Amazon Redshift DC2 migration method with a buyer case examine

This can be 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 cases, guiding customers emigrate to both Redshift RA3 cases or Redshift Serverless. Redshift RA3 cases and Serverless undertake a design that separates storage and compute, gives new options similar to information sharing, concurrency scaling for writes, zero-ETL , and cluster relocation.

On this submit, we share insights from one among our clients’ migration from DC2 to RA3 cases. The shopper, a big enterprise within the retail trade, operated a 16-node dc2.8xlarge cluster for enterprise intelligence (BI) and ETL workloads. Going through rising information volumes and disk capability limitations, they efficiently migrated to RA3 cases utilizing a Blue-Inexperienced deployment method, attaining improved ETL question efficiency and expanded storage capability whereas sustaining price effectivity.

Amazon Redshift structure varieties

Amazon Redshift gives two deployment choices: Provisioned mode, the place you select the occasion kind and variety of nodes and handle resizing as wanted, and Redshift Serverless, which routinely provisions information warehouse capability and intelligently scales the underlying sources. The next diagram compares these two structure varieties.

Provisioned clusters require you to find out cluster dimension prematurely, however you possibly can optimize prices by buying Reserved Situations (RI) or scheduling pause and resume actions. Serverless routinely provisions sources as wanted, with a pay-per-use mannequin the place you solely pay for compute sources consumed. Each providers assist 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 computerized scaling primarily based on queuing. Serverless gives management-free computerized scaling for variable workloads with AI-driven optimization that scales primarily based on workload complexity and information volumes. For extra particulars, confer with Evaluating Amazon Redshift Serverless to an Amazon Redshift provisioned information warehouse.

Buyer case examine: Migration from DC2 cases

This part describes the client’s migration from Amazon Redshift DC2 to RA3 occasion varieties. The migration used a Blue-Inexperienced deployment method that minimized downtime whereas attaining each price optimization and efficiency enchancment.

The shopper’s workload had the next traits:

Use circumstances

The shopper had the next key use circumstances for his or her Amazon Redshift deployment:

1. Question through BI software throughout enterprise hours
   1. Excessive quantity of learn queries
   2. Peak entry throughout Mondays and starting of months
2. Information processing in early morning
   1. Concentrated write queries for information loading and transformation
3. Regular-state workload traits
   1. Run queries greater than 16 hours every day

Necessities

The shopper had the next key necessities for his or her Amazon Redshift migration:

1. Efficiency
   1. Use auto-scaling (similar to concurrency scaling) throughout peak entry durations
2. Information dimension
   1. Disk capability growth wanted
3. Price Administration
   1. Simple finances prediction and administration
   2. Make the most of low cost providers for long-term utilization
4. Compatibility
   1. Preserve compatibility with present functions and BI instruments
   2. Keep away from endpoint adjustments
5. Availability
   1. Most downtime of 8 hours acceptable throughout migration
6. Community
   1. Don’t modify the present 2-Availability Zone (AZ) subnet configuration
7. When emigrate
   1. To be performed throughout low-load days and hours
   2. Deliberate downtime doable inside 8 hours

Key concerns in system design, implementation, and operation included prolonged operation hours, ease of finances prediction and administration, price optimization by means of Reserved Situations (RI), and sustaining compatibility with present methods (avoiding endpoint adjustments). The shopper evaluated Amazon Redshift Serverless, which provided engaging options similar to a pay-per-use mannequin, computerized scaling capabilities, and the potential for higher worth efficiency for variable workloads. Whereas each Redshift Serverless and provisioned clusters may successfully assist their workload patterns, the client selected the provisioned mannequin with RA3 nodes, leveraging their years of operational expertise with provisioned environments, present RI technique, and established capability planning method.

