It is a visitor submit by Satoru Ishikawa, Options Architect at Classmethod in partnership with AWS.
In April 2025, AWS introduced the deprecation of Amazon Redshift DC2 situations, guiding customers emigrate to both Redshift RA3 situations or Redshift Serverless. Redshift RA3 situations and Serverless undertake a design that separates storage and compute, presents new options corresponding to information sharing, concurrency scaling for writes, zero-ETL , and cluster relocation.
On this submit, we share insights from considered one of our clients’ migration from DC2 to RA3 situations. The shopper, a big enterprise within the retail business, operated a 16-node dc2.8xlarge cluster for enterprise intelligence (BI) and ETL workloads. Dealing with rising information volumes and disk capability limitations, they efficiently migrated to RA3 situations utilizing a Blue-Inexperienced deployment strategy, attaining improved ETL question efficiency and expanded storage capability whereas sustaining price effectivity.
Amazon Redshift structure sorts
Amazon Redshift presents two deployment choices: Provisioned mode, the place you select the occasion sort and variety of nodes and handle resizing as wanted, and Redshift Serverless, which robotically provisions information warehouse capability and intelligently scales the underlying assets. The next diagram compares these two structure sorts.
Provisioned clusters require you to find out cluster dimension upfront, however you possibly can optimize prices by buying Reserved Situations (RI) or scheduling pause and resume actions. Serverless robotically provisions assets as wanted, with a pay-per-use mannequin the place you solely pay for compute assets consumed. Each companies help migration between one another and provide the identical options together with SQL, zero-ETL, and Federated Question capabilities. For particular pricing particulars, see Amazon Redshift pricing.
Provisioned clusters are appropriate for large-scale, predictable workloads and provide automated scaling primarily based on queuing. Serverless supplies management-free automated scaling for variable workloads with AI-driven optimization that scales primarily based on workload complexity and information volumes. For extra particulars, discuss with Evaluating Amazon Redshift Serverless to an Amazon Redshift provisioned information warehouse.
Buyer case examine: Migration from DC2 situations
This part describes the shopper’s migration from Amazon Redshift DC2 to RA3 occasion sorts. The migration used a Blue-Inexperienced deployment strategy that minimized downtime whereas attaining each price optimization and efficiency enchancment.
The shopper’s workload had the next traits:
Use instances
The shopper had the next key use instances for his or her Amazon Redshift deployment:
- Question by way of BI instrument throughout enterprise hours
- Excessive quantity of learn queries
- Peak entry throughout Mondays and starting of months
- Information processing in early morning
- Concentrated write queries for information loading and transformation
- Regular-state workload traits
- Run queries greater than 16 hours every day
Necessities
The shopper had the next key necessities for his or her Amazon Redshift migration:
- Efficiency
- Use auto-scaling (corresponding to concurrency scaling) throughout peak entry durations
- Information dimension
- Disk capability enlargement wanted
- Value Administration
- Straightforward funds prediction and administration
- Make the most of low cost companies for long-term utilization
- Compatibility
- Preserve compatibility with current functions and BI instruments
- Keep away from endpoint adjustments
- Availability
- Most downtime of 8 hours acceptable throughout migration
- Community
- Don’t modify the present 2-Availability Zone (AZ) subnet configuration
- When emigrate
- To be performed throughout low-load days and hours
- Deliberate downtime doable inside 8 hours
Key concerns in system design, implementation, and operation included prolonged operation hours, ease of funds prediction and administration, price optimization via Reserved Situations (RI), and sustaining compatibility with current programs (avoiding endpoint adjustments). The shopper evaluated Amazon Redshift Serverless, which provided engaging options corresponding to a pay-per-use mannequin, automated scaling capabilities, and the potential for higher value efficiency for variable workloads. Whereas each Redshift Serverless and provisioned clusters might successfully help their workload patterns, the shopper selected the provisioned mannequin with RA3 nodes, leveraging their years of operational expertise with provisioned environments, current RI technique, and established capability planning strategy.
Options of RA3 occasion sort
Constructed on the AWS Nitro System, RA3 situations with managed storage undertake an structure that separates computing and storage, permitting impartial scaling and separate billing for every element. These situations use high-performance SSDs for warm information and Amazon S3 for chilly information, offering ease of use, cost-effective storage, and quick question efficiency. For extra particulars, discuss with Amazon Redshift RA3 situations with managed storage.
Migration conditions
The shopper had the next migration conditions in place:
- The shopper used a Redshift cluster with 16 nodes of dc2.8xlarge configuration.
- The shopper selected a Blue-Inexperienced deployment strategy for migration, the place they’d restore from a snapshot to RA3 occasion sort, enabling fast rollback if obligatory.
