Storage optimization – Introduction to Serverless on AWS
Storage optimization
Modern cloud applications ingest huge volumes of data—operational data, metrics, logs, etc. Teams that own the data might want to optimize their storage (to mini‐ mize cost and, in some cases, improve performance) by isolating and keeping only business-critical data.
Managed data services provide built-in features to remove or transition unneeded data. For example, Amazon S3 supports per-bucket data retention policies to either delete data or transition it to a different storage class, and DynamoDB allows you to configure the Time to Live (TTL) value on every item in a table. The storage optimization options are not confined to the mainstream data stores; you can specify the message retention period for each SQS queue, Kinesis stream, API cache, etc.
DynamoDB manages the TTL configuration of the table items efficiently, regardless of how many items are in a table and how many of those items have a TTL timestamp set. However, in some cases, it can take up to 48 hours for an item to be deleted from the table. Consequently, this may not be an ideal solution if you require guaranteed item removal at the exact TTL time.
AWS Identity and Access Management (IAM)
AWS IAM is a service that controls the authentication and authorization of access to AWS services and resources. It helps define who can access which services and resources, under which conditions. Access to a service or resource can be granted to an identity, such as a user, or a resource, such as a Lambda function. The object that holds the details of the permissions is known as a policy and is stored as a JSON document, as shown in Example 1-1.
Example 1-1. IAM policy to allow read actions on DynamoDB Orders table
{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: [
“dynamodb:BatchGet*”,
“dynamodb:Get*”,
“dynamodb:Query”
],
“Resource”: “arn:aws:dynamodb:eu-west-1:12890:table/Orders”
}
]
}