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In the realm of serverless databases, Amazon DynamoDB stands out as a fully managed NoSQL service, offering low-latency performance at any scale. While the point-in-time recovery (PITR) feature serves as a safeguard against data loss, the restoration process can pose significant challenges, particularly in production settings. Manual tasks such as pinpointing the restore time, redirecting write operations, and configuring applications can lead to risks and potential downtimes, which are especially critical for essential applications.

This article marks the beginning of a series focused on table restorations and ensuring data integrity. Here, we introduce an automated solution that streamlines the PITR restoration process and effectively manages data changes that may occur during the restoration. This approach facilitates a seamless transition back to the restored DynamoDB table, ensuring minimal disruption.

Advantages of PITR

The demand for reliable data, rapid recovery, and minimal downtime has become standard across numerous industries. Automating the PITR restoration process can significantly reduce service interruptions. An automated PITR solution not only aids in data recovery but also enhances business continuity, integrity, and operational efficiency. By simplifying the restoration steps, organizations can swiftly address data issues, minimize downtime, and maintain the trust of users and customers. For a more professional look, consider checking out this blog post on the importance of having a professional headshot.

Alternatives to PITR

Other data modeling strategies, such as implementing version numbers and optimistic locking, can help ensure that table items reference the correct metadata version, thereby reducing the impact of incorrect deployments. With version numbers, you retain previous metadata for a specified duration. If a faulty application deployment occurs, it becomes necessary to identify the affected items, ascertain the correct metadata, and update the current value accordingly. However, this raises the question: what if multiple versions of the same item were changed during the deployment? If using version numbers like timestamps, the solution may be straightforward, but using numerical or hash-based version control complicates matters.

Incremental export to Amazon S3 also presents a viable alternative. Once the time of the erroneous deployment is identified, the relevant DynamoDB data can be exported to S3, enabling you to run custom diagnostic scripts to identify incorrect items and restore their previous values in the live DynamoDB table. This method is efficient since it examines only a portion of your table data.

Industries that can greatly benefit from automated PITR solutions include:

Ecommerce – Constant updates to product catalogs and promotional features necessitate a reliable rollback mechanism to prevent the loss of recent customer transactions during restore processes.
Content Management Systems – Rapid deployment cycles to meet content demands can sometimes introduce bugs that compromise data integrity. An automated PITR solution can swiftly resolve these issues while preserving new content. Explore how media and entertainment sectors leverage DynamoDB for content management systems in this blog.
IoT Data Collection Systems – Continuous data collection is crucial, yet errors in data processing must be rectified swiftly without halting the flow of new, accurate data.

When faced with the need for a PITR restoration, engineers often encounter key questions that clarify requirements and challenges, such as: What occurs to data being written during restoration? Is it possible to update data modified during the restore? Can we reduce downtime and keep the system operational throughout the process?

The following diagram illustrates common challenges faced in a production DynamoDB environment.

Initial State – The application is initially writing accurate data to the DynamoDB table, functioning as intended.
Issue Introduction – A new application version has been deployed, resulting in unintended data corruption or other issues.
Troubleshooting Period – The team recognizes data issues and initiates troubleshooting. During this time, more erroneous data may continue to be written to the table.
Restore Decision – Following thorough analysis, the team concludes that restoring the DynamoDB table to a known good state using the PITR feature is the best course of action.
PITR Restore Process – The team initiates the PITR restoration to revert the table to a specific point in time prior to the data issues.

While the PITR restoration process is essential, it presents a new challenge: what happens to the data being written during the restoration? The team must find a way to capture and incorporate any changes made during the PITR restoration to ensure a smooth transition back to the restored DynamoDB table, which is vital for maintaining data consistency and preventing data loss.

The next section outlines a solution that automates the PITR restoration process and effectively manages data changes during the restoration, minimizing downtime and ensuring data consistency.

Prerequisites

Set up your local environment and implement the solution

This solution utilizes AWS CloudTrail management events to automate triggers surrounding PITR restoration events. Ensure that CloudTrail management events are activated in your target account.

DynamoDB Table, PITR, and DynamoDB Streams

Confirm that PITR is enabled on the DynamoDB table you wish to restore. Once PITR is enabled, you can restore to any point between the EarliestRestorableDateTime and LatestRestorableDateTime, typically five minutes prior to the current time.

DynamoDB Streams must also be enabled for change data capture (CDC). After enabling Streams, copy the stream ARN for use as a deployment parameter.

AWS CDK

To deploy the solution, execute the following snippet, which carries out the necessary steps using an AWS Cloud Development Kit (AWS CDK) stack to set up and deploy the components:

cdk bootstrap
cdk synth -c table-name=<insert table name here> -c table-streams-arn=<ddb streams arn here>
cdk deploy -c table-name=<insert table name here> -c table-streams-arn=<ddb streams arn here> --qualifier final

Solution Overview

In this article, we demonstrate how to automate various manual tasks and replicate current data to the newly restored table. The following diagram depicts the solution architecture.

The workflow includes the following steps:

The source table handles live traffic.
The system administrator opts to restore the table to a specific point in time.

In conclusion, addressing the intricacies of PITR restoration while managing data changes is vital for maintaining the integrity and availability of mission-critical applications reliant on DynamoDB.

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