- The shopper applied cluster switching and rollback via endpoint switching utilizing cluster identifier rotation.
- Moreover, to enhance efficiency with excessive concurrency, they transitioned the transaction isolation stage from SERIALIZABLE ISOLATION to SNAPSHOT ISOLATION.
Cluster migration strategies
There have been two migration choices accessible: Elastic Resize and Basic Resize.
Amazon Redshift’s Basic Resize performance had been enhanced, for resizing to RA3 occasion sorts, considerably lowering the write-unavailable interval. Primarily based on PoC testing, after initiating the resize, the cluster’s standing was modifying for 16 minutes earlier than it turned accessible. Primarily based on these outcomes, the shopper proceeded with the Basic Resize strategy.
Cluster sizing
Sizing concerned figuring out the occasion sort and variety of nodes for the migration goal. Sizing factors thought of workload traits corresponding to CPU-intensive (queries utilizing excessive CPU), I/O-intensive (queries with excessive information learn/write), or each.When migrating from DC2 occasion sorts, extra nodes could be required relying on workload necessities. Nodes had been added or eliminated primarily based on the computing necessities for obligatory question efficiency.
Evaluating configurations with comparable cluster prices when it comes to occasion dimension and depend, for a dc2.8xlarge 16-node cluster, the really useful configuration was 8 nodes of ra3.16xlarge. The next was the fee comparability within the Tokyo Area:
- Really useful: dc2.8xlarge 16-node cluster => ra3.16xlarge * 8-node cluster
- $97.52/h (6.095/h * 16 nodes) => $122.776/h (15.347/h * 8 nodes)
- Value-focused: dc2.8xlarge 16-node cluster => ra3.16xlarge * 6-node cluster
- $97.52/h (6.095/h * 16 nodes) => $92.082/h (15.347/h * 6 nodes)
For this migration, the shopper proceeded with a cost-efficient 6-node ra3.16xlarge cluster to remain inside current funds constraints. Nevertheless, since this node depend might face throughput limitations throughout sure occasions, they enabled concurrent scaling for the RA3 occasion sort to deal with spike entry.
Concurrency scaling supplies as much as 1 hour of free credit per day for every lively cluster, accumulating as much as 30 hours. On-demand utilization charges apply when exceeding this free tier.Whereas the shopper selected to implement concurrency scaling, Elastic Resize to briefly enhance nodes throughout peak masses was additionally thought of however rejected on account of on-demand prices for added nodes and the transient disconnection interval throughout switching.
Managed storage price
RA3 situations use Redshift Managed Storage (RMS), which is charged at a set GB-month charge. The shopper’s roughly 2 TB of information required together with storage prices within the estimates. For pricing particulars, see Amazon Redshift pricing.
Migration step from DC2 to RA3
After creating an RA3 cluster from the DC2 cluster’s snapshot, the shopper swapped the cluster identifiers. The next diagram reveals this course of.
- Take a snapshot of the present DC2 cluster.
- Restore RA3 cluster from the snapshot with a unique cluster identifier (Basic Resize)
- Swap the cluster identifiers between the present DC2 cluster and the brand new RA3 cluster.
If any points come up after the cluster swap, you possibly can rapidly roll again by returning the unique DC2 cluster to its unique cluster identifier.
Word: Restore from a snapshot
Operating the restore operation utilizing CLI instructions is really useful to attenuate operational errors and guarantee reproducibility. The next is a pattern command.
Manufacturing migration period
The time required for the restore and traditional resize steps can range considerably relying on information quantity and goal cluster specs. The shopper performed a rehearsal beforehand to measure the precise required time.
Check outcomes
Earlier than the manufacturing migration, the shopper created a check cluster by restoring a snapshot to the RA3 occasion sort. Whereas Redshift Check Drive is often helpful for workload testing, this buyer confronted distinctive constraints: enabling audit logging of their manufacturing cluster would require configuration adjustments, cluster restarts, and complicated approval processes below their strict change administration insurance policies. To handle this, they developed a customized load testing instrument that captured workload patterns utilizing Amazon Redshift system views (SYS_QUERY_HISTORY and SYS_QUERY_TEXT), which preserve 7 days of question historical past. The instrument replayed 55,755 historic queries with 50-way parallelism in opposition to each DC2 and RA3 clusters, evaluating metrics together with question execution time, CPU utilization, and disk I/O. Question end result caching was disabled throughout testing to make sure correct comparisons.
