Scheduled review blocks (like setting aside 10am and 3pm for code review) sound reasonable until you try implementing them. Some teams report shorter queues and smoother sprint cycles. Others find the practice creates bottlenecks and misses urgent fixes. The difference is rarely commitment or discipline. It’s whether the underlying conditions support batching as an architecture.
Scheduled processing of emails was found to reduced interruptions and emotional exhaustion when participants handled high volumes (25+ emails per day) and worked in organizations that didn’t expect immediate responses. But in low-volume settings or where quick replies were expected, batching provided minimal benefit. The effects also wore off within two weeks as old habits and external pressures reasserted themselves.
The moderators matter more than the practice itself. Batching works when arrival rate is high enough to justify context-switching costs and when response expectations allow delayed processing. When either condition fails, batching creates friction without delivering benefits.
The Batch-Stream Decision Link to heading
Distributed systems face the same architectural choice of when to batch events for efficient processing versus when to stream them for immediate handling. Batch processing uses off-peak resources to handle large volumes where real-time response isn’t needed. Stream processing handles continuous data flow where immediate insights justify higher infrastructure costs and complexity.
The trade-off is capacity utilization versus latency. Batching amortizes overhead across many items but delays each individual item. Streaming processes each item immediately but maintains always-on infrastructure. Neither is universally better. The choice depends on volume, latency requirements, and cost constraints.
For code review, the parallel holds. Interrupt-driven review (streaming) means responding immediately to each PR, accepting context-switching costs to minimize latency. Batched review means scheduled blocks (say, 10am and 3pm) where reviewers process accumulated PRs, protecting focus time but introducing delay for individual PRs.
The email batching research suggests this works when PR volume is high enough that constant interruptions fragment the day (analogous to 25+ emails) and when stakeholders accept same-day response rather than same-hour. It breaks down when volume is low enough that interruptions don’t dominate, or when expectations demand sub-hour turnaround.
When Batching Breaks Down Link to heading
Queue saturation is the hard limit. A hospital operations study found a tipping point where employees could no longer compensate by working faster. Beyond that threshold, small surges in arrivals created sustained backlogs because the system had no buffer to absorb variation.
For review queues, saturation happens when arrival rate approaches or exceeds processing capacity. When batching frequency is too low relative to arrival rate, PRs accumulate faster than batches can clear them. Then even the 24-hour response expectation Google’s engineering practices recommend will starts slipping, and engineers will escalate to get unblocked.
Batching also assumes reviewers can sustain focus during the batch window. In reality, review effectiveness drops sharply beyond 400 LOC at once or after 90 minutes. If the batch accumulates more PRs than a reviewer can process in that window, the queue persists and the batching strategy fails. You’re still interrupt-driven, just with added latency.
Beyond capacity limits, there’s a scheduling dimension. Research on task switching found that people who freely organized their own schedule performed worse than those forced to work sequentially, suggesting that scheduled structure matters independent of individual preferences. Batching enforces this structure by treating review as a single extended task rather than dozens of micro-tasks scattered across the day.
Implementation Patterns Link to heading
I’ve seen teams implemented identical to this 3PM code review rule. A fixed one-hour block where everyone stops to review pending PRs. 3PM was choosen through trial and error to accomodate everyone’s availability across timezones. The pattern solved forgotten reviews (both parties waiting for the other), strategic delays (reviewers hoping someone else would go first), and sprint-end rushes where review and fixing happened simultaneously under pressure. Similar to the article, the teams reported shorter PR queues, smoother charts, and fewer escalation requests to leadership.
I’ve also seen the alternative pattern mentioned of morning-first work well as well as teams doing both 10 AM and 3 PM during crunch time before a big release. All of these patterns create predictable review windows while allowing urgent items to be handled immediately outside the batch.
The key to all these patterns is predictability. Also, batching doesn’t prohibit reviews outside the scheduled window, it just guarantees minimum review capacity. If a production incident needs immediate review, interrupt-driven handling makes sense. If a dependency-blocking change needs quick feedback, streaming it through makes sense. Batching as the default architecture for the common case also makes sense.
AI-generated code Link to heading
AI-generated code increases both arrival rate (more PRs) and review time. This pushes review queues closer to saturation. Batching becomes more essential if volume rises because context-switching costs compound, but the longer per-PR review time means each batch processes fewer items.
The tension is that AI makes batching more necessary (higher λ means more interruptions to avoid) and less effective (higher review time means batches clear less work). Focusing on batching discipline will have limited benefits. Expect more improvements by increasing service rate (μ) through more reviewers, smaller PRs, or automated checks that shift what humans verify.
Scheduled review blocks work when arrival rate and response expectations align with batch frequency. They break down when volume is too low to justify overhead, expectations demand sub-hour response, or queue saturation means batches can’t keep pace. The architectural choice isn’t batching versus interruption-driven, it’s understanding when each pattern fits the constraints you’re operating under.
Batching has the potential to enable both fast response and thorough review by creating windows for thorough review while meeting same-day response expectations. But it only works if the queue doesn’t saturate and if “same-day” actually satisfies stakeholders. When it doesn’t, you need different architecture, not better habits.
External pressures and organizational norms override individual scheduling preferences, which is why the email batching benefits faded within two weeks. If stakeholders routinely ask “did you see my PR?” after submission or feel obligated to send a slack message for each PR, then batching is fighting the culture. Then architecture needs organizational support, not just individual discipline.
So when teams report different results from scheduled review blocks, both are right. The practice works under specific conditions and fails when those don’t hold. The question isn’t whether batching is good architecture. It’s whether it fits the constraints you’re operating under. Measure your arrival rate, check actual response expectations, watch your queue depth. Those tell you which architecture makes sense, not whether your team is disciplined enough to stick with a calendar block.