AWS Redshift turns AQUA for better query performance
Before we jump into the topic, let us understand the current limitations in Redshift and how AQUA addresses it.
Issues in current Redshift Architecture : With the introduction of RA3 nodes, Redshift provides options to scale and pay for compute and storage independently(same approach like Snowflake). However, the current data architectures with centralized storage (S3) requires data movement to compute clusters for its processing. So, any complex data operation needs a lot of resources to transfer data between nodes.
AQUA -Introduction : AQUA brings the compute closer to storage by processing data in-place on the cache memory. By doing this, data transfer time across nodes get reduced to a huge extent. In addition, it utilizes AWS-designed processors and a scale-out architecture to accelerate data processing. AQUA will be available only on RA3 nodes.
AQUA Architecture :
As mentioned earlier, AQUA achieves this functionality by 1) Bringing Compute closer to Storage, 2) Leveraging AWS-Designed processors, 3) Scale out architecture.
- Compute Closer to Storage : In order to avoid networking bandwidth limitations and eliminate unnecessary data movement between storage and compute cluster, AQUA reduces query execution time by running tasks like filtering, aggregation ..etc closer to the storage layer.
- AWS-Designed Processors : AQUA uses AWS-designed processors that possess AWS Nitro chips to speed up data encryption and compression.
- Scale out Architecture : Large chunks of data can be processed in parallel and automatically scales out to add more capacity.
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