The Tool Desk
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What WLM controls
WLM defines query queues and routing behavior through settings in a Redshift parameter group. It influences which queries run together and how the cluster allocates resources; it does not guarantee a fixed concurrency level or a universal performance gain. See AWS’s WLM configuration overview.
A practical design starts by identifying distinct workload needs—such as interactive analysis, scheduled transformations, and reporting—then deciding whether routing, priority, short-query handling, or additional capacity addresses each need. Measure the result rather than assuming a particular WLM mode or queue layout will be faster.
Choose automatic or manual WLM
| Consideration | Automatic WLM | Manual WLM |
|---|---|---|
| Concurrency and memory | Redshift adjusts concurrency and memory allocation as query demands change. | Administrators set queue concurrency and memory. A queue’s memory is divided among its query slots, so more slots mean less memory per slot. |
| Best fit | AWS recommends automatic WLM in most cases. | Useful when specialized workloads require explicit queue-level controls. |
| Operational trade-off | Less direct tuning of queue concurrency and memory. | Requires managing the concurrency/memory trade-off and validating settings against actual query behavior. |
AWS documents up to eight user-defined queues for automatic WLM. For manual WLM, AWS recommends 15 or fewer total query slots in its implementation guidance and documents an upper limit of 50 slots across user-defined queues. These are product guidance and configuration limits, not evidence that a particular slot count improves performance. Consult AWS’s WLM implementation guidance and manual WLM tutorial before choosing settings.
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Route queries into queues
WLM can assign queries based on user groups, query groups, or user roles. Where supported, assignment options include wildcards. Queries that do not match an assignment use the default queue. Review the assignment criteria and precedence in AWS’s queue assignment documentation.
- Identify the users, applications, or query patterns that need distinct handling.
- Choose an assignment method—user group, query group, or role—and configure matching criteria in the WLM parameter-group settings.
- Check that each intended query matches the right queue and that unmatched queries have an appropriate default-queue path.
- Verify queue names in metrics and monitoring. Renaming a queue can require updating alarms or reports that refer to it.
Set priorities with automatic WLM
Automatic WLM supports queue-level query priorities. Queries associated with a queue inherit its priority, so priority is a way to express relative importance among routed work—not a guarantee that every query will finish by a deadline. AWS explains the behavior in its query priority documentation.
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Use query monitoring rules as guardrails
Query monitoring rules (QMRs) define metric predicates and an action for matching queries. A rule can include up to three predicates; AWS documents a maximum of 25 rules per queue and 25 across all queues. Depending on the WLM configuration and rule, actions include logging, hopping in manual WLM, or aborting a query. See AWS’s QMR documentation for supported metrics and actions.
Use a rule to respond to a defined workload condition, then review the resulting logs and query behavior. A rule is a guardrail, not a replacement for investigating query design or understanding system behavior.
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Handle short queries with SQA
Short Query Acceleration (SQA) prioritizes eligible short-running queries while they wait in user-defined queues. AWS allows a dynamically assigned maximum runtime or a fixed threshold from 1 to 20 seconds. A query that exceeds the selected threshold moves to the first matching WLM queue. SQA can avoid the need to maintain a separate queue just for short queries in many workflows, but eligibility still matters. Details are in the SQA documentation.
Consider concurrency scaling for queue pressure
Concurrency scaling can route eligible queries to added cluster capacity when concurrency in a queue where the feature is enabled is exceeded. It is not an assurance that every query can use that capacity: eligibility rules and limits apply. Confirm that the relevant workload and queue configuration qualify before relying on it; AWS documents the requirements in its concurrency scaling guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply changes carefully
WLM changes do not all have the same activation behavior. Check each property’s dynamic or static status and plan any required restart or rollout accordingly. AWS documents the distinction in its dynamic and static properties reference. QMR changes are documented to apply without a cluster restart; do not assume that applies to every WLM setting.
Quick Recap
- Make one focused change at a time where practical, so its effect is easier to assess.
- Check the property behavior and activation requirements before applying the parameter-group change.
- Validate routing, queue behavior, and query outcomes after the change, including any dependent alarms or reports.
- Keep a rollback path for changes that cause unexpected queueing or query behavior.
A workload-led decision checklist
- Start with automatic WLM unless a measured requirement calls for explicit manual controls.
- Separate workloads only when routing or isolation solves a concrete operational need.
- Use queue priority to express relative importance, and QMRs to define clear operational guardrails.
- Evaluate SQA for eligible short queries waiting behind longer work.
- Consider concurrency scaling only after confirming queue configuration and query eligibility.
- Judge changes from observed workload behavior; do not treat published limits or recommendations as performance predictions.
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