\n<\/strong>Concurrency Scaling handles sudden spikes in query traffic by automatically adding additional cluster capacity. When your primary cluster is overwhelmed with concurrent queries, Redshift seamlessly routes excess queries to temporary, dynamically provisioned clusters called Concurrency Scaling clusters. These are fully managed by AWS and process queries in parallel, keeping performance consistent during peak loads.<\/p>\n
Once the workload subsides, the Concurrency Scaling clusters are automatically terminated, and you only pay for the resources used during the scaling period \u2014 making it a cost-effective way to absorb unpredictable query traffic without permanently over-provisioning your primary cluster.<\/p>\n
How It Works
\n<\/strong><\/h2>\n![\"Redshift](\"https:\/\/www.tothenew.com\/blog\/wp-ttn-blog\/uploads\/2026\/09\/Screenshot-2026-09-15-at-10.03.28-PM.png\")
Redshift Concurrency Scaling<\/p><\/div>\n
Query routing<\/strong> \u2014 when concurrent queries exceed the primary cluster’s capacity, Redshift automatically routes the excess to Concurrency Scaling clusters.
\nDynamic provisioning <\/strong>\u2014 AWS provisions additional clusters in the background, as exact replicas of your primary cluster’s schema and metadata.
\nParallel execution <\/strong>\u2014 queries run in parallel across the primary and Concurrency Scaling clusters, keeping response times fast and consistent.
\nAutomatic termination <\/strong>\u2014 once the workload drops back down, the Concurrency Scaling clusters are terminated and billing for them stops.<\/p>\nBenefits<\/strong><\/h2>\nImproved query performance<\/strong> \u2014 distributing queries across multiple clusters keeps response times fast even during peak usage.
\nCost-effectiveness<\/strong> \u2014 you pay for additional clusters only while they’re in use; during quiet periods you’re billed only for the primary cluster.
\nSeamless integration<\/strong> \u2014 no manual intervention required; it works automatically on top of your existing Redshift setup.
\nScalability<\/strong> \u2014 handles both sudden spikes and gradual volume increases without a manual resize.<\/p>\nWhen to Use It
\n<\/strong>
\nConcurrency Scaling earns its keep in a few recurring scenarios:<\/h2>\nPeak workloads<\/strong> \u2014 predictable spikes, like business-hours traffic or scheduled reporting windows.
\nUnpredictable workloads<\/strong> \u2014 variable query patterns where you can’t forecast when demand will increase.
\nHigh-concurrency environments<\/strong> \u2014 many users or applications querying the same warehouse simultaneously.<\/p>\nHow to Enable It<\/strong>
\nConcurrency Scaling is managed through Redshift’s Workload Management (WLM) feature, on a per-queue basis.<\/p>\nConfigure WLM<\/strong> \u2014 enable Concurrency Scaling for specific query queues.
\nSet the Concurrency Scaling mode per queue:auto \u2014 Redshift automatically decides when to use Concurrency Scaling based on workload.
\noff<\/strong> \u2014 disables it for that queue.
\non<\/strong> \u2014 always uses Concurrency Scaling for that queue.
\nMonitor usage<\/strong> \u2014 track Concurrency Scaling activity and cost via the Redshift console or CloudWatch.
\nExample WLM configuration:<\/p>\n{
\n{
\n\"wlm\": [
\n{
\n\"query_concurrency_scaling_mode\": \"auto\",
\n\"query_queue\": [
\n{
\n\"queue_name\": \"reporting_queue\",
\n\"query_groups\": [\"reporting\"]
\n}
\n]
\n}
\n]
\n}<\/code>
\nBest Practices<\/strong>
\nMonitor costs<\/strong> \u2014 Concurrency Scaling is cost-effective, but usage should still be tracked via the Redshift console or Cost Explorer to avoid surprises.
\nOptimize query queues<\/strong> \u2014 assign queries to WLM queues based on priority and resource needs, so critical queries get the resources they’re entitled to.
\nPair with Short Query Acceleration (SQA)<\/strong> \u2014 SQA prioritizes short-running queries, complementing Concurrency Scaling’s handling of overall concurrency.
