance. However, you should also consider normalization principles, where you store data in separate tables to avoid redundancy and ensure data integrity.
Hierarchical Data Modeling: DynamoDB supports hierarchical data structures, allowing you to represent complex relationships between entities within a single table. This can be achieved using composite primary keys or nested attributes (such as maps and lists) within items.
Primary Key Design: Carefully design your table's primary keys to ensure efficient data access and distribution across partitions. Choose partition keys that evenly distribute your workload and sort keys that facilitate efficient querying and sorting of data within partitions.
Secondary Indexes: Utilize secondary indexes to support alternative access patterns and queries beyond the primary key. Consider the trade-offs between local secondary indexes (LSIs) and global secondary indexes (GSIs), including their impact on write throughput, storage costs, and query flexibility.
Composite Keys: Use composite primary keys (partition key and sort key) to model hierarchical relationships and support range queries within partitions. When designing composite keys, consider the access patterns of your queries and how the data will be accessed.
Optimizing Read and Write Operations: Strive to minimize the number of read and write operations required to access and modify your data. Use efficient data structures, such as maps and sets, to store multiple values within a single attribute and reduce the size of your items.
Batch Operations: Take advantage of DynamoDB's batch operations, such as BatchGetItem and BatchWriteItem, to efficiently retrieve and manipulate multiple items in a single request. This can reduce latency and improve overall throughput for bulk operations.
Data Types and Encoding: Choose appropriate data types for your attributes based on the nature of your data and the operations you plan to perform. DynamoDB supports various data types, including strings, numbers, binary data, sets, lists, and maps. Additionally, consider data encoding techniques, such as base64 encoding for binary data, to optimize storage and transmission.
By carefully considering these factors and principles when modeling your data in DynamoDB, you can design efficient and scalable database schemas that meet the performance and scalability requirements of your applications.
Partitioning and Throughput in DynamoDB
Partitioning and throughput provisioning are critical aspects of designing scalable and performant applications with Amazon DynamoDB. DynamoDB partitions data across multiple servers to achieve high availability and horizontal scalability. Understanding how partitioning works and how to provision throughput effectively is essential for optimizing performance and managing costs. Here are key concepts related to partitioning and throughput in DynamoDB:
Partitioning: DynamoDB partitions data into multiple partitions based on the partition key of each item. Each partition is stored and managed independently by DynamoDB. The partition key value determines the partition in which an item is stored. DynamoDB automatically scales the number of partitions based on the workload and the size of the data.
Partition Keys: Choosing an appropriate partition key is crucial for evenly distributing workload and avoiding hot partitions. A hot partition occurs when a disproportionate amount of read or write activity is directed to a single partition, leading to throttling and performance degradation. Design partition keys that distribute workload evenly across partitions to avoid hot partitions.
Partition Throughput: DynamoDB provisions throughput capacity in terms of read capacity units (RCUs) and write capacity units (WCUs). RCUs represent the number of strongly consistent read operations per second, while WCUs represent the number of write operations per second. Provisioned throughput capacity is allocated at the partition level, so evenly distributing workload across partitions is essential for maximizing throughput.
Provisioned Throughput: When creating a DynamoDB table, you specify the desired provisioned throughput capacity in terms of RCUs and WCUs. Provisioned throughput can be adjusted dynamically using the AWS Management Console, AWS CLI, or AWS SDK based on the changing workload requirements. Provisioned throughput capacity is billed based on the provisioned capacity and the duration it is provisioned.
Auto Scaling: DynamoDB offers auto scaling, a feature that automatically adjusts provisioned throughput capacity in response to changes in workload demand. Auto scaling ensures that your application can handle varying levels of traffic without manual intervention. You can enable auto scaling for read and write capacity independently, and DynamoDB will automatically adjust capacity based on utilization metrics.
On-Demand Capacity Mode: In addition to provisioned throughput, DynamoDB offers on-demand capacity mode, which eliminates the need for manual capacity provisioning. With on-demand capacity mode, you pay for the read and write requests your application makes without managing capacity manually. On-demand capacity mode is suitable for unpredictable workloads or applications with infrequent access patterns.
Monitoring Throughput: Monitor DynamoDB throughput metrics using Amazon CloudWatch to ensure that your application's performance requirements are met. Key metrics to monitor include consumed capacity, throttled requests, and provisioned capacity utilization. Use CloudWatch alarms to trigger notifications or automated actions in response to throughput-related events.
