Amazon Redshift now supports Elastic resize
Development Amazon Redshift now supports Elastic resize

One of the major pain points for me with Amazon Redshift has always been the coupling between storage and compute. Competitors like Snowflake and Google’s BigQuery offer independent compute and storage, which means easier (and quicker) scaling in times of increased load. Redshift’s main drawback in the scalability sense has been that it can take up to 24 hours to resize your cluster (during which it’s in read-only mode), meaning there’s a lot of pressure to get your cluster configuration spot on before you go into production. Redshift’s provision of elasticity is just not up to par with most of Amazon’s other services. While Redshift Spectrum helps with this, it’s not a solution to the issue of scalability for an existing cluster.

In the lead up to re:Invent, Amazon last night dropped a load of really neat announcements (server-side encryption for DynamoDB as standard, SSE support for SNS), among which was the reveal of Elastic resize for Redshift. As an aside, if this is the stuff they’re announcing now, there should be some really nice announcements at re:Invent.

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Redshift Spectrum finally supports Enhanced VPC routing
Development Redshift Spectrum finally supports Enhanced VPC routing

What seems like an age ago, I spotted a setting on one of our Redshift clusters that suggested Enhanced VPC routing support for Redshift Spectrum might be on the way. After waiting a while, and waiting some more, and then waiting some more, it seems that Amazon have finally released this into the wild, and Redshift Spectrum now works with clusters that have Enhanced VPC routing available!

As of Build 1.0.4349 or Build 1.0.4515, this functionality will be available in Redshift. It hasn’t made it into the official announcements yet, but it has popped up on the Redshift forums here: https://forums.aws.amazon.com/ann.jspa?annID=6197

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SQS vs SNS for Lambda Dead Letter Queues
Development SQS vs SNS for Lambda Dead Letter Queues

Serverless computing and event-driven functions are what it’s all about at the moment. But what happens when the event trigger fires, and your process then encounters an error? How do you recover from this given the event has since passed and may never happen again? This is a common question in AWS when working with their serverless, event-driven Lambda Functions.

Fortunately, AWS lets you define Dead Letter Queues for this very scenario. This option allows you to designate either an SQS queue or SNS topic as a DLQ, meaning that when your Lambda function fails it will push the incoming event message (and some additional context) onto the specified resource. If it’s SNS you can send out alerts or trigger other services (maybe even a retry of the same function - although watch out for infinite loops), or any combination of the above, given its fanout nature. If it’s SQS you can persist the message and process it with another service.

So let’s look at both options in a little more detail.

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picnicerror.net is a personal blog for me to post various ideas, thoughts and discoveries through both my day to day work in marketing technology and general hobbies and interests.

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