How would you support heavy reporting queries without slowing down normal application traffic?

Design reporting queries without hurting OLTP traffic

Protects transactional traffic from reporting load through freshness requirements, bounded asynchronous jobs, tuned summaries, workload isolation, replicas or warehouses, and explicit capacity gates.

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I first define the report's freshness, date range, dimensions, frequency, expected rows, and interactive versus export use. A small selective report may run on the primary with a supporting index and strict limits. A heavy report becomes an asynchronous job with bounded concurrency and durable results. Repeated aggregates can use summary tables or a materialized read model, while broader analytics may belong on a readable replica or warehouse so normal writes and point reads keep predictable capacity.

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OLTP versus reporting workloads, read models, replicas, indexes, isolation, batching, and operational controls.

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Why this question matters
Reporting and OLTP optimize for different work. Transactional requests need short predictable operations, while reports may scan, join, sort, and aggregate large histories; sharing capacity without limits lets a useful report become a production incident.
I first define the report's freshness, date range, dimensions, frequency, expected rows, and interactive versus export use. A small selective report may run on the primary with a supporting index and strict limits. A heavy report becomes an asynchronous job with bounded concurrency and durable results. Repeated aggregates can use summary tables or a materialized read model, while broader analytics may belong on a readable replica or warehouse so normal writes and point reads keep predictable capacity.
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For example, a yearly order export returns 202 with a job id instead of holding an API request open. A worker reads bounded chunks or a consistent snapshot, writes the file to durable storage, and exposes progress and expiry. Per-user and global concurrency, maximum range, cancellation, and timeout protect the database. If users need daily totals within fifteen minutes, an incremental summary table with a visible freshness timestamp is usually better than repeatedly grouping raw order lines. A replica reduces direct query competition, but lag and any shared upstream capacity remain part of the contract.

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Moving a heavy query to a worker but leaving database concurrency unbounded.
Assuming a read replica has zero lag or zero source cost.
Using snapshot isolation as a substitute for bounding scans and memory grants.
Hiding report freshness and letting users compare different data moments unknowingly.

How would you support heavy reporting queries without slowing down normal application traffic?

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