By understanding partitioning and throughput concepts in DynamoDB, you can design scalable and efficient database schemas, provision throughput capacity effectively, and monitor performance to optimize resource utilization and cost efficiency.
Consistency Models in DynamoDB
Consistency models define how updates to data are propagated and observed by clients in a distributed system like Amazon DynamoDB. DynamoDB offers two consistency models: eventual consistency and strong consistency. Understanding these consistency models is crucial for designing applications that meet your consistency and performance requirements. Here's an overview of each consistency model in DynamoDB:
Eventual Consistency:
In an eventually consistent system like DynamoDB, updates to data are propagated asynchronously across multiple replicas, and there is no guarantee that all replicas will have the latest version of the data at any given time.
After a write operation is acknowledged by DynamoDB, there may be a short delay before the updated data is visible to all subsequent read operations. During this time, different clients may observe different versions of the data.
Eventual consistency offers high availability and low latency for read and write operations but sacrifices strong consistency guarantees. It is suitable for applications where eventual convergence of data is acceptable and where low latency is a priority.
Strong Consistency:
In a strongly consistent system like DynamoDB, updates to data are propagated synchronously across all replicas, and all subsequent read operations will return the latest version of the data.
When you choose strong consistency for read operations in DynamoDB, the read operations are guaranteed to reflect the effects of all prior write operations that have been acknowledged by DynamoDB.
Strong consistency ensures that all clients observe a consistent view of the data, eliminating the possibility of stale or outdated reads. However, strong consistency may incur higher latency and lower availability compared to eventual consistency.
Consistency Levels:
DynamoDB allows you to specify the consistency level for read operations at the table or individual request level. You can choose between eventually consistent reads and strongly consistent reads based on your application's requirements.
By default, read operations in DynamoDB are eventually consistent, providing high availability and low latency. You can opt-in to strong consistency for specific read operations that require immediate access to the latest data.
Use Cases:
Eventual consistency is suitable for applications where eventual convergence of data is acceptable, such as content delivery, caching, or analytics systems.
Strong consistency is preferred for applications where strict consistency guarantees are required, such as financial transactions, inventory management, or real-time collaboration.
Trade-offs:
Choosing between eventual consistency and strong consistency involves trade-offs between availability, latency, and consistency guarantees. Evaluate your application's requirements and choose the appropriate consistency model based on your specific use case.
By understanding the differences between eventual consistency and strong consistency in DynamoDB, you can design applications that balance consistency requirements with performance and availability constraints.
Querying and Indexing in DynamoDB
Querying and indexing are fundamental operations in Amazon DynamoDB that allow you to efficiently retrieve and manipulate data based on specific criteria. DynamoDB provides powerful querying capabilities to access data from tables and indexes, enabling flexible and efficient data retrieval for various use cases. Here's an overview of querying and indexing in DynamoDB:
Query Operation:
The query operation allows you to retrieve items from a DynamoDB table based on specified criteria.
Queries are performed using the table's primary key or secondary indexes, allowing for efficient retrieval of specific items or ranges of items.
Primary key queries retrieve items based on the partition key and optionally the sort key, allowing for precise item retrieval within partitions.
Secondary index queries enable querying based on alternate attributes, providing flexibility in accessing data using different access patterns.
Queries can be performed using filter expressions to further refine the results based on attribute values.
Scan Operation:
The scan operation allows you to retrieve all items from a DynamoDB table or a subset of items based on specified criteria.
Scans examine every item in the table and apply filtering criteria to include or exclude items from the result set.
Scans are less efficient than queries, especially for large tables, as they involve reading every item in the table and evaluating filter expressions.
Scans should be used judiciously and primarily for scenarios where querying based on indexes or primary keys is not feasible.
Indexing:
DynamoDB supports secondary indexes, which allow you to query data based on attributes other than the primary key.
Local Secondary Indexes (LSIs) are indexes that have the same partition key as the table's primary key but a different sort key. They are useful for querying data within a single partition.
Global Secondary Indexes (GSIs) are indexes with a different partition key and optionally a different sort key from the table's primary key. They provide flexibility in querying data across partitions.
Indexes can be created when creating a table or added later using the AWS Management Console, AWS CLI, or AWS SDKs.
Querying with Indexes:
When querying with secondary indexes, you can specify the index name and query criteria to retrieve items based on the indexed attributes.
Index queries support both key conditions and filter expressions, allowing for precise retrieval and filtering of items based on attribute values.
