In this post, I’ll go through the inner workings of our Lambda Function.
DynamoDb is an AWS fully managed NoSQL service, which provides a fast and predictable data store. We’ve been using it for several microservices in the past 18 months, and one feature that is sorely missed, are incremental backups.
AWS provides an option to take snapshots of your table using a service called DataPipeline. At a high level, what this does is:
1. Create an EMR (Elastic Map-Reduce) cluster
2. Perform a parallel full scan of the table in question (while consuming read units) into JSON data
3. This JSON data can be uploaded to S3 or similiar
DynamoDb to S3 Template in Data Pipeline Architect
The issue I have with this is, that the backup is not an “point in time” snapshot, it is essentially scanning the table (which can take hours) while the table is essentially still live.
Our requirements for DPO (Data Point Objective) is 30 minutes. Which basically means, if “shit hits the fan”, we can only have 30 minutes of data loss (in the worst case). This is our contractual agreement with our clients.
Given this, we have been investigating ways to solve this problem, which has led us to creating incremental backups for DynamoDb, stored in an S3 versioned bucket.
In the next post, I’ll walk through the details of our implementation, and provide the source code of the Lambda Function.