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---
title: Upgrading Redshift from RA3 to RG-Faster Queries Lower Cost
description: Halodoc migrated its production Amazon Redshift warehouse from RA3 to the new Graviton-based RG instances and cut total costs by 32%. The team ran eleven...
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og:description: Halodoc migrated its production Amazon Redshift warehouse from RA3 to the new Graviton-based RG instances and cut total costs by 32%. The team ran eleven...
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# Upgrading Redshift from RA3 to RG-Faster Queries Lower Cost

**[Halodoc](https://daily.dev/sources/halodoc)** · 12 min read · 0 upvotes · 0 comments

## Summary

Halodoc migrated its production Amazon Redshift warehouse from RA3 to the new Graviton-based RG instances and cut total costs by 32%. The team ran eleven validation checks covering connections, permissions, data masking, pipelines and query plans before switching via a 10-15 minute elastic resize. COPY loads of 6 million rows dropped from 145 to 24 seconds, and five heavy reporting tables ran 2.1 to 5.2 times faster. Dashboard queue time fell 32% and pipeline queue time fell 83%, while concurrency-scaling spend dropped 76%.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blogs.halodoc.io/upgrading-redshift-from-ra3-to-rg-and-achieving-faster-queries-at-32-lower-cost>

## Questions this post answers

### What performance and cost difference did Halodoc see migrating Amazon Redshift from RA3 to RG instances?

Halodoc saw COPY loads of 6 million rows drop from 145 to 24 seconds, five heavy reporting tables run 2.1 to 5.2 times faster, dashboard queue time fall 32% and pipeline queue time fall 83%. Concurrency-scaling spend dropped 76% and overall Redshift costs, including storage, fell about 32%.

_Weighing a Redshift RA3 to RG migration? daily.dev surfaces real-world benchmarks like this to inform the decision._

### What should I test before migrating a Redshift cluster from RA3 to RG instances?

Test every path that touches the cluster: connections from all tools, users/permissions/custom functions, data loading via COPY, end-to-end pipelines (both Spark and direct SQL paths), data export formats, maintenance jobs, data masking policies, Spark integration, streaming cursors, and query plan comparisons, since RG's new vectorized engine can change execution plans even though the SQL dialect is unchanged.

_Planning a similar cloud data warehouse upgrade? daily.dev helps engineers track migration playbooks like this one._

### Why did Redshift RG instances run queries faster than RA3 according to query plan analysis?

RG reads external S3 tables directly on compute nodes instead of through RA3's separate scanning layer, distributes only needed data based on join keys instead of broadcasting whole tables (RA3 broadcast a 10-million-row table into four subqueries), and benefits from JIT ANALYZE keeping statistics current, whereas RA3 was planning with out-of-date statistics that misjudged row counts.

_Debugging slow Redshift query plans? daily.dev keeps engineers current on warehouse engine internals like these._

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---

Tags: [#aws](https://daily.dev/tags/aws), [#database](https://daily.dev/tags/database), [#data-engineering](https://daily.dev/tags/data-engineering)

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