Best Practices:
Design efficient data models and choose appropriate primary keys and secondary indexes based on your application's access patterns.
Use queries for precise item retrieval based on primary keys or secondary indexes whenever possible, and use scans sparingly for scenarios where querying is not feasible.
Optimize query performance by designing efficient index structures, minimizing data size, and leveraging filter expressions to reduce result sets.
By mastering querying and indexing concepts in DynamoDB, you can effectively retrieve and manipulate data to meet your application's requirements while optimizing performance and efficiency.
Best Practices for DynamoDB
To effectively utilize Amazon DynamoDB and ensure optimal performance, scalability, and cost efficiency, it's essential to follow best practices when designing, implementing, and managing your DynamoDB tables. These best practices encompass various aspects of DynamoDB usage, including data modeling, throughput provisioning, indexing, and optimization strategies. Here are key best practices for DynamoDB:
Data Modeling Best Practices:
Design efficient data models based on your application's access patterns and query requirements.
Denormalize data where necessary to minimize the number of read and write operations required to access and update data.
Use composite primary keys (partition key and sort key) to model hierarchical relationships and support range queries within partitions.
Throughput Provisioning Best Practices:
Provision read and write capacity units (RCUs and WCUs) based on your application's expected workload and performance requirements.
Monitor throughput metrics using Amazon CloudWatch and adjust provisioned capacity dynamically to accommodate changing workload demands.
Consider using auto scaling to automatically adjust throughput capacity based on utilization metrics, ensuring optimal resource allocation and cost efficiency.
Indexing Best Practices:
Create appropriate secondary indexes to support alternate access patterns and query requirements.
Choose the right type of index (local secondary index or global secondary index) based on your query patterns and scalability needs.
Use sparse indexes to reduce index size and improve query performance by indexing only the necessary attributes.
Query and Scan Best Practices:
Prefer queries over scans whenever possible to minimize latency and throughput consumption.
Use filter expressions to narrow down query results and reduce the size of result sets, especially for scan operations.
Limit scan operations to small datasets or scenarios where querying based on indexes or primary keys is not feasible.
Optimization Best Practices:
Optimize data access patterns by designing efficient data models, indexes, and queries.
Minimize item size and attribute count to reduce storage costs and improve read and write performance.
Leverage DynamoDB features such as partition keys, sort keys, and secondary indexes to optimize query performance and reduce latency.
Monitoring and Management Best Practices:
Monitor DynamoDB performance metrics using Amazon CloudWatch and set up alarms to notify you of performance issues or capacity constraints.
Use AWS Trusted Advisor to identify potential optimization opportunities and cost-saving measures for your DynamoDB tables.
Regularly review and optimize your DynamoDB table configurations, throughput capacity, and data access patterns to ensure optimal performance and cost efficiency over time.
By following these best practices, you can design scalable, efficient, and cost-effective DynamoDB solutions that meet the performance and scalability requirements of your applications while minimizing operational overhead and costs.
DynamoDB Transactions
Transactions in Amazon DynamoDB provide atomicity, consistency, isolation, and durability (ACID) guarantees for multiple read and write operations across one or more items within a single or multiple tables. Transactions ensure that either all of the operations within the transaction are applied, or none of them are applied, maintaining data integrity and consistency. Here's an overview of transactions in DynamoDB:
Transactional Operations:
DynamoDB transactions support two types of operations: transactGetItems and transactWriteItems.
transactGetItems allows you to perform multiple GetItem operations within a single transaction to retrieve multiple items from one or more tables.
transactWriteItems allows you to perform multiple PutItem, UpdateItem, DeleteItem, or ConditionCheck operations within a single transaction to modify multiple items in one or more tables.
Atomicity and Consistency:
DynamoDB transactions provide atomicity, ensuring that either all of the operations within a transaction are successfully applied, or none of them are applied. This prevents partial updates or inconsistencies resulting from failed operations.
Transactions also provide consistency by enforcing serializability, ensuring that transactions appear to execute sequentially without interfering with each other, even in a distributed environment.
Isolation:
DynamoDB transactions provide isolation, meaning that the changes made by one transaction are not visible to other transactions until the transaction is committed.
Transactions are isolated from each other to prevent concurrent transactions from accessing or modifying the same data simultaneously, avoiding data corruption or conflicts.
Durability:
DynamoDB transactions provide durability by ensuring that once a transaction is committed, its changes are durable and persist even in the event of failures or system crashes.