\nSet limits<\/strong> \u2014 cap the maximum number of Concurrency Scaling clusters to control cost and prevent over-provisioning.<\/p>\nReal-World Example<\/strong>
\nPicture an e-commerce company during a holiday sale. Marketing, sales, and analytics teams are all running complex reporting queries at the same time, right when the business needs those insights fastest. Without Concurrency Scaling, the primary cluster would buckle under the combined load, and every team’s dashboards would slow down together \u2014 at the worst possible moment.<\/p>\nWith Concurrency Scaling enabled, the excess load is automatically routed to additional clusters, so every team gets real-time access without any of them competing for the same fixed capacity. Once the sale ends and query volume drops, the extra clusters terminate on their own, and the company only pays for the capacity it actually used during the surge.<\/p>\n
Conclusion<\/strong>
\nAmazon Redshift Concurrency Scaling turns unpredictable or high-concurrency query load from a capacity-planning problem into a pay-as-you-go one. By automatically provisioning additional clusters during peak times and tearing them down afterward, it keeps performance consistent without forcing you to size your primary cluster for the worst day of the year.<\/p>\nIf you haven’t already, enabling Concurrency Scaling on the right WLM queues \u2014 paired with cost monitoring and SQA \u2014 is a low-effort way to make sure your warehouse stays responsive exactly when demand is highest, without paying for that headroom the rest of the time.<\/p>\n
\u00a0<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"As your data grows and your user base expands, your Amazon Redshift cluster faces increasing demands from concurrent queries. Without proper management, this can lead to performance bottlenecks, longer query times, and frustrated users. Fortunately, Amazon Redshift offers a feature called Concurrency Scaling to address exactly this problem. This post covers what it is, how […]<\/p>\n","protected":false},"author":1983,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":0,"footnotes":""},"categories":[2348],"tags":[1892],"class_list":["post-69665","post","type-post","status-publish","format-standard","hentry","category-devops-technology","tag-devops"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
Redshift Concurrency Scaling<\/p><\/div>\n
Query routing<\/strong> \u2014 when concurrent queries exceed the primary cluster’s capacity, Redshift automatically routes the excess to Concurrency Scaling clusters. Improved query performance<\/strong> \u2014 distributing queries across multiple clusters keeps response times fast even during peak usage. Peak workloads<\/strong> \u2014 predictable spikes, like business-hours traffic or scheduled reporting windows. How to Enable It<\/strong> Configure WLM<\/strong> \u2014 enable Concurrency Scaling for specific query queues. Real-World Example<\/strong> With Concurrency Scaling enabled, the excess load is automatically routed to additional clusters, so every team gets real-time access without any of them competing for the same fixed capacity. Once the sale ends and query volume drops, the extra clusters terminate on their own, and the company only pays for the capacity it actually used during the surge.<\/p>\n Conclusion<\/strong> If you haven’t already, enabling Concurrency Scaling on the right WLM queues \u2014 paired with cost monitoring and SQA \u2014 is a low-effort way to make sure your warehouse stays responsive exactly when demand is highest, without paying for that headroom the rest of the time.<\/p>\n \u00a0<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" As your data grows and your user base expands, your Amazon Redshift cluster faces increasing demands from concurrent queries. Without proper management, this can lead to performance bottlenecks, longer query times, and frustrated users. Fortunately, Amazon Redshift offers a feature called Concurrency Scaling to address exactly this problem. This post covers what it is, how […]<\/p>\n","protected":false},"author":1983,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":0,"footnotes":""},"categories":[2348],"tags":[1892],"class_list":["post-69665","post","type-post","status-publish","format-standard","hentry","category-devops-technology","tag-devops"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
\nDynamic provisioning <\/strong>\u2014 AWS provisions additional clusters in the background, as exact replicas of your primary cluster’s schema and metadata.
\nParallel execution <\/strong>\u2014 queries run in parallel across the primary and Concurrency Scaling clusters, keeping response times fast and consistent.
\nAutomatic termination <\/strong>\u2014 once the workload drops back down, the Concurrency Scaling clusters are terminated and billing for them stops.<\/p>\nBenefits<\/strong><\/h2>\n
\nCost-effectiveness<\/strong> \u2014 you pay for additional clusters only while they’re in use; during quiet periods you’re billed only for the primary cluster.
\nSeamless integration<\/strong> \u2014 no manual intervention required; it works automatically on top of your existing Redshift setup.
\nScalability<\/strong> \u2014 handles both sudden spikes and gradual volume increases without a manual resize.<\/p>\nWhen to Use It
\n<\/strong>
\nConcurrency Scaling earns its keep in a few recurring scenarios:<\/h2>\n
\nUnpredictable workloads<\/strong> \u2014 variable query patterns where you can’t forecast when demand will increase.
\nHigh-concurrency environments<\/strong> \u2014 many users or applications querying the same warehouse simultaneously.<\/p>\n
\nConcurrency Scaling is managed through Redshift’s Workload Management (WLM) feature, on a per-queue basis.<\/p>\n
\nSet the Concurrency Scaling mode per queue:auto \u2014 Redshift automatically decides when to use Concurrency Scaling based on workload.
\noff<\/strong> \u2014 disables it for that queue.
\non<\/strong> \u2014 always uses Concurrency Scaling for that queue.
\nMonitor usage<\/strong> \u2014 track Concurrency Scaling activity and cost via the Redshift console or CloudWatch.
\nExample WLM configuration:<\/p>\n{
\n{
\n\"wlm\": [
\n{
\n\"query_concurrency_scaling_mode\": \"auto\",
\n\"query_queue\": [
\n{
\n\"queue_name\": \"reporting_queue\",
\n\"query_groups\": [\"reporting\"]
\n}
\n]
\n}
\n]
\n}<\/code>
\nBest Practices<\/strong>
\nMonitor costs<\/strong> \u2014 Concurrency Scaling is cost-effective, but usage should still be tracked via the Redshift console or Cost Explorer to avoid surprises.
\nOptimize query queues<\/strong> \u2014 assign queries to WLM queues based on priority and resource needs, so critical queries get the resources they’re entitled to.
\nPair with Short Query Acceleration (SQA)<\/strong> \u2014 SQA prioritizes short-running queries, complementing Concurrency Scaling’s handling of overall concurrency.
\nSet limits<\/strong> \u2014 cap the maximum number of Concurrency Scaling clusters to control cost and prevent over-provisioning.<\/p>\n
\nPicture an e-commerce company during a holiday sale. Marketing, sales, and analytics teams are all running complex reporting queries at the same time, right when the business needs those insights fastest. Without Concurrency Scaling, the primary cluster would buckle under the combined load, and every team’s dashboards would slow down together \u2014 at the worst possible moment.<\/p>\n
\nAmazon Redshift Concurrency Scaling turns unpredictable or high-concurrency query load from a capacity-planning problem into a pay-as-you-go one. By automatically provisioning additional clusters during peak times and tearing them down afterward, it keeps performance consistent without forcing you to size your primary cluster for the worst day of the year.<\/p>\n