![]() ![]() Costsįinally, we always want to look at costs. Interestingly, even with my tiny datasets (with sample datasets dropped and no tables), Redshift Serverless created automatic recovery points that were circa 200MB in size. For example: aws redshift-data list-databases -database dev -profile XXXXXX the “redshift-data” API works against Redshift Serverless) so there’s no new CLI API for it. Using the APIĪs reported above, I got to use the AWS CLI against Redshift Serverless a bit quicker than I intended □, but I can report that the standard AWS CLI objects all seem to work (e.g. So, I was left with a “dev” database that has zero run cost and is empty (zero tables, etc.) but can’t be dropped – maybe a bit of a “preview” feature, I think. With “normal” Redshift we would generally just blow the cluster away (and therefore any databases, etc.) to tidy up after an evaluation session, but of course you can’t do this with Redshift Serverless. The console gave me an error when trying to “drop database”, presumably because it was connected to it… ERROR: database "sample_data_dev" is being accessed by other users One little issue was I couldn’t easily find a way to fully tidy up afterwards via the console, so had to use the AWS CLI to drop the “sample_data_dev” database it had created for me. I played with the sample datasets – all behaved as you’d expect…er…like a database…so nothing to report there. month-end reporting) to be offloaded to an on-demand model – therefore, you can run a smaller RA3 cluster and save costs. marketing) using data shares, rather than serving them from the original Redshift cluster. So if you’ve already got an RA3 Redshift cluster, you can create datamarts for other departments (e.g. Redshift Serverless does add an interesting new option to the data architect’s toolbox, as it can now be used to provide an entirely on-demand reporting datastore for specific user communities. The architecture changes made to Redshift back in 2019 to introduce RA3 instances (splitting the scaling relationship between compute and storage) are fundamental to delivering this serverless flexibility, as well as the new data sharing capability. ![]() I’d be keen to assess this rather more scientifically when we have the chance. However, after leaving it for a week, there was a noticeable start-up delay (in the console and via the CLI – maybe 2-3 minutes). The response was still quick after a deliberate 24-hour rest. I didn’t get any sense of a “cold start time” – always a potential concern with serverless offerings, and something that continues to be a consideration with Lambda, Glue, SageMaker, etc. So basically, it does what it says on the tin! Response TimesĪfter setting up the preview service, the initial query performance/experience via the revamped v2 query editor seemed fine. This is a good thing – PaaS is much easier to adopt with less operational responsibilities and fewer infrastructure choices to make. it’s just Redshift □ but without specifying the cluster configuration. On first use, my immediate reaction was… it’s a bit unexciting, i.e. ![]()
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