DynamoDB guarantees that committed transactions are durable and will not be lost due to system failures or outages.
Usage Considerations:
Use transactions for scenarios that require atomicity and consistency across multiple data operations, such as financial transactions, inventory management, or complex data updates.
Transactions incur additional costs and may have performance implications compared to individual data operations, so use them judiciously for critical operations that require transactional guarantees.
Transactions are limited to a maximum of 25 unique items or up to 4 MB of data per transaction, so ensure that your transactions comply with these limits.
Implementation:
Use the TransactWriteItems and TransactGetItems operations in the DynamoDB API or SDKs to perform transactional operations.
Define the list of operations to be performed within a transaction, including PutItem, UpdateItem, DeleteItem, or ConditionCheck operations, and execute them as a single unit of work.
Handle transactional errors and retries gracefully to ensure robustness and reliability in your application.
By leveraging transactions in DynamoDB, you can ensure data integrity, consistency, and reliability for critical operations, enabling you to build robust and scalable applications that meet stringent transactional requirements.
Backup and Restore in DynamoDB
Backup and restore capabilities are essential for ensuring data durability, compliance, and disaster recovery in Amazon DynamoDB. DynamoDB offers several options for backing up and restoring tables, allowing you to protect your data against accidental deletion, corruption, or other data loss events. Here's an overview of backup and restore options in DynamoDB:
On-Demand Backups:
DynamoDB supports on-demand backups, allowing you to create full backups of your tables at any time. On-demand backups capture the entire table data, including all items, indexes, and settings.
You can create on-demand backups using the AWS Management Console, AWS CLI, or AWS SDKs. Simply select the table you want to backup and initiate the backup process.
On-demand backups are stored in Amazon S3 and are incremental, meaning that only the data that has changed since the last backup is stored, reducing storage costs and backup duration.
Point-in-Time Recovery (PITR):
DynamoDB offers point-in-time recovery (PITR) to enable continuous and granular recovery of tables to any point in time within the last 35 days.
PITR automatically backs up table data and maintains incremental backups for the specified recovery window, allowing you to restore tables to any specific point in time within that window.
PITR backups capture changes made to tables in real-time, providing comprehensive data protection and recovery capabilities for DynamoDB tables.
Restore from Backup:
You can restore a DynamoDB table from an on-demand backup or PITR backup to create a new table with the same data and settings as the original table at the time of the backup.
When restoring from a backup, you can specify the target table name, provisioned throughput capacity, and other settings for the restored table.
Restoring from backup is a straightforward process that can be initiated using the AWS Management Console, AWS CLI, or AWS SDKs, providing flexibility and ease of use.
Backup and Restore Considerations:
Regularly backup your DynamoDB tables to protect against data loss and ensure data durability and compliance with regulatory requirements.
Define backup retention policies and schedules based on your data retention and recovery objectives, considering factors such as compliance requirements, recovery point objectives (RPOs), and recovery time objectives (RTOs).
Test backup and restore procedures regularly to verify their effectiveness and reliability in real-world scenarios, ensuring that you can recover your data quickly and accurately in the event of a data loss event.
By leveraging backup and restore capabilities in DynamoDB, you can safeguard your data against loss or corruption, maintain data availability and compliance, and minimize the impact of data-related incidents on your applications and business operations.
Monitoring and Metrics in DynamoDB
Monitoring and metrics are essential for maintaining the health, performance, and efficiency of Amazon DynamoDB tables and applications. DynamoDB provides comprehensive monitoring capabilities through Amazon CloudWatch, allowing you to monitor various performance metrics, set up alarms, and gain insights into the behavior of your DynamoDB tables. Here's an overview of monitoring and metrics in DynamoDB:
Amazon CloudWatch Metrics:
DynamoDB publishes a wide range of metrics to Amazon CloudWatch, providing visibility into the operational health and performance of your DynamoDB tables.
Key metrics include:
Read and write capacity utilization: Measures the percentage of provisioned read and write capacity units consumed by your table.
Throttled read and write events: Indicates the number of read and write requests that were throttled due to exceeding provisioned capacity limits.
Successful and failed requests: Tracks the number of successful and failed read and write requests to your table.
Conditional check failed requests: Counts the number of conditional check failed requests, where conditional writes failed due to attribute conditions not being met.
Consumed and provisioned throughput: Provides insights into the utilization of read and write capacity units compared to provisioned capacity.
CloudWatch Alarms:
You can set up CloudWatch alarms to monitor specific metrics and trigger automated actions or notifications when predefined thresholds are exceeded.
