{ "story_id": "3f01941bdad9ab5cceebf6e4d31831af", "desk": "drm3", "revision": 1, "published_at": "2026-09-04T11:00:00.000Z", "content_hash": "06cd551a7e92ef0e7377863aa139b61ed04197e2accfca5a51733cc37515f5dc", "hash_basis": "sha256 over `headline\\ndek\\nprose`, plus `\\n` + the canonical citations JSON when any source is placed, plus `\\n#blog` for blogs", "basis": { "headline": "Amazon Redshift rg.large Instances Now Support Single-Node Clusters", "dek": "Amazon Redshift rg.large instances now support single-node clusters for cost-effective testing.", "prose": "Amazon Redshift rg.large instances, powered by AWS Graviton processors, now support single-node clusters. [^1]\n\nOnur Satici stated that Vortex achieves approximately 30 times faster S3-to-GPU scans than Parquet and approximately 100 times or more faster random access than Parquet, thanks to its encodings and compression. [^2]\n\nCustomers can now create a single-node rg.large cluster for smaller workloads that do not require high availability, offering a cost-effective option to conduct proofs of concept and tests quickly. [^3]\n\nRG instances deliver up to 2.4x faster performance running data warehouse and data lake workloads when compared to previous generation RA3 instances, at 30% lower price per vCPU. [^4]\n\nSingle-node support for rg.large clusters is available on P204 or later patch versions. [^5]\n\nOnur Satici claimed that Vortex uses lightweight and cascading encodings instead of block compression, which allows compute on compressed data, random access, and aggregations, and that some of these computations can be faster than on decompressed data. [^6]\n\nOnur Satici described a 'decision tax' in ML training iteration, where changing a data mix or filter with current tools requires reprocessing the entire data (re-tokenizing, reshuffling, and reloading), whereas with Vortex, users can simply change the scan, query, or filter without reprocessing. [^7]\n\nOnur Satici explained that Vortex is a columnar file format similar to Parquet but with an extensible spec, a separation of logical types from physical types, and a design that does as little as possible as late as possible, pushing projections, filters, and aggregations from the compute engine into the file format. 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