BI question efficiency
BI queries had been examined utilizing the customized load testing instrument. The outcomes characterize the common execution time from 15 check runs of 55,755 queries executed with 50-way parallelism. With out concurrency scaling, the dc2.8xlarge 16-node cluster averaged 45.82 seconds per question, whereas the ra3.16xlarge 6-node cluster averaged 91.30 seconds. This indicated that RA3 situations confirmed longer execution occasions for brief and medium queries in a direct migration with out optimizations. Nevertheless, enabling concurrency scaling improved RA3 efficiency progressively. With concurrency scaling enabled at most 2 clusters, the ra3.16xlarge 6-node cluster achieved a median of 72.48 seconds per question, a 21% enchancment over the non-scaled configuration.
| Node Sort / Variety of nodes | Common Question Time |
| ra3.16xlarge 6-node cluster | 72.48 seconds |
ETL question efficiency comparability
For long-running ETL queries (execution time better than 10 minutes), the RA3 cluster demonstrated higher efficiency than DC2. These outcomes represented a direct migration of the shopper’s workload with no optimizations utilized.
- For the Giant-scale information load workload 1, the ra3.16xlarge cluster accomplished the question 28% sooner than the dc2.8xlarge cluster (41 minutes vs. 57 minutes).
- For the Advanced transformation workload 1, the ra3.16xlarge cluster was 23% sooner (1 hour 1 minute vs. 1 hour 20 minutes).
These outcomes indicated that the RA3 node sort was extra performant for time-intensive information loading and transformation duties. The upper CPU utilization values for RA3 advised simpler compute useful resource utilization.
| Node Sort / Variety of nodes | Common Question Time | MAXCPU% |
| ra3.16xlarge 6-node cluster | 41 minutes 09 seconds | 11:45 |
| dc2.8xlarge 16-node cluster | 57 minutes 07 seconds | 10:85 |
| Node Sort / Variety of nodes | Common Question Time | MAXCPU% |
| ra3.16xlarge 6-node cluster | 1 hour 01 minutes 33 seconds | 74:23 |
| dc2.8xlarge 16-node cluster | 1 hour 20 minutes 36 seconds | 53:58 |
Efficiency tuning
Primarily based on the check outcomes, the shopper recognized that RA3 confirmed longer execution occasions for brief and medium BI queries however sooner efficiency for long-running ETL queries in comparison with DC2. To optimize total efficiency, they targeted on figuring out sluggish queries and continuously referenced tables, prioritizing optimizations with the very best impression.
Efficiency tuning technique
The shopper thought of a number of optimization methods to leverage RA3’s architectural benefits. One key technique concerned pre-processing ad-hoc quick and medium question workloads throughout low-load durations, creating pre-processed tables or materialized views for queries that repeatedly carried out joins, aggregations, filters, and projections. RA3’s separated compute and storage structure, with cost-effective large-scale storage, supported this strategy.
Changing common views to materialized views
Evaluation of sluggish queries revealed using joins in views, and continuously referenced tables had been being accessed a number of occasions via these views. As a countermeasure, the shopper changed continuously used common views with materialized views, eradicating pointless information ranges and redundant columns.
Amazon Redshift helps incremental updates of materialized view contents by way of the REFRESH MATERIALIZED VIEW command, enabling environment friendly information updates.
Materialized views and question rewrite
By changing common views to materialized views, current queries could also be robotically optimized via the “question rewrite” characteristic supplied by the question planner. For extra particulars, discuss with “Computerized question rewriting to make use of materialized views“.
Computerized tuning with AutoMV
On the DC2 cluster, disk utilization persistently exceeded 80%, which disabled the AutoMV characteristic on account of inadequate disk house. With RA3’s expanded storage, automated tuning via AutoMV turned doable, resulting in additional efficiency enhancements. For extra particulars about AutoMV, discuss with Automated materialized views.
Efficiency tuning outcomes
After making use of these optimizations, the shopper achieved the next outcomes:
- Maintained current efficiency whereas controlling price will increase
- Achieved larger CPU utilization whereas sustaining throughput
- Enhanced dynamic throughput throughout peak load durations utilizing concurrency scaling’s automated scaling
Conclusion
On this submit, you realized how a big retail enterprise efficiently migrated from Amazon Redshift DC2 to RA3 situations. The Blue-Inexperienced deployment strategy enabled a secure migration with fast rollback functionality, whereas the separated compute and storage structure of RA3 supplied flexibility to deal with rising information volumes. Though RA3 confirmed completely different efficiency traits for brief BI queries in comparison with DC2, the shopper achieved vital enhancements in long-running ETL question efficiency (as much as 28% sooner for information masses and 23% sooner for complicated transformations). By leveraging RA3-specific options corresponding to materialized views and AutoMV, they optimized total question efficiency whereas sustaining price effectivity via Reserved Situations and concurrency scaling.
To proceed your RA3 migration journey, see Finest practices for upgrading from Amazon Redshift DC2 to RA3 and Amazon Redshift Serverless and Resize Amazon Redshift from DC2 to RA3 with minimal or no downtime for added steering and finest practices.
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