Alarms can be configured to notify you via email, SMS, or other notification mechanisms when metrics breach configured thresholds, allowing you to take proactive actions to address performance issues or capacity constraints.
Dashboard and Graphs:
CloudWatch provides customizable dashboards and graphs to visualize DynamoDB metrics and performance trends over time.
You can create custom dashboards with metrics of interest, configure graphs to display relevant data points, and monitor the health and performance of your DynamoDB tables in real-time.
Integration with AWS Lambda:
You can use AWS Lambda functions to process DynamoDB Streams and react to changes in your DynamoDB tables in real-time.
Lambda functions can be triggered by DynamoDB Stream events, allowing you to perform custom processing, notifications, or remediation actions based on changes to your data.
Performance Insights:
DynamoDB Performance Insights provides a comprehensive view of DynamoDB performance metrics, query latency, and resource utilization.
Performance Insights allows you to identify and troubleshoot performance bottlenecks, optimize query patterns, and improve the efficiency of your DynamoDB queries.
Auto Scaling and Capacity Planning:
Monitor CloudWatch metrics to understand the utilization of provisioned capacity and adjust capacity provisioning dynamically using auto scaling.
Use CloudWatch metrics and insights to optimize capacity planning, identify workload patterns, and forecast capacity requirements based on historical data and trends.
By leveraging monitoring and metrics in DynamoDB through Amazon CloudWatch, you can gain visibility into the performance and health of your DynamoDB tables, set up proactive alerts and notifications, and optimize capacity provisioning and performance tuning to ensure optimal performance and reliability of your DynamoDB applications.
Cost Optimization in DynamoDB
Cost optimization is crucial for effectively managing expenses while leveraging the scalability and performance benefits of Amazon DynamoDB. By adopting cost-effective strategies and optimizing resource utilization, you can minimize DynamoDB-related expenses without compromising application performance or reliability. Here's an overview of cost optimization techniques for DynamoDB:
Right-Sizing Provisioned Capacity:
Provision read and write capacity units (RCUs and WCUs) based on your application's actual workload and performance requirements.
Use CloudWatch metrics and performance insights to monitor capacity utilization and adjust provisioned capacity dynamically to match changing workload patterns.
Avoid over-provisioning capacity units to minimize costs, but ensure that you have sufficient headroom to handle peak traffic and avoid throttling.
Utilizing On-Demand Capacity Mode:
Consider using on-demand capacity mode for DynamoDB tables with unpredictable or fluctuating workloads.
On-demand capacity mode eliminates the need for manual capacity planning and provisioning, allowing you to pay only for the read and write requests your application makes without upfront commitments.
Use on-demand capacity mode for development, testing, and low-traffic production workloads to optimize costs and simplify capacity management.
Optimizing Data Models and Access Patterns:
Design efficient data models and access patterns to minimize read and write operations and reduce data storage costs.
Denormalize data where appropriate to minimize the number of read operations required to access related data.
Use composite keys, secondary indexes, and filter expressions to optimize queries and reduce the amount of data retrieved from DynamoDB.
Archiving and Deleting Unused Data:
Regularly review and identify unused or obsolete data in your DynamoDB tables.
Archive historical data to long-term storage solutions such as Amazon S3 Glacier or Amazon S3 Glacier Deep Archive to reduce storage costs while retaining data for compliance or historical analysis purposes.
Delete expired or unnecessary data from DynamoDB tables to reclaim storage space and minimize storage costs.
Monitoring and Analyzing Costs:
Monitor DynamoDB-related costs using AWS Cost Explorer and AWS Billing and Cost Management dashboards.
Analyze cost trends, usage patterns, and cost drivers to identify opportunities for optimization and cost-saving measures.
Set up billing alerts and budget notifications to receive notifications when DynamoDB-related costs exceed predefined thresholds, allowing you to take corrective actions promptly.
Use Spot Instances for Secondary Indexes:
Consider using DynamoDB on-demand capacity for the main table and utilize DynamoDB Spot Instances for non-critical or less frequently accessed secondary indexes.
DynamoDB Spot Instances offer significant cost savings compared to standard provisioned capacity, allowing you to reduce costs without sacrificing performance or reliability.
By adopting these cost optimization strategies, you can effectively manage DynamoDB-related expenses, optimize resource utilization, and maximize cost efficiency while leveraging the scalability, performance, and reliability benefits of Amazon DynamoDB.