Options of RA3 occasion kind

Constructed on the AWS Nitro System, RA3 cases with managed storage undertake an structure that separates computing and storage, permitting unbiased scaling and separate billing for every element. These cases use high-performance SSDs for decent information and Amazon S3 for chilly information, offering ease of use, cost-effective storage, and quick question efficiency. For extra particulars, confer with Amazon Redshift RA3 cases with managed storage.

Migration stipulations

The shopper had the next migration stipulations in place:

• The shopper used a Redshift cluster with 16 nodes of dc2.8xlarge configuration.
• The shopper selected a Blue-Inexperienced deployment method for migration, the place they’d restore from a snapshot to RA3 occasion kind, enabling fast rollback if obligatory.
• The shopper applied cluster switching and rollback by means of 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 out there: Elastic Resize and Traditional Resize.

Amazon Redshift’s Traditional Resize performance had been enhanced, for resizing to RA3 occasion varieties, considerably lowering the write-unavailable interval. Based mostly on PoC testing, after initiating the resize, the cluster’s standing was modifying for 16 minutes earlier than it turned out there. Based mostly on these outcomes, the client proceeded with the Traditional Resize method.

Cluster sizing

Sizing concerned figuring out the occasion kind and variety of nodes for the migration goal. Sizing factors thought-about workload traits similar to CPU-intensive (queries utilizing excessive CPU), I/O-intensive (queries with excessive information learn/write), or each.When migrating from DC2 occasion varieties, further nodes is likely to 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 by way of occasion dimension and rely, for a dc2.8xlarge 16-node cluster, the beneficial configuration was 8 nodes of ra3.16xlarge. The next was the fee comparability within the Tokyo Area:

1. Really useful: dc2.8xlarge 16-node cluster => ra3.16xlarge * 8-node cluster
   1. $97.52/h (6.095/h * 16 nodes) => $122.776/h (15.347/h * 8 nodes)
2. Price-focused: dc2.8xlarge 16-node cluster => ra3.16xlarge * 6-node cluster
   1. $97.52/h (6.095/h * 16 nodes) => $92.082/h (15.347/h * 6 nodes)

For this migration, the client proceeded with a cost-efficient 6-node ra3.16xlarge cluster to remain inside present finances constraints. Nonetheless, since this node rely may face throughput limitations throughout sure occasions, they enabled concurrent scaling for the RA3 occasion kind to deal with spike entry.

Concurrency scaling gives 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 client selected to implement concurrency scaling, Elastic Resize to quickly enhance nodes throughout peak masses was additionally thought-about however rejected as a result of on-demand prices for extra nodes and the temporary disconnection interval throughout switching.

Managed storage price

RA3 cases 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 client swapped the cluster identifiers. The next diagram reveals this course of.

1. Take a snapshot of the present DC2 cluster.
2. Restore RA3 cluster from the snapshot with a special cluster identifier (Traditional Resize)
3. Swap the cluster identifiers between the present DC2 cluster and the brand new RA3 cluster.

If any points come up after the cluster change, you possibly can shortly roll again by returning the unique DC2 cluster to its unique cluster identifier.

Be aware: Restore from a snapshot

Working the restore operation utilizing CLI instructions is beneficial to attenuate operational errors and guarantee reproducibility. The next is a pattern command.

aws redshift restore-from-cluster-snapshot 
--cluster-identifier for-ra3-20250207 
--snapshot-identifier cm-cluster-for-ra3-20250207 
--cluster-subnet-group-name cm-cluster 
--vpc-security-group-ids sg-1234567a sg-2345678b sg-3456789c 
--cluster-parameter-group-name cm-cluster 
--node-type ra3.16xlarge 
--number-of-nodes 6 
--port 5439 
--no-publicly-accessible 
--enhanced-vpc-routing 
--availability-zone ap-northeast-1a 
--preferred-maintenance-window sat:17:00-sat:17:30 
--automated-snapshot-retention-period 14 
--iam-roles 'arn:aws:iam::123456789012:function/AmazonRedshift-CommandsAccessRole' 'arn:aws:iam::123456789012:function/AmazonRedshift-Spectrum' 
--maintenance-track-name present

Manufacturing migration period

The time required for the restore and traditional resize steps can differ considerably relying on information quantity and goal cluster specs. The shopper performed a rehearsal beforehand to measure the precise required time.

