Redshift concurrency scaling: How it works

Amazon Redshift is a popular cloud data warehouse solution known for its speed and scalability. As businesses grow and user demand increases, it's common to face performance issues during query spikes—especially when multiple users or applications are trying to access data at the same time. This is where Redshift Concurrency Scaling comes into play.

In this blog, we’ll break down how Redshift Concurrency Scaling works, its benefits, how to enable it, and best practices to make the most of it.


🚦 What is Redshift Concurrency Scaling?

Concurrency Scaling is a feature of Amazon Redshift that automatically adds temporary, additional clusters to handle bursts of user queries—without impacting performance. These clusters are fully managed, on-demand, and only chargeable beyond your free limits.

In simple terms:

When your main cluster is overwhelmed with concurrent queries, Redshift spins up extra capacity so users don't have to wait.


⚙️ How Does It Work?

Redshift maintains a query queue for incoming SQL queries. When the number of concurrent queries exceeds your main cluster’s concurrency limit (typically 15 for a single queue), Redshift evaluates whether to offload the excess queries.

Here’s what happens:

Query Spike Occurs

When user queries exceed what the main cluster can handle concurrently, Redshift detects the overload.


Concurrency Scaling is Triggered

Redshift automatically launches one or more concurrency scaling clusters to process the overflow queries.


Queries Are Offloaded

Eligible queries—such as read-only SELECT queries—are routed to the new temporary cluster.


No User Impact

From the end user's perspective, queries continue to run without delay, maintaining consistent performance.


Clusters Shut Down Automatically

Once the workload subsides, the temporary clusters are shut down. Billing applies only for the time those clusters are running.


🧪 Which Queries Are Eligible?

Only certain types of queries can be routed to concurrency scaling clusters:

Eligible:

Read-only queries (SELECT)

Short-running or dashboard queries

Queries with low I/O and CPU

Not Eligible:

Write or DML queries (INSERT, UPDATE, DELETE)

Transactions or complex stored procedures

Queries involving temporary tables


💰 Cost and Billing

Amazon offers one hour of free concurrency scaling per day, per cluster. This is often sufficient for many workloads. If your demand exceeds this, you'll be charged per-second billing for any extra time, based on your cluster size.

You can monitor usage and billing in the AWS Console > Redshift > Concurrency Scaling section.


✅ Benefits of Concurrency Scaling

⚡ Improved Query Performance during peak load

🔄 Automatic and Scalable—no manual intervention required

💸 Cost-Efficient with free usage included

🧘 Consistent User Experience for dashboards and analysts


🧭 Best Practices

Use Workload Management (WLM) to configure separate queues and assign query priorities.

Monitor usage with CloudWatch Metrics to understand when and why concurrency scaling is triggered.

Optimize your queries to reduce dependency on concurrency scaling for day-to-day performance.

Set up alerts for billing thresholds if your usage might exceed free limits.


✅ Conclusion

Amazon Redshift Concurrency Scaling is a smart solution to handle unpredictable query loads without sacrificing performance. By automatically provisioning additional resources during spikes, it ensures users get fast query results—even when demand surges.

If you're using Redshift for analytics, dashboards, or data applications, enabling concurrency scaling can dramatically enhance performance reliability and user satisfaction.


Learn AWS Data Engineer with Data Analytics

Read More: AWS Glue with JDBC connections: Best practices
Read More: Creating dynamic dashboards in QuickSight from Athena
Read More: How to backfill data in Kinesis and Glue jobs

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