Thanks for the reply. I figured for a dataset of that size, the main bottleneck might be not indexing -- maybe RedShift stores row data in a higher-latency medium while keeping indexes in-memory. Curious, I checked the documentation[1] and found this:
> Amazon Redshift doesn’t require indexes or materialized views and so uses less space than traditional relational database systems.
Reading through the rest of their FAQ, it sounds like they echo your conclusion -- RedShift shines the most for use-cases where the dataset is large enough that, to use PostgreSQL, you'd have to shard out multiple instances.
Comments
Thanks for the reply. I figured for a dataset of that size, the main bottleneck might be not indexing -- maybe RedShift stores row data in a higher-latency medium while keeping indexes in-memory. Curious, I checked the documentation[1] and found this:
> Amazon Redshift doesn’t require indexes or materialized views and so uses less space than traditional relational database systems.
Reading through the rest of their FAQ, it sounds like they echo your conclusion -- RedShift shines the most for use-cases where the dataset is large enough that, to use PostgreSQL, you'd have to shard out multiple instances.
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[1] http://aws.amazon.com/redshift/faqs/#0030