Take a look at outcomes

Earlier than the manufacturing migration, the client created a check cluster by restoring a snapshot to the RA3 occasion kind. Whereas Redshift Take a look at Drive is usually helpful for workload testing, this buyer confronted distinctive constraints: enabling audit logging of their manufacturing cluster would require configuration adjustments, cluster restarts, and sophisticated approval processes underneath their strict change administration insurance policies. To handle this, they developed a customized load testing software that captured workload patterns utilizing Amazon Redshift system views (SYS_QUERY_HISTORY and SYS_QUERY_TEXT), which keep 7 days of question historical past. The software 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 consequence caching was disabled throughout testing to make sure correct comparisons.

BI question efficiency

BI queries had been examined utilizing the customized load testing software. The outcomes symbolize 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 cases confirmed longer execution occasions for brief and medium queries in a direct migration with out optimizations. Nonetheless, enabling concurrency scaling improved RA3 efficiency progressively. With concurrency scaling enabled at most 2 clusters, the ra3.16xlarge 6-node cluster achieved a mean of 72.48 seconds per question, a 21% enchancment over the non-scaled configuration.

ETL question efficiency comparability

For long-running ETL queries (execution time larger than 10 minutes), the RA3 cluster demonstrated higher efficiency than DC2. These outcomes represented a direct migration of the client’s workload with no optimizations utilized.

• For the Massive-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 kind was extra performant for time-intensive information loading and transformation duties. The upper CPU utilization values for RA3 urged more practical compute useful resource utilization.

Efficiency tuning

Based mostly on the check outcomes, the client 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 general efficiency, they targeted on figuring out sluggish queries and often referenced tables, prioritizing optimizations with the very best affect.

Efficiency tuning technique

The shopper thought-about 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 method.

Changing common views to materialized views

Evaluation of sluggish queries revealed using joins in views, and often referenced tables had been being accessed a number of occasions by means of these views. As a countermeasure, the client changed often used common views with materialized views, eradicating pointless information ranges and redundant columns.

Amazon Redshift helps incremental updates of materialized view contents through the REFRESH MATERIALIZED VIEW command, enabling environment friendly information updates.

Materialized views and question rewrite

By changing common views to materialized views, present queries could also be routinely optimized by means of the “question rewrite” characteristic supplied by the question planner. For extra particulars, confer with “Automated question rewriting to make use of materialized views“.

Automated tuning with AutoMV

On the DC2 cluster, disk utilization constantly exceeded 80%, which disabled the AutoMV characteristic as a result of inadequate disk house. With RA3’s expanded storage, computerized tuning by means of AutoMV turned doable, resulting in additional efficiency enhancements. For extra particulars about AutoMV, confer with Automated materialized views.

Efficiency tuning outcomes

After making use of these optimizations, the client achieved the next outcomes:

• Maintained present efficiency whereas controlling price will increase
• Achieved increased CPU utilization whereas sustaining throughput
• Enhanced dynamic throughput throughout peak load durations utilizing concurrency scaling’s computerized scaling

Conclusion

On this submit, you realized how a big retail enterprise efficiently migrated from Amazon Redshift DC2 to RA3 cases. The Blue-Inexperienced deployment method enabled a protected 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 totally different efficiency traits for brief BI queries in comparison with DC2, the client achieved important enhancements in long-running ETL question efficiency (as much as 28% sooner for information masses and 23% sooner for advanced transformations). By leveraging RA3-specific options similar to materialized views and AutoMV, they optimized general question efficiency whereas sustaining price effectivity by means of Reserved Situations and concurrency scaling.

To proceed your RA3 migration journey, see Greatest 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 extra steerage and finest practices.

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